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Research Methods Guide: Research Design & Method

• Introduction
• Survey Research
• Interview Research
• Data Analysis
• Resources & Consultation

Tutorial Videos: Research Design & Method

Research Methods (sociology-focused)

Qualitative vs. Quantitative Methods (intro)

Qualitative vs. Quantitative Methods (advanced)

FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

Research design is a plan to answer your research question.  A research method is a strategy used to implement that plan.  Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively.

Which research method should I choose ?

It depends on your research goal.  It depends on what subjects (and who) you want to study.  Let's say you are interested in studying what makes people happy, or why some students are more conscious about recycling on campus.  To answer these questions, you need to make a decision about how to collect your data.  Most frequently used methods include:

• Observation / Participant Observation
• Focus Groups
• Experiments
• Secondary Data Analysis / Archival Study
• Mixed Methods (combination of some of the above)

One particular method could be better suited to your research goal than others, because the data you collect from different methods will be different in quality and quantity.   For instance, surveys are usually designed to produce relatively short answers, rather than the extensive responses expected in qualitative interviews.

What other factors should I consider when choosing one method over another?

Time for data collection and analysis is something you want to consider.  An observation or interview method, so-called qualitative approach, helps you collect richer information, but it takes time.  Using a survey helps you collect more data quickly, yet it may lack details.  So, you will need to consider the time you have for research and the balance between strengths and weaknesses associated with each method (e.g., qualitative vs. quantitative).

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• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

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The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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Home » Education » Difference Between Research Methods and Research Design

Difference Between Research Methods and Research Design

Main difference – research methods vs research design.

Research methods and research design are terms you must know before starting a research project. Both these elements are essential to the success of a research project. However, many new researchers assume research methods and research design to be the same. Research design is the overall structure of a research project. For example, if you are building a house, you need to have a good idea about what kind of house you are going to build; you cannot do anything without knowing this. A research design is the same – you cannot proceed with the research study without having a proper research design. Research methods are the procedures that are used to collect and analyze data. Thus, the main difference between research methods and research design is that research design is the overall structure of the research study whereas research methods are the various processes, procedures, and tools used to collect and analyze data.

1. What are Research Methods?      – Definition, Features, Characteristics

2. What is Research Design?      – Definition, Features, Characteristics

What are Research Methods

Research methods are concerned with the various research processes, procedures, and tools – techniques of gathering information, various ways of analyzing them. Research problems can be categorized into two basic sections: qualitative research and quantitative research . Researchers may use one or both of these methods (mixed method) in their research studies. The type of research method you choose would depend on your research questions or problem and research design.

The main aim of a research study is to produce new knowledge or deepen the existing understanding of a field. This can be done by three forms.

Exploratory research – identifies and outlines a problem or question

Constructive research – tests theories and suggests solutions to a problem or question

Empirical research – tests the viability of a solution using empirical evidence

What is a Research Design

Research design is the overall plan or structure of the research project. It indicates what type of study is planned and what kind of results are expected from this project. It specifically focuses on the final results of the research. It is almost impossible to proceed with a research project without a proper research design. The main function of a research design is to make sure that the information gathered throughout the research answers the initial question unambiguously. In other words, the final outcomes and conclusions of the research must correspond with the research problems chosen at the beginning of the research.

A research design can be,

Descriptive (case study, survey, naturalistic observation, etc.)

Correlational (case-control study, observational study, etc.)

Experimental (experiments)

Semi-experimental (field experiment, quasi-experiment, etc.)

Meta-analytic (meta-analysis)

Review ( literature review , systematic review)

Research Methods : Research methods are the procedures that will be used to collect and analyze data.

Research Design: Research design is the overall structure of the research.

Research Methods: Research methods focus on what type of methods are more suitable to collect and analyze the evidence we need.

Research Design: Research design focuses on what type of study is planned and what kind of results are expected from the research.

Research Methods: Research methods depend on the research design.

Research Design: Research design is based on the research question or problem.

De Vaus, D. A. 2001. Research design in social research. London: SAGE.

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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Creswell, J. W. (2014). Research Design: Qualitative, Quantitative and Mixed Methods Approaches (4th ed.). Thousand Oaks, CA: Sage

The book Research Design: Qualitative, Quantitative and Mixed Methods Approaches by Creswell (2014) covers three approaches-qualitative, quantitative and mixed methods. This educational book is informative and illustrative and is equally beneficial for students, teachers and researchers. Readers should have basic knowledge of research for better understanding of this book. There are two parts of the book. Part 1 (chapter 1-4) consists of steps for developing research proposal and part II (chapter 5-10) explains how to develop a research proposal or write a research report. A summary is given at the end of every chapter that helps the reader to recapitulate the ideas. Moreover, writing exercises and suggested readings at the end of every chapter are useful for the readers. Chapter 1 opens with-definition of research approaches and the author gives his opinion that selection of a research approach is based on the nature of the research problem, researchers' experience and the audience of the study. The author defines qualitative, quantitative and mixed methods research. A distinction is made between quantitative and qualitative research approaches. The author believes that interest in qualitative research increased in the latter half of the 20th century. The worldviews, Fraenkel, Wallen and Hyun (2012) and Onwuegbuzie and Leech (2005) call them paradigms, have been explained. Sometimes, the use of language becomes too philosophical and technical. This is probably because the author had to explain some technical terms.

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Conducting a well-established research requires deep knowledge about the research designs. Doing research can be likened to jumping into the sea which may transform into a huge ocean if the researcher is not experienced. As a PhD candidate and a novice researcher, I believe that the book "Research Design: Qualitative, Quantitative and Mixed Methods Approaches" by J.W. Creswell is a true reference guide for novice researchers since it is the most comprehensive and informative source with its reader-friendly structure.

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John W. Creswell was previously a professor in educational psychology in the University of Nebraska–Lincoln. He moved to the University of Michigan in 2015 as a professor in the Department of Family Medicine. He has published many articles and close to 27 books on mixed methods. Professor Creswell is also one of the founding members of the Journal of Mixed Methods Research. He was a Fulbright scholar in South Africa in 2008 and Thailand in 2012. In 2011, he served as a visiting professor in the School of Public Health of Harvard University. In 2014, he became the Chairman of the Mixed Methods International Research Association. Professor Creswell has a personal website called “Mixed Methods Research” at http://johnwcreswell.com/. The site contains the information about his background, his own blog, consulting works and published books. He also posted replies questions from academic researchers and practitioners in the blog.

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To understand educational research, you now have the map (the steps that exist in the process of research) and the different paths you can take (quantitative and qualitative). Now we will explore some distinguishing features along the qualitative research design. These features are the research designs you can use to collect, analyze, and interpret data using quantitative and qualitative research. Some of the research designs may be familiar; others may be new, such as how these paths can converge with two designs called mixed methods research and action research. The discussion of designs will provide a more advanced understanding of educational research on your journey.

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What is Descriptive Research? Definition, Methods, Types and Examples

Descriptive research is a methodological approach that seeks to depict the characteristics of a phenomenon or subject under investigation. In scientific inquiry, it serves as a foundational tool for researchers aiming to observe, record, and analyze the intricate details of a particular topic. This method provides a rich and detailed account that aids in understanding, categorizing, and interpreting the subject matter.

Descriptive research design is widely employed across diverse fields, and its primary objective is to systematically observe and document all variables and conditions influencing the phenomenon.

After this descriptive research definition, let’s look at this example. Consider a researcher working on climate change adaptation, who wants to understand water management trends in an arid village in a specific study area. She must conduct a demographic survey of the region, gather population data, and then conduct descriptive research on this demographic segment. The study will then uncover details on “what are the water management practices and trends in village X.” Note, however, that it will not cover any investigative information about “why” the patterns exist.

Table of Contents

What is descriptive research?

If you’ve been wondering “What is descriptive research,” we’ve got you covered in this post! In a nutshell, descriptive research is an exploratory research method that helps a researcher describe a population, circumstance, or phenomenon. It can help answer what , where , when and how questions, but not why questions. In other words, it does not involve changing the study variables and does not seek to establish cause-and-effect relationships.

Importance of descriptive research

Now, let’s delve into the importance of descriptive research. This research method acts as the cornerstone for various academic and applied disciplines. Its primary significance lies in its ability to provide a comprehensive overview of a phenomenon, enabling researchers to gain a nuanced understanding of the variables at play. This method aids in forming hypotheses, generating insights, and laying the groundwork for further in-depth investigations. The following points further illustrate its importance:

Provides insights into a population or phenomenon: Descriptive research furnishes a comprehensive overview of the characteristics and behaviors of a specific population or phenomenon, thereby guiding and shaping the research project.

Offers baseline data: The data acquired through this type of research acts as a reference for subsequent investigations, laying the groundwork for further studies.

Allows validation of sampling methods: Descriptive research validates sampling methods, aiding in the selection of the most effective approach for the study.

Helps reduce time and costs: It is cost-effective and time-efficient, making this an economical means of gathering information about a specific population or phenomenon.

Ensures replicability: Descriptive research is easily replicable, ensuring a reliable way to collect and compare information from various sources.

When to use descriptive research design?

Determining when to use descriptive research depends on the nature of the research question. Before diving into the reasons behind an occurrence, understanding the how, when, and where aspects is essential. Descriptive research design is a suitable option when the research objective is to discern characteristics, frequencies, trends, and categories without manipulating variables. It is therefore often employed in the initial stages of a study before progressing to more complex research designs. To put it in another way, descriptive research precedes the hypotheses of explanatory research. It is particularly valuable when there is limited existing knowledge about the subject.

Some examples are as follows, highlighting that these questions would arise before a clear outline of the research plan is established:

• In the last two decades, what changes have occurred in patterns of urban gardening in Mumbai?
• What are the differences in climate change perceptions of farmers in coastal versus inland villages in the Philippines?

Characteristics of descriptive research

Coming to the characteristics of descriptive research, this approach is characterized by its focus on observing and documenting the features of a subject. Specific characteristics are as below.

• Quantitative nature: Some descriptive research types involve quantitative research methods to gather quantifiable information for statistical analysis of the population sample.
• Qualitative nature: Some descriptive research examples include those using the qualitative research method to describe or explain the research problem.
• Observational nature: This approach is non-invasive and observational because the study variables remain untouched. Researchers merely observe and report, without introducing interventions that could impact the subject(s).
• Cross-sectional nature: In descriptive research, different sections belonging to the same group are studied, providing a “snapshot” of sorts.
• Springboard for further research: The data collected are further studied and analyzed using different research techniques. This approach helps guide the suitable research methods to be employed.

Types of descriptive research

There are various descriptive research types, each suited to different research objectives. Take a look at the different types below.

• Surveys: This involves collecting data through questionnaires or interviews to gather qualitative and quantitative data.
• Observational studies: This involves observing and collecting data on a particular population or phenomenon without influencing the study variables or manipulating the conditions. These may be further divided into cohort studies, case studies, and cross-sectional studies:
• Cohort studies: Also known as longitudinal studies, these studies involve the collection of data over an extended period, allowing researchers to track changes and trends.
• Case studies: These deal with a single individual, group, or event, which might be rare or unusual.
• Cross-sectional studies : A researcher collects data at a single point in time, in order to obtain a snapshot of a specific moment.
• Focus groups: In this approach, a small group of people are brought together to discuss a topic. The researcher moderates and records the group discussion. This can also be considered a “participatory” observational method.
• Descriptive classification: Relevant to the biological sciences, this type of approach may be used to classify living organisms.

Descriptive research methods

Several descriptive research methods can be employed, and these are more or less similar to the types of approaches mentioned above.

• Surveys: This method involves the collection of data through questionnaires or interviews. Surveys may be done online or offline, and the target subjects might be hyper-local, regional, or global.
• Observational studies: These entail the direct observation of subjects in their natural environment. These include case studies, dealing with a single case or individual, as well as cross-sectional and longitudinal studies, for a glimpse into a population or changes in trends over time, respectively. Participatory observational studies such as focus group discussions may also fall under this method.

Researchers must carefully consider descriptive research methods, types, and examples to harness their full potential in contributing to scientific knowledge.

Examples of descriptive research

Now, let’s consider some descriptive research examples.

• In social sciences, an example could be a study analyzing the demographics of a specific community to understand its socio-economic characteristics.
• In business, a market research survey aiming to describe consumer preferences would be a descriptive study.
• In ecology, a researcher might undertake a survey of all the types of monocots naturally occurring in a region and classify them up to species level.

These examples showcase the versatility of descriptive research across diverse fields.

Advantages of descriptive research

There are several advantages to this approach, which every researcher must be aware of. These are as follows:

• Owing to the numerous descriptive research methods and types, primary data can be obtained in diverse ways and be used for developing a research hypothesis .
• It is a versatile research method and allows flexibility.
• Detailed and comprehensive information can be obtained because the data collected can be qualitative or quantitative.
• It is carried out in the natural environment, which greatly minimizes certain types of bias and ethical concerns.
• It is an inexpensive and efficient approach, even with large sample sizes

Disadvantages of descriptive research

On the other hand, this design has some drawbacks as well:

• It is limited in its scope as it does not determine cause-and-effect relationships.
• The approach does not generate new information and simply depends on existing data.
• Study variables are not manipulated or controlled, and this limits the conclusions to be drawn.
• Descriptive research findings may not be generalizable to other populations.
• Finally, it offers a preliminary understanding rather than an in-depth understanding.

To reiterate, the advantages of descriptive research lie in its ability to provide a comprehensive overview, aid hypothesis generation, and serve as a preliminary step in the research process. However, its limitations include a potential lack of depth, inability to establish cause-and-effect relationships, and susceptibility to bias.

Frequently asked questions

When should researchers conduct descriptive research.

Descriptive research is most appropriate when researchers aim to portray and understand the characteristics of a phenomenon without manipulating variables. It is particularly valuable in the early stages of a study.

What is the difference between descriptive and exploratory research?

Descriptive research focuses on providing a detailed depiction of a phenomenon, while exploratory research aims to explore and generate insights into an issue where little is known.

What is the difference between descriptive and experimental research?

Descriptive research observes and documents without manipulating variables, whereas experimental research involves intentional interventions to establish cause-and-effect relationships.

Is descriptive research only for social sciences?

No, various descriptive research types may be applicable to all fields of study, including social science, humanities, physical science, and biological science.

How important is descriptive research?

The importance of descriptive research lies in its ability to provide a glimpse of the current state of a phenomenon, offering valuable insights and establishing a basic understanding. Further, the advantages of descriptive research include its capacity to offer a straightforward depiction of a situation or phenomenon, facilitate the identification of patterns or trends, and serve as a useful starting point for more in-depth investigations. Additionally, descriptive research can contribute to the development of hypotheses and guide the formulation of research questions for subsequent studies.

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Jul 11, 2024

What is field research? Meaning, methods, and examples

Insights • Aakash Jethwani • 11 Mins reading time

In the realm of research methodologies, field study, often called field research, stands out as a pivotal approach to understanding real-world phenomena through direct observation and interaction within natural settings. 

Unlike controlled experiments, it captures genuine behaviors and social interactions, providing rich and detailed insights. Field research offers a firsthand look at reality, whether you’re exploring cultural traditions, studying social issues, or understanding consumer habits. 

For businesses, this method is invaluable for improving product design, enhancing usability, and making informed decisions based on real-life data. 

Let’s start with this blog to explore field research meaning and significance and provide field research examples to illustrate its diverse applications.

Field research meaning

Field research encompasses the systematic study conducted outside controlled environments, where researchers directly engage with subjects in their natural contexts. It involves observation, interaction, and data collection in real-world settings, aiming to capture user behaviors, interactions, and phenomena as they naturally occur. 

Unlike experiments conducted in artificial setups, field study enables researchers to explore and understand the complexities of human societies, wildlife habitats, consumer behaviors, and cultural practices within their natural environments. 

This methodological approach provides rich, contextual insights that contribute to a deeper understanding of various disciplines and phenomena, enhancing the validity and applicability of research findings in practical and real-world contexts. 

Let’s look at some field research examples to understand this concept better.

Field research examples

1. study of indigenous tribes.

Researchers visit and live among indigenous communities to study their cultures, traditions, languages, and social structures firsthand. 

They observe daily life, participate in rituals and activities, and conduct interviews to understand how these communities function and interact with the surrounding environment.

2. Urban ethnography

This involves studying people’s behaviors, interactions, and cultures in urban settings like cities or neighborhoods. 

