research articles on quality management system

Increasing the value of quality management systems

International Journal of Quality and Service Sciences

ISSN : 1756-669X

Article publication date: 27 July 2021

Issue publication date: 14 September 2021

Over one million organisations have a quality management system (QMS) certified to the ISO 9001 standard; however, the system requires a lot of resources and its value has been questioned. This critique also leads to a questioning of the strategic relevance of quality management. The purpose of this paper is to explore how different types of uses of QMS correlate with management perceptions of quality management in terms of respect, cost and strategic importance.

Design/methodology/approach

The paper is based on a mixed method data collection strategy, quantitative data being collected from a survey in 8 organisations ( n = 108) and qualitative data being collected from 12 interviews with quality managers in 12 different organisations.

The paper shows that a compliance-oriented QMS usage will more likely lead to a view of quality management as costly and of little respect, than a business or improvement-oriented QMS usage. Moreover, it nuances the view on compliance-oriented usage, showing that it is mainly documentation that negatively influences how management views quality management, whereas standardisation that is part of the compliance-oriented use is perceived as more value-adding.

Originality/value

This paper suggests three types of QMS use, namely, business management, improvement, and compliance-oriented use, and that a wise selection of how to use the QMS will affect the respect, strategic importance and cost that management associates with quality management.

Quality management system
Quality Management
Quality audit

Gremyr, I. , Lenning, J. , Elg, M. and Martin, J. (2021), "Increasing the value of quality management systems", International Journal of Quality and Service Sciences , Vol. 13 No. 3, pp. 381-394. https://doi.org/10.1108/IJQSS-10-2020-0170

Emerald Publishing Limited

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate andcreate derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Introduction

Today, more than one million companies and organisations globally are certified in accordance with ISO 9001 ( ISO – International Organization for Standardization, 2018 Survey). In organisations’ quality management work, a substantial amount of time and focus is given to the quality management systems (QMS) ( Elg et al. , 2011 ). Thus, it is important that QMS adds value to the organisations ( Lenning and Gremyr, 2017 ). The interest in QMS has further grown by its potential to support sustainability efforts through integrated management systems, or by improving environmental management systems based on lessons learned from QMS ( Siva et al. , 2016 ). This potential has, however, not yet been fully exploited, and it is suggested that increased formalization and bureaucracy, induced by a certified QMS, is a reason stated for cases in which quality management hinders rather than support implementation of sustainability efforts ( Allur et al. , 2018 ; Barouch and Kleinhans, 2015 ). Even with a focus on QMS per se , that is, not as a support for an environmental management system, QMS has been subject to critique for hindering creativity, being detached from actual practice and providing limited support for quality improvement ( Poksinska et al. , 2006 ), having negative effects on process compliance ( Gray et al. , 2015 ; Karapetrovic et al. , 2010 ) and can limit focus to production and management systems instead of supporting sustainable development and green innovation ( Li et al. , 2018 ).

At the same time, evidence suggests that QMS provides a critical and established structure with potential to create value ( Rönnbäck et al. , 2009 ), contribute to product quality and operational performance ( Iyer et al. , 2013 ; Kafetzopoulos et al. , 2015b ), increase net asset value ( Ochieng et al. , 2015 ) and support continuous improvement ( Lenning and Gremyr, 2017 ). To ensure that the QMS contributes to as much value as possible, it is vital to have support from management and an appreciation of quality management work ( Beer, 2003 ; Dubey et al. , 2018 ; Joiner, 2007 ; Kaynak, 2003 ; Kafetzopoulos et al. , 2015a ; Lakhal et al. , 2006 ), and that management shows and communicates their awareness of the purpose of the QMS ( Zelnik et al. , 2012 ).

This paper aims to contribute to the existing body of research on QMS by describing different ways of using a QMS (drawing on Maguad, 2006 ); detailing and nuancing the understanding of why QMS might be perceived as non-value-adding ( Lenning and Gremyr, 2017 ; Poksinska et al. , 2006 ); and extending research evaluating the impact of QMS beyond a focus on financial performance ( Aba et al. , 2015 ; Cândido et al. , 2016 ). For practitioners, this paper aims to support a broadened understanding of how different usage of a QMS impact managements’ perception of quality management, which in turn possibly impact their willingness to invest resources in QMS.

Drawing on the various ways of operationalizing quality management proposed by ( Maguad, 2006 ), this study investigates three types of QMS usage: QMS as support for developing the quality of an offering; QMS as a tool for daily management; and QMS as a tool for standardization and documentation. The purpose of this paper is to explore how these three different types of uses of QMS correlate with management perceptions of quality management in terms of respect, cost and strategic importance. This study focuses on certified QMS, and a QMS is defined as a part of a management system regarding quality, based upon a set of interconnected or interacting elements of an organization to establish the organisation, operation, policies, objectives and processes to achieve those objectives (ISO 9000, 2015). Thus, such a system of elements can be viewed as a tool and support to reach an organisations’ objectives. In the following section, some background to QMS usage and the three ways of using QMS are provided, after which methods, findings and discussion of the findings are given. Finally, conclusions are drawn.

Theoretical background

Born with the ideas of Deming, Shewart, Juran and Ishikawa nearly four decades ago, quality management has evolved to become an established management philosophy and area of research ( Hackman and Wageman, 1995 ). This philosophy has been presented as being based upon three pillars, namely principles, practices and techniques ( Dean and Bowen, 1994 ). The principles are given as customer focus, continuous improvement and teamwork.

The ISO 9001 management system standard, being a common basis for a QMS, has become universal in its application (ISO Survey, 2018), as well as a central theme in quality management research ( Carnerud, 2018 ). ISO 9001 is claimed to have the potential for contributing to quality improvement ( Sousa and Voss, 2002 ) and improved operational performance ( Kaynak, 2003 ; Psomas and Pantouvakis, 2015 ). However, the value and the effect of a QMS is argued to depend on different factors, such as management attitudes and purposes ( Willar et al. , 2015 ), but also on quality management maturity, implementation strategy and people involvement ( Poksinska, 2010 ).

The type of motivation for implementing a QMS is also said to influence the performance of the system. Organisations focusing on real quality improvements and organisational needs achieve higher benefits from their QMS implementation in areas like quality and operational improvement, compared to those organisations that implement and seek certification of their QMS for external motives, for example, image or customer requirements ( Boiral and Amara, 2009 ; del Castillo-Peces et al. , 2018 ; Poksinska et al. , 2002 ; Sampaio et al. , 2009 ). Thus, a QMS implemented based upon external requirements, tends to focus more on compliance and control and less on organisational efficiency ( Alič and Rusjan, 2010 ).

In the following section, three different ways of working with QMS will be outlined. The three ways draw on Maguad (2006) who argued that quality in the 21st century could be categorised based on orientation in three different directions: business management, improvement and compliance. However, it is said that all three orientations must coincide for an organisation to be successful in their quality work ( Maguad, 2006 ).

Quality management systems as a tool for daily management

Maguad (2006) argued that business management-oriented quality demands an integrated deployment of strategy, and attention to critical success factors, including vision of the business, markets, and core processes. It also requires involvement from top management and every employee in continuous improvement efforts ( Maguad, 2006 ). On an overall level, Sadikoglu and Zehir (2010) studied relationships between quality practices and multiple performance measures and revealed that all practices studied – training, employee management, continuous improvement, information and analysis – were significantly and positively correlated with measures of employee performance, innovation performance, and firm performance. For QMS, it has been shown that they have effects not only on effectivity, product and service quality but also on employees and employers, for example, related to health and safety at the workplace ( Levine and Toffel, 2010 ). Furthermore, Levine and Toffel (2010) show that after being certified, firms experienced a growth in both sales and employment considerably quicker compared to firms that were not certified. Thus, the authors argued that management should consider an ISO 9001 certification as valuable.

If QMS is used as a support for managing the organisation, management will likely show respect for quality management and not view it as cost-driving but rather as being of strategic importance.

Quality management systems as a support for developing the quality of the offering

An improvement-oriented view of quality promotes an integrated approach for process improvement, involves the whole organisation, and has a wide range of applications, such as on service and support operations ( Maguad, 2006 ). In a study of service employees who interact with customers, Coo and Verma (2002) found that the employee’s perceptions of the implemented QMS had an impact on service quality of the actual offering, in terms of reliability, responsiveness, assurance, empathy and tangibles ( Parasuraman et al. , 1988 ), and in turn of the firm’s performance. Coo and Verma (2002) further believe that one success factor of these perceptions were strong leaders who were involved in promoting quality management.

If QMS is seen as supportive of the development of the quality of the organisation’s offering, management will likely show respect for quality management, not viewing it as cost-driving but rather as being of strategic importance.

Quality management systems as a tool for documentation and standardization

A focus on providing documentation, developing procedures and ensuring consistency is said to result in a compliance-oriented approach to quality management ( Maguad, 2006 ). Implementing a QMS standard like ISO 9000 drives standardization. How standardization impacts an organisation can depend on three variables: what is standardized, how the implementation is done, and to what extent activities and processes are standardized ( Poksinska, 2007 ). First, if there is a low motivation for implementing a QMS, it is shown to result in that organization only fulfil the minimum requirements of the ISO 9000. Fulfilling only the minimum requirements may result in the implementation of a QMS that focuses only on describing the existing work practices – that is, standardizing present practices instead of practising the standard ( Poksinska , 2007, 2010 ). Second, if the result of a standardization is positive or negative is also affected by how the standard is implemented. Thus, if the standardization is done with employee involvement (enabling), supporting changes to deficient practices, or if the standard is implemented top-down (coercive), where management wants to discipline work ( Poksinska, 2007 ). Finally, the level of standardization needs to be right, as too high a level of standardization will reduce employees’ work motivation ( Poksinska (2007) .

If QMS is used as a tool for documentation and standardization, management will likely show little respect for quality management and view quality management as cost-driving and lacking in strategic importance.

Methodology

Research instrument

The study was based on a concurrent mixed method data collection strategy ( Creswell et al. , 2007 ) using both quantitative and qualitative data. Quantitative data were gathered using a survey instrument, developed through a literature review, input from senior practitioners, as well as researchers, and input from previously validated questionnaires. Specifically, this paper draws on a set of items focusing on the main function of the QMS ( Poksinska et al. , 2006 ) and management’s perceptions of quality ( Elg et al. , 2011 ) ( Table 1 ).

How would you describe the main role or purpose of the QMS?

How is the QMS used in your organisation?

How do you think management view/perceive the QMS?

For the survey, respondents from eight large-sized Swedish organisations (>1000 employees each) participated in the study (see Table 2 ). Each participating organisation identified 30–50 respondents on different hierarchical levels. The respondents within each organisation were chosen from employees who had dedicated time and responsibility for quality work. The total number of responses was 249 (response rate = 81%), the number of respondents per organisation ranged from 16 to 51. For this paper, the subset of questions used in the analysis focused on management perceptions of quality management and the overall view of the QMS. These questions were only asked of respondents with management responsibilities and resulted in a subset of 108 respondents.

For the interviews, the interviewee sample consisted of twelve quality managers (IP 1–12) with dedicated time and responsibility for quality work. Sample selection was based on organisations offering both products and services, and having established quality management work structures. The sampled organisations covered the following industries: forestry industry, equipment manufacturers, electronics industry, mechanical industry, med-tech industry, logistics industry, and aviation engineering. The interviewees in these organisations focused both product and service quality. Selection was also based on each interviewee having broad areas of responsibility for quality work and also unmediated access to higher management levels, thereby ensuring a relevant knowledge base concerning management perceptions of quality management in general, and the QMS in particular.

Data collection

The survey was administered by e-mail, including a customized invitation letter for each organization and a link to the survey (using the Web-based tool SurveyMonkey). The survey was open for one month per organization, including two rounds of reminders. The interviews were recorded and then transcribed verbatim.

Data analysis

Since the analysed statements in the quantitative data are jointly exhaustive, answers for which no alternative was chosen were considered to be missing values. After excluding rows containing missing values, 108 of the original 249 observations remained. Of these, nine had rows containing the answer “no opinion”. Since this answer cannot be interpreted as an ordinal value, these observations were excluded as well, resulting in a sample of 99 observations. Spearman’s rank correlation coefficient was used to evaluate the monotonic relationships between the ordinal variables. To depend the understanding of the correlations, the mixed method design was exploited as qualitative interview data was used to further the comprehension of the correlations. Hence, focus was on understanding the relevance and meaning of the correlations.

For the analysis of the qualitative data, the transcriptions of the interviews were uploaded into the QSR NVivo 12 software program. A coding scheme was devised using the theory of grounded propositions (see above). The interviews were then subjected to a thematic text analysis using a deductive cross-case analysis strategy ( Miles and Huberman, 1994 ). Data analysis was done by first reading through all the interviews. By using the theoretically derived coding scheme, coding can be described as influenced by the theoretical underpinnings of the propositions and as descriptive by “attributing a class of phenomena to a segment of text” ( Miles and Huberman, 1994 , p.57), based on the grounded propositions. The content of the coded data was thematically analysed whereby general similarities (or discrepancies) between the interviewees could be identified. Finally, the thematic content was evaluated against the conceptual and theoretical underpinnings to further understand the data and draw conclusions. An overview of the coding scheme with quotes illustrating how the data analysis was performed is featured in Table 3 . The results per se will be further elaborated on in the findings section.

Each code category was labelled either to signify a positive view – the use of QMS is viewed with respect in daily work, QMS is viewed as cost reducing, and the use QMS is viewed as strategically important – or to signify a negative view – the use of QMS is not viewed with respect in daily work, QMS is viewed as cost increasing and the use of QMS is not viewed as strategically important.

The study took several steps to achieve acceptable research quality, for example all questions in the survey were based on established instruments, and triangulation of data with questionnaire data and interview data was used to corroborate the findings.

On an overall level, the data shows that the respondents to a large extent agree with all the statements regarding the function and use of the QMS in their organisation ( Table 4 ).

It appears that QMS as a “tool to handle documentation”, “tool for standardisation”, and as having a “significant impact on how the organisation works” are the three statements where most respondents to some extent agree and in other words recognise their way of working with QMS. For statements where a group of respondents do not agree at all, the three other statements stand out. The statement for which most respondents do not agree is that QMS is “a tool that supports efficient management of our organisation”, followed by QMS is “a tool that helps us to fulfil our customers’ needs”, and QMS is “a tool for managing our quality work and improve the quality of our products/services”. As QMS and activities related to designing, implementing, and maintaining the system is a large part of what a quality function does, it arguably will influence how managers view quality management overall. Figure 1 shows the correlations between the level of agreement on the statements related to the function of QMS, and management’s view on quality management in terms of respect, cost and strategic importance.

