clinical research informatics definition

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Article Contents

A conceptual model of clinical research informatics, new contributions to the clinical research informatics knowledge base, looking forward, competing interests, provenance and peer review.

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Clinical research informatics: a conceptual perspective

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Michael G Kahn, Chunhua Weng, Clinical research informatics: a conceptual perspective, Journal of the American Medical Informatics Association , Volume 19, Issue e1, June 2012, Pages e36–e42, https://doi.org/10.1136/amiajnl-2012-000968

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Clinical research informatics is the rapidly evolving sub-discipline within biomedical informatics that focuses on developing new informatics theories, tools, and solutions to accelerate the full translational continuum: basic research to clinical trials (T1), clinical trials to academic health center practice (T2), diffusion and implementation to community practice (T3), and ‘real world’ outcomes (T4). We present a conceptual model based on an informatics-enabled clinical research workflow, integration across heterogeneous data sources, and core informatics tools and platforms. We use this conceptual model to highlight 18 new articles in the JAMIA special issue on clinical research informatics.

Clinical research informatics (CRI) is the rapidly evolving sub-discipline within biomedical informatics that focuses on developing new informatics theories, tools, and solutions to accelerate the full translational continuum 1 , 2 : basic research to clinical trials (T1), clinical trials to academic health center practice (T2), diffusion and implementation to community practice (T3), and ‘real world’ outcomes (T4). 3 Two recent factors accelerating CRI research and development efforts are (1) the extensive and diverse informatics needs of the NIH Clinical and Translational Sciences Awards (CTSAs), 4 – 6 and (2) the growing interest in sustainable, large-scale, multi-institutional distributed research networks for comparative effectiveness research. 7 – 9 Given the large landscape that comprises translational science, CRI scientists are asked to conceive innovative informatics solutions that span biological, clinical, and population-based research. It is therefore not surprising that the field has simultaneously borrowed from and contributed to many related informatics disciplines.

Paralleling the growth in CRI prominence, JAMIA has received an increasing number of CRI submissions. In 2010, five published articles were completely focused on CRI, 10 – 14 while in 2011 this number rose to 23, 15 – 37 accounting for 11.5% of all JAMIA articles for that year. There was a special section focused on CRI papers in the December 2011 supplement issue. Much of the increase can be attributed to publications from awardees of the CTSA, since publication rate is related to funding. 38 , JAMIA publications acknowledging CTSA funding rose from three in 2009 39 – 41 to four in 2010 14 , 42 – 44 and 15 in 2011. 15 , 17 , 19 , 36 , 45 – 55 Some of the articles were not exclusively focused on CRI, but were directly related, covering many different topics that are highly relevant to CRI: data models and terminologies, 27 , 56 – 68 natural language processing (NLP), 16 , 50 , 61 , 69 – 99 surveillance systems, 48 , 65 , 80 , 100 – 110 and privacy technology and policy. 33 , 111 – 117 This 2012 CRI supplement adds 18 new publications to this growing field.

To provide guidance on the CRI innovations represented in this special supplement, we developed the conceptual model in figure 1 . This figure illustrates how CRI integrates clinical and translational research workflows in addition to core informatics methodologies and principles into a framework that reflects the unique informatics needs of translational investigators. The model is organized around three conceptual components: workflows; data sources and platforms; and informatics core methods and topics.

A conceptual model for clinical research informatics consisting of an informatics-enabled clinical research workflow, heterogeneous data sources, and a collection of informatics methods and platforms. EHR, electronic health records; IDR, integrated data repositories; PHR, personal health records.

The central structure that establishes the unique context for CRI is the informatics-enabled clinical research workflow . The elements and sequence of this workflow should be familiar as it reflects the key phases in the scientific model of knowledge discovery. 118 Unlike diagrams that appear in traditional research methodology textbooks, figure 1 applies an informatics-centric perspective to each step and contains two translational workflow cycles, which reflect the use of CRI technologies in both early (‘T1–T2’) and later (‘T3–T4’) translational phases. 119 , 120 The ‘inner’ cycle represents translational discoveries within carefully controlled study conditions in a limited number of clinical trial sites. The ‘outer’ cycle represents the later stages of clinical translational research, where implementation and dissemination tasks become more prominent across community practices. The later stages of clinical translational research are represented by implementation-oriented translational activities such as evidence generation and synthesis, personalized evidence application, and population surveillance.

New scientific knowledge, both hypothesis-generating and hypothesis-testing, begins with a research question that drives the investigative process. While previous studies may suggest possible new research questions, ultimately this step reflects the creative insight of a well-trained translational investigator. During the early planning phases, study feasibility assessment and cohort identification are important tasks for ensuring that sufficient study participants and data exist to move the proposed study forward. Eligibility alerting, which leverages the growing use of electronic health records (EHRs) to notify physicians of their patients' eligibility for clinical trials, is one of the major informatics solutions to address the leading cause of failures in clinical studies—the inability to recruit sufficient study participants. 121 , 122 Obtaining informed consent is a critical step in clinical research recruitment. Advanced interactive human–computer educational systems could reduce the burden for investigators and improve the understanding of risks and benefits by patients. Data collection and analyses follow naturally after patients are enrolled, but are often seen (erroneously) as the sole use of informatics by most investigators. As shown in figure 1 , CRI supports the cycle for converting data into knowledge by encompassing data analysis, evidence generation, and evidence synthesis. Population surveillance seeks to discover unmet community-based health needs, which can be used to drive another set of research questions.

Reflecting the expanding scope of data sources that are commonly used to drive clinical and translational research, figure 1 highlights CRI's emphasis on data integration across EHRs or over time to form integrated longitudinal data repositories, which in turn are integrated across institutions to form multi-institutional federated data networks. A wide range of additional sources of data is reflected in figure 1 : personal health records, registries, claims databases, public reports, and social media that contain patient self-reported outcome data. This list is intentionally incomplete—it is intended only to highlight the endless variety of both ‘traditional’ and ‘non-traditional’ data sources, such as in-home continuous monitoring, public and specialized social networks, and geo-location data. Significant CRI research has focused on the challenges of data integration across disparate data sources that may differ in concept specificity (granularity), representation, syntax, and semantics. 123 – 128 Similarly, a large body of informatics research has developed alternative models for data federation across independent data sources, including distributed, federated, and mediator-based architectures. 8 , 9 , 129 – 132 Two of the largest efforts to develop large-scale data integration and distributed data sharing environments specifically directed toward clinical and translational research are caBIG from the National Cancer Institute and BIRN from the National Center for Research Resources (now part of the National Center for Advancing Translational Sciences). 31 , 133 – 135 Some CRI investigators are adopting and adapting these architectures to meet the needs of multi-institutional data sharing networks.

The need to support the above informatics-enabled clinical research workflows and to strengthen the national research capacity have led to new developments in CRI core topics and techniques. Many technologies used to solve CRI needs have been borrowed from other informatics disciplines and adapted to meet CRI requirements. The bottom portion in figure 1 highlights the major core research topics in CRI, including secondary use of clinical data for research, distributed queries, data integration, record linkage, data quality assessment, integrated data models and terminologies, and a set of common informatics methods, including human–computer interaction, knowledge management, NLP, information extraction, and text classification. Each core topic builds upon and extends fundamental informatics theories and methodologies that are implemented and assembled into functioning CRI solutions. This supplement contains 18 articles that focus on various aspects of CRI workflow, applications, or research topics. The articles contribute to either a CRI workflow task or an underlying core CRI technology or platform or both, as illustrated in figure 1 .

Integrated clinical data repositories or federated data networks are considered a fundamental infrastructure for biomedical and translational research. With the establishment of the US national CTSA consortium, which currently consists of 60 participating institutions, there is a pressing need to develop and share best practices for clinical data integration in support of clinical research. MacKenzie et al ( see page e119 ) conducted a survey among 28 CTSAs and the NIH Clinical Center. 136 This study identified several data integration trends among the CTSA programs, such as a growing presence of centralized integrated data repositories and master patient indexing tools. Another key finding is the increasing movement away from homegrown solutions to more broadly used integration platforms such as i2b2. 13 , 41 , 137

Popular applications of integrated data repositories for clinical and translational research include retrospective data analyses and identification of research participants to improve clinical research recruitment, 40 but few institutions have leveraged real-time streams to enrich data. Ferranti et al ( see page e68 ) designed and implemented an open-source, data-driven cohort recruitment system called The Duke Integrated Subject Cohort and Enrollment Research Network (DISCERN). 32 This system combines both retrospective warehouse data and real-time clinical events via Health Level Seven (HL7) messages to immediately alert study personnel of potential recruits as they become eligible. Real-time data feeds are critical when the required clinical findings have not yet been loaded into the warehouse but have been captured contemporaneously during patient care. The use of both retrospective and real time data provides an interesting example of how multiple data sources may be required to capture important details for cohort discovery.

Extending the capacity of a single institutional data repository to support translational studies, Anderson et al ( see page e60 ) used the i2b2 data warehouse software to implement a multi-institutional federated data network for population-based cohort discovery. 37 This infrastructure links de-identified data repositories from three CTSA institutions to support federated queries to identify potentially eligible patients for clinical trial studies. This distributed data-sharing network requires a harmonized common data model, value sets, and data access policies across all participating institutions. It demonstrates the ability for a distributed network containing de-identified patient data to provide aggregated patient counts. An important finding is that while multi-institutional cohort discovery allows for queries to interrogate extremely large patient populations, harmonization of inter-institutional policies, semantics, and use cases is perhaps more important and challenging than technical harmonization.

Motivated by a different use case but using a similar approach, Buck ( see page e46 ) leveraged a widely adopted EHR system in New York City to develop a clinical and public health research platform. This research infrastructure participates in a city-wide distributed query network to support population-based data queries with provider-specific alerting and communication capabilities. 35 This virtual network aggregates distributed count information and reports, and disseminates shared decision support alerts and secure messaging directly into provider EHR email accounts. This project illustrates how a common EHR system, with common documentation, codes, and standards, can be used to monitor community health and facilitate communications between clinical and public health practitioners.

Both of these articles highlight the importance of using standard software, data models, and data semantics to enable large-scale research infrastructures and to achieve interoperability across organizations.

Recruitment is the primary and most costly barrier to clinical and translational research. 138 This supplement contains two articles that contribute to the literature on informatics solutions for boosting recruitment. 20 , 139 Embi and Leonard ( see page e145 ) evaluated the response patterns over time to EHR-based clinical trial alerts using a randomized clinical trial. 139 The authors observed that responses to clinical trial alerts declined gradually over prolonged exposure. However, recruitment performance remained higher than baseline despite this decline in responsiveness to trial alerts over time. The authors found that, while there were no differences in the loss of performance between specialists and generalists, there was a significantly bigger loss of alert responsiveness in community-based practitioners compared to academic practitioners. This study is another reminder that one person's critical alert is another person's disruptive annoyance.

Obtaining informed consent remains a labor-intensive step in clinical research recruitment. The study from Tait et al ( see page e43 ) proposed a novel interactive consent program that enables patients to specify their preferences to participate in pediatric clinical trials. 20 The interactive computer program contains both child- and parent-appropriate animations of a clinical trial of asthma and shows that innovative technologies can open new possibilities for eliminating workflow barriers in translational research. The improved understanding of key clinical trial concepts by both children and adults indicates that this approach should be explored in more depth as more powerful hand-held tablet devices become widely available.

Besides the use of clinical data to facilitate clinical trial recruitment, broadened secondary use of clinical data has been on the rise. Secondary data use requirements have resulted in the development of new approaches to deriving actionable knowledge from the mass of patient data in structured fields, unstructured text, and handwritten notes. 103 , 140 , 141 For example, adapting the results of large-scale clinical studies to individual patients remains challenging. Jiang et al ( see page e137 ) investigated model adaptation challenges in risk prediction for individual patients and developed a patient-driven adaptive prediction technique (ADAPT) to improve personalized risk estimation for clinical decision support. 140 This method selects the best risk estimation model from a set of models for an individual patient. The technique examines individualized confidence intervals based on an individual's data to select the ‘best’ risk prediction. This very simple, computationally inexpensive approach shows better performance using receiver operating characteristic (ROC) and goodness-of-fit tests compared to alternative model-selection approaches.