Researchers immerse themselves in these environments to observe social dynamics, community relationships, and cultural practices unique to urban life. This helps them understand how urban societies work and evolve.

3. Wildlife tracking

Researchers use various techniques, such as GPS collars, camera traps, and direct observation, to track and study animals in their natural habitats. 

The behaviors of wildlife, migration patterns, preferred habitats, and the effects of environmental changes on animal populations are all better understood by researchers because of this fieldwork.

4. Consumer behavior studies

Researchers conduct field research in shopping malls, retail stores, or online platforms to observe and analyze consumer behavior. 

They study how people make purchasing decisions, their preferences for products or services, and their overall shopping experiences. This research is crucial for businesses to understand market trends and consumer needs.

5. Usability testing in context

This involves testing the usability of products or services in real-world settings where they are used. Researchers observe how users interact with devices, software, websites, or apps to identify usability issues, user preferences, and areas for improvement. 

Usability testing in context provides insights into how well products meet user needs and expectations in their everyday environments.

You may like to read about the difference between field studies vs ethnographic studies vs contextual inquiry

Reasons for conducting a field study

Field study is essential for gaining deep insights and understanding across various disciplines due to several key reasons:

1. Contextual understanding

Field research allows researchers to study phenomena in their natural environments, providing a contextual understanding beyond controlled settings. It lets them observe how environment, culture, and social dynamics influence behaviors and outcomes. 

For example, studying how people interact in their neighborhoods gives insights that might be missed in a lab.

2. Behavioral insights

Field research yields authentic and nuanced behavioral insights by observing behaviors directly in real-world settings. Researchers can see how people react in specific situations, which helps them understand decision-making processes, habits, and responses to stimuli. 

This direct observation is crucial for developing theories that accurately reflect real-life behaviors.

3. Cultural and social insights

Field research is invaluable for studying cultural practices, traditions, and social structures within natural contexts. It provides opportunities to immerse in diverse communities and understand their values, rituals, and daily lives. 

This field research fosters cultural sensitivity and enhances understanding of societal norms, helping researchers appreciate and respect cultural diversity.

4. Exploratory research

Field research often serves as exploratory research, where researchers explore new phenomena or test hypotheses in real-world settings. It allows for flexible and adaptive methods to uncover unexpected findings or patterns that might not be apparent in theoretical frameworks alone. 

This exploratory nature of field research contributes to expanding knowledge and generating new ideas.

5. Intervention and application

Field research also plays a crucial role in applied research and interventions. By studying problems or challenges in situ, researchers can develop and test practical solutions tailored to specific contexts. 

This approach ensures that interventions are relevant, practical, and feasible, addressing real-world issues directly.

Also, read why field research is needed across different disciplines

When is field research conducted?

Field research is conducted across diverse contexts and disciplines to explore, describe, evaluate, and monitor phenomena in their natural settings. It provides invaluable insights into real-world complexities and behaviors.

1. Exploratory studies

Field research is often conducted in exploratory studies when researchers aim to investigate new phenomena or explore unfamiliar topics. 

By immersing themselves in the field, researchers gather preliminary data and insights that help formulate hypotheses or refine research questions for further study.

2. Descriptive studies

In descriptive studies, field research describes and documents specific behaviors, characteristics, or phenomena in their natural settings. 

Researchers observe and record details without manipulating variables, aiming to comprehensively understand what exists and how it functions in real-world contexts.

3. Evaluation and monitoring

Field research is crucial for evaluating programs, policies, or interventions implemented in real-world settings. Researchers conduct ongoing monitoring to assess outcomes, measure impacts, and identify areas for improvement. 

This type of research helps stakeholders make informed decisions based on empirical data and feedback from the field.

4. Longitudinal studies

Longitudinal studies involve observing subjects over extended periods, sometimes years or decades, to track changes or developments over time. 

Field research in longitudinal studies allows researchers to capture evolving behaviors, trends, and influences within natural environments, providing insights into developmental trajectories or long-term effects.

5. Cross-cultural comparisons

Field research is essential for cross-cultural comparisons to understand how behaviors, beliefs, or social practices vary across different cultures or geographical regions. 

Researchers collect data from multiple cultural contexts, comparing similarities, differences, and underlying factors that shape cultural variations.

Types of field research

Field research encompasses various methodologies tailored to different research objectives and data collection approaches:

1. Qualitative field research

This type of field research focuses on understanding phenomena through in-depth exploration and interpretation of experiences, behaviors, and social interactions within natural settings. 

Researchers use participant observation, interviews, and open-ended surveys to gather rich, descriptive data. This approach emphasizes capturing meanings, perceptions, and contextual factors that shape individuals’ experiences and behaviors.

2. Quantitative field research

Quantitative field research involves collecting numerical data and analyzing it statistically to identify patterns, relationships, and trends. Researchers use structured surveys, experiments, or systematic observations to gather data from large samples in real-world environments. 

This approach emphasizes measurement, objectivity, and generalizability of findings, allowing researchers to draw statistically valid conclusions about populations or phenomena.

3. Mixed methods field research

Mixed methods is a type of field research that combines qualitative and quantitative approaches to leverage their strengths and comprehensively understand complex phenomena. Researchers integrate data collection methods and analyses to triangulate findings, enhancing the validity and depth of research outcomes. 

This method gives researchers a more comprehensive understanding of research issues by enabling them to capture both the depth of qualitative insights and the breadth of quantitative data.

Field research methods

The field research methods employ various ways to collect data and gain insights directly from natural settings:

1. Participant observation

Researchers immerse themselves in the studied environment, actively participating in activities and observing behaviors firsthand. With this approach, social interactions, user behaviors, and cultural customs can be thoroughly understood in natural settings.

2. Interviews and focus groups

Researchers conduct structured or semi-structured interviews with individuals or facilitate group discussions in focus groups. These methods gather qualitative data through direct interaction, probing questions, and group dynamics, offering insights into attitudes, perceptions, and experiences.

3. Surveys and questionnaires

This type of field study method collects large quantitative data from respondents. Researchers design structured instruments to gather information on attitudes, behaviors, preferences, or demographics, providing statistical insights into population patterns and trends.

4. Document analysis

Researchers analyze written or recorded materials relevant to the research topic, such as texts, reports, archives, or multimedia sources. Document analysis uncovers historical context, policy documents, organizational records, or cultural artifacts, offering valuable insights into trends, perspectives, and changes over time.

5. Sampling techniques

This technique selects a representative subset of the population for study. Researchers use methods such as random, stratified, or purposive sampling to ensure the sample reflects the diversity and characteristics of the larger population, enhancing the generalizability of findings.

6. Field experiments

Researchers carry out controlled experiments in natural environments to change variables and track their impact on relevant outcomes. Field experiments allow researchers to study cause-and-effect relationships in real-world conditions, providing empirical evidence to test hypotheses and inform practical applications.

Steps to conduct a field study

Conducting a field study involves several systematic steps to ensure rigorous research and meaningful findings:

1. Define research objectives

Define the objective and goal of the study, outlining what you aim to achieve and the questions you seek to answer through your research in the field.

2. Literature review

Conduct a thorough examination of existing literature on your research topic. This will assist you in identifying knowledge gaps, understanding theoretical frameworks, and guiding your research design and methods.

3. Research design

Develop a research design that aligns with your objectives and chosen methodology (qualitative, quantitative, or mixed methods). Decide on data collection methods, sampling strategies, and experimental or observational techniques.

4. Obtain permissions and clearances

Obtain necessary permissions and clearances from relevant authorities or stakeholders, especially if your study involves human subjects, sensitive environments, or requires access to restricted areas.

5. Prepare data collection tools

Design and prepare data collection tools, such as interview guides, survey questionnaires, observation protocols, or experimental setups. Ensure these tools are valid, reliable, and appropriate for your research context.

6. Pilot testing

Launch a pilot test of your data collection tools and procedures to identify and address any practical issues, refine questions, and ensure the effectiveness of your approach before full-scale implementation.

7. Data collection

Collect data according to your planned methodology and procedures. This may involve conducting interviews, administering surveys, observing behaviors, or performing experiments in the field setting.

8. Data analysis

Examine the collected data using appropriate analytical techniques. This may involve coding, thematic analysis, or narrative interpretation for qualitative data. Using statistical methods to analyze patterns, relationships, and trends for quantitative data.

9. Validation and triangulation

Validate your findings by comparing and contrasting data from different sources or methods (triangulation). This helps to ensure the reliability and credibility of your results by corroborating evidence across multiple perspectives.

10. Report and dissemination

Compile your findings into a comprehensive report with an introduction, methodology, results, discussion, and conclusions. Communicate your findings, implications, and recommendations for future research or practical applications.

In conclusion, understanding field research meaning is essential for understanding real-world user needs and informing the design of effective, user-centered solutions. By immersing themselves in the natural environment, researchers can gain invaluable insights that may not be captured through other methods. 

At Octet , our experienced field researchers leverage various field research methods to uncover these insights, which we then translate into actionable recommendations to drive innovation and foster deeper connections between products/services and users. 

By partnering with Octet, you can harness the power of field research to create solutions that truly resonate with your target audience.

1. Why is field study important?

Field research is essential because it allows researchers to gain a deep, contextual understanding of real-world user needs and behaviors. 

By immersing themselves in the natural environment, researchers can uncover insights that may not be captured through other research methods, such as surveys or lab studies. 

These insights can inform the design process, leading to more effective, user-centered solutions that resonate with the target audience.

2. What is the objective of the field study?

The primary objective of field research is to develop a comprehensive understanding of the user’s environment, experiences, and pain points. 

Through the observation of users in their natural environments, researchers are able to determine the fundamental aspects that impact their decisions and behaviors. 

This information can then guide the design and development of products, services, or interventions more appropriate for the target population.

3. What do you mean by field of study?

The term “field of study” refers to the specific academic or professional discipline in which field research is conducted. 

This can include various fields, such as anthropology, sociology, psychology, marketing, product design, or human-computer interaction. 

The field of study determines the research methods, theoretical frameworks, and analytical approaches used to collect and interpret the data gathered through field research.

Read more on:

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What is a Research Design? Importance and Types

Why Research Design is Important for a Researcher?

A research design is a systematic procedure or an idea to carry out different tasks of the research study. It is important to know the research design and its types for the researcher to carry out the work in a proper way.

The purpose of research design is that enable the researcher to proceed in the right direction without any deviation from the tasks. It is an overall detailed strategy of the research process.

The design of experiments is a very important aspect of a research study. A poor research design may collapse the entire research project in terms of time, manpower, and money.

7 Importance of Research Design – iLovePhD

What is a Research Design in Research Methodology ?

A research design is a plan or framework for conducting research. It includes a set of plans and procedures that aim to produce reliable and valid data. The research design must be appropriate to the type of research question being asked and the type of data being collected.

A typical research design is a detailed methodology or a roadmap for the successful completion of any research work. ilovephd.com

Importance of Research Design

A Good research design consists of the following important points:

• Formulating a research design helps the researcher to make correct decisions in each and every step of the study.
• It helps to identify the major and minor tasks of the study.
• It makes the research study effective and interesting by providing minute details at each step of the research process.
• Based on the design of experiments (research design), a researcher can easily frame the objectives of the research work.
• A good research design helps the researcher to complete the objectives of the study in a given time and facilitates getting the best solution for the research problems .
• It helps the researcher to complete all the tasks even with limited resources in a better way.
• The main advantage of a good research design is that it provides accuracy, reliability, consistency, and legitimacy to the research.

How to Create a Research Design?                      

According to Thyer, the research design has the following components:

• A researcher begins the study by framing the problem statement of the research work.
• Then, the researcher has to identify the sampling points, the number of samples, the sample size, and the location.
• The next step is to identify the operating variables or parameters of the study and detail how the variables are to be measured.
• The final step is the collection, interpretation, and dissemination of results.

Considerations in selecting the research design

The researchers should know the various types of research designs and their applicability. The selection of a research design can only be made after a careful understanding of the different research design types . The factors to be considered in choosing a research design are

• Qualitative Vs quantitative
• Basic Vs applied
• Empirical Vs Non-empirical

Types of Research Design?

There are four main types of research designs: experimental, observational, quasi-experimental, and descriptive.

• Experimental designs: are used to test cause-and-effect relationships. In an experiment, the researcher manipulates one or more independent variables and observes the effect on a dependent variable.
• Observational designs are used to study behavior without manipulating any variables. The researcher simply observes and records the behavior.
• Quasi-experimental designs are used when it is not possible to manipulate the independent variable. The researcher uses a naturally occurring independent variable and controls for other variables.
• Descriptive designs are used to describe a behavior or phenomenon. The researcher does not manipulate any variables, but simply observes and records the behavior.

I hope, this article would help you to know about what is research design, the types of research design, and what are the important points to be considered in carrying out the research work.

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What is a PhD? A Comprehensive Guide for Indian Scientists and Aspiring Researchers

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How to Write a Research Proposal: A Complete Guide

A research proposal is a piece of writing that basically serves as your plan for a research project. It spells out what you’ll study, how you’ll go about it, and why it matters. Think of it as your pitch to show professors or funding bodies that your project is worth their attention and support.

This task is standard for grad students, especially those in research-intensive fields. It’s your chance to showcase your ability to think critically, design a solid study, and articulate why your research could make a difference.

In this article, we'll talk about how to craft a good research proposal, covering everything from the standard format of a research proposal to the specific details you'll need to include. 

Feeling overwhelmed by the idea of putting one together? That’s where DoMyEssay comes in handy.  Whether you need a little push or more extensive guidance, we’ll help you nail your proposal and move your project forward. 

Research Proposal Format

When you're putting together a research proposal, think of it as setting up a roadmap for your project. You want it to be clear and easy to follow so everyone knows what you’re planning to do, how you’re going to do it, and why it matters. 

Whether you’re following APA or Chicago style, the key is to keep your formatting clean so that it’s easy for committees or funding bodies to read through and understand.

Here’s a breakdown of each section, with a special focus on formatting a research proposal:

• Title Page : This is your first impression. Make sure it includes the title of your research proposal, your name, and your affiliations. Your title should grab attention and make it clear what your research is about.
• Abstract : This is your elevator pitch. In about 250 words, you need to sum up what you plan to research, how you plan to do it, and what impact you think it will have.
• Introduction : Here’s where you draw them in. Lay out your research question or problem, highlight its importance, and clearly outline what you aim to achieve with your study.
• Literature Review : Show that you’ve done your homework. In this section, demonstrate that you know the field and how your research fits into it. It’s your chance to connect your ideas to what’s already out there and show off a bit about what makes your approach unique or necessary.
• Methodology : Dive into the details of how you’ll get your research done. Explain your methods for gathering data and how you’ll analyze it. This is where you reassure them that your project is doable and you’ve thought through all the steps.
• Timeline : Keep it realistic. Provide an estimated schedule for your research, breaking down the process into manageable stages and assigning a timeline for each phase.
• Budget : If you need funding, lay out a budget that spells out what you need money for. Be clear and precise so there’s no guesswork involved about what you’re asking for.
• References/Bibliography : List out all the works you cited in your proposal. Stick to one citation style to keep things consistent.

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Research Proposal Structure

When you're writing a research proposal, you're laying out your questions and explaining the path you're planning to take to tackle them. Here’s how to structure your proposal so that it speaks to why your research matters and should get some attention.

Introduction

An introduction is where you grab attention and make everyone see why what you're doing matters. Here, you’ll pose the big question of your research proposal topic and show off the potential of your research right from the get-go:

• Grab attention : Start with something that makes the reader sit up — maybe a surprising fact, a challenging question, or a brief anecdote that highlights the urgency of your topic.
• Set the scene : What’s the broader context of your work? Give a snapshot of the landscape and zoom in on where your research fits. This helps readers see the big picture and the niche you’re filling.
• Lay out your plan : Briefly mention the main goals or questions of your research. If you have a hypothesis, state it clearly here.
• Make it matter : Show why your research needs to happen now. What gaps are you filling? What changes could your findings inspire? Make sure the reader understands the impact and significance of your work.

Literature Review

In your research proposal, the literature review does more than just recap what’s already out there. It's where you get to show off how your research connects with the big ideas and ongoing debates in your field. Here’s how to make this section work hard for you:

• Connect the dots : First up, highlight how your study fits into the current landscape by listing what others have done and positioning your research within it. You want to make it clear that you’re not just following the crowd but actually engaging with and contributing to real conversations. 
• Critique what’s out there : Explore what others have done well and where they’ve fallen short. Pointing out the gaps or where others might have missed the mark helps set up why your research is needed and how it offers something different.
• Build on what’s known : Explain how your research will use, challenge, or advance the existing knowledge. Are you closing a key gap? Applying old ideas in new ways? Make it clear how your work is going to add something new or push existing boundaries.