First, P1 ’s focus on a business management-oriented use of QMS relates to two functions of QMS: impact on work and efficient management ( Table 1 ). These two functions of QMS correlate negatively to management viewing quality management as with a lack of respect and as being costly. On the other hand, there is a positive correlation to viewing quality management as being of strategic importance. Hence, the data points in the same directions as outlined in proposition 1. The findings from the interviews partly support proposition 1 in that management views the impact of QMS on efficient management as positive (e.g. IP8, IP10, IP12). For example, IP7 states that: “The current management at […] has a clear quality aware mentality that benefits everybody […] that works with quality”. However, management can also be perceived as showing a “lack of interest to QMS as to the purpose of quality management work” (IP1).

Second, P2 encompasses the statements on QMS as a tool focused on customer needs and a tool impacting product/service quality; these two concepts constitute what this paper refers to as an improvement-oriented use of QMS. In the same way as the statements underlying P1 , the statements of “customer needs” and “product/service quality” correlate positively to management acknowledging the strategic importance of quality management. Moreover, there are negative correlations with quality being viewed with little respect and as a costly activity. Looking at the correlation values, these are largest for the statement regarding “customer needs”, which might depend on a larger variation in the responses. The findings from the interviews are mostly in favour of P2 (e.g. IP4, IP8, IP12). Key customer requirements such as sustainability (IP4), and also the function of collecting customer information and understanding customer needs (IP3) is perceived by the management as being directly facilitated by QMS. As an example, IP12 states that: “Auditing is still a big part, because that’s one way you can tell how you’re adhering to what your customers want”. IP4 described the benefit of QMS supporting organizational success like this: “And we have this in order, it will be a competitive advantage, and it’s coming globally; it’s coming in all areas.” However, there are also perceptions of management only perceiving the use of QMS for improvement as a “tick in the box”. The interviews show various degrees of understanding QMS as a tool for improvement by management levels (e.g. IP2, IP8).

Third and last, P3 refers to a compliance-oriented use of QMS and concerns documentation and standardization. The correlations are small, but the results are mixed as compared to the other two propositions. The statement viewing QMS as a tool for documentation, displays correlations supporting parts of P3 . That is, it positively correlates with little respect for quality management and a view of it as being costly. However, the statement on documentation does not correlate with quality management being seen as strategic. Moving to the other statement on a compliance-oriented QMS use (“standardization”), the correlations do not support P3 . The use of QMS as a tool for standardization negatively correlates with all three views on quality management. It does not appear supportive of a view on quality management as costly, or of it being little respected. However, it does have a negative correlation with quality management being viewed as strategic (as outlined in P3 ). Again, the correlations are small and further investigation is needed. The interview findings related to P3 are somewhat ambiguous. Regarding management perceptions that QMS, primarily used as a tool for documentation, increases both work and costs and also reduces respect, the findings support P3 (e.g. IP1, IP2, IP5, IP6). Concerning perceptions of QMS used as a tool for standardization, statements on QMS as filling regulatory purposes recur (e.g. IP8, IP9, IP11). Standardization is viewed as both an imperative and something that is self-evident and “the right thing to do” (IP8) with references to safety and brand perception in order not to “run into problems” (IP9).

To support improved QMS usage and increase the perceived value added by a QMS, there is a need to move beyond the broad conception of QMS usage and move towards a more detailed analysis. This paper contributes to research on QMS by outlining three different ways of using QMS, rather than studying QMS usage overall. Drawing on Maguad (2006) three types of QMS usage are described as being oriented towards business management, improvement or compliance.

First, the business management-oriented use of QMS is operationalised by QMS “significantly impacting the way an organisation works”, and “is a tool that supports efficient management of an organisation”. As assumed in proposition 1, these functions appear to support that management will likely show respect for quality management and not view it as cost-driving but rather as being of strategic importance. This is in line with previous research by, for example, Bunney and Dale (1997) establishing that deployment of quality initiatives will be more successful if they are perceived as closely connected to – and potentially improving upon – current work practices.

Second, the improvement-oriented use of QMS is based on QMS as “a tool that help us to fulfil our customers’ needs”, and “a tool for managing our quality work and improve the quality of our products/services”. The proposed impact of these functions is supported, thus ensuring respect for quality management and not viewing it as costly but as strategic ( P2 ). Hence, using QMS to fulfil customer needs and improve the quality of the product or service will positively impact management perception of quality management overall. Previous research has shown that improved quality of the product/service will lead to increased customer satisfaction and loyalty ( Honore Petnji Yaya et al. , 2011 ; Parasuraman et al. , 1988 ), and that improved product/service quality is a benefit of QMS ( Psomas and Pantouvakis, 2015 ). Thus, if QMS is used in a way that can be linked to improved quality and customer satisfaction, this will likely impact management perception of the value added by the QMS.

Third, the results are more mixed in relation to P3 that QMS is used as “a tool for documentation” and “standardization”. This would be correlated with management showing little respect for quality management, viewing it as cost-driving, and not viewing it as strategic. As management perception and support is critical for QMS implementation ( Willar et al. , 2015 ), it is critical to minimize the risk with a too strong focus on documentation conveying a view of QMS as bureaucratic ( Allur et al. , 2018 ) rather than a respected and value-adding activity. However, a certification is still of value as a qualifier in certain business relations ( Boiral and Amara, 2009 ; del Castillo-Peces et al. , 2018 ). This might be a reason that the documentation focus does not appear to have the anticipated negative correlation with management viewing quality management as strategic value. Moreover, a standardisation-focussed use of QMS does not appear to reduce respect for quality management nor lead to it being seen as costly. Perhaps this can be linked to Poksinskàs (2007, 2010) notion of practising the standard rather than standardising current practices. In other words, if standardisation is done with an improvement approach rather than one of pure documentation, it will likely be perceived as beneficial. This is also linked to the function of QMS as having “impact on work”, which is classified as a business management-oriented QMS usage. If this is practised and QMS is allowed to impact actual practices, it will likely mean that QMS is used to standardise and at the same time improve existing work practices.

Overall, the findings support literature pointing to challenges of QMS in terms of focus on compliance rather than organisational efficiency ( Alič and Rusjan, 2010 ), and sometimes not being relevant for actual practice ( Poksinska et al. , 2006 ). However, by distinguishing QMS usage in the three orientations presented above, this study indicates that the documentation focus is what might be the cause for many negative perceptions of the value of QMS. On the other hand, many respondents fully agree that QMS is “a tool that helps us to fulfil our customers’ needs”, which has a relatively high correlation with management viewing quality management as strategic. Contrary to the view of limited value from QMS, this paper supports Poksinska (2007) and Lenning and Gremyr (2017) in that there is potential value in QMS, and that this perceived value will increase if QMS usage is mainly business management- and improvement-oriented, although wisely documented and standardised processes are also required to maintain a certified QMS. An important issue highlighted in the interviews is the risk of using QMS as “quality washing” by management. The interviews indicate that there is still a need to further increase knowledge and understanding within higher management levels on the value of QMS.

The data set underlying this paper is limited in size and the correlations established from the quantitative data are small, yet the qualitative data also supports the propositions. To further establish how an organisation should work with QMS to gain as much benefit as possible, more empirical studies on the three orientations (i.e. business management, improvement and compliance oriented) to QMS are suggested.

Conclusions

Based on an extended view of QMS, this paper has elaborated on three types of QMS use: business management, improvement and compliance-oriented use. The purpose was to explore how these three differing types of uses of QMS correlate with management perceptions of quality management in terms of respect, cost, and strategic importance. Overall, the conclusion is that different ways of working with QMS does not only impact the value of QMS per se , rather it also influences management’s respect for and view of quality management. In terms of difference between the three types of QMS usage, there is a correlation between business management- and improvement-oriented uses of QMS with quality management being respected, and viewed as strategic and not cost-driving. Earlier research has suggested a compliance-oriented use of QMS was the reason for many of the negative perceptions of QMS that in turn was suspected to lead to negative views on quality management in general. However, the findings of this study are somewhat contradictory to this and provide a more nuanced picture showing that, in general, compliance-oriented views might not drive negative perceptions and that it is useful to operationalise compliance into documentation and standardisation. It is suggested that a perception of QMS as having limited value is mainly due to a focus on documentation, whereas work on standardization, which is also part of a compliance-oriented QMS, does not carry similar negative implications. In summary, this study highlights how the perceived strategic value of quality management can be increased through a deliberate design, and choice of an organisation’s ways of using QMS.

research articles on quality management system

Correlation matrix

Statements		Scale
To what extent do you agree with the following statements about the function of your QMS?	Our QMS has a significant impact on how our organisation works. (impact on work)	0 = No opinion/do not know 1 = Do not agree 2 = Partly agree 3 = Agree to a large extent 4 = Fully agree
	Our QMS is a tool that supports efficient management of our organisation. (efficient management)
	Our QMS is a tool that helps us to fulfil our customers’ needs. (customer needs)
	Our QMS is a tool for managing our quality work and improving the quality of our products/services. (product/service quality)
	Our QMS is a tool to handle documentation. (documentation)
	Our QMS is a tool to standardise our processes. (standardisation)
To what extent do you agree with the following statements: “In our organisation management …”	… shows little respect for QM in our daily work. (little respect)	0 to 10 0 = No opinion/do not know 1 = Do not agree 10 = Fully agree
	… regards quality management as a costly activity. (costly)
	… acknowledges the strategic importance of quality management. (strategic)

Overview of organisations in the survey

Organisation	No. of respondents in survey
Life-science company Component manufacturing company Government body Energy supply company Telecommunications company Regional hospital Manufacturing company A Manufacturing company B	51 20 41 25 18 16 38 40 = 249

Coding scheme with illustrative examples

Propositions (P)	Coding categorization	Illustrative examples
P1: QMS for business management	Perceived impact on work	“I'd like to think the more and more we get people involved, the more and more they can see why they need to have it, so, I’d like to think we're alright”. (IP11)
P1: QMS for business management	Perceived management efficiency	“… but otherwise we are heading in the direction of an integrated management system that covers an energy environment health and safety and quality, um, and it’s called for in our business, everybody knows that, everybody works for that, um, we have established documented processes that we want to make sure are lean and also effective”. (IP2)
P2: QMS for improvement	Perceived customer need fulfilment	“But to, to sum it up also so that I’ve understood it, the way that you get information about like different types of issues and customer complaints is both from your customers, the big customers who are Skyping you or sending you whatever issues that might be, it’s from the end customers and from your field engineers”. (IP3)
P2: QMS for improvement	Perceived effect on product/service quality	“Um, what the quality management system should do for us is it should set standards for operation and objectives for continuing improvement in whichever discipline you’re talking about”. (IP2)
P3: QMS for compliance (P3)	Perception as a tool for documentation	“There are many, many documents that are apparently only written for the occasions when an auditor comes to see them. I would say that this is not very useful”. (IP1)
P3: QMS for compliance (P3)	Perception as a standardizing process	“The updated ISO9001-standard of 2015 has as well eased this transition, since the new standard is more business-oriented than the previous one”. (IP7)

Note:

Statement	1 = Do not agree	3 = Agree to a large extent	4 = Fully agree
Impact on work	5	78	16
Efficient management	19	69	11
Customer needs	13	66	20
Product/service quality	12	73	14
Documentation	4	77	18
Standardisation	5	73	21

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Acknowledgements

The authors are grateful for the support from the Swedish Quality Management Academy and the organisations participating in this study. Further, we acknowledge financial support from the Production Area of Advance at Chalmers and the HELIX Competence Centre at Linköping University.

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Implementing quality management systems to close the AI translation gap and facilitate safe, ethical, and effective health AI solutions

Shauna M. Overgaard ORCID: orcid.org/0000-0002-4494-0008 1 ,
Megan G. Graham ORCID: orcid.org/0009-0008-3456-9250 1 ,
Tracey Brereton 1 ,
Michael J. Pencina 2 ,
John D. Halamka ORCID: orcid.org/0000-0003-2305-6755 1 ,
David E. Vidal ORCID: orcid.org/0009-0009-6268-8481 1 &
Nicoleta J. Economou-Zavlanos ORCID: orcid.org/0009-0000-4078-9809 2

npj Digital Medicine volume 6 , Article number: 218 ( 2023 ) Cite this article

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The integration of Quality Management System (QMS) principles into the life cycle of development, deployment, and utilization of machine learning (ML) and artificial intelligence (AI) technologies within healthcare settings holds the potential to close the AI translation gap by establishing a robust framework that accelerates the safe, ethical, and effective delivery of AI/ML in day-to-day patient care. Healthcare organizations (HCOs) can implement these principles effectively by embracing an enterprise QMS analogous to those in regulated industries. By establishing a QMS explicitly tailored to health AI technologies, HCOs can comply with evolving regulations and minimize redundancy and rework while aligning their internal governance practices with their steadfast commitment to scientific rigor and medical excellence.

QMS as a framework for health AI

The advancements in healthcare software, encompassing artificial intelligence, machine learning (AI/ML), and Software as a Medical Device (SaMD), have brought about opportunities for transformative changes in clinical workflows and patient care to effectively meet patient and clinician needs. However, healthcare software exists within a complex regulatory and technical landscape 1 . The need for more readiness among healthcare organizations (HCOs) magnifies the disparity in translating research into effective predictive clinical decision support interventions. Without a collaborative enterprise approach, the intricate nature of this system delays the translation of AI solutions into clinical practice. Characterized by the continuous evolution and maturation of AI/ML capabilities, such as large language models (LLMs), this ecosystem escalates the demand for software-driven clinical solutions and a regulatory framework that must effectively adapt to govern the distinctive nature of in-house-built and procured software 2 . The growing engagement of HCOs in AI calls for alignment among diverse stakeholders, encompassing industry, academic institutions, and the medical community. This alignment should focus on harmonizing assurance standards for health AI technologies, but also practices and infrastructure to enable HCOs to develop and deploy AI solutions meeting rigorous medical-grade standards while ensuring accountability across all involved parties. While regulatory authorities, AI coalitions, medical device manufacturers, and the medical informatics community have acknowledged the current gap not only in common standards but also in the maturity of HCOs to develop and/or deploy health AI, a primary concern for HCOs remains unresolved: “How might our enterprise establish a coordinated, robust strategy that ensures the safe, effective, and ethically sound delivery of AI/ML in day-to-day patient care?” 3 , 4 , 5 , 6 , 7 .