Mathias, Gossett, and Baker 141 ( see page e96 ) describe a retrospective study using EHR data to estimate the incidence of inappropriate use of cervical cancer screening. Using manual chart review to validate the accuracy of their electronic query, they were able to determine that most low-risk women were receiving Pap tests more frequently than recommended. Of particular interest, Mathias provides the actual query logic used to identify study participants. Excluding the lines that generate the analytic data set, the code required to identify the study cohort occupies three full pages, highlighting that the EHR, while providing access to detailed clinical data, requires very complex query logic to ensure that the right patients have been extracted. Their study shows that EHR data can play an important role in monitoring unnecessary test orders and containing healthcare costs.

Li and colleagues ( see page e51 ) describe the use of seasonally adjusted alerting thresholds in a disease surveillance system to obtain improved outbreak detection performance during epidemic and non-epidemic seasons of hand-foot-and-mouth disease. 103 Their conclusions indicate that, for diseases with known seasonal variability, different thresholds may be most appropriate for optimizing high sensitivity and low false alarm rates without reducing the time to outbreak detection.

A patient's data is often scattered in data repositories from multiple organizations. Therefore, record linkage is a critical step in integrating data about patients obtained from different data sources. To address information fragmentation and incompleteness problems that are common to many data repository developers, Duvall and colleagues ( see page e54 ) 33 describe their experience performing record linkage between a large institutional enterprise data warehouse and a statewide (Utah) population database. The results of record linkage were then validated using a state cancer registry. They developed a Master Subject Index, which has become an increasing popular method to identify the same person in multiple data sources to support linked data discovery. The project used a commercial record linkage tool based on probabilistic record matching. An analysis of their findings indicated the strong negative impact of missing values in fields used in the record linkage algorithm.

A common concern related to secondary use of clinical data is data quality. In this supplement, three articles present different methods for data quality assurance: the use of imputation; rule-based error detection; and knowledge-based approaches leveraging semantic web and UMLs' semantic network knowledge. Sariyar, Borg, and Pommerening ( see page e76 ) 22 focus on systematic approaches for dealing with missing values that occur in fields that are used to perform record linkage. Their ‘measure of success' for alternative approaches is the accuracy of record linkage following the application of alternative methods. Using both real and simulated data and four alternative linkage scoring methods based on classification and regression trees (CART), they show that assuming that a missing value always represents a non-match is a computationally efficient heuristic with only a small loss in accuracy compared to alternative algorithms that are substantially more complex.

Rather than using imputation, McGarvey and colleagues ( see page e125 ) describe a multi-faceted approach to improving data completeness and quality in a multi-center breast and colon cancer family registry. 142 The authors implemented a rule-based validation system that facilitates error detection and correction for research data centers. Evaluation over a 2-year period showed a decrease in the numbers of errors per patient in the database and a concurrent increase in data consistency and accuracy. While their approach improved efficiency and operational effectiveness, an important finding is the need to establish data-quality governance that explicitly acknowledges the shared responsibilities between members of the data coordinating center and the data collection sites in improving the overall quality of research data. As additional data validation routines were implemented, their findings highlight the oft-stated observation that ‘you cannot improve what you do not measure.’

Common data elements (CDEs) have emerged as an effective way to represent reusable, semantically defined data collection items. Jiang et al ( see page e129 ) 143 evaluated the semantic consistency of CDE value sets contained in the NCI caDSR repository. This paper presents a new methodology for assessing the quality of value set terms using a clever mapping between CDEs and the UMLS semantic network's 15 semantic groups and 133 semantic types. 143 Elements in a value set were considered inconsistent if a member of the value set mapped to a different type or group in the UMLS semantic network. This effort highlights the critical need to constantly evaluate the very large body of CDEs to ensure that these elements, which are critical to future data sharing efforts, are themselves consistent and correct.

The previous articles focused on the reuse of structured data elements. Another common challenge to reusing clinical data for clinical research is to extract information from unstructured data sources, such as text and images. Therefore, various methods for NLP, text classification, information extraction, and optical character recognition (OCR) have been developed to address this challenge. This supplement includes three articles providing examples of the above methods. 24 , 144 , 145

NLP has emerged as a critical technology in large-scale clinical research. 146 Savova ( see page e83 ) describes the use of NLP to extract drug treatment information from breast cancer therapy notes. 145 Extracted information was combined with structured information from an electronic prescribing system and integrated into a common treatment timeline. This work shows how integration of information from both structured and unstructured data sources can result in data sets that are richer in content than can be provided by either data source alone. Although not a focus of this paper, it is striking to note that the NLP pipeline required 12 different computational processes to annotate the text, most of which are part of the OpenNLP toolset, and numerous public-domain coding systems.

Rasmussen et al ( see page e90 ) extended conventional information extraction tasks from data fields or electronic text to scanned handwritten forms using an OCR processing pipeline. 24 The proposed pipeline leverages the capabilities of existing third-party OCR engines and provides the flexibility offered by a modular system. Pipeline-based architectures are common in NLP solutions, as illustrated by the Savova article described previously. Rasmussen's results show that the OCR pipeline significantly reduces human effort on chart abstraction. Rasmussen's focus on OCR reminds us that an enormous body of historical medical information exists in handwritten text notes. Informatics tools that can eliminate or reduce manual chart abstraction would make these data more accessible for clinical research.

Many studies use manual chart reviews to classify patients. Manual methods are not just time-consuming: they are prone to classification bias. Using adverse event reports, Ong, Magrabi, and Coiera ( see page e110 ) 144 show how statistical classification methods can be used to classify extreme risk (Severity Code Assessment level one) reports with high accuracy. As seen in other uses of statistical classifiers, performance was better when the training set consisted of a narrow set of conditions (specifically, patient misidentification errors) rather than a diverse population of events.

An important resource for information retrieval in clinical data is the wide range of semantic knowledge resources such as UMLS and SNOMED-CT. Given the importance of data models and semantic knowledge for CRI, much work has been focused on improving the quality of these critical knowledge resources. López-García ( see page e102 ) describes a usability-driven pruning technique to study the modularity of SNOMED-CT. 147 This study concludes that graph-traversal strategies and frequency data from an authoritative source can prune large biomedical ontologies and produce useful segmentations that still exhibit acceptable coverage for annotating clinical data. Similarly, Wu et al ( see page e149 ) investigate the frequency of UMLS terms in clinical notes across multiple institutions' clinical data warehouses. 148 The authors found that only 3.56% of UMLS terms were empirically attested in clinical notes, implying that a lightweight lexicon could be developed to improve the efficiency of NLP systems for clinical notes.

From all the diversity of workflow applications, methods, and knowledge resources that we see represented in this special issue, we not only identify a steadily growing literature in classic CRI topics such as data integration or federation, information retrieval, and data analysis, but we also note some emerging new areas, such as interactive consenting and individualized decision support. We expect the CRI research agenda will continue to evolve to become more precise, predictive, preemptive, and participatory, in parallel with the development of ‘4P medicine’. 149 We anticipate more patient-centered research decision support and innovative consent programs to strengthen patient participation and participation, including specifying how an individual's research data will be used and by whom. 150 We also expect more CRI research that is informed by and responsive to patient or population needs. We encourage investigators developing new methods and tools that accelerate clinical and translational research to continue to contribute to the explosive growth in the peer-reviewed literature in clinical research informatics.

Dr Kahn was supported in part by NIH/NCRR Colorado CTSI Grant Number UL1 RR025780 and AHRQ R01HS019908 (Scalable Architecture for Federated Translational Inquiries Network) and AHRQ R21 HS19726-01A. The contents are the authors' sole responsibility and do not necessarily represent official NIH views. Dr Weng was support by grants R01LM009886 and R01LM010815 from the National Library of Medicine, grant UL1RR024156 from the National Center for Research Resources, and AHRQ grant R01HS019853.

Commissioned; internally peer reviewed.

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Home > Resources > Health Informatics > What is Clinical Informatics?

What is Clinical Informatics?

• Published February 16, 2017
• Updated May 2, 2024

Clinical informatics is the study of information technology and how it can be applied to the healthcare field. It includes the study and practice of an information-based approach to healthcare delivery in which data must be structured in a certain way to be effectively retrieved and used in a report or evaluation. Clinical informatics can be applied in a range of healthcare settings including hospitals, physician’s practices, the military, and other locations.

Clinical Informaticist Job Duties

Providers in today’s healthcare industry increasingly rely on data and technology to provide treatments for patients. Physicians, nurses, dentists, pharmacists , rehab therapists, assistants and a host of others collect and share data to formulate and implement a treatment plan for a patient. Along the way, they use the latest in technological equipment, computers, software, tablets, smartphones and even apps to gather and distribute information. All of this information must be collected, stored, interpreted, analyzed and implemented into a treatment plan.

A clinical informaticist may serve in a multitude of roles, depending on the size of the healthcare setting. Typically, these professionals evaluate the existing information systems and recommend improvements to functionality. Clinical informaticists may study a data entry or visual image storage system or interact with those who need access to records. They may train staff on system use, build interfaces, troubleshoot software and hardware issues, and work across multiple departments to integrate the sharing of information. They document and report their findings and work to implement improvements. The ultimate goal is to manage the costs while improving patient outcomes.

Clinical Informatics Jobs Outlook

Though clinical informatics has been widespread in the healthcare industry since the 1970s, a stimulus bill passed by Congress in 2009 included a mandate that medical providers convert paper records to electronic data by 2014 to continue receiving Medicaid and Medicare payments.

The American Medical Informatics Association (AMIA) achieved one of its goals in 2011 when the American Board of Medical Specialties recognized clinical informatics as a subspecialty. The first board certifications were awarded late in 2013.

“This field is exploding,” Charles Friedman, director of the health informatics program at the University of Michigan-Ann Arbor, told U.S. News and World Report in 2014. “Access to health information on the Web is taking off at a meteoric pace. It’s creating enormous employment opportunities.”

Clinical Informatics Job Descriptions

In the growing field of clinical informatics, specialized roles are taking shape that focus on specific areas of healthcare, including positions in medical informatics, nursing informatics, pharmacy informatics, and nutrition informatics.

Medical Informatics

The U.S. National Library of Medicine defines medical informatics as the study of the design, development and adaptation of IT-based innovations in healthcare services delivery, management and planning. For example, when a patient goes for tests, medical informaticists ensure those results are quickly and securely accessible to doctors as part of the patient’s electronic health record (EHR) . This technology can be applied to payment systems and transactions through government agencies and insurance companies.

Nursing Informatics

Doctors and patients discussing treatment options rely on data. The nursing informatics role, as defined by the American Nurses Association, serves to integrate data, information and knowledge to support the decision-making process of patients and their providers. A nurse in this position knows how to store and access medical information and how to keep the facility’s IT systems up to date.

Pharmacy Informatics

When it comes to prescribing and administering medications, electronic communication is rapidly replacing the pen-and-prescription pad ways of the past. In this emerging field, a pharmacy informaticist uses both medical and computer knowledge to improve the efficiency and accuracy of the medication process for the patient and the providers.

Nutritional Informatics

Food- and nutrition-related information may be an important part of a patient’s treatment plan. Nutrition informaticists assist in the storage, organization and retrieval of data that will help dieticians, doctors and patients make informed choices in this continually evolving area. A person in this role might be working with software that would create a checklist of considerations based on a diagnosis, medications, allergies and other values.

Clinical Informatics Salary information

The impact of the federal mandate regarding EHRs is still being felt as many medical providers and facilities respond. The demand for applicants with both medical and technological knowledge is growing. Job growth in clinical informatics could reach about 21% through 2020, according to the U.S. Bureau of Labor Statistics.

The American Health Information Management Association (AHIMA) estimates mid-range salaries for those in clinical informatics roles to reach more than $85,000 a year. Managers in this profession may earn as much as $200,000. However, it is important to note that geographical location, position requirements, the employer and other factors may affect those numbers, so research is necessary.

Educational Requirements for Clinical Informatics Jobs

This field of study is most often pursued by healthcare professionals with a passion for technology. For example, nurses often transition into informatics through graduate-level informatics programs, which teach students how to build medical applications (EHRs), how to abide by patient privacy laws, and how to better understand healthcare policy and economics.

Clinical informaticists typically start with at least a bachelor’s degree and often earn a graduate degree that includes technical instruction combined with courses in medical practice and hands-on experience working with electronic patient records at healthcare facilities.

Many employers look for applicants with a Master’s degree in Health Informatics , Healthcare Management, or Quality Management.

Many colleges offer online video-based e-learning and interactive virtual classrooms, where students can gain the credentials to pursue a career in clinical informatics on their terms.