Aims and Objectives

Let's talk about the aims and objectives of your research. This is where you set out what you want to achieve and how you plan to get there:

• Main Goal : Start by stating your primary aim. What big question are you trying to answer, or what hypothesis are you testing? This is your research's main driving force.
• Detailed Objectives : Now, break down your main goal into smaller, actionable objectives. These should be clear and specific steps that will help you reach your overall aim. Think of these as the building blocks of your research, each one designed to contribute to the larger goal.

Research Design and Method

This part of your proposal outlines the practical steps you’ll take to answer your research questions:

• Type of Research : First off, what kind of research are you conducting? Will it be qualitative or quantitative research , or perhaps a mix of both? Clearly define whether you'll be gathering numerical data for statistical analysis or exploring patterns and theories in depth.
• Research Approach : Specify whether your approach is experimental, correlational, or descriptive. Each of these frameworks has its own way of uncovering insights, so choose the one that best fits the questions you’re trying to answer.
• Data Collection : Discuss the specifics of your data. If you’re in the social sciences, for instance, describe who or what you’ll be studying. How will you select your subjects or sources? What criteria will you use, and how will you gather your data? Be clear about the methods you’ll use, whether that’s surveys, interviews, observations, or experiments.
• Tools and Techniques : Detail the tools and techniques you'll use to collect your data. Explain why these tools are the best fit for your research goals.
• Timeline and Budget : Sketch out a timeline for your research activities. How long will each phase take? This helps everyone see that your project is organized and feasible.
• Potential Challenges : What might go wrong? Think about potential obstacles and how you plan to handle them. This shows you’re thinking ahead and preparing for all possibilities.

Ethical Considerations

When you're conducting research, especially involving people, you've got to think about ethics. This is all about ensuring everyone's rights are respected throughout your study. Here’s a quick rundown:

• Participant Rights : You need to protect your participants' rights to privacy, autonomy, and confidentiality. This means they should know what the study involves and agree to participate willingly—this is what we call informed consent.
• Informed Consent : You've got to be clear with participants about what they’re signing up for, what you’ll do with the data, and how you'll keep it confidential. Plus, they need the freedom to drop out any time they want.
• Ethical Approval : Before you even start collecting data, your research plan needs a green light from an ethics committee. This group checks that you’re set up to keep your participants safe and treated fairly.

You need to carefully calculate the costs for every aspect of your project. Make sure to include a bit extra for those just-in-case scenarios like unexpected delays or price hikes. Every dollar should have a clear purpose, so justify each part of your budget to ensure it’s all above board. This approach keeps your project on track financially and avoids any surprises down the line.

The appendices in your research proposal are where you stash all the extra documents that back up your main points. Depending on your project, this could include things like consent forms, questionnaires, measurement tools, or even a simple explanation of your study for participants. 

Just like any academic paper, your research proposal needs to include citations for all the sources you’ve referenced. Whether you call it a references list or a bibliography, the idea is the same — crediting the work that has informed your research. Make sure every source you’ve cited is listed properly, keeping everything consistent and easy to follow.

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Writing a research proposal can be straightforward if you break it down into manageable steps:

• Pick a strong research proposal topic that interests you and has enough material to explore.
• Craft an engaging introduction that clearly states your research question and objectives.
• Do a thorough literature review to see how your work fits into the existing research landscape.
• Plan out your research design and method , deciding whether you’ll use qualitative or quantitative research.
• Consider the ethical aspects to ensure your research is conducted responsibly.
• Set up a budget and gather any necessary appendices to support your proposal.
• Make sure all your sources are cited properly to add credibility to your work.

If you need some extra support, DoMyEssay is ready to help with any type of paper, including crafting a strong research proposal. 

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• Volume 9, Issue 8
• Defining and identifying the critical elements of operational readiness for public health emergency events: a rapid scoping review
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• René English 1 ,
• Heather Carlson 2 ,
• Heike Geduld 3 ,
• Juliet Charity Yauka Nyasulu 1 ,
• Quinette Louw 4 ,
• Karina Berner 4 ,
• http://orcid.org/0000-0002-2441-2566 Maria Yvonne Charumbira 4 ,
• Michele Pappin 1 ,
• Michael McCaul 5 ,
• Conran Joseph 4 ,
• Nina Gobat 2 ,
• Linda Lucy Boulanger 2 ,
• Nedret Emiroglu 2
• 1 Division of Health Systems and Public Health, Department of Global Health , Stellenbosch University Faculty of Medicine and Health Sciences , Cape Town , South Africa
• 2 Country Readiness and Strengthening Department, World Health Emergencies Programme , World Health Organization , Geneva , Switzerland
• 3 Department of Family and Emergency Medicine, Faculty of Medicine and Health Sciences , Stellenbosch University Division of Emergency Medicine , Stellenbosch , South Africa
• 4 Division of Physiotherapy, Department of Health and Rehabilitation Sciences , Stellenbosch University Faculty of Medicine and Health Sciences , Cape Town , South Africa
• 5 Centre for Evidence-based Health Care, Division of Epidemiology and Biostatistics, Department of Global Health , Stellenbosch University , Cape town , South Africa
• Correspondence to Professor René English; renglish{at}sun.ac.za

Introduction COVID-19 showed that countries must strengthen their operational readiness (OPR) capabilities to respond to an imminent pandemic threat rapidly and proactively. We conducted a rapid scoping evidence review to understand the definition and critical elements of OPR against five core sub-systems of a new framework to strengthen the global architecture for Health Emergency Preparedness Response and Resilience (HEPR).

Methods We searched MEDLINE, Embase, and Web of Science, targeted repositories, websites, and grey literature databases for publications between 1 January 2010 and 29 September 2021 in English, German, French or Afrikaans. Included sources were of any study design, reporting OPR, defined as immediate actions taken in the presence of an imminent threat, from groups who led or responded to a specified health emergency. We used prespecified and tested methods to screen and select sources, extract data, assess credibility and analyse results against the HEPR framework.

Results Of 7005 sources reviewed, 79 met the eligibility criteria, including 54 peer-reviewed publications. The majority were descriptive reports (28%) and qualitative analyses (30%) from early stages of the COVID-19 pandemic. Definitions of OPR varied while nine articles explicitly used the term ‘readiness’, others classified OPR as part of preparedness or response. Applying our working OPR definition across all sources, we identified OPR actions within all five HEPR subsystems. These included resource prepositioning for early detection, data sharing, tailored communication and interventions, augmented staffing, timely supply procurement, availability and strategic dissemination of medical countermeasures, leadership, comprehensive risk assessment and resource allocation supported by relevant legislation. We identified gaps related to OPR for research and technology-enabled manufacturing platforms.

Conclusions OPR is in an early stage of adoption. Establishing a consistent and explicit framework for OPRs within the context of existing global legal and policy frameworks can foster coherence and guide evidence-based policy and practice improvements in health emergency management.

• Public Health

Data availability statement

Data are available on reasonable request. The rapid scoping review protocol can be publicly accessed on the Open Science Framework (OSF) platform ( https://osf.io/39q4b/ ). The datasets used and/or analysed during the scoping review are available from the corresponding author on reasonable request.

This is an open access article distributed under the terms of the Creative Commons Attribution IGO License ( CC BY 3.0 IGO ), which permits use, distribution, and reproduction in any medium, provided the original work is properly cited. In any reproduction of this article there should not be any suggestion that WHO or this article endorse any specific organization or products. The use of the WHO logo is not permitted. This notice should be preserved along with the article’s original URL.

https://doi.org/10.1136/bmjgh-2023-014379

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Operational readiness (OPR) has emerged as a crucial but relatively unexplored concept in the context of health emergencies.

WHAT THIS STUDY ADDS

OPR is in an early stage of adoption with variable understandings of what it entails. This study highlights a need for conceptual clarity and consistency in describing OPR to build a coherent body of evidence that can underpin policy and practice. Key OPR actions aligned with five core subsystems of Health Emergency Preparedness Response and Resilience (a global, integrated framework for health emergency management) are identified.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Instruments to evaluate country-level preparedness under the International Health Regulations require evidence of readiness planning. The most recent global policy framework to strengthen the global architecture for health emergencies also signposts the critical role of readiness. This scoping review has provided a foundation for global expert deliberations and agreement on OPR, which is an important step forward towards a coherent body of evidence and to advance policy and practice for improved health emergency management.

Introduction

A key lesson learnt from the global and national response to COVID-19 is the critical importance of early action. COVID-19 caught many countries off guard, and the consequences of delayed responses were severe in terms of public health as well as socioeconomic impacts. To prevent and mitigate the impact of future events, countries must strengthen their capabilities for rapid mobilisation to proactively respond in anticipation of an imminent threat. To this end, operational readiness (OPR) has emerged as an important part of efforts to strengthen the global architecture for health emergency preparedness, response and resilience (HEPR). 1 HEPR, WHO’s new strategic framework, is intended to guide, inform and resource collective efforts to strengthen the key interlinked national, regional and global multisectoral capacities sitting at the intersection of health security, primary healthcare and health promotion.

In the context of the health emergency cycle, OPR arises at the intersection between preparedness planning and response. 2 By promptly mobilising specific resources and strategies in the face of a high-priority and imminent threat, countries can enhance their ability to respond swiftly and efficiently by strategic deployment of well-defined capabilities, plans and actions that are tailored to the specific threat. The importance of this neglected phase in the health emergency cycle has catalysed related global policy initiatives. Instruments to evaluate country preparedness for emergency response under the International Health Regulations (IHR) require evaluation of country-level OPR planning, as seen in the Joint External Evaluation (JEE) 3.0’s Health Emergency Management Capacity, which targets risk-based plans for readiness and existence of an emergency readiness assessment. 3 The WHO’s proposals for a strengthened HEPR architecture across core domains of governance, finance and systems require OPR and capacities in five core subsystems: Collaborative Surveillance; Community Protection; Safe and Scalable Care; Access to Countermeasures and Emergency Coordination, along with OPR plans in Emergency Coordination. 1 4 Currently, there is no WHO guidance related to standardised emergency readiness assessments and readiness planning. To achieve the promise of strengthened OPR policy and practice, closer specification is needed to define what OPR involves and how it works, and the methodologies and approaches used to implement and operationalise it.

To underpin WHO technical products for OPR, we conducted a rapid scoping evidence review to examine the definitions and critical elements of OPR for public health emergencies caused by new or re-emerging infectious diseases and other public health threats in the context of the latest global policy frameworks for health emergency management. 5 This review is important given the absence of a standardised checklist of ‘must haves’ to inform the development of a country contingency plan in the face of an emergency.

Objectives of our review were (a) to identify how OPR has been conceptualised and defined; (b) to elicit critical elements of ‘OPRs’ in the context of key global policy frameworks, such as the WHO Global Health Security Framework, HEPR and JEE 3.0. 3 4 6 Anticipating a large and diverse body of evidence and given the need for a rapid output from this work, we conducted a rapid, scoping review following well-recognised methods. 7–9 We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews 10 checklist for reporting. Our study protocol is published 5 and registered (doi:10.17605/OSF.IO/6SYAH).

Eligibility criteria

We included articles that:

Reported on OPRs, defined for this review, as those immediate action(s) required to preposition response actions to acute, proximal or imminent hazards and/or threats (eg, an infectious disease outbreak or a natural disaster threat), that is, an all-hazards approach 5 in the context of health emergencies, that is, disasters and major incidents (natural and otherwise) including emerging and re-emerging infectious disease threats with the potential to significantly impact a population’s health; and described actions of emergency response groups or organisations at national, regional or global levels.

Types: English, German, French or Afrikaans language peer-reviewed original articles or reviews published between 1 January 2010 and 29 September 2021, publicly available policy frameworks and programme reports, published conference reports or electronic theses, relevant grey literature and documents for which full texts or abstracts were available.

We excluded articles that:

Focused exclusively on longer-term preparedness actions (ie, an imminent threat was not explicitly defined) or response actions (ie, actions to respond to an active public health emergency), reported on contexts beyond health emergencies or did not focus on disease prevention and control.

Search strategy

We developed and ran a search structured by population (health systems/community), concept (readiness/preparedness/risk/planning) and context (emergencies/diseases/natural disasters) in MEDLINE, Embase and Web of Science databases (see online supplemental table S1 for detailed search strategies for the electronic databases). We searched various targeted repositories, websites and databases for grey literature 11 (see online supplemental box S1 ). We also used forward and backward citation tracking.

Supplemental material

Selection of sources.

Search outcomes were imported into Rayyan V.0.1.0 software (Rayyan Systems, Massachusetts, USA) for screening, checking of duplicates and final selection. 8 12 Our approach to citation screening aimed to balance rigour and speed, consistent with rapid reviews and adapted from the Cochrane Rapid Reviews Methods Group’s guidance for rapid reviews, 8 including guidance on addressing the methodological challenges faced during COVID-19 rapid reviews. 9

Screening occurred at three levels (title, abstract and full report). The review team agreed on screening decisions upfront and agreed on guidelines after piloting for consistency. 8 13 For piloting, two reviewers (MP and MYC) independently and in duplicate screened 100 titles and abstracts, followed by discussion with three senior authors (RE, QL and MM) to refine screening decisions. Category coding by study design and keywords for excluded articles at the title and abstract level were agreed and set in Rayyan.

After this, one reviewer (MP) screened 20% of the initially identified titles and screened abstracts to remove irrelevant reports. A second reviewer (MYC) verified excluded titles and abstracts. 8 Conflicts and uncertainties were resolved by discussion with senior authors (RE, HG or QL). To ensure that all texts could be assessed in detail against the eligibility criteria within the limited time frame of the rapid review, 9 full-text screening was independently conducted by eight reviewers (MYC, MP, KB, JCYN, CJ, QL, RE and HG) with the yield divided among them. Discrepancies were resolved through discussion.

Selection of grey literature

Grey literature search outputs were screened at two levels (title and body of the report) and recorded by one reviewer (MP). A second content expert (HG) verified the included sample. 8 9

Data extraction and management

Two reviewers (MYC and QL) extracted data from journal articles, and one (MP) from grey literature; an additional reviewer (RE) checked for accuracy in both instances. 14 Data were deductively coded in ATLAS.ti V.9 (Scientific Software Development) ( https://atlasti.com/ ) and extracted into a custom-built, pilot-tested MS Excel spreadsheet, according to preset criteria The data extraction form was revised after pilot testing and consultation with WHO and amended 14 to reflect the study authors’ affiliations and the WHO region in which the study was conducted. Uncertainties were discussed by the full review team. It was not necessary to contact the study authors.

Credibility of evidence in the included articles was assessed based on the information source and type. 8–10 Two reviewers (MP and MYC) appraised the included sources for descriptive purposes and incorporated the results narratively in the reflective summaries of the charting findings.

Data analysis and presentation

To analyse data, we (QL, RE, HG, CJ and JCYN) used qualitative thematic analysis with deductive synthesis, 15–17 against the following preidentified thematic categories: leadership, governance and coordination; country risk assessment; operational planning and coordination; contingency finance; health facility capacity and service delivery; health workforce/human resources; early warning or surveillance and health information systems; community resilience and risk communications; logistics or supply chain for access to essential medicines; WHO readiness and partner readiness. New themes were also identified. A revision of this analysis (HC and NG) used the new HEPR global architecture as an organising frame. 4

Patient and public involvement

As this study presents a scoping review of already published literature, patient and public involvement was not applicable.

Of 7005 citations identified in the database (n=6827) and grey literature (n=178) searches, we included 78 (54 peer-reviewed publications; 25 grey literature) ( figure 1 ). The study characteristics are highlighted in online supplemental table 2A, B .

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PRISMA flow diagram. PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews.

Online supplemental table S2A characteristics of peer-reviewed studies on the definitions of OPR according to emergency type.

Online supplemental table S2B characteristics of grey literature publications on the definitions of OPR according to emergency type.