We propose using the Quality Management System (QMS) framework to offer HCOs a consistent and adaptable structure to translate research-based health AI technology into clinical practice systematically and transparently. QMS is a structured framework that documents processes, procedures, and responsibilities to achieve quality policies and objectives. The QMS framework effectively manages evolving regulatory requirements, promotes continuous improvement, and ensures adherence to cutting-edge standards over the life cycle of the design, development, deployment, and maintenance of regulated healthcare software 8 . QMS’s are often certified to external standards (e.g., ISO 13485), thus demonstrating organizational commitment to quality, continuous improvement, and regulatory compliance. Aligning standards with risk-based approaches facilitates the least burdensome path for an HCO to meet regulatory requirements and maintain compliance 9 . Thus, the streamlined incorporation of these regulatory requirements into business processes via the QMS assures enduring safety, effectiveness, ethicality, regulatory compliance, and alignment with organizational and user needs as AI-enabled methodologies, such as LLMs, evolve 10 .

We aim to elucidate the primary components of a QMS (Fig. 1 ) 8 , 9 , 11 , namely People & Culture, Process & Data, and Validated Technology, as the impetus for HCO’s strategic efforts to integrate research rigor and clinical excellence into a cohesive system and close the AI translation gap.

Primary components of a Quality Management System (QMS).

Establishing a proactive culture of quality

In HCOs, AI/ML technologies are often initiated as siloed research or quality improvement initiatives. However, when these AI technologies demonstrate potential for implementation in patient care, development teams may encounter substantial challenges and backtracking to meet the rigorous quality and regulatory requirements 12 , 13 . Similarly, HCO governance and leadership may possess a strong foundation in scientific rigor and clinical studies; however, without targeted qualifications and training, they may find themselves unprepared to offer institutional support, regulatory oversight, or mobilize teams toward interdisciplinary scientific validation of AI/ML–enabled technologies required for regulatory submissions and deployment of SaMD. Consequently, the unpreparedness of HCOs exacerbates the translation gap between research activities and the practical implementation of clinical solutions 14 . The absence of a systematic approach to ensuring the effectiveness of practices and perpetuating them throughout the organization can lead to operational inefficiencies or harm. Thus, HCOs must first contend with a culture shift when faced with quality control rigor inherent to industry-aligned software development and deployment, specifically design controls, version control, installation qualification, operational qualification, performance qualification, that primarily focuses on end-user acceptance testing and the product meeting its intended purpose (improving clinical outcomes or processes compared to the standard of care or the current state), and the traceability and auditability of proof records (Table 1 ).

Consider that even in cases where a regulatory submission is not within the scope, it remains imperative to adhere to practices encompassing ethical and quality principles. Examples of such principles identified by the Coalition for Health AI and the National Institute for Standards and Technology (NIST) include effectiveness, safety, fairness, equity, accountability, transparency, privacy, and security 3 , 7 , 15 , 16 , 17 , 18 , 19 , 20 . It is also feasible that the AI/ML technology could transition from a non-regulated state to a regulated one due to updated regulations or an expanded scope. In that case, a proactive approach to streamlining the conversion from a non-regulatory to a regulatory standard should address the delicate balance of meeting baseline requirements while maintaining a least-burdensome transition to regulatory compliance.

As utilized by the FDA for regulating SaMD, a proactive culture of quality recognizes the same practices familiar to research scientists well-versed in informatics, translational science, and AI/ML framework development. For example, the FDA has published good machine learning practices (GMLP) 21 that enumerate its expectations across the entire AI/ML life cycle grounded in emerging AI/ML science. The FDA’s regulatory framework allows for a stepwise product realization approach that HCOs can follow to augment this culture shift. This stepwise approach implements ethical and quality principles by design into the AI product lifecycle, fostering downstream compliance while allowing development teams to innovate and continuously improve and refine their products. Using this approach allows for freedom to iterate at early research stages. As the product evolves, the team is prepared for the next stage, where prospectively planned development, risk management, and industry-standard design controls are initiated. At this stage, the model becomes a product, incorporating all the software and functionality needed for the model to work as intended in its clinical setting. QMS procedures outline practices, and the records generated during this stage create the level of evidence expected by industry and regulators 22 , 23 . HCOs may either maintain dedicated quality teams responsible for conducting testing or employ alternative structures designed to carry out independent reviews and audits.

Upon deployment, QMS rigor increases again to account for standardized post-deployment monitoring and change management practices embedded in QMS procedures (Fig. 2 ). By increasing formal QMS consistency as the AI/ML gets closer to clinical deployment, the QMS can minimize disruption to current research practices and embolden HCO scientists with a clear pathway as they continue to prove their software safe, effective, and ethical for clinical deployment.

Staged process for applying increasing regulatory rigor throughout product realization.

Establishing risk-based design, development, and monitoring

The medical device industry has utilized a risk-based infrastructure for years to support a least burdensome approach to designing, developing, and deploying healthcare technologies 9 , 24 . This approach systematically enables HCOs to proactively focus resources on key areas of concern, such as safety, equity, and data privacy, to prevent errors and malfunctions and promote a culture of accountability and continuous improvement.

Risk-based practices have been extended to healthcare AI/ML in not only the medical device domain, such as with AAMI’s Technical Information Report 34971 25 , but more broadly in emerging frameworks such as the NIST AI Risk Management Framework 3 , the Whitehouse Blueprint for an AI Bill of Rights 5 , the Coalition for Health AI Blueprint for Trustworthy AI Implementation Guidance and Assurance for Healthcare 26 , and the Health AI Partnership Key Decision Points 27 , 28 . Risk management is grounded in the intended use and informed by a prospective risk management plan. It follows the process of identification, enumeration, mitigation, and monitoring (Fig. 3 ) to analyze and classify potential sources of harm (known as hazards) caused by the healthcare software or its impact on the clinical workflow. As the healthcare software is designed and developed, features or attributes that reduce or minimize the risk (known as mitigations) are included in the product design; for example, incorporating features that improve the user experience or providing user training or documentation to clarify how the software should or should not be used. As risks and potential issues are anticipated for the health software’s implementation, a risk management plan is put in place, a document articulating how safety, bias, and other anticipated risks will be identified and resolved. Risks continue to be monitored, reported, and reviewed after the software is deployed to ensure the software remains safe for use. Systematic feedback, monitoring, and corrective & preventive action (CAPA) frameworks are key to identifying and triaging issues, escalating issues to relevant accountable departments of the organization depending on their severity, performing root-cause analysis, and continuously controlling risks and improving the AI technology.

Example QMS risk management plan and risk assessment phases. Risks are identified, assessed and analyzed, mitigated and controlled, and continuously monitored. Reporting is performed at pre-defined intervals.

Risk-based practices formalized and implemented within a QMS will systematically identify risks associated with an AI solution, document mitigation strategies, and offer a framework for objective testing and auditing of individual technology components. Further, such technologies can be informed by AI/ML and software life cycle best practices to address common issues within phases of the AI lifecycle. This allows for capturing performance metrics across various levels of rigor and data transparency in requirements, version, and design controls. These insights from initial testing can then support the calibration and maintenance of AI solutions during deployment, guided by a multidisciplinary governance system to proactively mitigate future risks 26 . Moreover, establishing a change management plan and access controls can eliminate business continuity risks, providing transparency into responsible parties and outlining the risks of any given change. Back-up (downtime) processes are in place in the event that risk cannot be managed, and the technology needs to be turned off. Effectively, a risk-based approach ensures the proper rigor and controls are in place at the right time throughout the product life cycle.

Establishing a compliance-facilitating infrastructure

The regulations for healthcare software are evolving. Software may or may not be regulated based on its intended use or by changes to regulatory agency enforcement. A QMS that facilitates compliance with applicable legal and regulatory requirements enables HCOs to design, implement, and deploy healthcare software to clinical practice while minimizing overall operational risk. A QMS fosters compliance to internal (e.g., institutional review board) and external (e.g., federal, and local regulatory) bodies by standardizing multi-faceted stakeholder responsibilities with its governance, allowing auditability and traceability through the appropriate evidence and documentation, maintaining an inventory of AI technologies developed and deployed, and hosting infrastructure that will allow document management and monitoring within the deployment platform.

A QMS involves establishing policies and standard operating procedures that outline the process for governance and prioritization, development, independent evaluation, maintenance and monitoring, issue reporting and safety surveillance. Procedures outline the roles and responsibilities of stakeholders such as design and testing responsibilities of the champion stakeholder representing the end-users in the product development process. Procedures should also articulate training and/or qualification requirements for the stakeholders participating in AI technology development teams as safety and other risks can be eliminated with stakeholder education. Procedures also outline the systems and communication channels available to the community impacted by the deployed algorithmic tools ensuring their compliance. Communication in a regulated QMS is bidirectional, where issues, safety surveillance and outcome data are gathered via real-time monitoring and tightly integrated with the risk management and patient safety operations of a given healthcare system to determine the behavior and impact on patients and their healthcare delivery.

Establishing an innovation infrastructure that facilitates compliance requires governance and leadership support to create a communicated mandate that all algorithmic tool-related activities impacting patient health comply with quality and ethical standards. For example, the governing body may have direct integration with existing IRB processes to ensure ethical conduct. With proper governance, algorithm inventory, and transparency, HCOs can begin to implement tools, testing, and monitoring capabilities into their QMS to reduce the burden and achieve safe, effective, ethical ML/AI at scale. Implementing QMS involves formal documentation encompassing quality, ethical principles, and processes, ensuring transparency and traceability to regulatory requirements.

HCOs can utilize a QMS framework to accelerate the translation of AI from research to clinical practice. A proactive quality culture, risk-based framework for design, development, monitoring, and compliance-oriented infrastructure enables continuous ethical review, ensuring the effectiveness, safety, and equity of AI/ML technologies and meet regulatory requirements. Implementing a QMS requires adaptability, customization, and interdisciplinary collaboration, fostering awareness, education, and organizational growth. Drawing on regulatory precedents and incorporating insights from expert stakeholders, the QMS framework enables HCOs to prioritize patient needs and foster trust in adopting innovative AI technologies, including those enabled by LLMs.

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Acknowledgements

We acknowledge Stephanie Bernthal, M.Ed., of Mayo Clinic, for her work creating the visualizations included in the manuscript.

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Quality management systems: An introduction

How do successful businesses thrive in our ever-more competitive world? Some are driven by a charismatic leader; others rely on the power of the collective. But there is one ingredient which, from corner store to corporate powerhouse, is essential for healthy long-term success. Quality.

That is why effective quality management is an imperative for any successful business today. In our age of innovation and rapidly shifting expectations, keeping pace with the times means committing to a journey of continuous improvement. And achieving this goal requires a foundation of sound quality management systems .

An effective quality management system (QMS) provides the means to consistently meet consumer expectations and deliver products and services with minimal waste. In today’s highly competitive global economy, having a QMS in place is the prerequisite for sustainable success.

What is a quality management system .

In the most simple terms, a quality management system is a clearly defined set of processes and responsibilities that makes your business run how it’s supposed to. Each organization tailors its own QMS, comprising a formal set of policies, processes and procedures established to elevate consumer satisfaction. A QMS guides organizations as they standardize and enhance quality controls across manufacturing, service delivery and other key business processes.

The core benefits of a QMS include:

Elevated consistency and standardization of processes and outputs
Reduced errors and increased operational efficiency
Improved customer satisfaction through the delivery of quality products and services
Continuous evaluation and improvement of organizational operations

What is a digital QMS?

A QMS can be delivered digitally rather than using paper checklists and forms. This saves organizations time, mitigates risk and minimizes the chance of human error. Implementing a digital QMS requires meticulous planning and execution, and needs to be designed to comply with relevant regulations and industry standards, incorporating robust digital security measures to protect data.

All of these approaches call for expert guidance.

Types of quality management systems

A QMS may be based on either domestic or international standards. Different QMSs respond to different needs and scenarios, and organizations can choose to implement just one, or integrate a blend of different approaches. Among the most common are:

Standardized systems : These set the bar for established standards and agreed-upon codes and practices, such as certifications against ISO standards. ISO 9001 outlines requirements for a comprehensive QMS and provides guidance for organizations looking to implement or improve their quality management strategy.
Total quality management (TQM) : TQM is a management philosophy centred on customer satisfaction through the active participation of every employee. Its goal is to support the continuous improvement of quality across all levels and business functions.
Lean management : Inefficiencies can result in unnecessary waste. Lean management strives to maximize customer value while minimizing waste using tools like value stream mapping, which helps fine-tune an organization’s processes for optimum efficiency.
Six Sigma : Although perfection is almost impossible to reach, the pursuit of it is still worthwhile. Six Sigma uses data-driven techniques in the pursuit of producing near-perfect products and services, with a defect rate of 3.4 per one million opportunities. While that’s not perfect, it is pretty close.

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Benefits of using a quality management system

There are numerous reasons to establish a QMS. Standardized processes improve efficiency and enhance productivity through the reduction, or even elimination, of redundancies and waste. Defect prevention reduces costs associated with reworking or scrapping.

QMS audits excel at recognizing potential problems before they occur, thereby significantly reducing risk. What’s more, a QMS streamlines the record-keeping process, with improved documentation facilitating traceability and accountability  – and aiding in regulatory compliance . A QMS also functions as a troubleshooting process, providing performance metrics and built-in audits to uncover weaknesses, establishing a solid foundation for improvement.

Consistent quality leads to happy, satisfied customers who become informal brand ambassadors within their communities. So they create further business opportunities and the potential for increased market share. Any real-world example of a QMS will aptly demonstrate this: Companies who have built a successful quality system are more likely to achieve their business goals, driving higher-loyalty, frictionless customer journeys.

Why is a quality management system important?

Every organization wants to strive for excellence. Because, ultimately, the quality of a product or service is what the customer gets out of it and is willing to pay for. Quality management plays a crucial role in delivering a superior experience, which in turn influences a company’s growth and performance.