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Clinical Research Informatics

Affiliations.

• 1 Information Technology Department, AP-HP, Paris, France.
• 2 Sorbonne University, University Paris 13, Sorbonne Paris Cité, INSERM UMR_S 1142, LIMICS, Paris, France.
• 3 The University of Gent, Gent, Belgium.
• PMID: 32823317
• PMCID: PMC7442510
• DOI: 10.1055/s-0040-1702007

Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2019.

Method: A bibliographic search using a combination of MeSH descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selected three best papers.

Results: Among the 517 papers, published in 2019, returned by the search, that were in the scope of the various areas of CRI, the full review process selected three best papers. The first best paper describes the use of a homomorphic encryption technique to enable federated analysis of real-world data while complying more easily with data protection requirements. The authors of the second best paper demonstrate the evidence value of federated data networks reporting a large real world data study related to the first line treatment for hypertension. The third best paper reports the migration of the US Food and Drug Administration (FDA) adverse event reporting system database to the OMOP common data model. This work opens the combined analysis of both spontaneous reporting system and electronic health record (EHR) data for pharmacovigilance.

Conclusions: The most significant research efforts in the CRI field are currently focusing on real world evidence generation and especially the reuse of EHR data. With the progress achieved this year in the areas of phenotyping, data integration, semantic interoperability, and data quality assessment, real world data is becoming more accessible and reusable. High quality data sets are key assets not only for large scale observational studies or for changing the way clinical trials are conducted but also for developing or evaluating artificial intelligence algorithms guiding clinical decision for more personalized care. And lastly, security and confidentiality, ethical and regulatory issues, and more generally speaking data governance are still active research areas this year.

Georg Thieme Verlag KG Stuttgart.

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Conflict of interest statement

Disclosure The authors report no conflicts of interest in this work.

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NLM Musings from the Mezzanine

Innovations in Health Information from the National Library of Medicine

Appreciating the Distinction: Clinical Informatics Research vs. Clinical Research Informatics

Guest post by Allison Dennis, PhD, Program Officer for the Division of Extramural Programs, National Library of Medicine.

The National Library of Medicine Division of Extramural Programs (EP) oversees NLM’s extramural research investments. One area that NLM invests in is using informatics methodologies and tools to understand and improve the way health care is delivered and health overall. In the ever-evolving health care landscape, the intersection of technology and research plays a pivotal role in advancing patient care and outcomes.

Two closely related yet different domains within this intersection are Clinical Informatics Research and Clinical Research Informatics. While their names may sound similar, these fields encompass different foci and methodologies. NLM funds research in both of these areas as they contribute to our mission in distinct ways.

Clinical Informatics Research

Clinical Informatics Research involves the study of information management and technology applications within health care settings. This research focuses on optimizing the use of information to improve patient care, streamline health care processes, and enhance overall system efficiency. Researchers in this field delve into topics such as electronic health records (EHRs), health information exchange, data interoperability, and the design and implementation of clinical decision support (CDS) systems. NLM’s interest in Clinical Informatics Research contrasts with that of other NIH institutes and centers because it seeks to unleash the broad potential of data and informatics to improve health care in general.

NLM supports many grants in the field of Clinical Informatics Research. These include NLM-supported research that is:

• Establishing an informatics framework to improve and automate the referral process between primary care providers and specialists
• Advancing automated documentation algorithms that interpret the dialogue between patients and clinicians, generate relevant encounter summaries, and develop new and improved ways to capture what happened during a visit
• Developing an adaptive CDS system that can adapt to variations in clinician fatigue levels in emergency departments

These studies have considerable potential to make health care more efficient and equitable. Their innovative data science and informatics approaches may improve health care transitions for patients, alleviate clinicians’ documentation burden, offer new ways to study implicit bias in clinical encounters, provide foundational knowledge for creating health information technology that adapts to clinicians and patients, and inform guidelines for the design and implementation of more responsive systems. Through investments in Clinical Informatics Research projects like these and the new and exciting ways they can improve health care delivery, NLM is excited to continue advancing its strategic priorities for enabling a future of data-powered health.

Clinical Research Informatics

Clinical Research Informatics is centered around leveraging informatics methodologies to enhance the research processes by introducing new paradigms for discovery and knowledge management. This field aims to innovate how data are harnessed to characterize, predict, prevent, diagnose, and treat disease more efficiently and accurately. As such, many NIH institutes and centers invest in Clinical Research Informatics research as it relates to particular health topics. However, NLM is uniquely interested in projects that provide broad and generalizable insights applicable to and relevant across disease domains.

Some of the many NLM-supported Clinical Research Informatics projects are:

• Tailoring innovative information retrieval methods to handle complex EHR data for cohort discovery
• Improving the generalizability of clinical trial findings to real-world populations through the development of new causal and statistical methods that address biases in cluster trials
• Creating risk prediction models that can learn from medical codes commonly found in the EHR and reduce the need for annotation from experts

These studies have the potential to enhance the data-driven capabilities of health-related research. They are developing domain-independent and reusable methods for leveraging data and models to design stronger clinical trials, better understanding and applying the knowledge generated from clinical trials in the real world, and using EHRs for precision medicine research. Through investments in Clinical Research Informatics, including projects like these, NLM continues to advance its strategic priorities for enabling a future of biomedical discovery.

Appreciating the Distinction, Funding Both

Both Clinical Informatics Research and Clinical Research Informatics play important roles in advancing health care delivery, improving patient outcomes, and driving innovation in health care. On one hand, Clinical Informatics Research focuses on optimizing the use of information technology in clinical settings to enhance workflow efficiency, patient safety, and communication among health care professionals. On the other, Clinical Research Informatics enables the efficient collection, analysis, and interpretation of vast amounts of data, facilitating evidence-based decision-making, and the development of new treatments and interventions. Together, these interdisciplinary fields contribute to the continuous evolution of health care practices, ultimately leading to better patient care and health outcomes. NLM is committed to supporting both Clinical Informatics Research and Clinical Research Informatics studies that elucidate and address the complex challenges facing modern health care systems and ensure the delivery of high-quality, patient-centered, and data-informed care.

Are You a Researcher with Innovative Ideas?

We encourage researchers interested in advancing data-driven capabilities and developing novel approaches to Clinical Informatics Research and Clinical Research Informatics to consider applying for NLM Research Grants in Biomedical Informatics and Data Science . Please reach out to an NLM Program Officer . We are always happy to discuss the scope of a potential project and appreciate the opportunity to review  draft specific aims .

Now is the perfect time to become part of the NLM-supported community that is creating the cutting-edge technologies needed to improve patient care and enhance health care delivery while advancing our ability to study and understand human health.

Allison Dennis, PhD

Dr. Dennis serves as the scientific contact for Bioinformatics, Translational Informatics, Personal Health Informatics, and the SBIR/STTR program in the NLM Extramural Research Program. Prior to joining NLM, Dr. Dennis was a Technical Lead in the NIH Office of Data Science Strategy, where she oversaw initiatives in artificial intelligence, and a Health Informatics Officer with the Office of the National Coordinator for Health IT, where she advanced health IT standards for scientific discovery. Dr. Dennis holds a PhD in Biology from Johns Hopkins University. She has nearly a decade of experience conducting data-driven biomedical research in the NIH Intramural Research Program.

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Clinical Research Informatics: Challenges, Opportunities and Definition for an Emerging Domain

• Journal of the American Medical Informatics Association 16(3):316-27
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Clinical Research Informatics

The Clinical Research Informatics Working Group's mission is to advance the discipline of Clinical Research Informatics (CRI) by fostering interaction, discussion and collaboration among individuals and groups involved or interested in the practice and study of CRI, and to serve as the home for CRI professionals within AMIA.

Clinical Research Informatics (CRI), as a subdomain of biomedical informatics, encompasses the technology and processes and principles and practices to support the breadth of activities included in the execution of clinical research involving human subjects and their data.

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CRI includes:

• Selection, implementation, development, and maintenance of a technology ecosystem to support clinical research activities and associated regulatory needs
• Optimization of electronic health record (EHR) systems and data to support research administration, participant recruitment and consenting, data capture, intervention implementation and other research activities supporting clinical research execution
• Management and workflow of data in research data repositories, data registries, data marts, data warehouses, and electronic data capture along with leveraging and simplifying the process of leveraging these standardized repositories via reporting and analytics
• Implementation science methods and translation of research into evidence-based practice
• Standardization of tools, techniques, and processes to support reproducibility of clinical research results, and outcomes
• Providing informatics tools to address the ethical, legal, and social issues that effect clinical research

The Clinical Research Informatics Working Group's mission is to advance the discipline of CRI by fostering interaction, discussion, and collaboration among individuals and groups involved or interested in the practice and study of CRI, and to serve as the home for CRI professionals within AMIA. The goals of the CRI Working group are to:

• Increase awareness and interaction of CRI domain with the various subdomains that it encompasses
• Provide a forum for discussion, collegial development, exchange, and information dissemination
• Identify, provide assistance in resolution of common issues in the CRI domain and form ad hoc groups for discussion
• Provide guidance, and engage community and discussion regarding the regulatory aspects of CRI, collect feedback and share/report the findings / understanding / consensus
• Provide education to AMIA members on the various aspects of CRI and its overlap with various domains/sub domains
• Provide support for members in their institutional settings to advance clinical research informatics
• Educate and inform the lay public regarding clinical research informatics to support enhanced people-centered research
• Provide platform and facilite discussions for CRI Leaders e.g. CRIO Discussion Forum

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Featured Publication

Clinical research informatics - third edition.

An Easy-to-Understand Clinical Research Glossary to Support Participants and Professionals

Blog June 18, 2024

Research assistants, clinical research coordinators, and other clinical research professionals are often the study team members with whom participants (and potential participants) interact the most when joining or learning about a research study, throughout their participation, and whenever they have questions. During the recruitment and consent process, as well as during the study, participants should be supported with clear explanations of what specific clinical research terms mean.  

To that end, The Multi-Regional Clinical Trials Center ( MRCT Center ) of Brigham and Women’s Hospital and Harvard has introduced the Clinical Research Glossary (CRG) , which offers plain language definitions along with additional contextual information that research professionals can use to empower participants and their families.  

The availability of these definitions for all sites and sponsors to use allows for a harmonized approach across the research ecosystem, increasing the likelihood of consistency in terminology across the study life cycle itself, and even between studies. This supports participants and the research enterprise, countering research misinformation and increasing researcher trustworthiness.  

Some key facts about the MRCT Center’s Clinical Research Glossary:

• The CRG is freely available and aims to provide clear definitions in plain language and helpful extra content (e.g., graphics and additional information for potential participants to consider when joining a study), so each concept is easier for every person to understand.  
• The CRG definitions have been included by the Clinical Data Interchange Standards Consortium  (CDISC) in its global clinical research standards, so more and more research leaders are turning to these definitions.  
• Every definition was co-created by the MRCT Center and a workgroup made up of volunteers with expertise across the clinical research ecosystem. We prioritize the patient voice and research advocates, while also drawing on the experience of medical writers, librarians, other nonprofit professionals, and sponsor representatives. Some of these definitions were even reviewed by fifth graders to ensure clarity of information.  
• Most terms found in the CRG have a bespoke image designed by the MRCT Center team, produced by graphic designers, and reviewed by the team of volunteers. The extra information goes through a similar process of multiple reviews before it is published on the website.  
• The MRCT Center will update the CRG over time, adding new words and refreshing old content as needed.  

This tool can be implemented in various ways:

• Use the CRG for onboarding to familiarize new staff with key clinical research terminology.  
• Print the CRG as a PDF for use during recruitment, consent, and study visits.  
• Incorporate CRG information into educational materials to aid participants in following study instructions and facilitate conversations.  
• Download the CRG as an Excel file to integrate with other site lexicons.  
• Provide the CRG to participants as an educational resource, either via link or on a study-specific website.  

As long as the Creative Commons license is followed, anyone can reuse the content and graphics.  

While we welcome all participation, we are especially grateful for the perspective of research professionals with direct, frequent participant engagement. Specifically, please share with us terms that need to be clarified or added to the CRG.  

What Makes the MRCT Center’s Clinical Research Glossary Different  

All words and definitions undergo a rigorous Public Review process governed by CDISC , ensuring that the CRG is a global standard. This annual process occurs every June and is open now from June 7 to July 5. Contribute to the vital process by clicking here for the Public Review survey .  