Definitions of OPR

Descriptions of OPR lacked clarity and consistency in definition and use. Nine primary research papers and one grey literature document provided explicit definitions of ‘readiness’ and/or ‘preparedness’ for infectious disease emergencies ( online supplemental table S3 ). 18–27 Of these, three 18 21 24 explicitly defined ‘readiness’ while the others used the term ‘preparedness’ in a way that was congruent with our working definition of OPR. The term readiness was used interchangeably with concepts of preparedness, response and recovery. In other included articles, the concept of readiness was reflected implicitly, as per our working definition.

Some included articles suggested that preparedness indicators, using tools like the State Party Self-Assessment Annual reporting tool (SPAR), could be used to indicate gaps for the purposes of targeting OPR actions. 24 28 Others suggested that a country’s OPR and response capacity depends on the strengths of its preparedness, with regular testing and updating of plans and capacities assessing country OPR. 22 26 However, some authors noted that countries’ responses to COVID-19 highlighted an incongruence between IHR compliance scores and response performance; for example, some countries with lower IHR scores demonstrated a better ability to contain COVID-19 at the early stages of the pandemic. 21 29 A lack of recently updated and tested plans and a lack of large-scale training and refresher courses or key actions for OPR, have been identified as reasons for inconsistency and weakness in previous responses. 23 25 Others have identified the activities they had taken as a result of lessons learned from similar diseases as a reason for more successful responses. 29 30 For example, rapid training and simulation exercises and leveraging specific expertise and experiences were considered important in preventing or mitigating an outbreak. 20 28 31

The nature of the imminent threat also influenced the scale and speed of OPR actions, along with the proximity to the hazard. 18–21 OPR could thus be considered the ‘operationalisation’ of hazard-specific capacities aimed at mitigation of a specific, identified risk. Triggering rapid action in response to an imminent threat was noted as a way to feedback and strengthen country capacities while effectively cutting costs of ‘firefighting’ public health emergencies.

While preparedness and OPR are used interchangeably by papers during this review, the reasonable abundance of literature dedicated to time-bound actions right before an event suggests they are different concepts. This observation prompts the necessity for a clear understanding of OPR and its differences from preparedness. Thus, OPR actions could build on overall preparedness levels but consist of time-sensitive activities focused on the imminent threat (eg, ensuring that the healthcare workforce has been recently trained for an imminent threat). These activities have been focused on ensuring that overall preparedness gaps are accounted for (eg, requesting international emergency medical teams (EMTs) to be ready to deploy if EMTs are unavailable in-country). In the following section, we detail the variety of OPR actions that have been taken in articles included in our review, in alignment with the HEPR subsystems. 20 28 31

Critical capabilities for OPR

Collaborative surveillance.

Previous emergencies highlighted the importance of a strong Early Warning System with capacity to improve disease outbreak detection for early action to localised health events. 32–34 Strong surveillance systems at all levels, rapid feedback of results and accessibility of information were described as critical for risk management and decision-making. 33 35 36 A critical review of epidemiological data linked with planning and decision-making to increase vigilance and real-time information sharing at all levels was viewed as critical to communicate changes in the incidence of disease, which could signal triggers. 22 35 37–40

Key OPR actions embedded in surveillance systems included updating case definitions for consistency in identifying and reporting cases, early investigation, proactive contract tracing training for all staff and rapidly updating guidance for clinicians. 18 19 23 41–44 Measures to rapidly ensure integration of various types of surveillance and to address gaps in information collection and sharing were noted. 19 37 40 Integration of human and animal health surveillance systems was viewed as critical, as was the importance of interoperability of surveillance systems. 38 40 The interconnectivity of surveillance systems has been stressed to ensure that actions taken, and information gathered in one part of the system are made aware to other parts. 22 45 For example, it was stressed that the occurrence of viral haemorrhagic fever in animals should activate enhanced surveillance. 38 The timely reconciliation of data from multiple sources has been noted as challenging without an escalation in trained staff, improved communication, information technology and accessibility to more remote locations. 32 The need to have epidemic data be open and transparent for decision-making was emphasised. 46

OPR actions taken for surveillance systems in anticipation of a disease outbreak were centred around detecting gaps and providing solutions, 19 47 improving case detection via procurement of supplies, distribution of case definitions and the deployment of screening teams, 28 44 47–49 improving reporting for Integrated Disease Surveillance and Response priority diseases 28 48 and strengthening specimen transportation and analysis. 47–49 Others included increased frequency of surveillance system results 36 and rapid delivery of updated training and mechanisms for data sharing. 28 29 50 Existing systems were leveraged for COVID-19 as a novel disease 37 or the private sector engaged to provide surge capacity. 31 Other efforts centred around digitising systems to improve flexibility of use and reporting times. 32 46 Contact tracing systems were established as OPR actions, 44 51 along with quarantine or isolation options, screening and referral pathways in community settings and dedicated transfers for suspect cases. 44 47 52

OPR actions for increasing diagnostics and laboratory capacity for surveillance included prepositioning laboratory supplies in high-risk areas which was described as key to facilitating the investigation of suspected cases (eg, specimen transportation containers, triple packages and gloves, transportation vehicles for specimens). 18 19 Electronic systems developed to improve laboratory results turnaround time, 19 the quick detection of hotspots 36 37 or digital contract tracing applications 37 were important developments implemented by countries by way of OPR actions. Lessons learnt from the digitalisation of contact tracing highlighted the importance of scaling up laboratory capacity to account for the increased demand for testing and to timeously ensure sufficient capability to test and process tests. 29 31 53 54 Mechanisms, if not available, should be rapidly instituted for sharing laboratory investigation data and establishing laboratory networks within and outside countries for timely diagnoses. 18 38

Included sources also signposted OPR actions for a collaborative approach to successful surveillance. For the rapid confirmation of novel influenza strains, for example, countries were successful in collaborating with WHO collaborating centres in their region. 35 Laboratory capacity in other countries were rapidly increased through the creation of laboratory networks. 18 42 In scenarios where a neighbouring country had a disease outbreak, cross-border surveillance teams have been established and the sharing of information between border countries improved and highlighted as a reason for the limited spill. 19 During COVID-19, surveillance was rapidly readied at the point of entries, including standard operating procedures for detected cases and awareness-raising sessions for personnel. 55 56

Community protection

Included articles highlighted key actions to upscale for rapidly involving and engaging affected communities in anticipation of an imminent threat. 22 57 These include rapidly providing updated information about the threat, including on identifying symptoms and any known public health and social measures, disseminated through numerous mechanisms and in a variety of languages to those at risk. 19 23 31 33 46 These should be adapted for all literacy levels. 58 Value was found in daily communications to build public trust. 37 Community volunteers were trained to carry out communal and door-to-door health education 19 32 or public websites containing epidemic reports to keep communities informed. 46

Further recommendations highlighted risk communications and public health and social measures to be rapidly readied to contain any potential community transmission. 18 21 48 51 59–61 These communications should allow the public to have a proper understanding of the perceived risk. 35 Other recommendations included working with local influencers to disseminate trusted information 47 and creating specialised focus messages for high-risk populations. 26 62 Crucially, there should be strong efforts for engaging vulnerable populations. 28 31 57

Plans and protocols should be in place for community-specific risk assessments to fill gaps in community OPR. 28 These assessments should focus on community perception, knowledge, preferred and accessible communication channels and existing barriers preventing community members from adopting promoted behaviours. 47 Plans should further account for resources for social security to support vulnerable communities. 40 To support this, community-based measures such as leveraging the community health workforce and community-based actors should be considered. 52 In this way, community needs and realities can be accounted for in the development of risk communication and community protection interventions. Misconceptions in the community should be identified and efforts made to dispel misinformation. 44

Some papers highlighted the early identification of vulnerable and remote population groups to ensure that their unique needs are well understood and addressed both in the design of interventions and in mitigating the impact of response interventions. 28 57 Accordingly, planning OPR should involve the input of communities, particularly organisations representing vulnerable groups, to inform community OPR. 47 Plans for response action should additionally consider secondary impacts or unintended consequences. For example, a clear lesson from COVID-19 related to the need for social security policies to mitigate the impacts of restrictive public health and social measures. 63 Policies for implementation should incorporate social security safety nets for communities, such as social health protection schemes or providing financial assistance for quarantined populations. 40 Plans should further be supported by partners. 64 Indirect health impacts should also be considered when OPR actions are implemented. 65 For example, some countries rapidly scaled up their capabilities for mental health services by implementing psychiatric hotlines 66 or providing stress management protocols. 48 Other indirect health impacts could include food insecurity; to prevent this, doorstep delivery of daily essentials 31 or provision of prepackaged meals 39 were planned.

Numerous papers highlighted the need for public health and social measures to be available rapidly and as early as possible, such as (for respiratory disease outbreaks) mask usage in public places when the risk level was high 31 36 46 52 63 and access to water, sanitation and hygiene, 44 48 with additional measures in place for individuals at risk of complications at the household level, such as using physical barriers, proper wearing of masks and environmental cleaning. 52 If non-existent, a strategy should be in place to assist in accelerating the containment of disease through imposing various public health and social measures, such as limits on local and international travel, the wearing of masks in public places, 37 social distancing, 67 bans or limits on mass gathering events 33 48 and closing educational institutions. 36 48 These measures were all implemented to a varying degree during COVID-19, with analyses finding that the earlier efforts of containment generally resulted in better containment early in the pandemic. 21 The measures taken should be weighed against the possibility of improving detection and spread through other methods, such as a rapid expansion of laboratory testing. 63 Public health and social measures should additionally take into account other likely risks—for example, countries with hurricane-prone areas during COVID-19 had to quickly revise their strategies to ensure social distancing in shelters. 39 If vaccines are available, a prioritisation policy should be developed to avoid ethical and political conflicts. 23

Safe and scalable care

For the health service to function during an emergency, they need a baseline quota of adequate staffing to perform core functions. 68 Included articles stressed OPR to surge additional healthcare personnel. 31 The healthcare workforce needs updated case definitions, transmission, clinical presentation, infection prevention and control (IPC), community surveillance and case management for the threat. 19 Capacity assessments can guide OPR to estimate the ability of health systems to contain the imminent threat 36 37 41 and to identify gaps. 29 36 Additional recommendations highlight that capacity modelling should integrate risks to the workforce during the response—previously, health workforce absenteeism has not always been considered in the development of staffing plans, leading to reduced response capacities. 57 When scaling up healthcare worker OPR for a threat, actions should also be taken to scale up the services to support them. 64 Health systems gaps have been addressed by increasing the space of intensive care unit beds in relevant facilities, human resource training and mobilisation 20 36 48 49 63 69–71 and reducing the workload (eg, patients with mild symptoms were managed at home in isolation). 46 Referral systems and safe pathways should be established. 36 42 52

COVID-19 highlighted the importance of maintaining essential health services during an emergency. Many studies under review did not immediately prioritise this when considering OPR for the imminent threat. Measures taken proactively to maintain essential health services and to reduce the stress on the health system were described, such as giving patients with chronic diseases a stockpile to prevent them from coming to the hospitals 31 72 and use of telemedicine. 31 40 It was recommended to establish referral systems and safe pathways to designated local isolation facilities and enhance case detection in healthcare facilities and the community. 47 Others emphasised their learnings from response to diseases before COVID-19 and maintaining the continuum of care 36 40 - for example, Korea created two systems (COVID-19 health system vs non-COVID-19 health system) to ensure continuity of non-COVID-19 needs and diverted the flow of patients through triage centres. 36 Measures were taken to safeguard hospitals not identified as part of the response, for example, using temperature checks or encouraging the use of masks. 33 40

Included articles also noted that staff protection and welfare should be strongly included in OPR planning, for example, to anticipate provision of personal protective equipment (PPE) and supplies for staff protection. 73 74 An IPC programme should be implemented before an outbreak. 33 38 63 Prepositioning of PPE supplies in high-risk districts has been recommended to enable a more rapid response, 19 or if the risk level is low, the availability of a regional reserve of PPE. 75 Where PPE was unavailable, production was quickly ramped up to be able to maintain inventory before the response 76 - others who did not do this noted that they suffered shortages during the response. 46 Regular training and simulation exercises were conducted for case management teams. 19 38 Psychosocial support and other interventions necessary to support staff welfare were also emphasised. 26 40 Others quickly put legislation into place to protect healthcare workers engaged in response from being attacked. 31

Access to countermeasures

There were fewer descriptions of OPR in this HEPR subsystem in comparison with others. When gearing up for response, countries have increased production and procurement by procuring from local industry, working with manufacturing companies to increase supply by, for example, adapting manufacturing facilities or establishing warehouses and transportation. 18 31 Numerous studies noted that they had extreme difficulty in obtaining the supplies they needed, 40 46 due to limited stockpiles and lack of finances to maintain them. 23 OPR actions for an imminent threat would focus on scaling up manufacturing plans and to ensure that a stockpile is in place.

Prepositioning essential supplies is essential for OPR, with an adequate supply of medical equipment to the frontline identified as vital for reducing health emergency risks. 77 Additionally, measures to quickly acquire and distribute medical supplies using government-set prices, prioritise frontline health professionals and vulnerable populations for the disbursement of medical countermeasures and promote local manufacturing were identified. 20 Other countries described OPR actions to introduce therapeutics, diagnostics and vaccines. 37

One study identified research topics such as system OPR, knowledge, attitudes and practices of the health workforce, epidemiology of the disease at the national level, best practices at the points of entries and isolation centres and infection-control measures as important to inform OPR actions. 78 Research should also support decision-making, cost-effectiveness, intervention effectiveness and the impact of these on pandemic trajectories. 50 79 Competing demands can limit the volume of research conducted which was considered a missed opportunity. 32 Early convening of expert groups to advise government was identified as useful for managing health service responses and OPR, and their work should as far as possible be informed by evidence (eg, scenario planning). 33 Health systems researchers occupying the highest levels of oversight across the sectors were said to enhance the use of evidence and data for decision-making. 36 Another paper noted that lessons learnt by regions found that funding for research and investigations during OPR and response should also be in place. 39

Emergency coordination

We identified several critical and overarching governance-related elements that facilitated OPR within regions and countries. Lessons from OPR or responses to previous diseases have demonstrated the importance of a coordinating body at regional or national levels 19 35 36 41 42 46 48 75 78 80 81 led by high-level officials. 19 48 80 These structures should provide leadership and coordination, 42 46 62 82 guidance and action plans, 36 and communication of critical information. 48 80 Strong and skilled leadership was a notable enabler 29 32 36 54 83 and was marked by active OPR involvement of the responsible health departments, and effective coordination with multiple stakeholders as the planning or response evolved. 29 32 54 82 84 Flexibility and adaptation, particularly during OPR, were important. 32

Many included articles emphasised the timely activation of coordination mechanisms and risk assessments to inform plans. 18 19 31 34 38 47 54 69 75 83 85 86 This involved the establishment and operationalisation of intersectoral and/or interdisciplinary teams (eg, task teams, 19 33 75 80 special councils 41 42 46 and command centres 30 41 ) to provide technical expertise, 25 42 78 87 prepare and coordinate the implementation of policy decisions 32 80 87 and guide lower health system-level or governmental-level structures or actors. 28 32 88 An Incident Management System was adopted in several countries with a dedicated lead, 32 35 36 83 89 and this was further recommended in the grey literature. 72 90 91 When operationalising these aspects for an efficient and effective response, the early establishment of clear roles and responsibilities, with a clear lead was considered vital and instrumental for later response success. 28 32 The highest levels of government should be involved, with an all-of-society and/or all-of-government approach. 32 35 69 70 79 87–89

To successfully implement coordination and response to an emergency, workforce management is key for a successful response. Actions taken include recruitment of staff from the private sector, healthcare students or retired or non-practising trained workers, 31 40 42 48 78 89 92 community health workers and community-based organisations 19 31 40 48 73 or volunteers. 19 48 89 Grey literature emphasised, actions in support of cross-border response teams or surge teams with rapid staff registration and accreditation systems, staff redeployment and reallocation, 18 72 93 and appropriate training. 18 90 94 95 Also critical was ensuring the availability of emergency medical services for immediate response and the early deployment of multidisciplinary Rapid Response Teams in high-risk groups. 23 31 53 83 87 89 Some papers emphasised prioritising actions which enable rapid deployment of these teams. 53 83