Here are six good reasons to consider investing in a quality management system:

Brand reputation : This is priceless, of course. A brand is more likely to gain international recognition when an organization surpasses established quality benchmarks.
Customer retention : Consistently meeting, or exceeding, customer needs and expectations fosters loyalty. When high standards are met or surpassed, why would customers go anywhere else?
Business sustainability : Consistently delivering excellence ensures and maintains a steady supply of customers. Doing business sustainably, and producing minimal waste, is the best way to grow and future-proof an organization.
Compliance : Meeting regulatory, safety and quality standards is a must and a QMS seamlessly facilitates this process.
Competitive edge : Higher-quality products and services give businesses a competitive advantage in complex times.
Staff engagement : Employees who feel they are involved in quality improvements tend to experience higher engagement and productivity.

Journey to excellence

Developing an effective quality management system doesn’t happen overnight, but requires careful planning and execution. So, what are some of the key steps to success for an organization starting out on its QMS journey?

Secure leadership commitment : Building a QMS requires alignment at the executive level.
Document processes : Identify and thoroughly document procedures associated with existing quality processes.
Define metrics : Performance-tracking metrics should be determined to ensure they meet QMS requirements.
Training : All employees will need initial and ongoing training in order to build understanding and engagement with the QMS.
Audits : Regular self-audits on processes and procedures will ensure compliance and effective implementation.
Review system performance : Regularly assess system performance in order to make improvements as needed.

It’s important to note that while the steps outlined above provide a high-level overview, building and sustaining an impactful QMS takes considerable effort and commitment across multiple areas of an organization.

ISO 9001 Quality management systems

The bottom line

In today’s competitive marketplace, maintaining high-quality standards is more crucial than ever. As a business owner, you’re aware that customers will continue coming if they know that you will deliver them the product or service they need. This calls for company processes that are reliable, effective, trustworthy and streamlined – aligning business objectives and bottom lines with consistency and excellence. While this may sound like a no-brainer, how do you ensure a formalized process that documents each step, the desired outcomes, ways to improve, and the end results?

A quality management system may be just the solution you’re looking for.

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A proposal of model for a quality management system in research testing laboratories

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Published: 08 November 2021
Volume 26 , pages 237–248, ( 2021 )

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Silvia Martínez-Perales ORCID: orcid.org/0000-0001-6940-6015 1 ,
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There is a broad consensus on the importance and advisability of testing laboratories adopting a Quality Management System (QMS) to support their work, no matter they are industrial or research oriented. However, laboratories involved in R&D have specific difficulties to implement a QMS due to the peculiar nature of their activity. This paper analyzes the main challenges and difficulties found by professionals when implementing a QMS in a research testing laboratory, based on the literature review and a questionnaire with 86 laboratories participating performed in collaboration with RedLab (Red de Laboratorios de la Comunidad de Madrid). After this analysis, a set of requirements for the competence of research testing laboratories based on ISO/IEC 17025 and UNE 166002 is defined, and an agile methodology for the fulfilment of these requirements is proposed.

System proposal for implementation of risk management in the context of ISO/IEC 17025

Experience in establishing a quality management system at a plant diagnostic laboratory in Fiji

Implementation of a Quality Management System According to ISO 9001:2015: The Case of a Textile Company

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Introduction

There is a broad consensus on the importance and advisability of testing laboratories adopting a Quality Management System (QMS) to support their work, no matter it is industrial oriented or a research oriented. However, laboratories involved in R&D testing have specific difficulties to implement a QMS due to the peculiar nature of their activity. Researchers and professionals have long discussed about the advisability of implementing a Quality Management System (QMS) in research testing laboratories. From the late 1990s (when ISO/IEC 17025 was first published [ 1 ]) to the present, the analysis on how these laboratories adopt quality management practices has gone through aspects such as the difficulties found, the critical success factors or the key indicators in the process of implementing a QMS. Also, there is a feeling that a QMS as stated in the existing standards does not offer a complete response to the needs of research testing laboratories in terms of scientific competence.

However, authors still agree on the benefits of a QMS on the research activity. Thus, the point is how to overcome the difficulties and how to incorporate scientific competence requirements to the traditional schemes for QMS in testing laboratories.

The first part of this paper includes a literature review focused on the hot topics regarding QMS in research testing laboratories: advantages and benefits of implementing a QMS; difficulties and limitations when implementing a QMS; and success factors for the implementation of a QMS. After the literature review, a questionnaire performed in collaboration with REDLAB (Red de Laboratorios de la Comunidad de Madrid) regarding QMS in research testing laboratories is presented. The results of this study support the findings in the literature review, and complete the picture of the difficulties and challenges found by research testing laboratories.

In the second part, two relevant standards are analyzed: ISO/IEC 17025 General requirements for the competence of testing and calibration laboratories [ 10 ] and UNE 166002 R&D&i management: R&D&i management system requirements [ 25 ] . The first is definitely the reference for any testing laboratory, no matter its scientific or industrial nature. The latter is used for this work as a basis to establish requirements for the scientific competence, which are not addressed by ISO/IEC 17025. The analysis of these standards results in a complete set of competence requirements for research testing laboratories.

At this point, the third part of the paper presents the proposal of an agile methodology that aims to fulfil the defined set of competence requirements trying to overcome the difficulties and limitations found.

Methodology

The objective of this work is giving a response to the three following research questions (RQs):

RQ1: Is there a real difference between industrial testing laboratories and research testing laboratories in terms of quality management?

RQ2: Is there an adequate normative context for the definition of a QMS is research testing laboratories?

RQ3: Is it possible to define a model for a QMS that overcomes the difficulties and limitations that research testing laboratories find when implementing and maintaining a quality management system?

RQ1 has been addressed by performing a systematic literature review based on Web of Science database. Also, a questionnaire regarding QMS aspects has been delivered to testing laboratories from REDLAB ( Red de Laboratorios de la Comunidad de Madrid/ Testing Laboratories Network in Community of Madrid ).

RQ2 has been addressed by reviewing the two relevant standards ISO/IE 17025 General requirements for the competence of testing and calibration laboratories and UNE 166002 R&D&i management: R&D&i management system requirements.

For RQ3, a model for a QMS based on agile principles has been defined. The model meets the technical and scientific requirements identified after the review of the relevant standards, which is a proof that may fit the purpose. Also, the agile focus has extensively proved to be a valid methodology for complex environments.

Literature review

Advantages and benefits of implementing a qms.

The convenience of implementing a QMS in research testing laboratories is widely recognized. The following are the main advantages and benefits found in the literature:

Need to count with quality management methods similar to those in the industry, in order to have the possibility of becoming supplier, subcontractor or partner ([ 2 , 3 , 4 , 9 , 21 , 23 , 24 ]);

Promotion of a mutual confidence among all parties with cooperation or funding purposes (customers, sponsors, scientists, authorities) ([ 3 , 5 , 9 , 19 , 20 , 21 , 22 , 24 ]);

Assurance of the technical and scientific competence ([ 2 , 3 , 5 , 9 , 18 , 19 , 22 , 23 , 24 ]);

Assurance of comparable research results, inside the laboratory during the phases of a project, or with other laboratories ([ 2 , 3 , 5 , 19 , 21 , 24 ]);

More efficient management of the scientific and technical activities in the laboratory ([ 3 , 20 , 24 ]);

Improvement of the structural organization thorough a better definition of functions and responsibilities ([ 3 , 20 ]);

Improvement of the equipment control [ 20 , 23 ];

Improvement of existing working habits [ 19 ];

Promotion of the knowledge management and staff qualification ([ 3 , 21 , 22 , 24 ]);

Improvement of staff commitment and satisfaction [ 24 ]

Difficulties and limitations

Once recognized the convenience of having a QMS in research testing laboratories, the point is that professionals find a number of difficulties in the implementation and maintenance. The main issues identified by authors are the following:

The excessive rigidity of a QMS limits the creative work which is strongly attached to research ([ 2 , 3 , 9 , 23 ]);

The excessive rigidity of a QMS increases bureaucratic work and paperwork ([ 2 , 7 , 19 , 23 , 24 ]);

The complexity of the research activity (with changing requirements, multiple groups, technical uncertainty) is hardly compatible to a QMS ([ 3 , 5 , 9 ]);

Lack of specific standards for the definition of a QMS in research organizations ([ 2 , 3 , 5 , 6 , 7 ]);

Research results are not limited to a test results, but include scientific production [ 3 ];

Difficulty to measure the cost of “non-quality”, and so it is difficult to justify the investment of resources in quality management tasks ([ 9 , 24 ]);

Lack of training in quality management among the researcher staff ([ 9 , 24 ]);

Lack of commitment to quality management among the researcher staff and management staff ([ 5 , 9 , 24 ]);

Lack of human resources dedicated to support the QMS ([ 20 , 21 , 22 ]);

Short-term contracts and high turnover ([ 19 , 21 , 22 ]);

Resistance to change ([ 24 ])

Success factors for implementing a QMS in research testing laboratories

The existing difficulties and limitations have pushed authors to reflect on the factors to take into account to successfully implement a QMS in testing research laboratories. These success factors are the following:

“bottom-up” design of the system, in order to reinforce awareness and commitment of the staff [ 8 ];

Simple, flexible and well-adapted documentation system ([ 9 , 19 , 21 ]);

Modular and non-redundant system [ 9 ];

Self-sustainable system ([ 9 , 21 ]);

The QMS must provide added value to the laboratory ([ 9 , 21 ]);

The QMS must consider not only general quality management aspects, but also specific aspects such as scientific competence, creativity-flexibility balance [ 5 ];

Tailoring of the QMS to the peculiarities of the laboratory ([ 19 , 21 , 23 ]);

Promotion of a culture of quality ([ 19 , 21 ]);

Management commitment [ 19 ];

Normative context

The reference standard for QMS in testing laboratories is ISO/IEC 17025. Numerous authors have analyzed the positive influence of having an implemented QMS according to ISO/IEC 17025 on laboratories performance [ 2 ]. However, many of them have called for the development of specific standards for research testing laboratories, which has not happened up to date. Today, two standards are used by testing laboratories as a reference for their QMS: ISO/IEC 17025 [ 10 ] and ISO 9001 [ 11 ]. Both ISO/IEC 17025 and ISO 9001 address aspects related to quality management. However, important differences exist between these two standards. While ISO/IEC 17025 defines general requirements for the competence of testing and calibration laboratories, ISO 9001 establishes requirements for a quality management system in any kind of organization, no matter the sector or the kind of activity being developed. In this line, ISO 17025 addresses technical and management requirements for the demonstration of the competence of testing laboratories, while ISO 9001 develops the requirements for the demonstration of the ability to provide products and services that meet the customer and regulatory requirements. ISO/IEC 17025 requirements contain the ones established by ISO 9001, and so the compliance to ISO/IEC 17025 principles implies the compliance to ISO 9001 principle (and not vice versa). As a last basic difference, it must be said that external recognition of a Quality Management System is subjected to a certification process in the case of ISO 9001, and to an accreditation process in the case of ISO/IEC 17025 to guarantee technical competence. [ 12 , 13 , 14 , 15 ] (among others) opt for ISO/IEC 17025 as a reference standard for research testing laboratories and recognize that the accreditation of a QMS against ISO/IEC 17025 adds value to the certification against ISO 9001. Cammann et al. [ 3 ] referred to Eurachem Guide [ 16 ], the guide for Quality Assurance for R&D and Non-Routine Analysis in the analytical chemistry field, based on the idea that laboratories performing non-routine measurements require a special approach in terms of quality management. Also, the British Department for Environment, Food & Rural Affairs published in May 2003 the “Joint Code of Practice for Research” [ 17 ], that applies to contractors funded by a number of British bodies, and addresses aspects related to the quality of research process and the quality of science, such as responsibilities, competence, project planning, quality control, health and safety, handling of samples and materials, facilities and equipment, documentation, records and field-based research.

The literature review suggests that these available standards do not consider the special difficulties, limitations and needs of research testing laboratories regarding quality management. In this work, the standard UNE 166002 [ 25 ] R&D&i management: R&D&i management system requirements is proposed as a basis to complement the scheme proposed by ISO/IEC 17025.

The purpose of UNE 166002 is to establish guidance and requirements for a management system based on the PDCA ( plan-do-check-act ) cycle, and suitable for any kind of organization involved in R&D&i. UNE 166002 addresses five general topics: context of the organization; leadership; planning; support to R&D&i; operational processes of R&D&i. There is a coincidence between ISO/IEC 17025 and UNE 166002 in the management of general aspects, and the latter includes a set of requirements that are not considered by ISO/IEC 17025. These requirements have to do with management of ideas, R&D&i vision and strategy, R&D&i policy and culture of innovation. Thus, the combination of ISO/IEC 17025 and UNE 166002 seems to be a good package as a standard framework for research testing laboratories.

Questionnaire

A study was carried out in collaboration with RedLab (Red de Laboratorios de la Comunidad de Madrid, Network of Laboratories of the Community of Madrid). The objective was to confirm the findings from the literature research in a working environment.

RedLab is an initiative of the General Directorate of Universities and Research founded in 2000 with the aim of bringing together the testing and calibration laboratories belonging to research centers and universities, disseminating their activity and supporting them in matters such as the quality and knowledge management. Currently 340 testing (300) and calibration (40) laboratories operating in Madrid (Spain) are members of this network. All the laboratories under the scope of this study are involved in R&D activities, since Redlab groups laboratories from universities and public research centers.

The questionnaire on which the study is based was distributed by RedLab to its members through the free access platform Typeform. The 40 questions in the questionnaire were grouped into seven blocks: (I) information about the respondent, (II and IV) information about the QMS implanted in the laboratory (maturity), (III) information about the tests carried out in the laboratory, (V) information on the critical points of the QMS, (VI) assessment of the QMS, (VII) benefits of the QMS. The questions were posed in different formats depending on the type of response expected: free text, form with a single answer, multiple answer form, numerical answer (0–10).

Participants description

378 people visited the questionnaire at Typeform. 115 valid and complete responses were received corresponding to testing and calibration laboratories. From these, responses from calibration laboratories were not considered for the purpose of this study, since the present work refers just to testing laboratories. After this filter, 88 responses corresponding to different laboratories were left, which is 29,33 % of the testing laboratories affiliated to RedLab. 2 out of the 88 laboratories declared not to have a QMS implanted. So, the analysis was done on 86 testing research laboratories.

The information obtained from the questionnaire was considered to be valid based on two aspects: the professional profile of the participants and their expertise in quality management systems.

Professionals who completed the questionnaire declared to be involved in the QMS implantation and maintenance. 76,74 % of the participants were laboratory managers and quality managers. The rest of them were technical managers, project managers and coordinators.