The MRCT Center Clinical Research Glossary is designed for patients, participants, their caregivers, and clinical research professionals. Use this resource to supplement your participant-facing materials, enhance your research conversations, and make clinical research a more equitable and accessible experience.  

Shared by the MRCT Center, with offices in Boston and Cambridge, Mass.  

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Clinical Trials Information System

Added on 18 June 2024: 'CTIS transparency rules' section

CTIS serves to implement EU pharmaceutical law in the Clinical Trials Regulation   (Regulation (EU) No 536/2014) .

The European Medicines Agency (EMA) maintains CTIS and the public website, together with the EU Member States, EEA countries and European Commission.

CTIS supports interactions between clinical trial sponsors (researchers or companies that run a clinical trial and collect and analyse the data) and regulatory authorities in the EU Member States and EEA countries, throughout the lifecycle of a clinical trial.

Anybody can view information held in CTIS on clinical trials in the EU and EEA, by using the searchable public website.

Access the public information and secure workspaces in CTIS:

• Clinical Trials website

For more information on the legal framework, including a three-year   transition period for clinical trial sponsors, see:

• Clinical Trials Regulation

Also on this topic

• Development of the Clinical Trials Information System

Secure workspaces

CTIS supports the business processes of clinical trial sponsors and national regulators via secure workspaces.

Clinical trial sponsors can use CTIS to apply for authorisation to run a clinical trial in up to 30 EEA countries via a single online application .

They can also carry out tasks including liaising with national regulators while a trial is ongoing and recording clinical trial results .

National regulators can use CTIS to collaborate on the evaluation and authorisation of a clinical trial in several EU/EEA countries.

They can also use it alongside other systems to work together on clinical trial oversight , including monitoring and assessing safety-related data in the context of a clinical trial.

Sponsor workspace

The sponsor workspace is a single online portal  for clinical trial sponsors and other organisations involved in running clinical trials to apply to carry out a trial in the EU Member States and EEA countries, submit data related to a trial and post trial results.

It has the following functionalities: 

• Manage users and user roles
• Compile clinical trial applications for new and updated trials
• Cross-reference to product documents in other clinical trials
• Submit clinical trial applications for assessment by Member States
• Receive alerts and notifications for ongoing trials in CTIS
• Respond to requests for information and view deadlines
• Search and access clinical trials
• Issue notifications related to key milestones in the trial lifecycle (e.g. start of recruitment, end of recruitment)
• Record clinical trial results
• Submit annual safety reports

It is for the following target users: 

• Clinical trial sponsors
• Marketing authorisation applicants and holders
• Other organisations involved in running clinical trials

For more information, see the infographic for sponsors : 

Clinical trials information system - Key information for sponsors on CTIS

English (EN) (512.67 KB - PDF)

Authority workspace

The authority workspace enables EU Member States, EEA countries and the European Commission to use CTIS to oversee the conduct of clinical trials in the EU/EEA. 

It has the following functionalities : 

• View clinical trial application dossiers
• Manage tasks related to the assessment of clinical trials
• Collaborate within and between Member States
• Download documents submitted by clinical trial sponsors
• Record inspections of sites and clinical trials
• Conduct Union controls
• Assess annual safety reports
• National competent authorities of EU Member States and EEA countries
• Ethics committees of EU Member States  and EEA countries (national processes regarding access to CTIS for ethics committees may vary)
• European Commission

Searching for clinical trials: the public portal

The public website has a search function  which anybody can use to find detailed information on clinical trials from 31 January 2022, based on the information contained in the Clinical Trials Information System (CTIS). 

Search for information in CTIS via the link below:

• CTIS public portal: search for clinical trials

Via this website, anybody can view information on individual clinical trials as soon as it becomes available, such as the following:

• EU clinical trial number
• Therapeutic area
• Details of the trial sponsor
• Start and end dates of participant recruitment and of the trial itself

Initially, the website gives access to information on a limited number of clinical trials, however the volume of trial-related information will increase as sponsors and regulators use CTIS to initiate and oversee clinical trials, in line with a  three-year transition period under the Clinical Trials Regulation .

Information on individual clinical trials initiated before 31 January 2023 under the Clinical Trials Directive is available:

• European Union Clinical Trials Register

The Clinical Trials Regulation requires  information stored in the CTIS database  to be publicly available, unless exempted to protect the following:

• Personal data
• Commercially confidential information (in particular the marketing-authorisation status of a medicine, unless there is an overriding public interest)
• Confidential communication between EU Member States during evaluations
• Supervision of clinical trials by EU Member States.

CTIS transparency rules

Information on clinical trials submitted in CTIS is made available on the CTIS public portal , in line with revised CTIS transparency rules applicable as of June 2024. 

To consult the revised rules, see: 

• Development of the Clinical Trials Information System: CTIS transparency rules

Processing of personal data

A joint controllership arrangement describes the processing of personal data in CTIS, in accordance with the General Data Protection Regulation and EU Data Protection Regulation. 

A range of actors may need to enter personal data into CTIS as part of clinical trial authorisation and supervision processes, including clinical trial sponsors, marketing authorisation applicants or holders, the European Commission, EMA, EU Member States and EEA countries.

The joint controllership arrangement describes the roles and responsibilities of each party regarding the processing of personal data in CTIS. It sets out the measures they must put in place to ensure that personal data in CTIS is securely processed, and covers how the parties are to handle any personal data breaches.

When accessing CTIS for the first time, CTIS workspace users will be made aware of the contents of the joint controllership arrangement before proceeding.

Joint Controllership Arrangement with regard to the Clinical Trials Information System (CTIS)

English (EN) (235.79 KB - PDF)

Questions and Answers on the Joint Controllership Arrangement and data protection matters related to the use of the Clinical Trials Information System

English (EN) (228.64 KB - PDF)

European Medicines Agency's data protection notice regarding personal data processing in the Clinical Trials Information System (CTIS)

English (EN) (147.71 KB - PDF)

External links

• European Union clinical trials register

Related content

• Clinical Trials Information System: training and support
• Clinical Trials Information System (CTIS): online modular training programme
• Clinical trials in human medicines
• Data submission on investigational medicines: guidance for clinical trial sponsors

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• Patient Care & Health Information
• Diseases & Conditions
• Neurofibromatosis type 1

Neurofibromatosis type 1 (NF1) is a genetic condition that causes changes in skin pigment and tumors on nerve tissue. Skin changes include flat, light brown spots and freckles in the armpits and groin. Tumors can grow anywhere in the nervous system, including the brain, spinal cord and nerves. NF1 is rare. About 1 in 2,500 is affected by NF1.

The tumors often are not cancerous, known as benign tumors. But sometimes they can become cancerous. Symptoms often are mild. But complications can occur and may include trouble with learning, heart and blood vessel conditions, vision loss, and pain.

Treatment focuses on supporting healthy growth and development in children and early management of complications. If NF1 causes large tumors or tumors that press on a nerve, surgery can reduce symptoms. A newer medicine is available to treat tumors in children, and other new treatments are being developed.

Neurofibromatosis type 1 (NF1) usually is diagnosed during childhood. Symptoms are seen at birth or shortly afterward and almost always by age 10. Symptoms tend to be mild to moderate, but they can vary from person to person.

Symptoms include:

• Flat, light brown spots on the skin, known as cafe au lait spots. These harmless spots are common in many people. But having more than six cafe au lait spots suggests NF1. They often are present at birth or appear during the first years of life. After childhood, new spots stop appearing.
• Freckling in the armpits or groin area. Freckling often appears by ages 3 to 5. Freckles are smaller than cafe au lait spots and tend to occur in clusters in skin folds.
• Tiny bumps on the iris of the eye, known as Lisch nodules. These nodules can't easily be seen and don't affect vision.
• Soft, pea-sized bumps on or under the skin called neurofibromas. These benign tumors usually grow in or under the skin but can also grow inside the body. A growth that involves many nerves is called a plexiform neurofibroma. Plexiform neurofibromas, when located on the face, can cause disfigurement. Neurofibromas may increase in number with age.
• Bone changes. Changes in bone development and low bone mineral density can cause bones to form in an irregular way. People with NF1 may have a curved spine, known as scoliosis, or a bowed lower leg.
• Tumor on the nerve that connects the eye to the brain, called an optic pathway glioma. This tumor usually appears by age 3. The tumor rarely appears in late childhood and among teenagers, and almost never in adults.
• Learning disabilities. It's common for children with NF1 to have some trouble with learning. Often there is a specific learning disability, such as trouble with reading or math. Attention-deficit/hyperactivity disorder (ADHD) and speech delay also are common.
• Larger than average head size. Children with NF1 tend to have a larger than average head size due to increased brain volume.
• Short stature. Children who have NF1 often are below average in height.

When to see a doctor

See a healthcare professional if your child has symptoms of neurofibromatosis type 1. The tumors are often not cancerous and are slow growing, but complications can be managed. If your child has a plexiform neurofibroma, a medicine is available to treat it.

Neurofibromatosis type 1 is caused by an altered gene that either is passed down by a parent or occurs at conception.

The NF1 gene is located on chromosome 17. This gene produces a protein called neurofibromin that helps regulate cell growth. When the gene is altered, it causes a loss of neurofibromin. This allows cells to grow without control.

Risk factors

Autosomal dominant inheritance pattern

In an autosomal dominant disorder, the changed gene is a dominant gene. It's located on one of the nonsex chromosomes, called autosomes. Only one changed gene is needed for someone to be affected by this type of condition. A person with an autosomal dominant condition — in this example, the father — has a 50% chance of having an affected child with one changed gene and a 50% chance of having an unaffected child.

The biggest risk factor for neurofibromatosis type 1 (NF1) is a family history. For about half of people who have NF1, the disease was passed down from a parent. People who have NF1 and whose relatives aren't affected are likely to have a new change to a gene.

NF1 has an autosomal dominant inheritance pattern. This means that any child of a parent who is affected by the disease has a 50% chance of having the altered gene.

Complications

Complications of neurofibromatosis type 1 (NF1) vary, even within the same family. Generally, complications occur when tumors affect nerve tissue or press on internal organs.

Complications of NF1 include:

• Neurological symptoms. Trouble with learning and thinking are the most common neurological symptoms associated with NF1. Less common complications include epilepsy and the buildup of excess fluid in the brain.
• Concerns with appearance. Visible signs of NF1 can include widespread cafe au lait spots, many neurofibromas in the facial area or large neurofibromas. In some people this can cause anxiety and emotional distress, even if they're not medically serious.
• Skeletal symptoms. Some children have bones that didn't form as usual. This can cause bowing of the legs and fractures that sometimes don't heal. NF1 can cause curvature of the spine, known as scoliosis, that may need bracing or surgery. NF1 also is associated with lower bone mineral density, which increases the risk of weak bones, known as osteoporosis.
• Changes in vision. Sometimes a tumor called an optic pathway glioma develops on the optic nerve. When this happens, it can affect vision.
• Increase in symptoms during times of hormonal change. Hormonal changes associated with puberty or pregnancy might cause an increase in neurofibromas. Most people who have NF1 have healthy pregnancies but will likely need monitoring by an obstetrician who is familiar with NF1.
• Cardiovascular symptoms. People who have NF1 have an increased risk of high blood pressure and may develop blood vessel conditions.
• Trouble breathing. Rarely, plexiform neurofibromas can put pressure on the airway.
• Cancer. Some people who have NF1 develop cancerous tumors. These usually arise from neurofibromas under the skin or from plexiform neurofibromas. People who have NF1 also have a higher risk of other forms of cancer. They include breast cancer, leukemia, colorectal cancer, brain tumors and some types of soft tissue cancer. Screening for breast cancer should begin earlier, at age 30, for women with NF1 compared to the general population.
• Benign adrenal gland tumor, known as a pheochromocytoma. This noncancerous tumor produces hormones that raise your blood pressure. Surgery often is needed to remove it.