Other important factors included threat-specific contingency planning at national and subnational levels for identifying preparedness gaps and actions to work around them, thus supporting rapid detection, response and containment. 18 19 35 83 89 Contingency plans helped to prioritise targeted actions 83 as well as identify and prioritise at-risk geographic areas and vulnerable communities. 40 57 Having recently updated or tested contingency plans in place was stated as essential to enhance OPR and effective response, 25 39 96 and these should support operations and logistics, help understand organisational structures and functions, and optimise resources. 44 68 93 They should further ensure critical infrastructure for health system functioning and ensure clinical and health service-level plans are detailed and able to assist in preparing for increased patient volumes or need for critical care services. 19 68 Contingency plans should incorporate past experiences and learnings from other outbreaks, changing contexts 52 and the results of simulation exercises conducted on the preparedness and response systems. 18 19 23 Countries with similar public health emergency experiences have been found to be better prepared than those without previous experience, 63 raising the importance of practice, via simulation exercises and training, for a new imminent threat. 19 23

Furthermore, country risk and vulnerability assessments should be available and guide risk assessment activities. 19 31 35 38 39 47 52 53 57 84 They were recommended to be focused on geographical areas with particularly high assessed risks 39 52 89 and related to prevention and control strategies. 19 47 84 The assessments should be conducted to ensure that the contingency plans contain appropriate OPR actions and consider local contexts 47 68 89 and can also be used to guide the prioritisation of actions. 47 89 Risk assessments for future waves or outbreaks should also be conducted, and updated worst-case scenarios incorporated into contingency plans. 39 63

OPR needs bespoke financial planning. 22 28 70 It was recommended that contingency funds be available for OPR, 83 ring-fenced and situated within a dedicated emergency programme. 19 50 70 There should be existing emergency financial management systems which allow for rapid, transparent and efficient use of funding. 40 42 Contingency funds were emphasised as particularly important as resources should not be diverted from necessary routine programmes. 25 50 Having contingency funds in place would ensure a few key capacities: first, earmarked resources for the hazard are ensured 22 and lead to rapid activation of key surveillance and early response activities. 25 50 Second, changes which may need to occur to financing healthcare services are already outlined, such as creating financial protection mechanisms for discontinued outpatient services or outlining how citizens or health insurance systems pay for screening and diagnostic testing. 42 Finally, contingency funds should cover workforce surge, including staff, supplies, training and workforce management. 73

This scoping review examined definitions and critical elements of OPR for public health emergencies. We sought to identify key actions that were mobilised in anticipation of an imminent threat framed in the latest conceptualisation of a global architecture for health emergency management. From 54 peer-reviewed publications and 24 grey literature sources, we found that the concept of OPR was in an early stage of adoption. Where the term was explicitly defined, these definitions lacked coherence and consistency and included articles that matched our working definition of OPR, often did not use the term. Our analysis highlights the important need for conceptual clarity regarding OPR. We agreed on a working definition of OPR at the outset of the review as those immediate actions taken in the presence of an imminent threat that is rapidly mobilised or prepositioned to respond to that threat. It was also often difficult to identify where the line between preparedness, OPR and response lay. For our purposes, these distinctions are relevant in so far as they can guide early detection and timely activation of key OPR capabilities in useful and practical ways. Put simply: when a hurricane is coming, you may rapidly begin to take measures to prevent damage to your house. These could include actions such as securing loose objects, protecting windows, turning off utilities and filling tubs with water. These actions, taken before a storm, would differ greatly from the years spent building and maintaining the house beforehand - ensuring the foundation is sound, and the roof has been well maintained. They would further differ from the actions would take immediately during and after the storm has hit.

This review was initiated during the dynamic and fast-moving context of a pandemic where important policy developments were advancing in parallel. To maximise the utility of this work, we reanalysed our findings to map to the HEPR framework once it became publicly available for wider discussion among WHO member states. Our analysis across the body of articles included in this review identified OPR actions that mapped to the five core subsystems considered critical to strengthen the global HEPR. Additionally, our review mostly identified national-level capabilities and provided less insight into key actions to activate subnational and local capabilities. This observation may reflect a limitation of our review, an under-reporting, or a need to further develop and define OPR at these levels.

Across articles included in this review, OPR actions were identified as those that aimed to fill gaps in a country’s capacities or to prepare for an early response. In this way, a key contribution of embedding OPR in health emergency management is in institutionalising prompt action as soon as a potential signal is detected. Of note are the many actions identified for emergency coordination, including strong, high-level leadership, governance and coordination, with clarity around the roles and responsibilities of the leaders and the coordination bodies. Collaborative surveillance that allows for early detection of signals is key for OPR in terms of triggering action. This is an underdeveloped part of readiness practice. Other important areas included rapid, integrated and interoperative health information systems for purposes such as surveillance, planning and decision-making, managing operations, and monitoring country responses. The ability to rapidly plan for, mobilise and manage resources (eg, human, PPE, financial) and scale-up services (eg, essential or laboratory) underpinned by supportive legislation were also identified. Clear and strong communication at the level of the policy-maker, within the services and in the community was also identified as crucial for optimal OPR. We note gaps related to research and manufacturing platforms enabled by technology and our analysis did not consider OPR actions at the intersection of the five subsystems, for example, the readiness of communities for early detection to support collaborative surveillance or for participation in clinical trials of novel medical countermeasures.

The review methodology has strengths and limitations. This work was done rapidly by a large team with the aim of underpinning practical technical products for OPR in health emergency risk management. A scoping review methodology was best suited to answer our research question, due to the broad base of evidence. 13 As far as possible, we followed expert group recommendations on the adaptations needed in the conduct of rapid reviews. 9 14 Our initial analysis mapped key thematic categories in the HEPR Framework. 1 To align with global policy developments that have led to the HEPR framework, we updated our analysis. In this process, we may have missed new articles that would add further insight into OPR experience. However, given the pragmatic focus for this review, and the global consensus work that has followed, it is unlikely that further updates to this review would significantly alter our key conclusions. Since this review, there has been significant progress in actions to strengthen the global HEPR architecture. A more thorough review of OPR, one for each of the subsystems, is needed to reflect the recently published breakdown of HEPR subsystems into capabilities. 4 Further, as OPR becomes engrained in health emergency response, a review to identify the optimal time frame needed to quickly and effectively operationalise the capabilities of every subsystem is needed. Additionally, the purpose of the review was not to identify how OPR actions have increased resilience. Future research is needed now that OPR has been defined to identify the OPR interventions which maximise populations; abilities to withstand an event and increase resilience. Finally, our review does not include a body of work on anticipatory actions, which aligns well with OPR. Anticipatory actions are defined as ‘actions taken ahead of predicted hazards to prevent or reduce acute humanitarian impacts before they fully unfold’. 97 They highlight OPR as part of emergency management, particularly for disaster management and in humanitarian contexts. 98 The outcome of these meetings reflects a growing consensus on the critical importance of OPR. The essence of OPR is to mobilise early action when a threat is on the horizon. The work reported in this paper is an important step to advancing this important and urgent agenda. Indeed, this work has now set a foundation for the more substantive and coherent development of the evidence in this important area and has provided input to readiness actions within the recently published IHR Benchmarks, and is informing the creation of readiness assessments and has informed the creation of a readiness course on OpenWHO. 99 100

Ethics statements

Patient consent for publication.

Not applicable.

Acknowledgments

We would like to thank Professor Taryn Young for guidance regarding the methodology and Hilmar Luckoff for editing earlier versions of the paper. The rapid scoping review was commissioned by the WHO to inform an Operational Readiness Framework for the Country Readiness Strengthening Department in the World Health Emergencies Program in WHO (Reference #: 2021/1145765; Unit: MST; Cluster: QNF/SCI).

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Handling editor Helen J Surana

Contributors Conceptualisation: RE, HG, QL, JCYN, NG and LLB; Data extraction: MYC and MP; Formal analysis: QL, RE, HG, CJ, JCYN, NG, HC and NE (synthesis); MYC, MP and KB (descriptive); Funding acquisition: RE; Methodology: QL, MM, KB, MP, MYC, CJ, NG and LLB; Project administration: JCYN; Software: MYC, MP, MM and KB; Source screening: MP, MYC, RE, HG, QL, KB and JCYN; Supervision: RE; Visualisation: QL and MYC; Writing–original draft preparation: KB, MYC, QL, MM, MP, CJ, RE, HG, JCYN, NG and LLB; Writing–review and editing: KB, MYC and NE; Writing–final version review: All authors have read and approved the final version of the report manuscript. RE is the nominated guarantor.

Funding This work was supported by the WHO (Reference: APW/RR/Readiness/2021/1145765). The manuscript development and publication were funded in part by the Wellcome Trust and the UK Foreign and Commonwealth Development Office under grant agreement 222037/A/20/Z and in part by the United States Agency for International Development (USAID) under grant agreement 720BHA21IO00300.

Disclaimer The authors alone are responsible for the views expressed in this article and they do not necessarily represent the views, decisions or policies of the institutions with which they are affiliated.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Guidance Regarding Methods for De-identification of Protected Health Information in Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule

This page provides guidance about methods and approaches to achieve de-identification in accordance with the Health Insurance Portability and Accountability Act of 1996 (HIPAA) Privacy Rule. The guidance explains and answers questions regarding the two methods that can be used to satisfy the Privacy Rule’s de-identification standard: Expert Determination and Safe Harbor 1 .  This guidance is intended to assist covered entities to understand what is de-identification, the general process by which de-identified information is created, and the options available for performing de-identification.

In developing this guidance, the Office for Civil Rights (OCR) solicited input from stakeholders with practical, technical and policy experience in de-identification.  OCR convened stakeholders at a workshop consisting of multiple panel sessions held March 8-9, 2010, in Washington, DC. Each panel addressed a specific topic related to the Privacy Rule’s de-identification methodologies and policies. The workshop was open to the public and each panel was followed by a question and answer period.  Read more on the Workshop on the HIPAA Privacy Rule's De-Identification Standard. Read the Full Guidance .

1.1 Protected Health Information 1.2 Covered Entities, Business Associates, and PHI 1.3 De-identification and its Rationale 1.4 The De-identification Standard 1.5 Preparation for De-identification

Guidance on Satisfying the Expert Determination Method

2.1 Have expert determinations been applied outside of the health field? 2.2 Who is an “expert?” 2.3 What is an acceptable level of identification risk for an expert determination? 2.4 How long is an expert determination valid for a given data set? 2.5 Can an expert derive multiple solutions from the same data set for a recipient? 2.6 How do experts assess the risk of identification of information? 2.7 What are the approaches by which an expert assesses the risk that health information can be identified? 2.8 What are the approaches by which an expert mitigates the risk of identification of an individual in health information? 2.9 Can an Expert determine a code derived from PHI is de-identified? 2.10 Must a covered entity use a data use agreement when sharing de-identified data to satisfy the Expert Determination Method?

Guidance on Satisfying the Safe Harbor Method

3.1 When can ZIP codes be included in de-identified information? 3.2 May parts or derivatives of any of the listed identifiers be disclosed consistent with the Safe Harbor Method? 3.3 What are examples of dates that are not permitted according to the Safe Harbor Method? 3.4 Can dates associated with test measures for a patient be reported in accordance with Safe Harbor? 3.5 What constitutes “any other unique identifying number, characteristic, or code” with respect to the Safe Harbor method of the Privacy Rule? 3.6 What is “actual knowledge” that the remaining information could be used either alone or in combination with other information to identify an individual who is a subject of the information? 3.7 If a covered entity knows of specific studies about methods to re-identify health information or use de-identified health information alone or in combination with other information to identify an individual, does this necessarily mean a covered entity has actual knowledge under the Safe Harbor method? 3.8 Must a covered entity suppress all personal names, such as physician names, from health information for it to be designated as de-identified? 3.9 Must a covered entity use a data use agreement when sharing de-identified data to satisfy the Safe Harbor Method? 3.10 Must a covered entity remove protected health information from free text fields to satisfy the Safe Harbor Method?

Glossary of Terms

Protected health information.

The HIPAA Privacy Rule protects most “individually identifiable health information” held or transmitted by a covered entity or its business associate, in any form or medium, whether electronic, on paper, or oral. The Privacy Rule calls this information protected health information (PHI) 2 . Protected health information is information, including demographic information, which relates to:

• the individual’s past, present, or future physical or mental health or condition,
• the provision of health care to the individual, or
• the past, present, or future payment for the provision of health care to the individual, and that identifies the individual or for which there is a reasonable basis to believe can be used to identify the individual. Protected health information includes many common identifiers (e.g., name, address, birth date, Social Security Number) when they can be associated with the health information listed above.

For example, a medical record, laboratory report, or hospital bill would be PHI because each document would contain a patient’s name and/or other identifying information associated with the health data content.

By contrast, a health plan report that only noted the average age of health plan members was 45 years would not be PHI because that information, although developed by aggregating information from individual plan member records, does not identify any individual plan members and there is no reasonable basis to believe that it could be used to identify an individual.

The relationship with health information is fundamental.  Identifying information alone, such as personal names, residential addresses, or phone numbers, would not necessarily be designated as PHI.  For instance, if such information was reported as part of a publicly accessible data source, such as a phone book, then this information would not be PHI because it is not related to heath data (see above).  If such information was listed with health condition, health care provision or payment data, such as an indication that the individual was treated at a certain clinic, then this information would be PHI.

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Covered Entities, Business Associates, and PHI

In general, the protections of the Privacy Rule apply to information held by covered entities and their business associates.  HIPAA defines a covered entity as 1) a health care provider that conducts certain standard administrative and financial transactions in electronic form; 2) a health care clearinghouse; or 3) a health plan. 3   A business associate is a person or entity (other than a member of the covered entity’s workforce) that performs certain functions or activities on behalf of, or provides certain services to, a covered entity that involve the use or disclosure of protected health information. A covered entity may use a business associate to de-identify PHI on its behalf only to the extent such activity is authorized by their business associate agreement.

See the OCR website https://www.hhs.gov/ocr/privacy/ for detailed information about the Privacy Rule and how it protects the privacy of health information.

De-identification and its Rationale

The increasing adoption of health information technologies in the United States accelerates their potential to facilitate beneficial studies that combine large, complex data sets from multiple sources.  The process of de-identification, by which identifiers are removed from the health information, mitigates privacy risks to individuals and thereby supports the secondary use of data for comparative effectiveness studies, policy assessment, life sciences research, and other endeavors.

The Privacy Rule was designed to protect individually identifiable health information through permitting only certain uses and disclosures of PHI provided by the Rule, or as authorized by the individual subject of the information.  However, in recognition of the potential utility of health information even when it is not individually identifiable, §164.502(d) of the Privacy Rule permits a covered entity or its business associate to create information that is not individually identifiable by following the de-identification standard and implementation specifications in §164.514(a)-(b).  These provisions allow the entity to use and disclose information that neither identifies nor provides a reasonable basis to identify an individual. 4 As discussed below, the Privacy Rule provides two de-identification methods: 1) a formal determination by a qualified expert; or 2) the removal of specified individual identifiers as well as absence of actual knowledge by the covered entity that the remaining information could be used alone or in combination with other information to identify the individual.

Both methods, even when properly applied, yield de-identified data that retains some risk of identification.  Although the risk is very small, it is not zero, and there is a possibility that de-identified data could be linked back to the identity of the patient to which it corresponds.

Regardless of the method by which de-identification is achieved, the Privacy Rule does not restrict the use or disclosure of de-identified health information, as it is no longer considered protected health information.

The De-identification Standard

Section 164.514(a) of the HIPAA Privacy Rule provides the standard for de-identification of protected health information.  Under this standard, health information is not individually identifiable if it does not identify an individual and if the covered entity has no reasonable basis to believe it can be used to identify an individual.

§ 164.514 Other requirements relating to uses and disclosures of protected health information. (a) Standard: de-identification of protected health information. Health information that does not identify an individual and with respect to which there is no reasonable basis to believe that the information can be used to identify an individual is not individually identifiable health information.

Sections 164.514(b) and(c) of the Privacy Rule contain the implementation specifications that a covered entity must follow to meet the de-identification standard. As summarized in Figure 1, the Privacy Rule provides two methods by which health information can be designated as de-identified.

Figure 1. Two methods to achieve de-identification in accordance with the HIPAA Privacy Rule.