80,23% of the participant laboratories declared to have a QMS implanted before 2013, which means a system with an over four-year life. Four years were considered to be an adequate period to admit a relevant expertise in quality management for several reasons. In a four-year cycle, a laboratory has typically closed a quality assurance plan, one (at least) calibration plan, one (or several) management reviews and one (or several) internal audits. Thus, in this period, the laboratory has had the opportunity to identify its weakness and to adapt the system to the activity. Only 3,49% of the participants declared to have implanted a QMS in the last year. So, major part of the participants was considered to have a solid experience in QMS.

Analysis of the results

As a previous step to the analysis, the participants were classified according to two criteria:

The nature of the test methods (standard or non-standard, being non-standard those methods that are not recognized by standards, and thus require validation);

The routine nature of the activity (the laboratory performs repeatedly the same set of tests).

For the classification, participants were asked to provide information about the nature of the test methods used at their laboratories and the routine nature of the activity performed. On this basis, they were allocated in four groups: laboratories that perform tests according to standard methods on a routine basis (group 1); laboratories that perform tests according to standard methods on a non-routine basis (group 2); laboratories that perform tests according to non-standard methods on a repetitive basis (group 3); laboratories that perform tests according to non-standard methods on a non-repetitive basis (group 4).

Laboratories in group 1 do not perform a research activity itself even though they support research organizations, since their activity is based on pre-defined validated methods, and they always execute the same set of tests. On the contrary, the activity developed by laboratories in group 4 implies the validation of methods and a continuous adaptation to execute different kind of tests, and so these are considered to be real research testing laboratories.

For the purpose of this work, the two groups of interest are groups 1 (which has a clear industrial-oriented activity) and 4 (which has a clear research itself –oriented activity). Group 1 is labelled as “Industrial group”, and group 4 is labelled as “Research group”. Table 1 shows the most relevant results obtained through the questionnaire, referring to:

Number of laboratories that have a QMS implemented under a specific scheme (ISO 9001; ISO/IEC 17025; other scheme; no QMS implemented);

Number of laboratories with an external recognition of the implemented QMS (ENAC accreditation; certification; none; other);

Number of laboratories that have a specific difficulty in the implementation of the QMS. This question was designed as a multiple choice question: laboratories could mark several options,

Degree of compliance to QMS requirements. This question was designed as a numerical answer in a 0–10 scale, being 0 “no compliance at all to the requirement” and 10 “absolute compliance to the requirement”;

Valuation of the QMS by the managerial and technical staff. This question was designed as a numerical answer in a 0–10 scale, being 0 a very negative valuation and 10 a very positive valuation;

Benefits of the QMS. This question was designed as a numerical answer in a 0–10 scale, being 0 “no recognized benefit in this aspect” and 10 “absolutely recognized benefit in this aspect”.

For the 0–10 scale questions, the table shows the mean values of the recorded answers.

After the results, a set of interesting observations were made:

QMS is a widely use tool, no matter the industrial or research nature of the laboratory;

Most of the research testing tools base their QMS on ISO 9001 instead of ISO/IEC 17025;

Greatest difficulty found by the professionals in the implantation of a QMS is the control of documentation;

Laboratories from group 1 meet quality assurance requirements in a higher degree than laboratories from group 4;

Technical and managerial staff from group 1 appreciate the benefits of a QMS more than those from group 4;

The most important benefit from the implementation of a QMS is the assurance of quality in the case of participants from group 1; however, the most important one is the knowledge management for group 4;

These basic observations reinforce the findings in the literature, and support the idea that there are differences in the approach to the QMS in testing laboratories depending on the industrial or research nature, and that there are clear key points to be improved in the implementation of a QMS.

Proposal of a QMS for research testing laboratories

After the literature review, the questionnaire results and the normative context, the result of this work is the proposal of a model for a Quality Management System for research testing laboratories. This model has been designed under the following principles:

Compliance to general competence requirements for testing laboratories established by ISO/IEC 17025;

Compliance to specific competence requirements for R&D&i organizations established by UNE 166002;

Consideration of the difficulties and limitations reported by authors and professionals in a research context.

Requirements, objectives, resources and planning change and evolve in any research, making it difficult to normalize activities and define rigid procedures. Activity in a research testing laboratory has these characteristics (which are similar to the ones attributed to projects), and this is the reason why a QMS based on standard procedures is not suitable for a research testing laboratory. At this point, the agile approach for QMS raises. These methodologies have been successfully implemented in quality assurance for software development projects, due to the fact that the agile approach deals with changing requirements and uncertain environments, which is similar to the situation found at research testing laboratories.

Thus, an agile approach for the QMS in research testing laboratories is proposed, based on the agile principles [ 26 ]:

Need to adapt to changing environment, versus the strict observation of a closed planning;

Incremental and cooperative execution of activities;

Priority of individuals and interactions over processes and tools;

Tight communication with parties involved in the activities;

Focus on motivated individuals;

Constant focus on technical excellence;

Regular reflection on the own activity to adjust and improve habits and procedures.

To be consistent to these principles, the model is based on the celebration of several events integrated in the testing activity milestones that act as a trigger to quality management tasks.

The proposed model includes:

A set of competence requirements;

A set of events: test readiness review (TRR), test follow-up review (TFR), post-test review (PTR) and management review (MR).

Figure 1 summarizes the QMS model, including the inputs for the definition, the agile principles taken into account and the proposal itself.

Proposed quality management system model

Competence requirements

After the analysis of the normative context, and taking into consideration the experts claims, a QMS exclusively based on ISO/IEC 17025 does not offer a complete response to research testing needs. In our proposal, requirements from ISO/IEC 17025 are completed with those from UNE 166002. As a result, four groups of requirements are set:

General requirements do not differ from those proposed by ISO/IEC 17025 (impartiality and confidentiality);

Resource requirements include those proposed by ISO/IEC 17025 and incorporate the need to create and maintain a R&D&i management unit and R&D&i units as defined by UNE 166002;

Process requirements include those proposed by ISO/IEC 17025 and incorporate the need to issue a test plan that must cover the following points: objectives and expected results, material and non-material resources, milestones, risk identification, support activities (technological surveillance, competitive intelligence);

Management requirements do not differ from those proposed by ISO/IEC 17025;

Research activity management requirements are incorporated as a new group, including the following: management of ideas, R&D&i vision and strategy, R&D&i policy and culture of innovation.

Table 2 shows the proposed set of requirements. Those coming from ISO/IEC 17025 are identified with the label in the standard. The new ones are identified with a sequential label with the format INV-n . Figure 2 is based on the schematic drawing according to ISO/IEC 17025 for the operational processes in the laboratory. Shaded elements refer to the resources and requirements in the proposed model (Table 3 ).

Schematic drawing of the QMS requirements

As aforementioned, an agile approach for the QMS is proposed, in order to achieve two main goals:

Enabling the integration of the QMS in the day to day routine, adapting the system to the real needs of the laboratory, promoting the commitment of the key personnel and searching for the self-sustainability of the system;

Removing the unnecessary quality requirements, by putting the focus on the test as a trigger of the quality events.

Three events are suggested around the test: the Test Readiness Review (TRR), the Test Follow-Up Review (TFR), and the Post-Test Review (PTR). Necessary attendants to these meetings are the laboratory manager, the R&D&i unit manager, the test engineer and the quality assurance manager. Optionally, the customers and partners may attend.

Test Readiness Review (TRR) The main purpose of the TRR is to ensure that all the necessary conditions for starting the test are met. The TRR meeting addresses management aspects (review of customer request for test, laboratory quotation), technical aspects (assurance of the EUT Equipment Under Test readiness for the beginning of the tests, readiness of measurement equipment and facilities, review of the staff qualification, risks assessment), scientific aspects (research line, scientific objectives and context). The output from the TRR includes the declaration of the EUT, measurement equipment and facilities readiness; the testing method validated and the declaration of qualified staff.

Test Follow-Up Review (TFR) TFR purpose is to enable a meeting point for all the parties to follow-up the test progress and review the evolution of the technical and scientific relevant aspects, such as changes in the test requirements, evolution of the EUT, risks plan update, partial results to be transferred to activities for dissemination and exploitation of scientific results (publications, seminars, others). Any change in the management, technical or scientific aspects that were approved at TRR and are reviewed at TFR must be conveniently recorded as an output of TFR.

Depending on the complexity of the test, the celebration of several TFRs may be useful for a close and efficient tracking of the activities.

Post-Test Review (PTR) The main purpose of the PTR is that all the necessary conditions for the closure of the test are met, and to compile the knowledge generated during the test. PTR must address the identification of deviation and non-conformances, the presentation of the final test results, the review of the scientific objectives planned at TRR and TFR, the exploitation of the test results. Also, knowledge management actions must be undertaken: record of lessons learned, planning of dissemination activities and customer satisfaction evaluation.

Since TRR, FTR and PTR are events triggered by the test evolution, holding these reviews is a natural action that serves the key activity, which is the test itself. So, quality management becomes integrated in the day-to-day activity of the laboratory, which turns into an increase in the staff commitment, a better adaptation to the real needs and a self-sustainability of the system.

A fourth event which is not triggered by the test itself is considered in the model. This event is the Management Review (MR), which must be held on a regular basis (typically once per year) and is oriented to strategic and managerial aspects. The main purpose of the MR is the review of the QMS by the managerial board (MB). MR must address all the key points that require the managerial commitment, including (but not only): the policy and strategy review (including the update of general objectives and scientific objectives), the performance assessment (based on results of internal and/or external audits, performance indicators and feedback from customers), the evaluation of the scientific impact, the assurance of the quality of the tests results, the review of actions (preventive, corrective, actions for improvement), the knowledge management initiatives, and the evaluation of suppliers.

Conclusions

This work was triggered by three research questions regarding quality management in research testing laboratories:

RQ3: Is it possible to define a model for a QMS that overcomes the difficulties and limitations that research testing laboratories find when implementing a maintaining a quality management system?

After applying the designed methodology, conclusions are:

RQ1: yes, there is a real difference between industrial testing laboratories and research testing laboratories in terms of quality management, as revealed by the literature review and supported by the results of the questionnaire. Research testing laboratories have specific difficulties, limitations and needs.

RQ2: no, there is not an adequate normative context, at least grouped on a single standard that addresses the dual nature of a research testing laboratory, as a testing laboratory and a R&D&i organization. The combination of two standards (ISO/IEC 17025 and UNE 166002) has been considered as a basis for this work.

RQ3: a model for QMS in research testing laboratories has been proposed. This model is the result of considering the difficulties and limitations reported by experts and professionals when implementing and maintaining a QMS in research testing laboratories, the success factors for the implementation and the agile principles. The model includes a set of competences requirements that follow the recommendations from ISO/IEC 17025 and incorporate the research and scientific approach from UNE 166002, and a set of reviews that enable the self-sustainability of the system and enable meeting points for the compliance to the aforementioned requirements.

The model has been built keeping in mind the following key aspects:

Observing the competence requirements for testing laboratories;

Simplifying the QMS and proposing a flexible approach;

Optimizing paperwork by reducing documentation and incorporating habits of continuous review;

Implanting following-up milestones to adequately manage the complexity of the research testing activities;

Promoting the innovation and communication culture;

Obtaining the maximum scientific return;

Adopting a self-sustainable model, in which the ordinary activity is a feedback for the maintenance of the QMS, thus reducing the resources dedicated to this task and improving the efficiency of the system.

Through this work, several references to the technical and managerial staff in relation to the implementation of a QMS have been done. The improvement of the staff qualification, commitment and satisfaction has been identified as one of the benefits from a QMS. On the other hand, the lack of training in quality management and the lack of commitment have been identified as difficulties for the implementation. Thus, this is a case of a vicious circle. The agile structure of the proposed model, built around the events (TRR, FTR, PTR and MR), aims to break this circle by involving the technical staff in the day-to-day maintenance and improvement of the system, and promoting the commitment of the managerial staff, which for sure is a success factor for the implementation of the QMS. Also, the specific needs (especially those related to scientific competence) and difficulties found by research testing laboratories have been taken into account.

There is a need that research organizations adopt QMS as an asset (and not as an obligation) to improve not also the management, but also the technical and scientific competence. This work, as a first step of our research, has tried to propose a tool to contribute to the success of a QMS in a kind of research organization, as research testing laboratories are.

Further research and limitations

The study based on the questionnaire refers to a reduced sample corresponding to testing laboratories from RedLab ( Red de Laboratorios de la Comunidad de Madrid , Network of Laboratories of the Community of Madrid). Data have not been collected nor analyzed under strict sampling and statistical rules. They cannot be interpreted as concluding results, but only as a support to the findings in the literature review.

Further research will include the verification of the model with experts, and the subsequent iteration on the proposal.

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Acknowledgements

The authors express their thanks to Mr. Raúl De Andrés from RedLab (Red de Laboratorios de la Comunidad de Madrid, Network of Laboratories of the Community of Madrid) for his inestimable help in reviewing and disseminating the questionnaire on which this work is based.

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Martínez-Perales, S., Ortiz-Marcos, I. & Ruiz, J.J. A proposal of model for a quality management system in research testing laboratories. Accred Qual Assur 26 , 237–248 (2021). https://doi.org/10.1007/s00769-021-01479-3

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Issue		9, 2018


Article Number		2
Number of page(s)		9
DOI
Published online		05 February 2018

1 Introduction

2 presentation of the laboratory and its quality policy, 3 implementation of a quality management system: actions undertaken, 4 discussion, analysis and improvements, 5 conclusion.