Neurofibromatosis type 1 care at Mayo Clinic

• Ferri FF. Neurofibromatosis. In: Ferri's Clinical Advisor 2024. Elsevier; 2024. https://www.clinicalkey.com. Accessed Feb. 21, 2024.
• Neurofibromatosis. National Institute of Neurological Disorders and Stroke. https://www.ninds.nih.gov/Disorders/Patient-Caregiver-Education/Fact-Sheets/Neurofibromatosis-Fact-Sheet. Accessed Feb. 21, 2024.
• Korf BR, et al. Neurofibromatosis type 1 (NF1): Pathogenesis, clinical features, and diagnosis. https://www.uptodate.com/contents/search. Accessed Feb. 21, 2024.
• Saleh M, et al. Neurofibromatosis type 1 system-based manifestations and treatments: A review. Neurological Sciences. 2023; doi:10.1007/s10072-023-06680-5.
• Neurofibromatosis. American Association of Neurological Surgeons. https://www.aans.org/en/Patients/Neurosurgical-Conditions-and-Treatments/Neurofibromatosis. Accessed Feb. 21, 2024.
• Neurofibromatosis. Merck Manual Professional Version. https://www.merckmanuals.com/professional/pediatrics/neurocutaneous-syndromes/neurofibromatosis. Accessed Feb. 21, 2024.
• Jankovic J, et al., eds. Neurocutaneous syndromes. In: Bradley and Daroff's Neurology in Clinical Practice. 8th ed. Elsevier; 2022. https://www.clinicalkey.com. Accessed Feb. 21, 2024.
• Armstrong AE, et al. Treatment decisions and the use of the MEK inhibitors for children with neurofibromatosis type 1-related plexiform neurofibromas. BMC Cancer. 2023; doi:10.1186/s12885-023-10996-y.
• Zitelli BJ, et al., eds. Neurology. In: Zitelli and Davis' Atlas of Pediatric Physical Diagnoses. 8th ed. Elsevier; 2023. https://www.clinicalkey.com. Accessed Feb. 21, 2024.
• Kellerman RD, et al. Neurofibromatosis (type 1). In: Conn's Current Therapy 2024. Elsevier; 2024. https://www.clinicalkey.com. Accessed Feb. 21, 2024.
• Babovic-Vuksanovic D (expert opinion). Mayo Clinic. March 26, 2024.
• Tamura R. Current understanding of neurofibromatosis type 1, 2 and schwannomatosis. International Journal of Molecular Sciences. 2021; doi:10.3390/ijms22115850.
• Legius E, et al. Revised diagnostic criteria for neurofibromatosis type 1 and Legius syndrome: An international consensus recommendation. Genetics in Medicine. 2021; doi:10.1038/s41436-021-01170-5.
• Find a doctor. Children's Tumor Foundation. https://www.ctf.org/understanding-nf/find-a-doctor/. Accessed Feb. 26, 2024.
• Ami TR. Allscripts EPSi. Mayo Clinic. April 18, 2024.

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Night eating syndrome: a review of etiology, assessment, and suggestions for clinical treatment.

1. Introduction

Aim of the Review

2. methodology, research question, 3. circadian and hormonal influences on night eating syndrome, 3.1. circadian rhythms, 3.2. hormonal influences, 4. psychological factors in night eating syndrome, 4.1. depression, anxiety disorders, and substance abuse, 4.2. emotion regulation.

• Accept and be aware of their own emotions;
• Stay focused on their own goals and inhibit impulsive behaviors when negative emotions arise;
• Use strategies appropriate to the situation to modulate the intensity and/or duration of emotional experiences;
• Be willing to experience and integrate negative emotions within oneself and live them as a significant part of life [ 27 ].

4.3. Emotion Regulation and Eating Pathology

4.4. emotional variations and interpersonal relationships, 4.5. rumination, 4.6. personality traits, 4.7. personality disorders, 4.8. psychological distress, 4.9. comorbid conditions, 4.10. patient perspectives on living with night eating syndrome.

• The darker side of NES: This theme represents the darker sides of patients’ behaviors and involves feelings of helplessness, contempt, self-loathing, and a loss of control. Patients also related to difficult memories concerning sexual, physical, and emotional abuse.
• The comforting side of NES: This theme involves soothing, regulating, emotional disconnecting, and a sense of calm, control, and the ability to function.

5. Differential Diagnosis

5.1. night eating syndrome and obesity-related eating behaviors, 5.2. night eating syndrome (nes) and binge eating disorder (bed).

• The impulsivity related to food intake in the evening.
• The need to contain the present anxiety using food in a compensatory way.
• The development of sleep-related rituals.
• The desire for food, which takes on the characteristics of a real craving. Craving is an uncontrollable desire for food intake that is not subject to any form of control by the individual.

5.3. Night Eating Syndrome (NES), Sleep Eating Disorders (SRED), and Night Sleep-Related Eating Disorder (NS-RED)

6. discussion, 6.1. considerations for implementing treatment for night eating syndrome, 6.2. comparison with previous literature and contribution to the field, 6.3. limitations, 7. conclusions and future directions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Bargagna, M.; Casu, M. Night Eating Syndrome: A Review of Etiology, Assessment, and Suggestions for Clinical Treatment. Psychiatry Int. 2024 , 5 , 289-304. https://doi.org/10.3390/psychiatryint5020020

Bargagna M, Casu M. Night Eating Syndrome: A Review of Etiology, Assessment, and Suggestions for Clinical Treatment. Psychiatry International . 2024; 5(2):289-304. https://doi.org/10.3390/psychiatryint5020020

Bargagna, Miria, and Mirko Casu. 2024. "Night Eating Syndrome: A Review of Etiology, Assessment, and Suggestions for Clinical Treatment" Psychiatry International 5, no. 2: 289-304. https://doi.org/10.3390/psychiatryint5020020

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Complementary, Alternative, or Integrative Health: What’s In a Name?

We’ve all seen the words “complementary,” “alternative,” and “integrative,” but what do they really mean?

This fact sheet looks into these terms to help you understand them better and gives you a brief picture of the mission and role of the National Center for Complementary and Integrative Health (NCCIH) in this area of research. The terms “complementary,” “alternative,” and “integrative” are continually evolving, along with the field, but the descriptions of these terms below are how we at the National Institutes of Health currently define them.

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According to a 2012 national survey, many Americans—more than 30 percent of adults and about 12 percent of children—use health care approaches that are not typically part of conventional medical care or that may have origins outside of usual Western practice. When describing these approaches, people often use “alternative” and “complementary” interchangeably, but the two terms refer to different concepts:

• If a non-mainstream approach is used  together with  conventional medicine, it’s considered “complementary.”
• If a non-mainstream approach is used  in place of  conventional medicine, it’s considered “alternative.”

Most people who use non-mainstream approaches also use conventional health care.

In addition to the terms complementary and alternative, you may also hear the term “functional medicine.” This term sometimes refers to a concept similar to integrative health (described below), but it may also refer to an approach that more closely resembles  naturopathy  (a medical system that has evolved from a combination of traditional practices and health care approaches popular in Europe during the 19th century).

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Integrative health brings conventional and complementary approaches together in a coordinated way. Integrative health also emphasizes multimodal interventions, which are two or more interventions such as conventional health care approaches (like medication, physical rehabilitation, psychotherapy), and complementary health approaches (like acupuncture, yoga, and probiotics) in various combinations, with an emphasis on treating the whole person rather than, for example, one organ system. Integrative health aims for well-coordinated care among different providers and institutions by bringing conventional and complementary approaches together to care for the whole person.

The use of integrative approaches to health and wellness has grown within care settings across the United States. Researchers are currently exploring the potential benefits of integrative health in a variety of situations, including pain management for military personnel and veterans, relief of symptoms in cancer patients and survivors, and programs to promote healthy behaviors.

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Whole person health refers to helping individuals, families, communities, and populations improve and restore their health in multiple interconnected domains—biological, behavioral, social, environmental—rather than just treating disease. Research on whole person health includes expanding the understanding of the connections between these various aspects of health, including connections between organs and body systems.

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• An NCCIH-funded study is developing an innovative, collaborative treatment model involving chiropractors, primary care providers, and mental health providers for veterans with spine pain and related mental health conditions.
• Other NCCIH-funded studies are testing the effects of adding mindfulness meditation, self-hypnosis, or other complementary approaches to pain management programs for veterans. The goal is to help patients feel and function better and reduce their need for pain medicines that can have serious side effects.
• For more information on pain management for military personnel and veterans, see NCCIH’s  Complementary Health Practices for U.S. Military, Veterans, and Families  webpage.

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• Massage therapy may lead to short-term improvements in pain and mood in patients with advanced cancer.
• Yoga may relieve the persistent fatigue that some women experience after breast cancer treatment, according to the results of a preliminary study.
• Tai chi or qigong have shown promise for managing symptoms such as fatigue, sleep difficulty, and depression in cancer survivors.
• For more information, see  NCCIH’s fact sheet on cancer .

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• Preliminary research suggests that yoga and meditation-based therapies may help smokers quit.
• In a study funded by the National Cancer Institute, complementary health practitioners (chiropractors, acupuncturists, and massage therapists) were successfully trained to provide evidence-based smoking cessation interventions to their patients.
• An NCCIH-funded study is testing whether a mindfulness-based program that involves the whole family can improve weight loss and eating behavior in adolescents who are overweight.
• For more information, see the NCCIH  Quitting Smoking  and  Weight Control  webpages.

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Complementary approaches can be classified by their primary therapeutic input (how the therapy is taken in or delivered), which may be:

• Nutritional (e.g., special diets, dietary supplements, herbs, and probiotics)
• Psychological (e.g., mindfulness)
• Physical (e.g., massage, spinal manipulation)
• Combinations such as psychological and physical (e.g., yoga, tai chi, acupuncture, dance or art therapies) or psychological and nutritional (e.g., mindful eating)

Nutritional approaches include what NCCIH previously categorized as natural products, whereas psychological and/or physical approaches include what was referred to as mind and body practices.

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These approaches include a variety of products, such as  herbs   (also known as botanicals),  vitamins and minerals , and  probiotics . They are widely marketed, readily available to consumers, and often sold as  dietary supplements .

According to the 2012 National Health Interview Survey (NHIS), which included a comprehensive survey on the use of complementary health approaches by Americans, 17.7 percent of American adults had used a dietary supplement other than vitamins and minerals in the past year. These products were the most popular complementary health approach in the survey. (See chart.) The most commonly used nonvitamin, nonmineral dietary supplement was fish oil.

Researchers have done large, rigorous studies on a few dietary supplements, but the results often showed that the products didn’t work for the conditions studied. Research on others is in progress. While there are indications that some may be helpful, more needs to be learned about the effects of these products in the human body, and about their  safety  and potential  interactions with medicines  and other natural products.

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Complementary physical and/or psychological approaches include tai chi , yoga , acupuncture , massage therapy , spinal manipulation , art therapy, music therapy, dance, mindfulness-based stress reduction, and many others. These approaches are often administered or taught by a trained practitioner or teacher. The 2012 NHIS showed that yoga, chiropractic and osteopathic manipulation , and meditation are among the most popular complementary health approaches used by adults. According to the 2017 NHIS , the popularity of yoga has grown dramatically in recent years, from 9.5 percent of U.S. adults practicing yoga in 2012 to 14.3 percent in 2017. The 2017 NHIS also showed that the use of meditation increased more than threefold from 4.1 percent in 2012 to 14.2 percent in 2017.

Other psychological and physical approaches include relaxation techniques   (such as breathing exercises and guided imagery),  qigong ,  hypnotherapy , Feldenkrais method, Alexander technique, Pilates, Rolfing Structural Integration, and Trager psychophysical integration.

Research findings suggest that several psychological and physical approaches, alone or in combination, are helpful for a variety of conditions. A few examples include the following:

• Acupuncture  may help ease types of pain that are often chronic, such as low-back pain, neck pain, and osteoarthritis/knee pain. Acupuncture may also help reduce the frequency of tension headaches and prevent migraine headaches.
• Meditation  may help reduce blood pressure, symptoms of anxiety and depression, and symptoms of irritable bowel syndrome and flare-ups in people with ulcerative colitis. Meditation may also benefit people with insomnia.
• Tai chi  appears to help improve balance and stability, reduce back pain and pain from knee osteoarthritis, and improve quality of life in people with heart disease, cancer, and other chronic illnesses.
• Yoga  may benefit people’s general wellness by relieving stress, supporting good health habits, and improving mental/emotional health, sleep, and balance. Yoga may also help with low-back pain and neck pain, anxiety or depressive symptoms associated with difficult life situations, quitting smoking, and quality of life for people with chronic diseases.

The amount of research on psychological and physical approaches varies widely depending on the practice. For example, researchers have done many studies on acupuncture, yoga, spinal manipulation, and meditation, but there have been fewer studies on some other approaches.