The first is the “Expert Determination” method:

(b) Implementation specifications: requirements for de-identification of protected health information. A covered entity may determine that health information is not individually identifiable health information only if: (1) A person with appropriate knowledge of and experience with generally accepted statistical and scientific principles and methods for rendering information not individually identifiable: (i) Applying such principles and methods, determines that the risk is very small that the information could be used, alone or in combination with other reasonably available information, by an anticipated recipient to identify an individual who is a subject of the information; and (ii) Documents the methods and results of the analysis that justify such determination; or

The second is the “Safe Harbor” method:

(2)(i) The following identifiers of the individual or of relatives, employers, or household members of the individual, are removed:

(B) All geographic subdivisions smaller than a state, including street address, city, county, precinct, ZIP code, and their equivalent geocodes, except for the initial three digits of the ZIP code if, according to the current publicly available data from the Bureau of the Census: (1) The geographic unit formed by combining all ZIP codes with the same three initial digits contains more than 20,000 people; and (2) The initial three digits of a ZIP code for all such geographic units containing 20,000 or fewer people is changed to 000

(C) All elements of dates (except year) for dates that are directly related to an individual, including birth date, admission date, discharge date, death date, and all ages over 89 and all elements of dates (including year) indicative of such age, except that such ages and elements may be aggregated into a single category of age 90 or older

(D) Telephone numbers

(L) Vehicle identifiers and serial numbers, including license plate numbers

(E) Fax numbers

(M) Device identifiers and serial numbers

(F) Email addresses

(N) Web Universal Resource Locators (URLs)

(G) Social security numbers

(O) Internet Protocol (IP) addresses

(H) Medical record numbers

(P) Biometric identifiers, including finger and voice prints

(I) Health plan beneficiary numbers

(Q) Full-face photographs and any comparable images

(J) Account numbers

(R) Any other unique identifying number, characteristic, or code, except as permitted by paragraph (c) of this section [Paragraph (c) is presented below in the section “Re-identification”]; and

(K) Certificate/license numbers

(ii) The covered entity does not have actual knowledge that the information could be used alone or in combination with other information to identify an individual who is a subject of the information.

Satisfying either method would demonstrate that a covered entity has met the standard in §164.514(a) above.  De-identified health information created following these methods is no longer protected by the Privacy Rule because it does not fall within the definition of PHI.  Of course, de-identification leads to information loss which may limit the usefulness of the resulting health information in certain circumstances. As described in the forthcoming sections, covered entities may wish to select de-identification strategies that minimize such loss.

Re-identification

The implementation specifications further provide direction with respect to re-identification , specifically the assignment of a unique code to the set of de-identified health information to permit re-identification by the covered entity.

If a covered entity or business associate successfully undertook an effort to identify the subject of de-identified information it maintained, the health information now related to a specific individual would again be protected by the Privacy Rule, as it would meet the definition of PHI.  Disclosure of a code or other means of record identification designed to enable coded or otherwise de-identified information to be re-identified is also considered a disclosure of PHI.

(c) Implementation specifications: re-identification. A covered entity may assign a code or other means of record identification to allow information de-identified under this section to be re-identified by the covered entity, provided that: (1) Derivation. The code or other means of record identification is not derived from or related to information about the individual and is not otherwise capable of being translated so as to identify the individual; and (2) Security. The covered entity does not use or disclose the code or other means of record identification for any other purpose, and does not disclose the mechanism for re-identification.

Preparation for De-identification

The importance of documentation for which values in health data correspond to PHI, as well as the systems that manage PHI, for the de-identification process cannot be overstated.  Esoteric notation, such as acronyms whose meaning are known to only a select few employees of a covered entity, and incomplete description may lead those overseeing a de-identification procedure to unnecessarily redact information or to fail to redact when necessary.  When sufficient documentation is provided, it is straightforward to redact the appropriate fields.  See section 3.10 for a more complete discussion.

In the following two sections, we address questions regarding the Expert Determination method (Section 2) and the Safe Harbor method (Section 3).

In §164.514(b), the Expert Determination method for de-identification is defined as follows:

 (1) A person with appropriate knowledge of and experience with generally accepted statistical and scientific principles and methods for rendering information not individually identifiable: (i) Applying such principles and methods, determines that the risk is very small that the information could be used, alone or in combination with other reasonably available information, by an anticipated recipient to identify an individual who is a subject of the information; and (ii) Documents the methods and results of the analysis that justify such determination

Have expert determinations been applied outside of the health field?

Yes. The notion of expert certification is not unique to the health care field.  Professional scientists and statisticians in various fields routinely determine and accordingly mitigate risk prior to sharing data. The field of statistical disclosure limitation, for instance, has been developed within government statistical agencies, such as the Bureau of the Census, and applied to protect numerous types of data. 5

Who is an “expert?”

There is no specific professional degree or certification program for designating who is an expert at rendering health information de-identified.  Relevant expertise may be gained through various routes of education and experience. Experts may be found in the statistical, mathematical, or other scientific domains.  From an enforcement perspective, OCR would review the relevant professional experience and academic or other training of the expert used by the covered entity, as well as actual experience of the expert using health information de-identification methodologies.

What is an acceptable level of identification risk for an expert determination?

There is no explicit numerical level of identification risk that is deemed to universally meet the “very small” level indicated by the method.  The ability of a recipient of information to identify an individual (i.e., subject of the information) is dependent on many factors, which an expert will need to take into account while assessing the risk from a data set.  This is because the risk of identification that has been determined for one particular data set in the context of a specific environment may not be appropriate for the same data set in a different environment or a different data set in the same environment.  As a result, an expert will define an acceptable “very small” risk based on the ability of an anticipated recipient to identify an individual.  This issue is addressed in further depth in Section 2.6.

How long is an expert determination valid for a given data set?

The Privacy Rule does not explicitly require that an expiration date be attached to the determination that a data set, or the method that generated such a data set, is de-identified information.  However, experts have recognized that technology, social conditions, and the availability of information changes over time.  Consequently, certain de-identification practitioners use the approach of time-limited certifications.  In this sense, the expert will assess the expected change of computational capability, as well as access to various data sources, and then determine an appropriate timeframe within which the health information will be considered reasonably protected from identification of an individual.

Information that had previously been de-identified may still be adequately de-identified when the certification limit has been reached.  When the certification timeframe reaches its conclusion, it does not imply that the data which has already been disseminated is no longer sufficiently protected in accordance with the de-identification standard.  Covered entities will need to have an expert examine whether future releases of the data to the same recipient (e.g., monthly reporting) should be subject to additional or different de-identification processes consistent with current conditions to reach the very low risk requirement.

Can an expert derive multiple solutions from the same data set for a recipient?

Yes.  Experts may design multiple solutions, each of which is tailored to the covered entity’s expectations regarding information reasonably available to the anticipated recipient of the data set.  In such cases, the expert must take care to ensure that the data sets cannot be combined to compromise the protections set in place through the mitigation strategy. (Of course, the expert must also reduce the risk that the data sets could be combined with prior versions of the de-identified dataset or with other publically available datasets to identify an individual.) For instance, an expert may derive one data set that contains detailed geocodes and generalized aged values (e.g., 5-year age ranges) and another data set that contains generalized geocodes (e.g., only the first two digits) and fine-grained age (e.g., days from birth).  The expert may certify a covered entity to share both data sets after determining that the two data sets could not be merged to individually identify a patient.  This certification may be based on a technical proof regarding the inability to merge such data sets.  Alternatively, the expert also could require additional safeguards through a data use agreement.

How do experts assess the risk of identification of information?

No single universal solution addresses all privacy and identifiability issues. Rather, a combination of technical and policy procedures are often applied to the de-identification task. OCR does not require a particular process for an expert to use to reach a determination that the risk of identification is very small.  However, the Rule does require that the methods and results of the analysis that justify the determination be documented and made available to OCR upon request. The following information is meant to provide covered entities with a general understanding of the de-identification process applied by an expert.  It does not provide sufficient detail in statistical or scientific methods to serve as a substitute for working with an expert in de-identification.

A general workflow for expert determination is depicted in Figure 2. Stakeholder input suggests that the determination of identification risk can be a process that consists of a series of steps.  First, the expert will evaluate the extent to which the health information can (or cannot) be identified by the anticipated recipients.  Second, the expert often will provide guidance to the covered entity or business associate on which statistical or scientific methods can be applied to the health information to mitigate the anticipated risk.  The expert will then execute such methods as deemed acceptable by the covered entity or business associate data managers, i.e., the officials responsible for the design and operations of the covered entity’s information systems.  Finally, the expert will evaluate the identifiability of the resulting health information to confirm that the risk is no more than very small when disclosed to the anticipated recipients.  Stakeholder input suggests that a process may require several iterations until the expert and data managers agree upon an acceptable solution. Regardless of the process or methods employed, the information must meet the very small risk specification requirement.

Figure 2.  Process for expert determination of de-Identification.

Data managers and administrators working with an expert to consider the risk of identification of a particular set of health information can look to the principles summarized in Table 1 for assistance. 6   These principles build on those defined by the Federal Committee on Statistical Methodology (which was referenced in the original publication of the Privacy Rule). 7 The table describes principles for considering the identification risk of health information. The principles should serve as a starting point for reasoning and are not meant to serve as a definitive list. In the process, experts are advised to consider how data sources that are available to a recipient of health information (e.g., computer systems that contain information about patients) could be utilized for identification of an individual. 8

Table 1. Principles used by experts in the determination of the identifiability of health information.

When evaluating identification risk, an expert often considers the degree to which a data set can be “linked” to a data source that reveals the identity of the corresponding individuals.  Linkage is a process that requires the satisfaction of certain conditions.  The first condition is that the de-identified data are unique or “distinguishing.”  It should be recognized, however, that the ability to distinguish data is, by itself, insufficient to compromise the corresponding patient’s privacy.  This is because of a second condition, which is the need for a naming data source, such as a publicly available voter registration database (see Section 2.6).  Without such a data source, there is no way to definitively link the de-identified health information to the corresponding patient. Finally, for the third condition, we need a mechanism to relate the de-identified and identified data sources. Inability to design such a relational mechanism would hamper a third party’s ability to achieve success to no better than random assignment of de-identified data and named individuals. The lack of a readily available naming data source does not imply that data are sufficiently protected from future identification, but it does indicate that it is harder to re-identify an individual, or group of individuals, given the data sources at hand. 

Example Scenario Imagine that a covered entity is considering sharing the information in the table to the left in Figure 3. This table is devoid of explicit identifiers, such as personal names and Social Security Numbers.  The information in this table is distinguishing, such that each row is unique on the combination of demographics (i.e., Age , ZIP Code , and Gender ).  Beyond this data, there exists a voter registration data source, which contains personal names, as well as demographics (i.e., Birthdate , ZIP Code , and Gender ), which are also distinguishing.  Linkage between the records in the tables is possible through the demographics.  Notice, however, that the first record in the covered entity’s table is not linked because the patient is not yet old enough to vote.

Figure 3.  Linking two data sources to identity diagnoses.

Thus, an important aspect of identification risk assessment is the route by which health information can be linked to naming sources or sensitive knowledge can be inferred. A higher risk “feature” is one that is found in many places and is publicly available. These are features that could be exploited by anyone who receives the information.  For instance, patient demographics could be classified as high-risk features.  In contrast, lower risk features are those that do not appear in public records or are less readily available.  For instance, clinical features, such as blood pressure, or temporal dependencies between events within a hospital (e.g., minutes between dispensation of pharmaceuticals) may uniquely characterize a patient in a hospital population, but the data sources to which such information could be linked to identify a patient are accessible to a much smaller set of people. 

Example Scenario An expert is asked to assess the identifiability of a patient’s demographics.  First, the expert will determine if the demographics are independently replicable .  Features such as birth date and gender are strongly independently replicable—the individual will always have the same birth date -- whereas ZIP code of residence is less so because an individual may relocate.  Second, the expert will determine which data sources that contain the individual’s identification also contain the demographics in question.  In this case, the expert may determine that public records, such as birth, death, and marriage registries, are the most likely data sources to be leveraged for identification.  Third, the expert will determine if the specific information to be disclosed is distinguishable .  At this point, the expert may determine that certain combinations of values (e.g., Asian males born in January of 1915 and living in a particular 5-digit ZIP code) are unique, whereas others (e.g., white females born in March of 1972 and living in a different 5-digit ZIP code) are never unique.  Finally, the expert will determine if the data sources that could be used in the identification process are readily accessible , which may differ by region.  For instance, voter registration registries are free in the state of North Carolina, but cost over $15,000 in the state of Wisconsin.  Thus, data shared in the former state may be deemed more risky than data shared in the latter. 12

What are the approaches by which an expert assesses the risk that health information can be identified?

The de-identification standard does not mandate a particular method for assessing risk.

A qualified expert may apply generally accepted statistical or scientific principles to compute the likelihood that a record in a data set is expected to be unique, or linkable to only one person, within the population to which it is being compared. Figure 4 provides a visualization of this concept. 13 This figure illustrates a situation in which the records in a data set are not a proper subset of the population for whom identified information is known.  This could occur, for instance, if the data set includes patients over one year-old but the population to which it is compared includes data on people over 18 years old (e.g., registered voters).

The computation of population uniques can be achieved in numerous ways, such as through the approaches outlined in published literature. 14 , 15   For instance, if an expert is attempting to assess if the combination of a patient’s race, age, and geographic region of residence is unique, the expert may use population statistics published by the U.S. Census Bureau to assist in this estimation.  In instances when population statistics are unavailable or unknown, the expert may calculate and rely on the statistics derived from the data set.  This is because a record can only be linked between the data set and the population to which it is being compared if it is unique in both.  Thus, by relying on the statistics derived from the data set, the expert will make a conservative estimate regarding the uniqueness of records. 

Example Scenario Imagine a covered entity has a data set in which there is one 25 year old male from a certain geographic region in the United States.  In truth, there are five 25 year old males in the geographic region in question (i.e., the population).  Unfortunately, there is no readily available data source to inform an expert about the number of 25 year old males in this geographic region.

By inspecting the data set, it is clear to the expert that there is at least one 25 year old male in the population, but the expert does not know if there are more.  So, without any additional knowledge, the expert assumes there are no more, such that the record in the data set is unique.  Based on this observation, the expert recommends removing this record from the data set.  In doing so, the expert has made a conservative decision with respect to the uniqueness of the record.

In the previous example, the expert provided a solution (i.e., removing a record from a dataset) to achieve de-identification, but this is one of many possible solutions that an expert could offer.  In practice, an expert may provide the covered entity with multiple alternative strategies, based on scientific or statistical principles, to mitigate risk.

Figure 4. Relationship between uniques in the data set and the broader population, as well as the degree to which linkage can be achieved.

The expert may consider different measures of “risk,” depending on the concern of the organization looking to disclose information.  The expert will attempt to determine which record in the data set is the most vulnerable to identification.  However, in certain instances, the expert may not know which particular record to be disclosed will be most vulnerable for identification purposes.  In this case, the expert may attempt to compute risk from several different perspectives. 

What are the approaches by which an expert mitigates the risk of identification of an individual in health information?

The Privacy Rule does not require a particular approach to mitigate, or reduce to very small, identification risk.  The following provides a survey of potential approaches.  An expert may find all or only one appropriate for a particular project, or may use another method entirely.

If an expert determines that the risk of identification is greater than very small, the expert may modify the information to mitigate the identification risk to that level, as required by the de-identification standard. In general, the expert will adjust certain features or values in the data to ensure that unique, identifiable elements no longer, or are not expected to, exist.  Some of the methods described below have been reviewed by the Federal Committee on Statistical Methodology 16 , which was referenced in the original preamble guidance to the Privacy Rule de-identification standard and recently revised.

Several broad classes of methods can be applied to protect data.  An overarching common goal of such approaches is to balance disclosure risk against data utility. 17   If one approach results in very small identity disclosure risk but also a set of data with little utility, another approach can be considered.  However, data utility does not determine when the de-identification standard of the Privacy Rule has been met.

Table 2 illustrates the application of such methods. In this example, we refer to columns as “features” about patients (e.g., Age and Gender) and rows as “records” of patients (e.g., the first and second rows correspond to records on two different patients).

Table 2. An example of protected health information.

A first class of identification risk mitigation methods corresponds to suppression techniques. These methods remove or eliminate certain features about the data prior to dissemination.  Suppression of an entire feature may be performed if a substantial quantity of records is considered as too risky (e.g., removal of the ZIP Code feature).  Suppression may also be performed on individual records, deleting records entirely if they are deemed too risky to share.  This can occur when a record is clearly very distinguishing (e.g., the only individual within a county that makes over $500,000 per year).   Alternatively, suppression of specific values within a record may be performed, such as when a particular value is deemed too risky (e.g., “President of the local university”, or ages or ZIP codes that may be unique).  Table 3 illustrates this last type of suppression by showing how specific values of features in Table 2 might be suppressed (i.e., black shaded cells).