List of figures

Research Article

An overview of Quality Management System implementation in a research laboratory

Valérie Molinéro-Demilly 1 * , Abdérafi Charki 2 , Christine Jeoffrion 3 , Barbara Lyonnet 4 , Steve O'Brien 5 and Luc Martin 6

1 Horticulture and Seeds Research Institute (IRHS-MRU 1345), INRA/Agrocampus Ouest/University of Angers-42, rue Georges Morel, 49071 Beucouzé Cedex, France 2 Angevin Research Laboratory in Systems Engineering (LARIS–EA 7315), University of Angers, 62 avenue Notre Dame du Lac, 49000 Angers, France 3 Psychology Laboratory of Pays de la Loire (LPPL-UPRES EA 4638), University of Nantes, BP 81 227, 44312 Nantes cedex 3, France 4 Economy and Management Laboratory (LEMNA), University of Nantes, Chemin de la Censive du Tertre, B.P. 81227, 44312 Nantes Cedex 3, France 5 Decision Support Systems Research Centre (CERADE), ESAIP School of Engineering, 18 rue du 8 mai 1945, 49180 St Barthélemy d'Anjou, France 6 Agricultural Research Centre for International Development (CIRAD), Avenue Agropolis, 34398 Montpellier Cedex 5, France

* Corresponding author: [email protected]

Received: 7 June 2017 Accepted: 11 November 2017

The aim of this paper is to show the advantages of implementing a Quality Management System (QMS) in a research laboratory in order to improve the management of risks specific to research programmes and to increase the reliability of results. This paper also presents experience gained from feedback following the implementation of the Quality process in a research laboratory at INRA, the French National Institute for Agronomic Research and details the various challenges encountered and solutions proposed to help achieve smoother adoption of a QMS process. The 7Ms (Management, Measurement, Manpower, Methods, Materials, Machinery, Mother-nature) methodology based on the Ishikawa ‘Fishbone’ diagram is used to show the effectiveness of the actions considered by a QMS, which involve both the organization and the activities of the laboratory. Practical examples illustrate the benefits and improvements observed in the laboratory.

Key words: Quality / research / reliability / management / measurement / manpower / methods / materials / machinery / mother-nature

Over recent years, a number of public sector research entities have been adopting a Quality process in order to improve their organization. In France, French standards association (AFNOR) formally recommends adoption of a Quality process by scientists [ 1 , 2 ]. However, implementation of a quality process in a public organization can come up against specific problems not encountered in a private organization [ 3 ]. Research requires both rigour and transparency in the production of knowledge, and involves specificities in terms of objectives, resources and organizational skills that can be very different from those of the industrial sector in which a Quality process has traditionally been found. In view of this, it is clear that the implementation of a Quality Management System (QMS) within a public research organization cannot be carried out in the same way as in industry [ 4 ]. Clearly, the specific challenges that may be encountered in a research laboratory need to be addressed via specific solutions and actions to ensure the success of a QMS.

In the literature, few papers [ 5 – 7 ] deal with the implementation impact of QMS in a research laboratory. Spencer et al. [ 5 ] underline the advantages in Quality assessment of qualitative research for evaluations of research programmes. The quality of scientific research is often uneven and lacking in credibility, making it difficult to make a confident, concrete assertion or prediction regarding evidence for improving practice or consumer outcomes [ 6 , 7 ]. The debate is also due, in part, to the lack of consensus on the specific standards for assessing Quality research. Edmondson et al. [ 8 ] introduce a framework for assessing and promoting methodological fit as an overarching criterion for ensuring quality field research. Baker [ 9 ], Begley et al. [ 10 ], Giesen et al. [ 11 , 12 ], Bareille et al. [ 13 ] show the importance of a Quality process in sciences for improving research management and reliability.

In this paper, we identify the advantages of implementing a QMS in a laboratory of INRA, the French National Institute for Agronomic Research, whose mission is to produce and publish knowledge gained through reliable results, train researchers, offer expertise, create, and innovate.

After presentation of the quality policy of the laboratory, several Quality main actions are developed and discussed using a modified Ishikawa diagram [7Ms: Management, Measurement, Manpower, Methods, Materials, Machinery, Mother-nature (environment)] in order to show the effectiveness of implementing the QMS, which involve both the organization and the activities of the laboratory.

Practical examples are presented to demonstrate the benefits and improvements achieved by implementing a QMS in a research laboratory, as well as the challenges encountered and the solutions proposed to deal with these. The methodology uses the first author's own feedback drawn from three years' experience as Quality Manager in an INRA Laboratory.

2.1 Organization of the laboratory

The research laboratory (or to give it the INRA term, Unit) under observation was created in January, 2012 and is a relatively complex structure, operating under the auspices of three separate Institutions: INRA (French national institute for agronomic research), a School of Engineering (Agrocampus Ouest) specialized in agronomy and horticulture, and a University (University of Angers). As regards INRA, the laboratory is attached to three different scientific divisions, each covering several disciplinary fields where the research constantly explores new ground. The laboratory is the result of the merger of four MRUs (Mixed Research Unit), and currently numbers some 230 staff members organized into 16 teams ( Fig. 1 ). From INRA's point of view, this is a Very large scale unit (VLSU), as the number of staff exceeds 100, whereas the average number of staff in an INRA Unit is 25. However, we have become increasingly accustomed over recent years to Units that merge with a view to pooling resources (i.e. sharing equipment and reducing the number of posts in Research Support Services while giving greater visibility to the Units). The laboratory is therefore of recent formation and has been subjected to extensive structural change.

The laboratory conducts research projects in seeds and horticulture. It is committed to an integrated approach of coordinated effort and expertise in the fields of genetics, epigenetics, genomics, pathology, physiology, ecophysiology, biochemistry, modelling, statistics, and bioinformatics.

Prior to the creation of the laboratory in 2012, the four former MRU (Mixed research unit) teams were located on different geographical sites. Figure 1 also shows the institutional membership of the laboratory staff. The INRA teams had already begun implementation of a Quality process in the year 2000.

MRU 1 had been internally audited by the INRA Quality task force in 2008 in accordance with INRA Guidelines Version 1 [ 14 ]. The result of this audit concerning management responsibility, documentation and resources management was highly complimentary reflecting the considerable efforts the MRU had made to meet the requirements of the INRA Guidelines version 1.

MRU 2, a Biology Resource Centre (BRC) has had ISO 9001 certification [ 15 ] since 2008. This BRC has achieved international renown and has a very dedicated Quality manager.

In MRU 3, a Quality process had been introduced. Quality, equipment and metrology managers were appointed in this research unit.

MRU 4 was operating under the auspices of a University that had not adopted a Quality process for its research departments. The same was true for the teams working for the School of Engineering, which had ISO 9001 certification for academic activities only but not for the research activities. Nevertheless, all university and engineering school teams were using laboratory notebooks, had drawn up operating procedures, conducted equipment inventories, implemented life cycle files or equipment monitoring logs, and observed the minimum requirements concerning external checking of pipettes and weighing scales.

The first one was due to administrative dissimilarities between the three institutions (INRA, the School of engineering and the university). This obstacle has been solved by delegating management of the new VLSU to INRA via a contractual agreement;

The second one concerned the multidisciplinary nature of the scientific community and the need to get individuals with different backgrounds and habits working efficiently together as well as to create synergy around Quality within the laboratory. This necessity had already been identified when the four MRUs were created, and became even more apparent when the VLSU came into being. The laboratory defined an objective of constructing a common QMS for all its research activities. One of the actions decided upon was the recruiting in September 2013 of a Quality manager to work full-time on Quality, health, safety and environment;

The Quality manager's first task was to establish an inventory of the existing situation, before moving the laboratory towards harmonization of all practices, bringing them in line with INRA guidelines version 2 [ 16 ]. However, teams that had made significant progress as regards quality felt that they were being made to regress following the merger and there has been a need to involve and remotivate them via the Quality actions undertaken;

The third one was the geographical spread of the teams. In 2012, all teams were still dispersed over four distant sites. Communication and common working were facilitated when the Institutions that benefit from county council funding received a brand new building, which enabled teams to be relocated to a single site during the summer months of 2015.

Institutional membership of laboratory staff.

2.2 The key to success: a committed Management Board

The success of a QMS depends on the commitment of staff, and most particularly that of top management. This commitment was formally expressed in a Quality policy statement (an obligatory step for any organization with ISO 9001 certification [ 15 ] or EN ISO/IEC 17025 accreditation [ 17 ]). The Quality policy outlines the objectives of the organization and the planned operational rollout of the associated action plan.

Guarantee reliability of measurable results via controlled methods and equipment;

Ensure traceability of research work;

Contribute to long-term conservation of data;

Guarantee quality of biological materials;

Guarantee quality of services provided by Biology Resource Centres (BRC);

Manage samples;

Contribute to human and environmental as well as collaborator risk management;

Ensure appropriate planning and organization of projects;

Harmonize practices, methods and operating procedures common to various teams;

Instigate appropriate and effective improvements.

2.3 Choosing Quality guidelines appropriate to a research organization

Convinced of the absolute necessity of the Quality process in the scientific environment, INRA officially embarked upon the Quality process in the year 2000. The INRA management coordination committee sent out its first Quality policy statement in March of that same year and instigated the INRA Quality task force. In 2005, INRA published its first Guidelines (Version 1) as well as introducing a self-assessment tool for the Units. These first Guidelines comprised five chapters: Quality Management and management responsibility; Documentation; Management of resources; Core activities; and Measurements, Analysis and improvement. In 2006, the first steps towards implementing the Quality process came into effect in INRA support services. A review of actions undertaken between 2000 and 2009 reveals the support given to the Quality process by the INRA Board of Management, the commitment of the research departments (12 out of 14), the commitment of the Units (25% in 2000 rising to 95% in 2004), and the application of international references such as ISO 9001 and EN ISO/IEC 17025 (15) for strategic platforms certified by the National commission for collective Tools (CNOC), as well as ISO 14001 [ 18 ] for Experimental Units, and ISO 9001 [ 15 ] or NF S 96-900 [ 19 ] for certified Biological resource centres.

INRA's next ambition was to extend the Quality process to research activities, thus bringing Quality to the very heart of INRA's activity. In 2012, the INRA Management coordination committee's new 2012–2016 Quality policy emerged. Version 2 [ 16 ] of the INRA Quality guidelines comprises five chapters: Quality management and responsibilities; Conducting research; Management of resources; Control of the documentation; and Measurements, analysis and improvement. This new version of the INRA Guidelines was presented to quality or metrology managers in laboratories.

This new guide is intended to be easy to read, using everyday language to ensure accessibility for the scientific community, since Quality terminology is rather specific and becoming familiar with it can take time. The INRA Quality task force also contributed to the drawing up of the NF X50-553 Standard (management of research activities) [ 2 ] and made sure the INRA Guidelines were consistent with this Standard. The INRA Guidelines deliberately make no reference to customers in order to avoid resistance from the scientific community to a concept commonly associated with the commercialization of knowledge. Version 2 of the INRA Guidelines is about accruement of experience and reinforcing continual improvement. It puts emphasis on conducting research as a process (design, implementation and publication/practical usefulness) with a view to managing and controlling the risks inherent during a research project. At the outset of the project, the person heading the research states the hypotheses involved, defines the experimental protocols, coordinates sampling/analyses/simulations, and interprets data and designates its uses.

The laboratory is required to draw up an inventory of all its research projects and establish research and/or experimental protocols. These protocols cover the objectives defined for the research project as well as the resources necessary to achieve them (methods, materials, resources, installations; persons and entities involved, provisional schedule, critical aspects requiring special attention and procedures for communication, retention period of samples and data, as well as any other specific criteria). The INRA version 2 Guidelines also put emphasis on management of methods: their formalization and validation, and the uncertainties associated with quantitative results. The version 2 INRA Guidelines come with a new dedicated self-assessment tool for the research units and specific tools for the implementation of the Quality process at national level: the INRA Quality task force is coordinated by a network of Quality managers located in centres across 17 different sites in France and the 13 scientific divisions. However, the ideal is not so easy to achieve in reality and many of the scientific divisions that were involved with the first version of the guidelines have since lost interest in the Quality process, and some centres are still without a Quality manager. The effect of this is to isolate the Quality managers in the units, just as these units undergo the process of merging and have growing staff levels.

When it comes to the VLSU, structural complexity complicates smooth coordination, as is evident in the case of the biology laboratory under observation: acceptance of the INRA guidelines needs to be achieved across 16 Laboratory teams (irrespective of the institute individuals belong to), in the centre of INRA Angers-Nantes, and in the three INRA scientific divisions (only one of which has a Quality manager).

At the same time, in the face of such extensive restructuring, the implementation of a QMS could actually be seen as an opportunity, offering the possibility on the one hand of managing risks specific to research activities, and on the other of enhancing cohesion between teams and ensuring that knowledge acquired is put to good purpose.

3.1 Managing the 7 Ms in a laboratory

The research community is agreed on the principle that scientific publications must be founded on reliable scientific data obtained in an environment where all factors capable of influencing the quality of a result (see Fig. 2 ) are tightly controlled [ 20 – 24 ]. These factors can be displayed in the manner of the Ishikawa Fishbone diagram with 7 principal categories (see Fig. 2 ): Machinery, Methods, Materials, Mother-nature (environment), Manpower, Management and Measurement.

Assessing the reliability of research results consists in attributing a confidence level relative to both the obtainment and the use of the results. In the case of research activities, it can be difficult to assess reliability with an appropriate confidence level but the minimum that can be expected is to be in control of all the factors mentioned in Figure 2 . The implementation of a QMS which integrates the principle of the 7 Ms constitutes an opportunity to ensure quality of research results, and to improve and obtain recognition of the work carried out in a research laboratory.

The main actions implemented in the laboratory under observation are described in the following sections, for each of the influence factors illustrated in Figure 2 . All actions that were put into effect came about as a result of the continual improvement dynamic brought to the laboratory by the existence of the QMS.

Ishikawa ‘Fishbone’ diagram (principle of 7 Ms).

3.2 Management and Manpower

The QMS constitutes a tool with which to control and steer the activities of the unit.

The laboratory has chosen to adopt an integrated approach to Quality management that includes aspects linked to prevention and sustainable development. A participative management style was chosen by the Management Board for implementation of the QMS [ 23 ] with the intention of encouraging inter-team and inter-discipline exchange. In September 2013, the Quality manager was appointed with a brief to implement and steer a Quality system common to all laboratory research teams. He has extensive independent powers to enable him to fulfil this brief, as well as an operating budget. He attends monthly steering committee meetings for the laboratory, at which any matters relating to Quality and prevention can be raised if necessary.

The danger was of the Quality manager finding himself shouldering this huge task single-handed. With the support of the laboratory manager, a Quality network was created with more than 60 researchers of the laboratory: the laboratory manager, the 16 research team leaders, the 16 Quality representatives (one per team), and 35 Equipment and Metrology representatives. The Quality representatives meet every two months. A mission letter was sent to the Quality manager, the Quality representatives and the Equipment and metrology representatives.

In order to help the laboratory's Quality manager and Quality representatives to deploy the Quality process among research teams, the Quality manager made good use of the commitment of students on work experience in the laboratory. The advice of their mentor, a specialist in Quality management and metrology, went a long way in ensuring implementation of the QMS was possible with the cooperation of all concerned. This tight collaboration had a number of positive offshoots and several actions have been dealt with, such as process mapping (see Fig. 3 ), a Quality manual, and procedures for document and equipment control, all of which advances formalization of process and operating procedures [ 15 ].