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Some complementary approaches may not neatly fit into either of these groups—for example, the practices of traditional healers, Ayurvedic medicine , traditional Chinese medicine , homeopathy , naturopathy , and functional medicine.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} NCCIH’s Role

NCCIH is the Federal Government’s lead agency for scientific research on complementary and integrative health approaches.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} NCCIH’s Mission and Vision

The mission of NCCIH is to determine, through rigorous scientific investigation, the fundamental science, usefulness, and safety of complementary and integrative health approaches and their roles in improving health and health care.

NCCIH’s vision is that scientific evidence informs decision making by the public, by health care professionals, and by health policymakers regarding the integrated use of complementary health approaches in a whole person health framework.

.header_greentext{color:green!important;font-size:24px!important;font-weight:500!important;}.header_bluetext{color:blue!important;font-size:18px!important;font-weight:500!important;}.header_redtext{color:red!important;font-size:28px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;font-size:28px!important;font-weight:500!important;}.header_purpletext{color:purple!important;font-size:31px!important;font-weight:500!important;}.header_yellowtext{color:yellow!important;font-size:20px!important;font-weight:500!important;}.header_blacktext{color:black!important;font-size:22px!important;font-weight:500!important;}.header_whitetext{color:white!important;font-size:22px!important;font-weight:500!important;}.header_darkred{color:#803d2f!important;}.Green_Header{color:green!important;font-size:24px!important;font-weight:500!important;}.Blue_Header{color:blue!important;font-size:18px!important;font-weight:500!important;}.Red_Header{color:red!important;font-size:28px!important;font-weight:500!important;}.Purple_Header{color:purple!important;font-size:31px!important;font-weight:500!important;}.Yellow_Header{color:yellow!important;font-size:20px!important;font-weight:500!important;}.Black_Header{color:black!important;font-size:22px!important;font-weight:500!important;}.White_Header{color:white!important;font-size:22px!important;font-weight:500!important;} For More Information

Nccih strategic plan.

NCCIH’s current strategic plan, Strategic Plan FY 2021 – 2025: Mapping a Pathway to Research on Whole Person Health , presents a series of goals and objectives to guide us in determining priorities for future research on complementary health approaches. 

NCCIH Clearinghouse

The NCCIH Clearinghouse provides information on NCCIH and complementary and integrative health approaches, including publications and searches of Federal databases of scientific and medical literature. The Clearinghouse does not provide medical advice, treatment recommendations, or referrals to practitioners.

Toll-free in the U.S.: 1-888-644-6226

Telecommunications relay service (TRS): 7-1-1

Website: https://www.nccih.nih.gov

Email: [email protected] (link sends email)

This publication is not copyrighted and is in the public domain. Duplication is encouraged.

NCCIH has provided this material for your information. It is not intended to substitute for the medical expertise and advice of your health care provider(s). We encourage you to discuss any decisions about treatment or care with your health care provider. The mention of any product, service, or therapy is not an endorsement by NCCIH.

Related Topics

NCCIH Strategic Plan FY 2021–⁠2025 Mapping a Pathway to Research on Whole Person Health

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Introduction to Clinical Research Informatics

• First Online: 01 January 2012

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Richesson, R.L., Andrews, J.E. (2012). Introduction to Clinical Research Informatics. In: Richesson, R., Andrews, J. (eds) Clinical Research Informatics. Health Informatics. Springer, London. https://doi.org/10.1007/978-1-84882-448-5_1

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Clinical Research Informatics and Electronic Health Record Data

R. l. richesson.

1 Duke University School of Nursing, Durham, NC, USA

M. M. Horvath

2 Health Intelligence and Research Services, Duke Health Technology Solutions, Durham, NC, USA

S. A. Rusincovitch

3 Duke Translational Medicine Institute, Duke University, Durham, NC, USA

The goal of this survey is to discuss the impact of the growing availability of electronic health record (EHR) data on the evolving field of Clinical Research Informatics (CRI), which is the union of biomedical research and informatics.

Major challenges for the use of EHR-derived data for research include the lack of standard methods for ensuring that data quality, completeness, and provenance are sufficient to assess the appropriateness of its use for research. Areas that need continued emphasis include methods for integrating data from heterogeneous sources, guidelines (including explicit phenotype definitions) for using these data in both pragmatic clinical trials and observational investigations, strong data governance to better understand and control quality of enterprise data, and promotion of national standards for representing and using clinical data.

Conclusions

The use of EHR data has become a priority in CRI. Awareness of underlying clinical data collection processes will be essential in order to leverage these data for clinical research and patient care, and will require multi-disciplinary teams representing clinical research, informatics, and healthcare operations. Considerations for the use of EHR data provide a starting point for practical applications and a CRI research agenda, which will be facilitated by CRI’s key role in the infrastructure of a learning healthcare system.

Introduction

The use of data derived from electronic health records (EHRs) for research and discovery is a growing area of investigation in clinical research informatics (CRI), defined as the intersection of research and biomedical informatics [ 1 ]. CRI has matured in recent years to be a prominent and active informatics sub-discipline [ 1 , 2 ]. CRI develops tools and methods to support researchers in study design, recruitment, and data collection, acquisition (including from EHR sources), and analysis [ 1 ]. To complement the “Big Data” theme of the IMIA 2014 Yearbook, this summary explores the impact of increasing volumes of EHR data on the field of CRI.

There is tremendous potential for leveraging electronic clinical data to solve complex problems in medicine [ 3 ]. The impact on the CRI domain is exemplified by a growing number of publications related to the use of EHRs, including medical record systems, algorithms and methods [ 4 ]. The analysis of existing clinical, environmental, and genomic data for predicting diseases and health outcomes is growing [ 5-7 ]. The regulatory and ethical challenges for using EHR data for research – though complex – are being addressed [ 8 , 9 ]. Research use of EHR data is inherent to the vision of the learning healthcare system [ 10 ]. In this context, CRI will play a central role bridging different perspectives from research and healthcare operations, particularly as they relate to new demonstrations of interventional clinical trials embedded within healthcare systems [ 11 ]. The more immediate uses of EHR data are for observational research (i.e., investigations that observe and explore patient phenomena related to the “natural” – rather than researcher controlled – assignment of interventions), because these designs have less inherent risk and disruption to clinical workflows than do interventional trials.

Definitions

Clinical research is the science that supports the evaluation of safety and effectiveness of therapeutics (medications and devices), diagnostic tools, and treatment regimens. Clinical research includes a variety of study designs and methods to support patient-oriented research (i.e., conducted with human subjects or their biospecimens), clinical trials, outcomes research, epidemiologic and behavioral studies, and health services research [ 12 ]. Clinical research informatics, then, is the branch of informatics that supports all these research activities, particularly the collection, management, and analysis of data for varied types of studies. Research approaches can be characterized broadly as either interventional (or experimental trials, where the researcher assigns treatments) or observational (where treatments are not assigned by the researcher). To date, CRI has focused largely on the support of interventional trials, but there is momentum around observational research and clinical data mining [ 6 ], both of which are particularly relevant to this IMIA Yearbook theme of “Big Data”. We defer to other issue authors for precise definitions of the term “Big Data,” but premise this discussion on the assumption that the large amounts of clinical and administrative data from institutional repositories and EHR systems qualify as Big Data. This summary and discussion, therefore, focus on informatics activities and trends related to the use of data collected from clinical practice for purposes of research and discovery.

Interventional Research

In interventional studies, researchers control the assignment of the intervention or treatment under investigation. In randomized controlled trials (RCTs) – the gold standard for evidence generation – researchers assign the participant to an intervention using randomization. The widespread availability of EHR systems in clinical practice are enhancing the potential for pragmatic clinical trials (PCTs), randomized controlled trials designed for broad generalizability, typically using multiple clinical sites and broader eligibility criteria. In contrast to explanatory trials, for which the goal is to detect the effects of new treatments, PCTs evaluate interventions in “real-world” practice conditions [ 13 ]. The routine implementation of PCTs is an important component of a learning health system [ 10 , 14 ]. Pragmatic trials require EHR data to identify research cohorts based on patient features and “clinical profiles”, including co-morbidities, severity, and health outcomes [ 14 ]. Current informatics challenges for PCTs include developing ethical and regulatory standards and public trust [ 8 , 9 ], integrating data from multiple databases, identifying appropriate study populations, unambiguously identifying procedures and treatments, and consistently and explicitly characterizing diseases in terms of progression, severity, and patient impact [ 14 ].

Observational Research

Observational research is non-experimental research encompassing different research designs (e.g., cross sectional, cohort, and case control) and directional components (prospective, retrospective, or non-longitudinal) [ 15 ]. The distinguishing factor is that there is no researcher-controlled assignment of treatment or intervention. In observational studies, the treatment occurs “naturally:” that is, as a result of patient factors and decisions made as part of routine healthcare delivery. In quasi-experimental design, the criteria used for treatment might be unknown, or determined using non-random methods (e.g., a summary score) outside the control of the researcher. A control group component in some observational study designs facilitates the evaluation treatment-outcome associations, making observational studies an appealing complement to RCTs [ 16-18 ]. Observational research principles underlie the growing use of patient registries for research [ 19-21 ] and management of chronic disease [ 22 ], quality measurement and improvement [ 23-37 ] activities, and comparative effectiveness research (CER) [ 28-30 ]. CER is the examination of available therapies relative to a broad range of health outcomes – or “what works best” in healthcare [ 16 ]. Because the goal of CER is to evaluate and compare real world treatments in large and diverse populations, the use of EHR data and observational research methods are essential [ 31-33 ].

Data mining is the exploratory and computationally-guided process of discovering patterns and signals in large data sets, which can then be used for hypothesis generation, predictive modeling, and other analytic activities. Data mining methods are counter to traditional hypothesis-based research, and instead developed in response to Big Data challenges in the business sector. Nonetheless, data mining has been embraced by some biostatisticians, and is gaining respect in the research community [ 6 ]. Data mining supports very large data sets obtained from legacy databases or data warehouses [ 34 ], and deals with the secondary analysis of clinical data, meaning the data are collected as a byproduct of routine clinical care and not purposely collected for research [ 6 ].

Research Fundamentals

The general process of research investigation includes formulating a research question, identifying relevant concepts and measures (variables), and collecting, analyzing, and interpreting data. A variety of statistical techniques can be used to demonstrate associations between patient features (e.g., laboratory value, genetic marker), experience (e.g., treatment), or events (e.g., onset of disease, hospitalization, death); these associations can sometimes be due to chance, bias, or confounding [ 35 ]. Bias is any systematic error that affects the estimates of the association under study, and can emerge from the identification of subjects (i.e., selection bias) or their evaluation (i.e., observation bias). Confounding results from the contamination or oversaturation of measured effects, influenced from related factors external to the study [ 35 ]. The strength behind RCTs is the belief that randomization eliminates confounding by ‘randomly distributing’ these factors – both known and unknown – across comparison groups. Both bias and confounding are major issues for observational studies [ 36 , 37 ] and CER in particular [ 16 , 37 ].

General research considerations for all research studies are the somewhat competing notions of validity and generalizability. Validity refers to confidence in the observed associations, and is increased when chance, bias, and confounding are well addressed. Bias and confounding can be minimized with strict eligibility criteria to limit the differences between comparison groups, but at the cost of making study populations ‘different’ from (or less generalizable to) the greater population.

Drawing from the literature of both the informatics and clinical research communities, we isolated important themes related to the use of electronic clinical data for research, including the heterogeneity and quality of EHR data, integrating data from multiple organizations, identifying research cohorts based on explicit clinical profiles, and the role of informatics in a learning health system. Emergent from these themes, a set of considerations for the use of EHR data is presented as a tool for coping with these challenges in the present and for guiding improvements for the future.

Current Themes Related to the Use of Electronic Clinical Data for Research

Important areas of informatics activity and recent advances are summarized below.

Heterogeneity of Data from EHRs

The definition and content of EHRs vary greatly [ 38 , 39 ]. However, reimbursement requirements and common healthcare delivery processes do result in broad areas of similar data collection across many health care providers. Common subject areas shared between most EHRs include patient demographics, healthcare encounters, diagnoses, procedures, laboratory findings, and vital signs. While there is commonality in subject areas, there is variation in how these concepts are operationalized into variables and codes [ 40 ]. What is notably missing from typical EHR data are standardized data related to disease severity, including disease-specific and general measures of patient functioning that are necessary for health outcomes research [ 41 ]. Estabrooks et al convened consensus groups of experts, patients, and stakeholders and identified critical patient-reported elements that should be systematically incorporated into EHRs for standard and continuous assessment, which include health behaviors (e.g., exercise), psychosocial issues (e.g., distress), and patient factors (e.g., demographics) [ 42 ].