Table 3. A version of Table 2 with suppressed patient values.

A second class of methods that can be applied for risk mitigation are based on generalization (sometimes referred to as abbreviation) of the information.  These methods transform data into more abstract representations.  For instance, a five-digit ZIP Code may be generalized to a four-digit ZIP Code, which in turn may be generalized to a three-digit ZIP Code, and onward so as to disclose data with lesser degrees of granularity.  Similarly, the age of a patient may be generalized from one- to five-year age groups. Table 4 illustrates how generalization (i.e., gray shaded cells) might be applied to the information in Table 2.

Table 4. A version of Table 2 with generalized patient values.

A third class of methods that can be applied for risk mitigation corresponds to perturbation .  In this case, specific values are replaced with equally specific, but different, values.  For instance, a patient’s age may be reported as a random value within a 5-year window of the actual age.  Table 5 illustrates how perturbation (i.e., gray shaded cells) might be applied to Table 2.  Notice that every age is within +/- 2 years of the original age.  Similarly, the final digit in each ZIP Code is within +/- 3 of the original ZIP Code.

Table 5. A version of Table 2 with randomized patient values.

In practice, perturbation is performed to maintain statistical properties about the original data, such as mean or variance.

The application of a method from one class does not necessarily preclude the application of a method from another class.  For instance, it is common to apply generalization and suppression to the same data set.

Using such methods, the expert will prove that the likelihood an undesirable event (e.g., future identification of an individual) will occur is very small.  For instance, one example of a data protection model that has been applied to health information is the k -anonymity principle. 18 , 19   In this model, “ k ” refers to the number of people to which each disclosed record must correspond.  In practice, this correspondence is assessed using the features that could be reasonably applied by a recipient to identify a patient.  Table 6 illustrates an application of generalization and suppression methods to achieve 2-anonymity with respect to the Age, Gender, and ZIP Code columns in Table 2.  The first two rows (i.e., shaded light gray) and last two rows (i.e., shaded dark gray) correspond to patient records with the same combination of generalized and suppressed values for Age, Gender, and ZIP Code.  Notice that Gender has been suppressed completely (i.e., black shaded cell).

Table 6, as well as a value of k equal to 2, is meant to serve as a simple example for illustrative purposes only.  Various state and federal agencies define policies regarding small cell counts (i.e., the number of people corresponding to the same combination of features) when sharing tabular, or summary, data. 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27   However, OCR does not designate a universal value for k that covered entities should apply to protect health information in accordance with the de-identification standard.  The value for k should be set at a level that is appropriate to mitigate risk of identification by the anticipated recipient of the data set. 28

Table 6. A version of Table 2 that is 2-anonymized.

As can be seen, there are many different disclosure risk reduction techniques that can be applied to health information. However, it should be noted that there is no particular method that is universally the best option for every covered entity and health information set.  Each method has benefits and drawbacks with respect to expected applications of the health information, which will be distinct for each covered entity and each intended recipient.  The determination of which method is most appropriate for the information will be assessed by the expert on a case-by-case basis and will be guided by input of the covered entity.

Finally, as noted in the preamble to the Privacy Rule, the expert may also consider the technique of limiting distribution of records through a data use agreement or restricted access agreement in which the recipient agrees to limits on who can use or receive the data, or agrees not to attempt identification of the subjects.  Of course, the specific details of such an agreement are left to the discretion of the expert and covered entity.

Can an Expert determine a code derived from PHI is de-identified?

There has been confusion about what constitutes a code and how it relates to PHI.  For clarification, our guidance is similar to that provided by the National Institutes of Standards and Technology (NIST) 29 , which states:

“ De-identified information can be re-identified (rendered distinguishable) by using a code, algorithm, or pseudonym that is assigned to individual records.  The code, algorithm, or pseudonym should not be derived from other related information* about the individual, and the means of re-identification should only be known by authorized parties and not disclosed to anyone without the authority to re-identify records.  A common de-identification technique for obscuring PII [Personally Identifiable Information] is to use a one-way cryptographic function, also known as a hash function, on the PII.

*This is not intended to exclude the application of cryptographic hash functions to the information.”

In line with this guidance from NIST, a covered entity may disclose codes derived from PHI as part of a de-identified data set if an expert determines that the data meets the de-identification requirements at §164.514(b)(1).  The re-identification provision in §164.514(c) does not preclude the transformation of PHI into values derived by cryptographic hash functions using the expert determination method, provided the keys associated with such functions are not disclosed, including to the recipients of the de-identified information.

Must a covered entity use a data use agreement when sharing de-identified data to satisfy the Expert Determination Method?

No. The Privacy Rule does not limit how a covered entity may disclose information that has been de-identified.  However, a covered entity may require the recipient of de-identified information to enter into a data use agreement to access files with known disclosure risk, such as is required for release of a limited data set under the Privacy Rule.  This agreement may contain a number of clauses designed to protect the data, such as prohibiting re-identification. 30 Of course, the use of a data use agreement does not substitute for any of the specific requirements of the Expert Determination Method. Further information about data use agreements can be found on the OCR website. 31   Covered entities may make their own assessments whether such additional oversight is appropriate.

In §164.514(b), the Safe Harbor method for de-identification is defined as follows:

(R) Any other unique identifying number, characteristic, or code, except as permitted by paragraph (c) of this section; and

When can ZIP codes be included in de-identified information?

Covered entities may include the first three digits of the ZIP code if, according to the current publicly available data from the Bureau of the Census: (1) The geographic unit formed by combining all ZIP codes with the same three initial digits contains more than 20,000 people; or (2) the initial three digits of a ZIP code for all such geographic units containing 20,000 or fewer people is changed to 000. This means that the initial three digits of ZIP codes may be included in de-identified information except when the ZIP codes contain the initial three digits listed in the Table below.  In those cases, the first three digits must be listed as 000.

OCR published a final rule on August 14, 2002, that modified certain standards in the Privacy Rule.  The preamble to this final rule identified the initial three digits of ZIP codes, or ZIP code tabulation areas (ZCTAs), that must change to 000 for release. 67 FR 53182, 53233-53234 (Aug. 14, 2002)).

Utilizing 2000 Census data, the following three-digit ZCTAs have a population of 20,000 or fewer persons. To produce a de-identified data set utilizing the safe harbor method, all records with three-digit ZIP codes corresponding to these three-digit ZCTAs must have the ZIP code changed to 000. Covered entities should not, however, rely upon this listing or the one found in the August 14, 2002 regulation if more current data has been published .

The 17 restricted ZIP codes are:

The Department notes that these three-digit ZIP codes are based on the five-digit ZIP Code Tabulation Areas created by the Census Bureau for the 2000 Census. This new methodology also is briefly described below, as it will likely be of interest to all users of data tabulated by ZIP code. The Census Bureau will not be producing data files containing U.S. Postal Service ZIP codes either as part of the Census 2000 product series or as a post Census 2000 product. However, due to the public’s interest in having statistics tabulated by ZIP code, the Census Bureau has created a new statistical area called the Zip Code Tabulation Area (ZCTA) for Census 2000. The ZCTAs were designed to overcome the operational difficulties of creating a well-defined ZIP code area by using Census blocks (and the addresses found in them) as the basis for the ZCTAs. In the past, there has been no correlation between ZIP codes and Census Bureau geography. Zip codes can cross State, place, county, census tract, block group, and census block boundaries. The geographic designations the Census Bureau uses to tabulate data are relatively stable over time. For instance, census tracts are only defined every ten years. In contrast, ZIP codes can change more frequently. Because of the ill-defined nature of ZIP code boundaries, the Census Bureau has no file (crosswalk) showing the relationship between US Census Bureau geography and U.S. Postal Service ZIP codes.

ZCTAs are generalized area representations of U.S. Postal Service (USPS) ZIP code service areas. Simply put, each one is built by aggregating the Census 2000 blocks, whose addresses use a given ZIP code, into a ZCTA which gets that ZIP code assigned as its ZCTA code. They represent the majority USPS five-digit ZIP code found in a given area. For those areas where it is difficult to determine the prevailing five-digit ZIP code, the higher-level three-digit ZIP code is used for the ZCTA code. For further information, go to: https://www.census.gov/programs-surveys/geography/guidance/geo-areas/zctas.html

The Bureau of the Census provides information regarding population density in the United States.  Covered entities are expected to rely on the most current publicly available Bureau of Census data regarding ZIP codes. This information can be downloaded from, or queried at, the American Fact Finder website (http://factfinder.census.gov).  As of the publication of this guidance, the information can be extracted from the detailed tables of the “Census 2000 Summary File 1 (SF 1) 100-Percent Data” files under the “Decennial Census” section of the website. The information is derived from the Decennial Census and was last updated in 2000.  It is expected that the Census Bureau will make data available from the 2010 Decennial Census in the near future.  This guidance will be updated when the Census makes new information available.

May parts or derivatives of any of the listed identifiers be disclosed consistent with the Safe Harbor Method?

No.  For example, a data set that contained patient initials, or the last four digits of a Social Security number, would not meet the requirement of the Safe Harbor method for de-identification.

What are examples of dates that are not permitted according to the Safe Harbor Method?

Elements of dates that are not permitted for disclosure include the day, month, and any other information that is more specific than the year of an event.  For instance, the date “January 1, 2009” could not be reported at this level of detail. However, it could be reported in a de-identified data set as “2009”.

Many records contain dates of service or other events that imply age.  Ages that are explicitly stated, or implied, as over 89 years old must be recoded as 90 or above.  For example, if the patient’s year of birth is 1910 and the year of healthcare service is reported as 2010, then in the de-identified data set the year of birth should be reported as “on or before 1920.”  Otherwise, a recipient of the data set would learn that the age of the patient is approximately 100.

Can dates associated with test measures for a patient be reported in accordance with Safe Harbor?

No. Dates associated with test measures, such as those derived from a laboratory report, are directly related to a specific individual and relate to the provision of health care. Such dates are protected health information.  As a result, no element of a date (except as described in 3.3. above) may be reported to adhere to Safe Harbor. 

What constitutes “any other unique identifying number, characteristic, or code” with respect to the Safe Harbor method of the Privacy Rule?

This category corresponds to any unique features that are not explicitly enumerated in the Safe Harbor list (A-Q), but could be used to identify a particular individual.  Thus, a covered entity must ensure that a data set stripped of the explicitly enumerated identifiers also does not contain any of these unique features.  The following are examples of such features:

Identifying Number There are many potential identifying numbers.  For example, the preamble to the Privacy Rule at 65 FR 82462, 82712 (Dec. 28, 2000) noted that “Clinical trial record numbers are included in the general category of ‘any other unique identifying number, characteristic, or code.’

Identifying Code A code corresponds to a value that is derived from a non-secure encoding mechanism.  For instance, a code derived from a secure hash function without a secret key (e.g., “salt”) would be considered an identifying element.  This is because the resulting value would be susceptible to compromise by the recipient of such data. As another example, an increasing quantity of electronic medical record and electronic prescribing systems assign and embed barcodes into patient records and their medications.  These barcodes are often designed to be unique for each patient, or event in a patient’s record, and thus can be easily applied for tracking purposes.  See the discussion of re-identification.

Identifying Characteristic A characteristic may be anything that distinguishes an individual and allows for identification.  For example, a unique identifying characteristic could be the occupation of a patient, if it was listed in a record as “current President of State University.”

Many questions have been received regarding what constitutes “any other unique identifying number, characteristic or code” in the Safe Harbor approach, §164.514(b)(2)(i)(R), above.  Generally, a code or other means of record identification that is derived from PHI would have to be removed from data de-identified following the safe harbor method.  To clarify what must be removed under (R), the implementation specifications at §164.514(c) provide an exception with respect to “re-identification” by the covered entity.  The objective of the paragraph is to permit covered entities to assign certain types of codes or other record identification to the de-identified information so that it may be re-identified by the covered entity at some later date. Such codes or other means of record identification assigned by the covered entity are not considered direct identifiers that must be removed under (R) if the covered entity follows the directions provided in §164.514(c).

What is “actual knowledge” that the remaining information could be used either alone or in combination with other information to identify an individual who is a subject of the information?

In the context of the Safe Harbor method, actual knowledge means clear and direct knowledge that the remaining information could be used, either alone or in combination with other information, to identify an individual who is a subject of the information.  This means that a covered entity has actual knowledge if it concludes that the remaining information could be used to identify the individual.  The covered entity, in other words, is aware that the information is not actually de-identified information.

The following examples illustrate when a covered entity would fail to meet the “actual knowledge” provision.

Example 1: Revealing Occupation Imagine a covered entity was aware that the occupation of a patient was listed in a record as “former president of the State University.”  This information in combination with almost any additional data – like age or state of residence – would clearly lead to an identification of the patient.  In this example, a covered entity would not satisfy the de-identification standard by simply removing the enumerated identifiers in §164.514(b)(2)(i) because the risk of identification is of a nature and degree that a covered entity must have concluded that the information could identify the patient.  Therefore, the data would not have satisfied the de-identification standard’s Safe Harbor method unless the covered entity made a sufficient good faith effort to remove the ‘‘occupation’’ field from the patient record.

Example 2: Clear Familial Relation Imagine a covered entity was aware that the anticipated recipient, a researcher who is an employee of the covered entity, had a family member in the data (e.g., spouse, parent, child, or sibling). In addition, the covered entity was aware that the data would provide sufficient context for the employee to recognize the relative.  For instance, the details of a complicated series of procedures, such as a primary surgery followed by a set of follow-up surgeries and examinations, for a person of a certain age and gender, might permit the recipient to comprehend that the data pertains to his or her relative’s case.  In this situation, the risk of identification is of a nature and degree that the covered entity must have concluded that the recipient could clearly and directly identify the individual in the data.  Therefore, the data would not have satisfied the de-identification standard’s Safe Harbor method.

Example 3: Publicized Clinical Event Rare clinical events may facilitate identification in a clear and direct manner.  For instance, imagine the information in a patient record revealed that a patient gave birth to an unusually large number of children at the same time.  During the year of this event, it is highly possible that this occurred for only one individual in the hospital (and perhaps the country).  As a result, the event was reported in the popular media, and the covered entity was aware of this media exposure.  In this case, the risk of identification is of a nature and degree that the covered entity must have concluded that the individual subject of the information could be identified by a recipient of the data.  Therefore, the data would not have satisfied the de-identification standard’s Safe Harbor method.

Example 4: Knowledge of a Recipient’s Ability Imagine a covered entity was told that the anticipated recipient of the data has a table or algorithm that can be used to identify the information, or a readily available mechanism to determine a patient’s identity.  In this situation, the covered entity has actual knowledge because it was informed outright that the recipient can identify a patient, unless it subsequently received information confirming that the recipient does not in fact have a means to identify a patient.  Therefore, the data would not have satisfied the de-identification standard’s Safe Harbor method.

If a covered entity knows of specific studies about methods to re-identify health information or use de-identified health information alone or in combination with other information to identify an individual, does this necessarily mean a covered entity has actual knowledge under the Safe Harbor method?

No.  Much has been written about the capabilities of researchers with certain analytic and quantitative capacities to combine information in particular ways to identify health information. 32 , 33 , 34 , 35   A covered entity may be aware of studies about methods to identify remaining information or using de-identified information alone or in combination with other information to identify an individual.  However, a covered entity’s mere knowledge of these studies and methods, by itself, does not mean it has “actual knowledge” that these methods would be used with the data it is disclosing.  OCR does not expect a covered entity to presume such capacities of all potential recipients of de-identified data.  This would not be consistent with the intent of the Safe Harbor method, which was to provide covered entities with a simple method to determine if the information is adequately de-identified.

Must a covered entity suppress all personal names, such as physician names, from health information for it to be designated as de-identified?

No. Only names of the individuals associated with the corresponding health information (i.e., the subjects of the records) and of their relatives, employers, and household members must be suppressed.  There is no explicit requirement to remove the names of providers or workforce members of the covered entity or business associate.  At the same time, there is also no requirement to retain such information in a de-identified data set.

Beyond the removal of names related to the patient, the covered entity would need to consider whether additional personal names contained in the data should be suppressed to meet the actual knowledge specification.  Additionally, other laws or confidentiality concerns may support the suppression of this information.

Must a covered entity use a data use agreement when sharing de-identified data to satisfy the Safe Harbor Method?