To ensure reliability of research results, it is essential from the outset to pay due regard to Human Resource management [ 23 , 25 ]. This consists in identifying the functions and skills required (in terms of knowledge, know-how and experience) and hence training needs, welcoming new recruits and retaining records of initial and ongoing training.

Every two years, at the activity meetings held between the members of staff managed by INRA and their line managers, a review is made of the different activities, of prospects, of skills acquired and needing to be developed, and of training needs. A training programme is thus established for the laboratory, and priorities are set in line with the laboratory's Guidelines. It has been noted that staff training in Quality and metrology needs to be developed [ 25 , 26 ] as the lack of this is slowing down the progress of the laboratory.

Laboratory process mapping proposed.

3.3 Methods

When analysing test results, researchers need to have at their disposal all the information that could have an influence on results [ 20 ]. Therefore the formalization of methods is essential. This consists in noting down all sample collection, measurements, analysis of apparatus used, kit lot numbers, the samples themselves, their identification numbers, storage temperatures, etc. In accordance with INRA Guidelines, these operations are written down in a laboratory notebook when the method is being set up; the operating procedure is in place once the method has been fully defined and is workable. INRA is in the process of developing electronic notebooks to further encourage their use by scientists and facilitate the traceability of information. The use of laboratory notebooks by scientists in INRA laboratories is a long-standing practice. Once a method is deemed reliable, it is transcribed in the operating procedure (using the model defined by the laboratory).

In the laboratory, research teams formalize the validation steps of their methods in accordance with the instructions in INRA guidelines version 2. In other words, the evidence is created to confirm that the method utilized is appropriate to the question being treated; any question of the conditions required to produce interpretable results with a known level of uncertainty can be answered.

Data management is also a crucial matter, one which the bioinformatics team at the laboratory would like to improve. The development of a Laboratory information management system (LIMS) is underway and will improve the management of samples (identification, localisation) tested and the traceability of their associated data. The objective is to be able to find easily where a sample comes from, whose it is, to which methods it relates, everything that has been done throughout its life cycle and how to use dispose of it [ 16 , 17 ].

The LIMS will also be used for the management of equipment (which will facilitate the work of the Equipment and Metrology Representatives), and also consumables so as to avoid the use of different product or reagent lots where this would impact upon results.

Document management is another essential factor that has to be properly handled by the laboratory. The laboratory lists the operating procedures that need to be formalized, schedules their realization, has them written up, and disseminates them via any means considered appropriate to enable them to be used in operational conditions. The laboratory defines and utilizes template documents for the writing of operating procedures. An initial list of documents has been created. It is updated by the Quality representatives in such a way that every scientist can be aware of all operating procedures in existence as well as of modifications to them. Documents created and validated as part of the QMS are made available for use by means of a document management tool. This tool is encountering a certain amount of resistance as some scientists object to this general availability of what they consider to be their own documents.

All researchers know that it is essential to describe precisely their methods and to validate and to improve their scientific works. It is also important to record correctly the validation methods used and the associated results and data. For the continuous improvement of the research laboratory, the useful QMS tools allow the laboratory to also share knowledge and better capitalize on a know-how.

3.4 Machinery and Measurement

The laboratory has responsibility for managing equipment that is subject to regulations or is identified as having an impact on the quality of research results. This empowers it to ensure that the purchasing, maintenance, calibration, and verification of equipment are conducted appropriately [ 27 – 29 ].

When it was created in 2012, the laboratory had eight different types of inventory for the listing of equipment. Critical equipment was not always identified as such and several different service-providers could be involved in the regulatory control of a single apparatus type depending on which teams used it. It was a matter of high priority to standardize the inventory and equipment management systems (pertaining to information such as model, make, serial number, commissioning date, person responsible, etc.). It took almost two years to develop an internal network with a referent for each team (a matter of 35 Equipment and metrology representatives) and collectively define their brief: to ensure regulatory verifications with a view to prevention (autoclaves, fume hoods, centrifuges, oxygen meters, etc.) and/or metrological verification and calibration (weighing-scales, pipettes, thermometers, incubators, water baths, etc.).

Each critical device identified has its own service-life file enabling the tracing of incidents and the monitoring of maintenance, verification, and/or calibration. When a piece of equipment fails a conformity check, the validity of all preceding results must be re-established. All operations pertaining to equipment are covered in the common equipment management and control procedures, and in equipment user, maintenance, calibration, verification and monitoring instructions. An annual schedule for both internal and external verification of critical equipment has been set up [ 27 ]. For example: weighing-scales identified as critical are periodically checked in-house with calibration weights and control charts [ 28 – 33 ]. The weighing-scales are also verified annually by an external service-provider. Weighing-scales that are identified as non-critical undergo in-house verification only. In molecular biology, pipetting of reagents is a critical activity which can have a significant impact on a result, especially where small volumes are concerned. Due to the number of pipettes in use, these make up a significant proportion of the equipment to be checked. A joint decision has therefore been made to perform verification in-house for pipettes with a volume above 10 μL and to use an external service provider for pipettes with a volume below 10 μL as well as for multichannel pipettes [ 33 , 34 ]. For temperature, the laboratory has acquired a reference thermometer, calibrated annually, with which to verify operational laboratory thermometers. For verification of more complex equipment such as thermal cyclers, a workgroup has been set up with the aim of developing a procedure to be used for in-house verification.

For machines that carry a degree of safety risk to the user, such as centrifuges, autoclaves, etc., regulatory checks are compulsory at the intervals defined in the relevant regulations. For autoclaves, an authorization given by an external body is required.

3.5 Mother-nature and Materials

The INRA guidelines require units to ensure proper monitoring, recording, and if possible control of ambient conditions when these have an impact on the quality of research results.

Discussions are currently underway with Equipment Managers in charge of freezers and cold rooms on the subject of identifying critical aspects requiring special attention where samples need to be stored at −80 °C. The laboratory stores pathogenic agents (bacteria and fungi), seeds, leaves, twig fragments, pieces of fruit, and also DNA, RNA, and proteins. In order to control the risks associated with poor cold storage conditions (at temperatures of −80 °C, −20 °C and +4 °C), several requirements have been pinpointed: the requirement for an on-site power generator, the installation of −80 °C freezers in an air-conditioned room, of a monitoring system for each freezer and cool room to ensure reliability (for a backup −80 °C freezer, for maintenance of freezers and cool rooms by an external company with a rapid response time in the event of failure) and, finally, for an in-house team capable of dealing with failures at weekends.

The INRA version 2 guidelines require laboratories to ensure correct cold storage of samples (cryopreservation, −80 °C, −20 °C and 4 °C). To satisfy this requirement the laboratory is in the course of defining a clear policy concerning management of freezers and refrigerators, as well as standardized numbering for all samples within the laboratory in order to ensure their traceability. The Quality representatives are also discussing protocols for the collection and acquisition of samples, types of packaging (e.g. tubes, plates, bottle, boxes, etc.), and methods of identifying the samples. A disposal policy for samples (post publication, at end of project.) and the scheduling of cleaning days are also under discussion.

The laboratory is responsible for the traceability of consumable and other products (chemical and phytosanitary products, solvents, biological reagents, etc.). The question of traceability is not handled in exactly the same way by every team. Nevertheless, all teams adhere to use-by dates and required storage conditions. The storage of consumables, other products and reagents must conform to regulations and manufacturer specifications. After the merging of the research units, which saw more than half the research teams move to a new building and the construction of new greenhouses, a massive sorting of chemical products was undertaken, with comprehensive inventories being drawn up and appropriate storage made available: clearly defined product bins ensure that acids, bases, inflammables and toxic and carcinogenic, mutagenic, toxic to reproduction (CMR) substances are kept separately from each other. Ventilated cabinets have been purchased for all the laboratory buildings. A special room dedicated to the preparation of phytosanitary products has been built near the new greenhouses. Chemical safety information has been centralized in a computerized folder to which everyone has access.

4.1 Measuring effectiveness of the system

The effectiveness of the system is measured via internal audits and the annual self-assessment tool implemented by the INRA Quality Task Force. An internal audit is organized by the INRA Quality task force every five years, a year before the HCERES (French High Council for Evaluation of Research and Higher Education) assessment of the laboratory. To the overall laboratory assessment are adjoined the Quality audit report, the ensuing action plan, the results of the action plan and the quality indicators selected. Nevertheless, it would be a positive step if the bodies assessing the laboratory were to pay closer attention to the efforts made by the laboratory towards enhancing reliability of results. In order to foster a more self-critical view and further the objectives of continual improvement, it is intended that the laboratory will, for the first time, conduct a Quality review at the end of the year to evaluate the Quality actions undertaken, assess their effectiveness, and define new objectives for the coming year based on the indicators defined by the laboratory for each of its processes. It is hoped by this means to give individuals a real opportunity to enhance their relationship with the Quality system and to instil dynamism in the pursuance of improvement. The Quality process is progressing well and awareness of the benefits attached to a QMS is growing within the laboratory.

4.2 Effect of QMS on organization of the laboratory

The INRA Management coordination committee recommends laboratories to undergo a Quality audit a year ahead of the HCERES assessment which takes place every five years. In response to the wish of management, therefore, an INRA internal audit was held in the VLSU in March, 2015 organized by the INRA Quality task force. The auditors took the time to audit every team (on every site) in accordance with the different requirements of the INRA version 2 guidelines. This very pedagogical action allowed scientists to measure in real terms the improvements made or needed to be made by their teams. This internal audit made it possible to draw up individual team-oriented action plans based on specific needs, followed-up with an action plan for the laboratory as a whole. The actions decided upon were prioritized according to three objectives: improvement of documentation management, of equipment management, and of cold-stored samples management (cryopreservation, −80 °C, −20 °C, 4 °C and lyophilisation). These objectives were then confirmed in the management mission statement, which was updated in 2016. The audit was therefore a very effective means of continuing to involve teams in the Quality process and of facilitating interaction between the teams and the Quality manager, and was also a means through which the collective objectives of the laboratory could be developed. This is in keeping with the concept of participative management put into effect by the laboratory management board.

4.3 Effect upon commitment and motivation of laboratory staff

The fact that the laboratory is under no obligation to pursue the certification objective means the scientific community may suffer a lack of motivation. However, this is actually a very positive situation: it allows staff the time it takes to become fully conversant with the new managerial process, one which actively encourages the participation of individuals, promotes a shared outlook, and fosters an ongoing critical regard of the organization of the laboratory. The process management constitutes a tool with which to steer laboratory activities with regard to key performance indicators. It involves every member of laboratory staff, favouring continual improvement of the operation, organization, and practices of the research laboratory via the Quality policy, Quality objectives, and results of self-assessment and audits.

In order to deepen the commitment of its scientists to the Quality process the laboratory is developing, in conjunction with its closest partners, a network of Quality managers, which it is intended will be broadened in order to benefit from the experience of other Organizations, such as INSERM (French National Institute of Health and Medical Research) and CIRAD (French Agricultural Research Centre for International Development). As the Quality process is not inscribed in the official duties of staff, implementation is not easy. Fortunately, the laboratory is able to count upon the commitment of its willing staff.

Recognition for individuals who participate in collective tasks needs to be increased. While the contribution of individuals to collective tasks such as prevention and risk management does come up at activity meetings and in competition for promotion, staff generally feel that only their scientific contribution (in the form of scientific communication and publication) is taken seriously. Only this, it seems, has any real effect on career development. In the light of this it is easy to understand why a number of laboratory staff takes little or no part in this type of collective activity.

This paper presents the different actions involved in setting up a QMS in a very large French research laboratory (very large scale unit) through a voluntary approach.

This paper clearly illustrates the effectiveness of the actions considered by looking at the 7M method and giving practical examples which involve both the organization and the activities of the laboratory.

Many improvements were made at the time of setting up the QMS in the laboratory. These have had a positive impact on the functioning and the activities of the laboratory.

Putting a QMS into place certainly improves the functioning of the laboratory since it provides information on where people are, what they are doing, how they are doing it, how what they do is being checked and how things can be anticipated. Quality tools allow laboratory staff to be accompanied in a spirit of continual improvement in order to maintain effectiveness and robust activities of research of the laboratory.

The management of quality also aims at opening up discussion so researchers can put meaning into their work and improve their research activities. The participative management aspect of the Quality process encourages a shift, initially on an individual basis but consequently at organization level, from wanting change to enjoying it. This participative style of management brings together different perspectives that enable anticipation, cooperation and innovation.

The QMS is still young and more needs to be achieved for it to be completely operational and cover all the processes linked to the activities of the laboratory. All the laboratory staff needs to acknowledge the QMS and become involved for it to function correctly. Efforts to increase researchers' awareness are continuing in the laboratory and in field work by showing, step by step, that the QMS exists to enable the laboratory and its quality staff to continue to progress from an organisational as well as scientific point of view.

Although it enjoys the support of the laboratory management, the implementation and development of a QMS is encountering resistance both from scientists and from the Institutions, notably in the latter case, for financial reasons: the IT tools, for example, that improve the management of documentation, equipment, consumables, and chemical products take time to develop satisfactorily and necessitate a training budget. And yet these tools help underpin the management of collective intelligence. Currently, the financial support of the Institutions contributes to the cost of fluids and research projects but provides nothing for the development of structural tools. Despite the economic pressures, scientists within the laboratory do willingly support the QMS. The laboratory could also take its work on the validation of the methods further, increasing emphasis on the estimation of uncertainties associated with results. Among other aspects that need to be improved are the control of outsourced activities and the evaluation of supplies and suppliers. It is perhaps useful at this point to refer to the experience of other laboratories: despite the difficulties encountered during the implementation phase of a QMS, of all those questioned who had been in a position to observe the changes to the organization of their laboratories, none expressed a wish to backtrack. This seems to reinforce the claim that a QMS, while admittedly demanding a certain effort from everybody in the laboratory during the implementation phase, does serve to enhance reliability and improve the functioning of a laboratory.

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Cite this article as : Valérie Molinéro-Demilly, Abdérafi Charki, Christine Jeoffrion, Barbara Lyonnet, Steve O'Brien, Luc Martin, An overview of Quality Management System implementation in a research laboratory, Int. J. Metrol. Qual. Eng. 9 , 2 (2018)

All Figures

	Institutional membership of laboratory staff.