In addition, there are multiple sources for some concepts that need to be well defined for meaningful analyses – within or across organizations. For example, medication usage can be identified using electronic orders, pharmacy fulfillment, administration records, or medication reconciliation. EHR data are inherently subject to the institution’s workflows and data-generating activities [ 43-45 ]. For research to be reproducible and for results to be valid, the source and limitations of different types of data within an organization must be clearly defined. Data provenance is the understanding of definitive or authoritative sources for particular data and any transformation of the data from their original state. This understanding is critical both for the valid use of these data in the present, and to drive future improvement in the quality of data from clinical systems [ 46 ]. Curcin et al have constructed a set of recommendations for modeling data provenance that includes the formal representation of relevant domain knowledge and business processes [ 47 ]. Hersh et al (2013) provide an illustrative model of data provenance, as part of their comprehensive description of caveats for the use of operational EHR data in research contexts [ 46 ]. Other caveats identified include the prevalence of missing and inaccurate data, the fragmentation of patient data across providers, operational features that introduce bias, inconsistencies in free text clinical notes, and differences in data granularity [ 46 ]. These aspects of EHR data are not generally reported, but likely have important implications for most research designs.

Data Quality

The notion of data quality is complex and context dependent [ 48 , 49 ]. Weiskopf presents a multi-dimensional (completeness, correctness, concordance, plausibility, and currency) model of data quality, as well as common data quality assessment methods that include comparison with gold standards, data source agreement, distribution comparison, and validity checks [ 50 ]. Accuracy and completeness [ 51 ] are the dimensions of quality that are most commonly assessed in both observational and interventional trials [ 23 , 52 ]. These dimensions closely indicate the capability of the data to support research conclusions, and have been prioritized in the high-profile Healthcare Systems Collaboratory, an NIH-funded exploratory collaboration to advance PCTs, cooperative research partnerships, and evidence-based healthcare) [ 52 ].

Challenges for Studies Involving Multiple Healthcare Organizations

Multi-site PCTs, safety surveillance, and observational research projects that identify patients from heterogeneous practice settings pose challenges for reconciling the variation in healthcare operations, widely disparate information systems, and differences in data capture fidelity. The impact of the selection of healthcare system and database on results of previously conducted studies is illustrated by a sobering study recently published in the American Journal of Epidemiology [ 53 ]. Using administrative claims data, Madigan et al systematically explored differences of relative risk and standard error estimates across a network of 10 health system data bases (130 million patients combined) for 53 drug-outcome test cases. They demonstrated variant results on studies in different clinical networks, despite identical sampling and statistical methods, in some cases reversing the drug-outcome associations detected. Authors concluded that 20% to 40% of observational database studies can swing from statistically significant in one direction to statistically significant in the opposite direction, depending on the choice of database [ 53 ]. The specific causes for this variance is unknown, but a growing number of methods reports are addressing approaches for using EHR data in observational research, including methods related to patient sampling and data quality [ 32 , 54 , 55 ].

Research studies are mandated to report patient characteristics for each study site as part of Consolidated Standards Of Reporting Trials (CONSORT) [ 56 ] and STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) [ 57 ] guidelines. Data from different healthcare organizations represent different patient populations, treatment patterns, and operational factors related to the collection, coding, and reporting of clinical and administrative data. Brown et al provided a set of recommendations for data quality checks and trend monitoring in distributed data networks for CER, using experiences from their multi-site networks [ 58 ]. Moving forward, features related to the EHR system will be a critical factor, and at times an unknown confounder, in research conducted with healthcare systems and electronic clinical data. There is growing appreciation that EHR structure and features might someday be reported as important qualifying data [ 59 ] and that it is important to have processes in place to monitor and characterize potential issues.

EHR Phenotypes

The heterogeneity of EHR data creates challenges for identifying the presence of any specific clinical condition – such as diabetes, heart disease, or obesity – in patient populations, and greatly limits opportunities to observe or learn from regional variation of disease patterns or trends over time [ 60 ]. For example, a recent report on trends in diabetes lists several conditions (including hypoglycemia, neuropathy, chronic kidney disease, peripheral vascular disease, cognitive decline) whose national prevalence could not be calculated due to lack of consistency of EHR documentation and definitions across the United States [ 61 ]. The methods for defining a clinical condition and applying it to EHR data are encompassed by the concept of EHR-driven computational phenotyping. Currently there are no standardized or well-established EHR-based definitions (or “phenotypes”) for most conditions, although many different operational definitions are used in different contexts. Within the past few years, a growing number of publications have emerged to describe methods related to EHR-driven phenotype definitions and resources for accessing and evaluating validated definitions [ 60 , 62-65 ] An important original work by Shivade et al reviews the development of phenotyping approaches using EHR data for identifying cohorts of patients [ 66 ]. Their assessment creates a useful framework of classification, but is focused specifically on cohort identification. In addition, there is a scarcity of information about performance (e.g., specificity, sensitivity, positive and negative predictive values) of these various conditions definitions used with EHR data. The estimation of validity, using any performance measure, requires a “gold standard,” defined as the best classification available for assessing the true or actual disease status. This requirement poses feasibility challenges because a “gold standard” does not often exist and must be constructed in order to be used for evaluation. Many EHR-based definition developers have conducted validation studies [ 65 , 67 , 68 ] but there is no standard approach or uniformly operationalized clinical “gold standard”. Further, the performance indications of phenotype definitions are not typically included in the reports of many research studies published in scientific journals. Although greatly needed, a standard process for validation of these definitions (including statistical considerations and procedures and qualifications for expert reviewers) does not yet exist. Standardized methods in this area could support measuring the impact of the different versions of the International Classification of Disease coding systems, a transition that will have broad impact across healthcare operations in the U.S. As discussed by Boyd et al, the sometimes-convoluted semantic relationships between mapping the same disease conditions from ICD-9-CM and ICD-10-CM will create complex logistics, with predicted repercussions to accuracy [ 69 ].

Although used predominantly to identify positive “cases” (or negative cases or controls) for research, an important application of phenotypes is in the definition of health outcomes. Here, the methods already developed with administrative and claims data should be a foundation for application to EHR data, in particular the well-constructed and mature work of the U.S. Food and Drug Administration’s Mini-Sentinel program [ 70 ]. Also utilizing claims data, Fox et al have described methods for expert review and consensus to define health outcome measurements in claims data [ 71 ].

The eMERGE consortium [ 65 ] and phenotype knowledge base [ 72 ] has lead the vanguard effort in many areas, including representation of phenotype definitions in characterizing eligibility criteria for clinical trials [ 64 ], metadata mapping [ 73 ], and a targeted evaluation of the National Quality Forum quality measure model [ 74 ]. This work is framed within its core objective of genetic research; in another context, Richesson et al have described a broader set of use cases for clinical research [ 14 ]. The SHARPn project [ 75 , 76 ] describes development of a data normalization infrastructure for phenotyping activity [ 77 ] and its new library [ 78 ] represents an important repository for implementation efforts, particularly for Meaningful Use application. Institutional infrastructure solutions [ 79 ], machine learning platforms [ 62 ], and user interfaces [ 79 , 80 ] are also a significant area of development.

Phenotype definitions based upon EHR data are inexorably tied to their health services context. Disparate processes are reflected within these data, including measurement of patient biological status, diagnostic progression, treatment decisions, and management of disease. As discussed by Hripcsak, the true patient state is interpreted through the health care model processes, which in turn informs and creates the basis for the phenotype itself [ 81 ]. The logic and parameters of each phenotype definition may lead to significantly different yields [ 82 ].

Due to the relatively recent development of phenotyping methods, most applications have been at single institution or with a relatively small group of collaborators. In order to achieve uniform implementation and reproducibility of results, especially among heterogeneous data sources for multi-site research and national networks, more expansion is needed for logical and consistent representation and dissemination across sites [ 83 ]. The development of robust and standardized validation methods is an important area for future development, and will ensure that individual phenotype definitions are widely generalizable. Further development of phenotyping methods and applicability within a variety of settings will become increasingly important for a broad set of applications in observational and interventional research settings.

Observational Data and Learning Health Systems

The vision of a Learning Healthcare System (LHS) has been described in both the EU and the US. The paradigm depends upon operationalizing proven insights from research into the health care delivery system [ 84 ]. In this environment, quality improvement and research studies increasingly use the same data sources, cohorts, and personnels. The core concept is a circular flow of data and knowledge between patients, clinicians, health provider organizations, and communities so that data related to the healthcare experience inform research, and research builds evidence, which in turn informs healthcare practices. Achieving this vision will require new ethical frameworks [ 8 , 9 ], robust methods for managing and analyzing observational data, and effective partnerships among healthcare systems [ 85 ]. Future work around the themes presented here will be essential to the vision of LHS, and CRI will play a key role. The growing appreciation for the generalizability and convenience of observation studies has increased their prominence on the evidence hierarchy, and observational research is gaining respect as critical part of the LHS infrastructure.

Both interventional and observational research methods are important components of the core vision of the learning healthcare system (IOM), as shown in Figure 1 , inspired by interpretations of the learning healthcare system as a continuous communication of data, information and knowledge between healthcare systems, researchers, and public.

Central role of CRI in a learning healthcare system. Adapted from: http://ehealth.johnwsharp.com/2012/12/14/the-learning-healthcare-system-and-order-sets/

There have been fruitful collaborations for integrating clinical data across large organizations, including models for networked research [ 86 ] and national pharmacovigilance [ 87 ]. We look forward to continued demonstration and dissemination of knowledge from the above, in particular their common challenge to convincingly demonstrate that they can overcome issues related to data quality, validity, and reproducibility, to which observational research and secondary use of clinical data are inherently vulnerable.

The ability to overcome these issues will be critical to combining data, applying guidelines, or comparing results across different settings. Essentially, generalized evidence synthesis (e.g., comparative effectiveness research or meta-analyses) of any kind will be dependent upon shared semantics and data quality [ 46 , 88-91 ]. This will require increased collaboration between the researcher and health system enterprises and commitment to quantify data quality, lineage, and provenance as part of an ongoing health informatics strategy. Informed by the experience of research and clinical networks that have successfully leveraged electronic clinical data for research insight and pharmacovigilance, we articulate the guidelines in the next section.

Considerations for Using EHR Data in Research Caveats and Guidelines

Regardless of the study design, there are some important issues that must be considered for research investigations involving EHR data. The following principles address risks to research integrity and points of attention for using EHR data, and also to describe outstanding challenges for clinical research informatics and informaticians in general.

• Data on a given cohort’s outcomes will be limited. Only a fraction of a patient’s lifetime care will be housed within any EHR [ 39 ], particularly in the U.S. Inconsistencies in defining a study population can affect the validity and generalizability of results. For example, readmissions can only be identified within those care sites where EHR data are available and patients can be linked across different locations and care delivery settings. Many research studies examine all-cause mortality as an endpoint; for deaths not directly observed within the inpatient setting, this is a very incomplete data point in most EHRs without supplementation by either the social security death index data, which has certain limitations, or the National Death Index (NDI), which can be costly to acquire [ 92 ].
• EHR adoption is continually evolving in healthcare environments. Observational studies require longitudinal data, but use of an EHR does not imply its data is consistent over time. A recent Black Book Ratings report has noted that 17% percent of surveyed health organizations planned to switch their current EHR to a new vendor by the end of 2013 in order to comply with growing government meaningful use requirements [ 93 ]. For researchers, this means that collected data may span multiple systems (i.e. different EHRs) and thus require separate data dictionaries as well as design and documentation of a strategy for spanning one or more timelines. Major upgrades to existing EHRs can also change the production data tables as well.
• Data quality will be an ongoing issue. Depending on the subject domain, some portion of data for a given field may be nonsensical and should be omitted. For example, it is possible that ‘dummy’ or ‘test’ patients may be left within a production EHR which could be discovered by scrutinizing patient keys that seem to have an unusual volume of encounters. At times, this may impact between 10%-15% of the EHR data examined. The decision to omit or restrict data will depend upon the project needs.
• Trustworthy data dictionaries are essential. A project-specific data dictionary should be created and include the following for each data element: completeness in the EHR, range of possible values, data types (e.g. integer, character), and definitions.
• The use of EHR data must be accompanied by an understanding of health care workflow. EHRs have plentiful time-stamps that could be used to understand the process of care, but care must be taken to understand exactly what triggers the recording of those dates and times. For example, a timestamp recorded for when a clinic visit starts can potentially precede a patient’s appointment if the provider opens the encounter early to get a head start on documentation.
• EHR data will reflect billing needs and not just clinical care. There may be concepts that have significance for billing but not care provision or disease progression. Researchers should be cautioned that diagnosis and procedure codes that were designed with billing and reimbursement uses in mind may not reflect a clinical state to the resolution needed for research purposes.
• Observational research using EHR data must be a team activity. Success requires partnerships between clinical staff, informaticians, and researchers. Given data complexity, a new discipline of health data science is emerging [ 94 ]. As of this writing, the National Institutes of Health announced the appointment of the Associate Director for Data Science, a new senior scientific position, charged to lead a series of NIH-wide strategic initiatives that collectively aim to capitalize on the exponential growth of biomedical research data such as from genomics, imaging, and electronic health records [ 95 ].
• Advocate for a research voice in the creation of organizational data governance. Quality EHR data is ultimately dependent upon a clear organizational data governance structure that enforces process, definitions, and standards. Researchers should have a voice within the governance structure to ensure that their needs are met and that they can communicate any data quality issues identified over the course of their investigations.