No. The Privacy Rule does not limit how a covered entity may disclose information that has been de-identified.  However, nothing prevents a covered entity from asking a recipient of de-identified information to enter into a data use agreement, such as is required for release of a limited data set under the Privacy Rule.  This agreement may prohibit re-identification. Of course, the use of a data use agreement does not substitute for any of the specific requirements of the Safe Harbor method. Further information about data use agreements can be found on the OCR website. 36   Covered entities may make their own assessments whether such additional oversight is appropriate.

Must a covered entity remove protected health information from free text fields to satisfy the Safe Harbor Method?

PHI may exist in different types of data in a multitude of forms and formats in a covered entity.  This data may reside in highly structured database tables, such as billing records. Yet, it may also be stored in a wide range of documents with less structure and written in natural language, such as discharge summaries, progress notes, and laboratory test interpretations.  These documents may vary with respect to the consistency and the format employed by the covered entity.

The de-identification standard makes no distinction between data entered into standardized fields and information entered as free text (i.e., structured and unstructured text) -- an identifier listed in the Safe Harbor standard must be removed regardless of its location in a record if it is recognizable as an identifier.

Whether additional information must be removed falls under the actual knowledge provision; the extent to which the covered entity has actual knowledge that residual information could be used to individually identify a patient. Clinical narratives in which a physician documents the history and/or lifestyle of a patient are information rich and may provide context that readily allows for patient identification.

Medical records are comprised of a wide range of structured and unstructured (also known as “free text”) documents.  In structured documents, it is relatively clear which fields contain the identifiers that must be removed following the Safe Harbor method.  For instance, it is simple to discern when a feature is a name or a Social Security Number, provided that the fields are appropriately labeled.  However, many researchers have observed that identifiers in medical information are not always clearly labeled. 37 . 38 As such, in some electronic health record systems it may be difficult to discern what a particular term or phrase corresponds to (e.g., is 5/97 a date or a ratio?).  It also is important to document when fields are derived from the Safe Harbor listed identifiers.  For instance, if a field corresponds to the first initials of names, then this derivation should be noted.  De-identification is more efficient and effective when data managers explicitly document when a feature or value pertains to identifiers.  Health Level 7 (HL7) and the International Standards Organization (ISO) publish best practices in documentation and standards that covered entities may consult in this process.

Example Scenario 1 The free text field of a patient’s medical record notes that the patient is the Executive Vice President of the state university.  The covered entity must remove this information.

Example Scenario 2 The intake notes for a new patient include the stand-alone notation, “Newark, NJ.”  It is not clear whether this relates to the patient’s address, the location of the patient’s previous health care provider, the location of the patient’s recent auto collision, or some other point.  The phrase may be retained in the data.

Glossary of terms used in Guidance Regarding Methods for De-identification of Protected Health Information in Accordance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule.  Note: some of these terms are paraphrased from the regulatory text; please see the HIPAA Rules for actual definitions.

Read the Full Guidance

Comments & Suggestions

In an effort to make this guidance a useful tool for HIPAA covered entities and business associates, we welcome and appreciate your sending us any feedback or suggestions to improve this guidance. You may submit a comment by sending an e-mail to [email protected] . 

Read more on the Workshop on the HIPAA Privacy Rule's De-Identification Standard

Acknowledgements

OCR gratefully acknowledges the significant contributions made by Bradley Malin, PhD, to the development of this guidance, through both organizing the 2010 workshop and synthesizing the concepts and perspectives in the document itself.  OCR also thanks the 2010 workshop panelists for generously providing their expertise and recommendations to the Department.

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Home » Applied Research – Types, Methods and Examples

Applied Research – Types, Methods and Examples

Table of Contents

Applied Research

Definition:

Applied research is a type of scientific inquiry that focuses on developing practical solutions to real-world problems. It involves the use of existing knowledge, theories, and techniques to address specific problems or challenges in a particular field or industry.

Applied research is often conducted in collaboration with industry or government partners, who provide funding and expertise to support the research. The results of applied research are typically intended to be directly applicable to the real world, and may involve the development of new products, technologies, or processes.

Types of Applied Research

Types of Applied Research are as follows:

Action Research

This type of research is designed to solve specific problems within an organization or community. The research involves collaboration between researchers and stakeholders to develop solutions to issues that affect the organization or community.

Evaluation Research

This type of research is used to assess the effectiveness of a particular program, policy, or intervention. Evaluation research is often used in government, healthcare, and social service settings to determine whether programs are meeting their intended goals.

Developmental Research

This type of research is used to develop new products, technologies, or processes. The research may involve the testing of prototypes or the development of new methods for production or delivery.

Diagnostic Research

This type of research is used to identify the causes of problems or issues. Diagnostic research is often used in healthcare, where researchers may investigate the causes of a particular disease or condition.

Policy Research

This type of research is used to inform policy decisions. Policy research may involve analyzing the impact of existing policies or evaluating the potential outcomes of proposed policies.

Predictive Research

This type of research is used to forecast future trends or events. Predictive research is often used in marketing, where researchers may use data analysis to predict consumer behavior or market trends.

Data Collection Methods

In applied research, data collection methods can be broadly classified into two categories: Quantitative and Qualitative methods:

Quantitative Data Collection

Quantitative research methods involve collecting numerical data that can be analyzed statistically. The most commonly used quantitative data collection methods in applied research include:

• Surveys : Surveys are questionnaires designed to collect data from a large sample of people. Surveys can be conducted face-to-face, over the phone, or online.
• Experiments : Experiments involve manipulating variables to test cause-and-effect relationships. Experiments can be conducted in the lab or in the field.
• Observations : Observations involve watching and recording behaviors or events in a systematic way. Observations can be conducted in the lab or in natural settings.
• Secondary data analysis: Secondary data analysis involves analyzing data that has already been collected by someone else. This can include data from government agencies, research institutes, or other sources.

Qualitative Data Collection

Qualitative research methods involve collecting non-numerical data that can be analyzed for themes and patterns. The most commonly used qualitative data collection methods in applied research include:

• Interviews : Interviews involve asking open-ended questions to individuals or groups. Interviews can be conducted in-person, over the phone, or online.
• Focus groups : Focus groups involve a group of people discussing a topic with a moderator. Focus groups can be conducted in-person or online.
• Case studies : Case studies involve in-depth analysis of a single individual, group, or organization.
• Document analysis : Document analysis involves analyzing written or recorded documents to extract data. This can include analyzing written records, audio recordings, or video recordings.

Data Analysis Methods

In applied research, data analysis methods can be broadly classified into two categories: Quantitative and Qualitative methods:

Quantitative Data Analysis

Quantitative data analysis methods involve analyzing numerical data to identify patterns and trends. The most commonly used quantitative data analysis methods in applied research include:

• Descriptive statistics: Descriptive statistics involve summarizing and presenting data using measures such as mean, median, mode, and standard deviation.
• Inferential statistics : Inferential statistics involve testing hypotheses and making predictions about a population based on a sample of data. This includes methods such as t-tests, ANOVA, regression analysis, and correlation analysis.
• Data mining: Data mining involves analyzing large datasets to identify patterns and relationships using machine learning algorithms.

Qualitative Data Analysis

Qualitative data analysis methods involve analyzing non-numerical data to identify themes and patterns. The most commonly used qualitative data analysis methods in applied research include:

• Content analysis: Content analysis involves analyzing written or recorded data to identify themes and patterns. This includes methods such as thematic analysis, discourse analysis, and narrative analysis.
• Grounded theory: Grounded theory involves developing theories and hypotheses based on the analysis of data.
• Interpretative phenomenological analysis: Interpretative phenomenological analysis involves analyzing data to identify the subjective experiences of individuals.
• Case study analysis: Case study analysis involves analyzing a single individual, group, or organization in-depth to identify patterns and themes.

Applied Research Methodology

Applied research methodology refers to the set of procedures, tools, and techniques used to design, conduct, and analyze research studies aimed at solving practical problems in real-world settings. The general steps involved in applied research methodology include:

• Identifying the research problem: The first step in applied research is to identify the problem to be studied. This involves conducting a literature review to identify existing knowledge and gaps in the literature, and to determine the research question.
• Developing a research design : Once the research question has been identified, the next step is to develop a research design. This involves determining the appropriate research method (quantitative, qualitative, or mixed methods), selecting the data collection methods, and designing the sampling strategy.
• Collecting data: The third step in applied research is to collect data using the selected data collection methods. This can include surveys, interviews, experiments, observations, or a combination of methods.
• Analyzing data : Once the data has been collected, it needs to be analyzed using appropriate data analysis methods. This can include descriptive statistics, inferential statistics, content analysis, or other methods, depending on the type of data collected.
• Interpreting and reporting findings : The final step in applied research is to interpret the findings and report the results. This involves drawing conclusions from the data analysis and presenting the findings in a clear and concise manner.

Applications of Applied Research

Some applications of applied research are as follows:

• Product development: Applied research can help companies develop new products or improve existing ones. For example, a company might conduct research to develop a new type of battery that lasts longer or a new type of software that is more efficient.
• Medical research : Applied research can be used to develop new treatments or drugs for diseases. For example, a pharmaceutical company might conduct research to develop a new cancer treatment.
• Environmental research : Applied research can be used to study and address environmental problems such as pollution and climate change. For example, research might be conducted to develop new technologies for reducing greenhouse gas emissions.
• Agriculture : Applied research can be used to improve crop yields, develop new varieties of plants, and study the impact of pests and diseases on crops.
• Education : Applied research can be used to study the effectiveness of teaching methods or to develop new teaching strategies.
• Transportation : Applied research can be used to develop new technologies for transportation, such as electric cars or high-speed trains.
• Communication : Applied research can be used to improve communication technologies, such as developing new methods for wireless communication or improving the quality of video calls.

Examples of Applied Research

Here are some real-time examples of applied research:

• COVID-19 Vaccine Development: The development of COVID-19 vaccines is a prime example of applied research. Researchers applied their knowledge of virology and immunology to develop vaccines that could prevent or reduce the severity of COVID-19.
• Autonomous Vehicles : The development of autonomous vehicles involves applied research in areas such as artificial intelligence, computer vision, and robotics. Companies like Tesla, Waymo, and Uber are conducting extensive research to improve their autonomous vehicle technology.
• Renewable Energy : Research is being conducted on renewable energy sources like solar, wind, and hydro power to improve efficiency and reduce costs. This is an example of applied research that aims to solve environmental problems.
• Precision Agriculture : Applied research is being conducted in the field of precision agriculture, which involves using technology to optimize crop yields and reduce waste. This includes research on crop sensors, drones, and data analysis.
• Telemedicine : Telemedicine involves using technology to deliver healthcare remotely. Applied research is being conducted to improve the quality of telemedicine services, such as developing new technologies for remote diagnosis and treatment.
• Cybersecurity : Applied research is being conducted to improve cybersecurity measures and protect against cyber threats. This includes research on encryption, network security, and data protection.

Purpose of Applied Research

The purpose of applied research is to solve practical problems or improve existing products, technologies, or processes. Applied research is focused on specific goals and objectives and is designed to have direct practical applications in the real world. It seeks to address problems and challenges faced by individuals, organizations, or communities and aims to provide solutions that can be implemented in a practical manner.

The primary purpose of applied research is to generate new knowledge that can be used to solve real-world problems or improve the efficiency and effectiveness of existing products, technologies, or processes. Applied research is often conducted in collaboration with industry, government, or non-profit organizations to address practical problems and create innovative solutions.

Applied research is also used to inform policy decisions by providing evidence-based insights into the effectiveness of specific interventions or programs. By conducting research on the impact of policies and programs, decision-makers can make informed decisions about how to allocate resources and prioritize interventions.

Overall, the purpose of applied research is to improve people’s lives by developing practical solutions to real-world problems. It aims to bridge the gap between theory and practice, and to ensure that research findings are put into action to achieve tangible benefits.

When to use Applied Research

Here are some specific situations when applied research may be appropriate:

• When there is a need to develop a new product : Applied research can be used to develop new products that meet the needs of consumers. For example, a company may conduct research to develop a new type of smartphone with improved features.
• When there is a need to improve an existing product : Applied research can also be used to improve existing products. For example, a company may conduct research to improve the battery life of an existing product.
• When there is a need to solve a practical problem: Applied research can be used to solve practical problems faced by individuals, organizations, or communities. For example, research may be conducted to find solutions to problems related to healthcare, transportation, or environmental issues.
• When there is a need to inform policy decisions: Applied research can be used to inform policy decisions by providing evidence-based insights into the effectiveness of specific interventions or programs.
• When there is a need to improve efficiency and effectiveness: Applied research can be used to improve the efficiency and effectiveness of processes or systems. For example, research may be conducted to identify ways to streamline manufacturing processes or to improve the delivery of healthcare services.

Characteristics of Applied Research

The following are some of the characteristics of applied research:

• Focus on solving real-world problems : Applied research focuses on addressing specific problems or needs in a practical setting, with the aim of developing solutions that can be implemented in the real world.
• Goal-oriented: A pplied research is goal-oriented, with a specific aim of solving a particular problem or meeting a specific need. The research is usually designed to achieve a specific outcome, such as developing a new product, improving an existing process, or solving a particular issue.
• Practical and relevant: Applied research is practical and relevant to the needs of the industry or field in which it is conducted. It aims to provide practical solutions that can be implemented to improve processes or solve problems.
• Collaborative : Applied research often involves collaboration between researchers and practitioners, such as engineers, scientists, and business professionals. Collaboration allows for the exchange of knowledge and expertise, which can lead to more effective solutions.
• Data-driven: Applied research is data-driven, relying on empirical evidence to support its findings and recommendations. Data collection and analysis are important components of applied research, as they help to identify patterns and trends that can inform decision-making.
• Results-oriented: Applied research is results-oriented, with an emphasis on achieving measurable outcomes. Research findings are often used to inform decisions about product development, process improvement, or policy changes.
• Time-bound : Applied research is often conducted within a specific timeframe, with deadlines for achieving specific outcomes. This helps to ensure that the research stays focused on its goals and that the results are timely and relevant to the needs of the industry or field.

Advantages of Applied Research

Some of the advantages of applied research are as follows:

• Practical solutions: Applied research is focused on developing practical solutions to real-world problems, making it highly relevant to the needs of the industry or field in which it is conducted. The solutions developed through applied research are often highly effective and can be implemented quickly to address specific issues.
• Improved processes: Applied research can help organizations to improve their processes, leading to increased efficiency and productivity. The research can identify areas for improvement, such as bottlenecks or inefficiencies, and provide recommendations for optimizing processes.
• Innovation: Applied research can lead to the development of new products, services, and technologies that can transform industries and create new opportunities for growth and innovation. The research can help organizations to identify unmet needs and develop new solutions to meet them.
• Collaboration : Applied research often involves collaboration between researchers and practitioners, leading to the exchange of knowledge and expertise. Collaboration can result in more effective solutions and can help to build partnerships between academia and industry.
• Increased competitiveness : Applied research can help organizations to stay competitive by enabling them to adapt to changing market conditions and customer needs. The research can provide insights into emerging trends and technologies, helping organizations to stay ahead of the curve.
• Economic growth: Applied research can contribute to economic growth by creating new industries and jobs. The research can lead to the development of new technologies and products that can drive economic growth and create new opportunities for entrepreneurship and innovation.

Limitations of Applied Research

Some of the limitations of applied research are as follows:

• Limited generalizability: Applied research often focuses on specific contexts and may not be generalizable to other settings. This means that the findings of applied research may not be applicable to other industries, regions, or populations.
• Time and resource constraints: Applied research is often conducted within a specific timeframe and with limited resources. This can limit the scope and depth of the research and may prevent researchers from exploring all possible avenues.
• Potential for bias: Applied research may be influenced by the interests and perspectives of the organization or industry funding the research. This can lead to a bias in the research and potentially compromise the objectivity and validity of the findings.
• Ethical considerations: Applied research may raise ethical concerns, particularly if it involves human subjects or sensitive issues. Researchers must adhere to ethical standards and ensure that the research is conducted in a responsible and respectful manner.
• Limited theoretical development: Applied research tends to focus on practical solutions and may not contribute significantly to theoretical development in a particular field. This can limit the broader impact of the research and may hinder the development of new theories and frameworks.
• Limited focus on long-term impact: Applied research often focuses on short-term outcomes, such as developing a new product or improving a process. This may limit the focus on long-term impacts, such as the sustainability of the solution or its broader implications for the industry or society.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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