CRI includes a growing collection of methods that offer methodological and technical solutions for processing clinical data, but guidelines for the valid assessment and analyses of these data are needed. Current clinical research data management practices are tailored to the rigor and regulatory requirements of clinical trials [ 96 ]. The secondary use of data generated from clinical care activities has created new challenges for data management and analysis, and the amount of data available greatly exceeds the number of analysts capable of making sense of it [ 97 ]. Interpretation and application of study findings will require teams of dedicated informatics professionals working collaboratively with researchers and practitioners. These multi-disciplinary teams should appreciate the fundamental research design principles, as well as organizational and workflow factors that affect the completeness and appropriateness of data for different research needs.

The above issues have emerged from the lack of representational standards for EHR data. Clinical researchers and informaticians have developed complex strategies to deal with the resulting EHR heterogeneity on a per-study basis; however, the identification of strategies and solutions will continue to be a major activity area for CRI and researchers alike.

Future Directions and Challenges

EHR technologies are evolving to permit not just management of patients at care sites, but also telemedicine and the management of population health. Vendors are exploring how to allow integration of the data generated by platforms for mobile technologies, and wearable devices [ 98-100 ]. These data streams bring new challenges as patients will use these resources to different depths; there is a large potential for missing data from patients on the less engaged side of the digital divide, especially due to lack of technical acumen or barriers to access [ 101 , 102 ].

EHRs contain information primarily generated during routine clinical care. As technology has evolved, there is a great deal of data generated outside the traditional healthcare system (e.g., wearable devices, social networks) with volume expected to increase exponentially; these data represent an important opportunity to understand the complete context of patient health and behavior, but will require integrated solutions for analytic use. Similarly, increased focus on socio-economic status will likely driven broader inclusion of different data types, including geospatial and patient-reported data, areas addressed by recommendations of the committee formed by the IOM to identify domains and measures that capture the social determinants of health to inform the development of recommendations for meaningful use of EHRs [ 102 ]. Images and video data represent other areas of largely untapped potential where challenges for interoperability have precluded large-scale analytic application [ 103 ].

As more data sources are available, there are also significant challenges associated with person-level disambiguation and linkage across sources. The U.S. lacks a unique person identifier used for healthcare settings, a strategy that has been adopted in other countries [ 104 ]. Multiple techniques exist to perform entity resolution and create an integrated view of all data points associated with the patient, but accuracy and performance of these methods is variable [ 105 ].

Emerging computational methods have arisen to address the demands of molecular analyses conducted upon large volumes of genetic data, including cloud-based solutions and the massively parallel processing supported by Hadoop, MapReduce, and other platforms [ 105 , 106 ]. As the availability and volume of clinical data increases, extending these technologies beyond the translational sciences offers great potential for overcoming some limitations of traditional relational databases management systems [ 107 ]. These emerging tools hold potential to support better prediction models, with the goal of supporting learning healthcare to achieve better outcomes of patient health. The future role of informatics will be to build upon successful clinical and research networks and collaborations to create a data infrastructure that will support a new generation of continuous learning [ 108 ]. This includes understanding the limitations of EHR data, operationalizing appropriate research questions for the data, and proactively devising approaches improving data quality for more robust uses. There are also opportunities for CRI professionals to formulate informatics research questions as well as provide leadership in building better approaches to data standards and exchange to support varied uses.

There is a great amount of activity surrounding the use of EHRs for observational research. Strategies are being designed by the CRI community to grapple with the complexities of observational and interventional study designs, data governance, technical integration issues, and query models. This work will continue to grow and inform the design and conduct of research as well as the eventual application of evidence based practice to complete the learning health system cycle.

We provide a set of principles, grounded in research design, to cope with the problems of leveraging EHR data for various research and learning objectives, and highlight outstanding and important areas for future research and attention. EHR data is complex and intertwined with business and operational aspects of an organization, which may be unfamiliar to researchers and statisticians accustomed to data from clinical trials and registries. Using EHR data points means that one must attempt to understand the workflow that created them, particularly if a strong program of data governance is not in place. Despite this, there is growing appreciation for the inherent limitations of these data as well as momentum to improve its content and quality for research. Successful strategies will address fundamentals of research design (including confounding, sampling, and measurement bias) while embracing data quality limitations.

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WHO Technical Advisory Group (TAG) on clinical and policy considerations for new Tuberculosis (TB) vaccines

The World Health Organization (WHO) is seeking experts to serve as members on Technical Advisory Group on clinical and policy considerations for new Tuberculosis (TB) vaccines.  This “Call for experts” provides information about the advisory group in question, the expert profiles being sought, the process to express interest, and the process of selection.

Tuberculosis (TB) is a leading cause of death from an infectious agent, and a major contributor to antimicrobial resistance. About a quarter of the world’s population has been infected with Mycobacterium tuberculosis, most living in low-and-middle income countries. This population is at risk of developing TB disease.  An estimated 10.6 million people fell ill with TB in 2022, an estimated 1.3 million people died.

WHO seeks to advance the development and deployment of new TB vaccines to prevent TB disease, with a focus on adolescents and adults in whom 90% of TB disease occurs.  Prevention of TB disease in these populations, responsible for most of the transmission, will significantly reduce disease and incidence in all those at risk, including unvaccinated infants, children, older adults and other key populations, such as people living with HIV. There are several vaccine candidates in the pipeline, many in late-stage development, and effort is needed now to co-ordinate and where feasible align on regulatory expectations, provide guidance on clinical aspects such as optimal clinical endpoints and on anticipated evidence needs for policy at the global, regional and national levels.  

WHO recently established the TB Vaccine Accelerator Council to facilitate the development, testing, authorization, & use of new TB vaccines.  This TAG would form part of the working groups associated with the TB Vaccine Accelerator.

Functions of the Technical Advisory Group on clinical and policy considerations for new Tuberculosis (TB) vaccines

• To provide independent evaluation of the scientific and strategic clinical, regulatory and policy aspects related to new TB vaccine candidates, including but not limited to safety, immunogenicity, efficacy, anticipated impact and effectiveness, resulting in recommendations on data requirements, study designs, clinical trial protocols and regulatory strategies.
• To advise WHO on evidence to support policy formulation, optimal programmatic delivery strategies and where appropriate implementation science to accelerate the pathway to recommendation, introduction and use of new TB vaccines at the country and global levels.
• To assist the secretariat in developing novel guidance / communication on WHO positions with respect to aspects such as clinical endpoints, case definitions, expected data and evidence needs, trade-offs, to inform investment and introduction decision-making.

Operations of the Technical Advisory Group (TAG) on clinical and policy considerations for new Tuberculosis (TB) vaccines

The TAG shall normally meet face-to-face at least once each year, and virtually up to 8 times per year. Interim teleconferences and review of draft documents may be required of the members.  The working language of the group will be English.

Who can express interest?

The Technical Advisory Group (TAG) on clinical and policy considerations for new Tuberculosis (TB) vaccines will be multidisciplinary, with members who have a range of technical knowledge, skills and experience relevant to novel TB vaccines, specifically their clinical development, regulatory approval, policy and implementation considerations to support WHO prequalification.  Approximately 20 individuals may be selected.

WHO welcomes expressions of interest from:

Scientists and healthcare professionals with expertise and/or experience in the following areas:

• Epidemiology, natural history and/or clinical experience of TB;
• Experience of TB control programme;
• Vaccine product development and regulatory strategy, particularly for vaccines targeted to adults and adolescents;
• Vaccine regulatory approval including in low- and middle-income countries;
• Vaccine related policy, particularly for vaccines targeted to adults and adolescents;
• Behavioural science
• Implementation science
• Community engagement
• Health economics and modelling in the field of TB and/or 
• Health systems and programme delivery.

Submitting your expression of interest

To register your interest in being considered for the TAG on clinical and policy considerations for new Tuberculosis (TB) vaccines, please submit the following documents by 12 July 2024  to [email protected] using the subject line “Expression of interest for the ‘Clinical and policy TAG on new TB vaccines’:

• A cover letter, indicating your motivation to apply and how you satisfy the selection criteria. Please note that, if selected, membership will be in a personal capacity. Therefore do not use the letterhead or other identification of your employer;
• Your curriculum vitae (including your nationality/ies) and
• A signed and completed Declaration of Interests (DOI) form for WHO Experts, available at https://www.who.int/about/ethics/declarations-of-interest .

After submission, your expression of interest will be reviewed by WHO.  Due to an expected high volume of interest, only selected individuals will be informed. 

Important information about the selection processes and conditions of appointment

Members of WHO advisory groups (AGs) must be free of any real, potential or apparent conflicts of interest. To this end, applicants are required to complete the WHO Declaration of Interests for WHO Experts, and the selection as a member of a AG is, amongst other things, dependent on WHO determining that there is no conflict of interest or that any identified conflicts could be appropriately managed (in addition to WHO’s evaluation of an applicant’s experience, expertise and motivation and other criteria).

All AG members will serve in their individual expert capacity and shall not represent any governments, any commercial industries or entities, any research, academic or civil society organizations, or any other bodies, entities, institutions or organizations. They are expected to fully comply with the Code of Conduct for WHO Experts ( https://www.who.int/about/ethics/declarations-of-interest ). AG members will be expected to sign and return a completed confidentiality undertaking prior to the beginning of the first meeting.

At any point during the selection process, telephone interviews may be scheduled between an applicant and the WHO Secretariat to enable WHO to ask questions relating to the applicant’s experience and expertise and/or  to assess whether the applicant meets the criteria for membership in the relevant AG.

The selection of members of the AGs will be made by WHO in its sole discretion, taking into account  the following (non-exclusive) criteria: relevant technical expertise; experience in international and country policy work; communication skills; and ability to work constructively with people from different cultural backgrounds and orientations .The selection of AG members will also take account of the need for diverse perspectives from different regions, especially from low and middle-income countries, and for gender balance.

If selected by WHO, proposed members will be sent an invitation letter and a Memorandum of Agreement. Appointment as a member of a AG will be subject to the proposed member returning to WHO the countersigned copy of these two documents.

WHO reserves the right to accept or reject any expression of interest , to annul the open call process and reject all expressions of interest at any time without incurring any liability to the affected applicant or applicants and without any obligation to inform the affected applicant or applicants of the grounds for WHO's action. WHO may also decide, at any time, not to proceed with the establishment of the AG, disband an existing TAG or modify the work of the AG.

WHO shall not in any way be obliged to reveal, or discuss with any applicant, how an expression of interest was assessed, or to provide any other information relating to the evaluation/selection process or to state the reasons for not choosing a member.

WHO may publish the names and a short biography of the selected individuals on the WHO internet.

AG members will not be remunerated for their services in relation to the AG or otherwise. Travel and accommodation expenses of AG members to participate in AG meetings will be covered by WHO in accordance with its applicable policies, rules and procedures.

The appointment will be limited in time as indicated in the letter of appointment.

If you have any questions about this “Call for experts”, please write to [email protected] well before the applicable deadline.