	Ishikawa ‘Fishbone’ diagram (principle of 7 Ms).

	Laboratory process mapping proposed.

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Quality Management

The ever-changing life of a quality management system, perhaps the most significant positive impact of qa programs is the improvement in customer satisfaction..

In the dynamic landscape of modern business, where competition is fierce and customer expectations are ever-evolving, organizations must prioritize quality in their products and services. Having a sound quality program crosses all aspects of business including areas we normally would not consider like our sales department.

During what was considered a standard build, a high rate of defects began to show up at a high rate that turned into a state of panic and large-scale root cause investigation. After countless hours of investigation into the manufacturing, inspection, and material specs, the trail led to the purchase order. It turns out that a custom product was sold as a standard build when sales crossed components not knowing the implications it would have on manufacturing.

This is where quality management system (QMS) programs play a pivotal role. A quality management system program is a systematic process designed to ensure that products and services meet predefined standards, customer expectations, and companywide standards that separate them from their competition. A common quality principle is known as “first time right” which is intended to address errors immediately at the source which includes every step from beginning to end.

What is a Quality Assurance Program

A quality assurance (QA) program is a comprehensive approach that encompasses all processes, methodologies, and activities aimed at delivering products or services that meet or exceed customer expectations. QA programs involve the establishment of systematic processes, guidelines, and standards to ensure consistency, reliability, and excellence throughout the product or service life cycle.

Components of a Quality Assurance Program

Process Improvement: QA programs focus on optimizing and refining processes to eliminate inefficiencies and reduce the likelihood of errors. These process controls can be as simple as a checklist that reminds employees of the steps needed and a point of measurement used to track the process improvements.
Standards and Compliance: QA programs establish industry-specific standards and compliance requirements. Adhering to these standards not only ensures the quality of products or services but also helps organizations meet ever-changing regulatory requirements, reducing the risk of legal issues.
Testing and Inspection: Rigorous testing and inspection procedures are integral to QA programs. This includes regular product testing, code reviews, inspector competency, and system audits to identify and rectify defects or deviations from established standards before products reach the customer.
Training and Development: QA programs prioritize the continuous development of personnel through training, proficiency, and competency programs. Well-trained employees are better equipped to understand and implement quality standards, contributing to the overall success of the organization.

The Positive Impacts of a Quality Assurance Program

Enhanced Customer Satisfaction: Perhaps the most significant positive impact of QA programs is the improvement in customer satisfaction. Products or services that consistently meet or exceed customer expectations lead to increased trust, loyalty, and positive word-of-mouth, ultimately contributing to business success.
Operational Efficiency: QA programs streamline processes and workflows, reducing errors and inefficiencies. This results in increased operational efficiency, decreased production costs, and improved resource utilization, leading to higher profit margins.
Risk Mitigation: By identifying and addressing potential issues early in the development or production phases, QA programs help mitigate risks. This proactive approach minimizes the chances of defects, recalls, or service failures, safety and bottom line.
Continuous Improvement: QA programs foster a culture of continuous improvement within organizations. Regular assessments and feedback loops enable teams to adapt to changing market conditions, incorporate emerging technologies, and stay ahead of the competition.

Perceived Negative Impacts of a Quality Assurance Program

Perception of Slowed Processes: The meticulous nature of QA processes can sometimes create the perception that projects take longer to complete. While this may be true to some extent, the tradeoff is a higher likelihood of delivering error-free and high-quality outcomes.
Increased Costs: Implementing and maintaining a comprehensive QA program requires financial investment. Some organizations may perceive this as an additional cost, especially in the short term. However, the long-term benefits in terms of reduced defects, improved efficiency, and customer satisfaction often outweigh the initial expenses.
Resistance to Change: Employees accustomed to existing processes may resist the changes introduced by QA programs. This resistance can hinder the successful implementation of QA initiatives. Effective change management strategies, including communication and training, are crucial to overcoming this challenge.
Potential for Over Regulation: In some cases, organizations may inadvertently create excessively rigid QA processes, leading to over regulation. This can stifle innovation and creativity, hindering the organization’s ability to adapt to evolving market trends.

Examples of High Performing Quality Programs

At the core of any good quality assurance program is the quality management system (QMS) which provides the foundation for a strong quality program that is flexible enough to accommodate industry and market changes. At the end of the day organizations want to influence their own change by improving overall quality, reducing wasteful re-works, and driving toward near perfect or zero defects within their products.

The changes need to be continuous and in small increments that will allow the organization to pivot quickly to subtle changes coming from process improvements, innovative production tools, or improved training. The biggest driver in operating an effective QMS is one that focuses on customer needs/expectations and not just industry or regulatory requirements. When an organization focuses only on the standards or specifications they are realistically only aiming at the minimum requirements and not striving for the excellence of a sound QMS.

Instilling regular meaningful internal audits is another tool that is used to put an organization at the top of their customers’ list as a means of continued process improvements. The best audits are concise while thorough and encourage open feedback without recourse. These audits prove to be powerful when it comes time to have external auditors drop in and find a tightly run organization so investing a little in internal audits will go a long way toward reduced root cause analysis that come from industry or customer audits.

The last but not least important factor of having a sound QMS program is a strong safety culture. By having a safe work environment also means less downtime from accidents or equipment failures. This factor is often overlooked but critical to top performing organizations.

Quality management systems are indispensable for organizations striving to deliver excellence in their products and services when implemented from the start of the process and not after the fact. Bringing in more work to the shop leads to more jobs, new products, new equipment, and improved employee satisfaction from knowing customers seek their products or services over the competition.

When implemented effectively, these programs contribute in ways that are impactful to the bottom line, safety, reputation and employee satisfaction. While there may be perceived negative impacts, such as increased costs and resistance to change, the long-term benefits far outweigh these challenges. Striking the right balance and fostering a culture that values quality at every stage of the process will position organizations for sustained success in today’s competitive business environment.

References:

The Quality Assurance Process: Roles, Methods & Tools - ProjectManager
What is quality assurance? and it's importance | Indeed.com UK
https://www.arenasolutions.com/blog/inside-look-at-four-companies-with-exceptional-quality-management-systems/
https://biomerics.com/BlogDetails/what-are-the-factors-of-a-good-quality-management-system-qms
Inside Look at Four Companies With Exceptional Quality Management System | Arena (arenasolutions.com)
https://qt9qms.com/case-studies/swagelok

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Eddie C. Pompa's career in NDT spans 30-plus years as an NDT Level III across the aerospace, oil & gas, and education sectors. He currently works at the Johnson Space Center in Houston as the Safety & Mission Assurance NDT Level III and continues to teach NDT classes at night at the local Lone Star College.

His career highlights include working on the Space Shuttle Endeavour, Alpha Magnetic Spectrometer, Columbia Accident investigation, Orion, Pressure vessels, Blow Out Preventers, and sharing these experiences with the next generation of NDT professionals. As an NDT advocate Eddie volunteers time with the local high school where NDT is taught as a career path by working to create meaningful experience opportunities for this generation of NDT professionals.

His passion for NDT has led to his NDT Hero creations that incorporate each NDT method into unique superheroes that work to protect and make our world safer by preventing disaster by implementing sound inspection and quality assurance processes across all sectors. His art can be seen on LinkedIn, Instagram, and Facebook where he aims to promote, inspire, and educate the world about NDT.

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J Clin Biochem Nutr
v.73(2); 2023 Sep
PMC10493209

Quality analysis of the clinical laboratory literature and its effectiveness on clinical quality improvement: a systematic review

Ahmed shabbir chaudhry.

1 Department of Medical Quality and Safety Science, Osaka City University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan

2 Department of Intensive Care Medicine, Osaka Women’s and Children’s Hospital, 840 Murodo-cho, Izumi, Osaka 594-1101, Japan

Etsuko Nakagami-Yamaguchi

3 Department of Medical Quality and Safety Science, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan

Associated Data

Quality improvement in clinical laboratories is crucial to ensure accurate and reliable test results. With increasing awareness of the potential adverse effects of errors in laboratory practice on patient outcomes, the need for continual improvement of laboratory services cannot be overemphasized. A literature search was conducted on PubMed and a web of science core collection between October and February 2021 to evaluate the scientific literature quality of clinical laboratory quality improvement; only peer-reviewed articles written in English that met quality improvement criteria were included. A structured template was used to extract data, and the papers were rated on a scale of 0–16 using the Quality Improvement Minimum Quality Criteria Set (QI-MQCS). Out of 776 studies, 726 were evaluated for clinical laboratory literature quality analysis. Studies were analyzed according to the quality improvement and control methods and interventions, such as training, education, task force, and observation. Results showed that the average score of QI-MQCS for quality improvement papers from 1981–2000 was 2.5, while from 2001–2020, it was 6.8, indicating continuous high-quality improvement in the clinical laboratory sector. However, there is still room to establish a proper system to judge the quality of clinical laboratory literature and improve accreditation programs within the sector.

Introduction

The robustness of the healthcare system relies upon the clinical laboratory because all the clinical decisions taken on patients by physicians mainly depend upon the clinical lab reports. ( 1 , 2 ) About 70–75% of medical diagnoses are obtained via clinical laboratory reports, making laboratory service quality directly impact healthcare quality. ( 3 , 4 ) Laboratory findings should be precise as possible, also at the same instance; all laboratory operations must be reliable with timely reporting resulting in a beneficial clinical setting. ( 5 ) Negligence during laboratory operations, including processing, assessing, and reporting, can cause severe consequences, including complications, lack of adequate treatment, and delay in correct and timely diagnosis, leading to unnecessary treatment and diagnostic testing. ( 6 – 8 ) A clinical laboratory is a complex set of cultures that include several activity steps, and many people make it unique and saucerful. The comprehensive set of these complex operations occurring during a testing process is called the path of the workflow. ( 9 ) The workflow path in a clinical laboratory initializes with the patient and finishes with reporting and comprehending the results. In any clinical lab setting, it is presumed that mistakes will be made in this process due to the high volume of samples, the limited number of staff, and the different steps implicated in the testing process. ( 10 , 11 ) Errors at any stage of the total testing process (TTP) can result in inaccurate laboratory outcomes. To guarantee the quality of the results, a reliable method for determining errors within the TTP is required. ( 12 )

Significance of quality in the medical laboratory

The term “quality” in the healthcare context has been properly defined by the Institute of Medicine (IOM). ( 13 ) It defines “quality of care as the extent to which health services for individuals and populations increase the probability of desired health outcomes and conform with current professional knowledge.” More recently, quality has been characterized as “doing the right things for the right people, at the right time and doing them right the first time.” In recent years, quality may entail different domains; there appears to be a consensus emerging that quality involves safety, effectiveness, appropriateness, responsiveness or patient-centered care, equity or access, and efficiency.

Importance of standardization

In the context of laboratory medicine, high-quality diagnostic testing (such as for patient safety) is often achieved through the application of standardized processes. Standardization helps to guarantee the accuracy and reproducibility of test outcomes and their appropriate application to the correct patient and also helps to ensure that the results are accurate. The accreditation agencies guarantee crucial points for standardization in laboratory medicine. There are several authorized CLIA accreditation agencies like the College of American Pathologists (CAP), Joint Commission (JCIA), Accreditation Commission for Health Care, Inc (ACHC), and American Association for Laboratory Accreditation, accreditation, which significantly influences quality improvement (QI) in medical laboratory. However, the international organization of standardization ISO is a non-governmental organization that offers a general framework for all procedural sections up to reporting results. Over the years, the establishment and maturity of each agency have brought significant improvement in the medical laboratory sector. The most crucial accreditation is ISO 15189 among all others because ISO 15189 fixates more on laboratory management systems and processes, e.g., The ISO 15189 standard includes requirements linked to the entire testing process, including pre-examination (i.e., pre-analytics), examination (i.e., analytics), and post-examination (i.e., post-analytics). These requirements include developing and implementing standard operating procedures, validation processes, staff training, internal and external quality control (EQC) measures, laboratory setup, and other aspects. In contrast, the other CLIA-approved laboratory accreditation program concentrates more on technical procedures implicated in testing, e.g., policy statement, certification standards, archive standards, and adequate laboratory testing.

The originality of this study

Several systematic analyses have been published on the quality and management of clinical laboratories, but none focus particularly on the overall QI of medical laboratories ( Supplemental Table 1 * ). This leaves a dent in our understanding of QI in clinical laboratory settings. ( 14 , 15 ) Regardless of the number of QIs in a medical laboratory context, the high-quality collective QI systematic review is insufficient, which limits our understating of this field and requires further advancement of QI reporting in the clinical laboratory.

Purpose of the study

This study sought to comprehensively review and evaluate published literature on QI in clinical laboratories. The goal was to provide researchers and professionals with a thorough overview of the present knowledge on quality control (QC) and improvement in medical laboratories. Furthermore, the study sought to determine areas for potential future research and developments in the field of QI in this setting.

Materials and Methods

Study design.

A systematic review is a technique for objectively summarizing prior research through a systematic and replicable process. ( 16 ) This review followed a three-stage design suggested by Tranfield et al. 2003. ( 16 , 17 ) During the planning stage, the choice of databases and keywords and the inclusion and exclusion criteria for selecting contextual articles were identified. The preferred reporting items for systematic reviews and Meta-analyses flow chart (Preferred Reporting Items for Systematic Reviews and Meta-Analyzes) was employed to illustrate selecting articles for inclusion in the final sample.

Data source

To guarantee comprehensive coverage of the literature, multiple databases were applied in the bibliometric analysis. ( 18 , 19 ) In this research, the Web of Science (WOS) core collection and PubMed were chosen for their significance to management and medical research. Three keywords were used to determine relevant articles: “quality control” in any of its forms, terms linked to quality processes such as “quality systems,” “quality improvement,” or “quality management,” and “clinical laboratory” to narrow the focus to the healthcare sector using different databases and these keywords helped to guarantee a comprehensive search of the literature on QC and improvement in clinical laboratories. ( 20 )

Study selection

The present analysis specializes in clinical laboratory QC and improvement research published between 1981 and 2021. To be added, the publication must be a research article and be written in English, with at least a title and summary available. Conference proceedings, letters, notes, reviews, editorials, summaries, and other types of publications were removed from the analysis.

Data processing

Before undertaking the study, we standardized the data to enhance the conformity of the results. We standardized the spelling of the author’s names and the formatting of journal affiliations and other data. We also revised to ensure that citations for each article were not counted multiple times when using both databases. Two authors worked independently to mitigate the risk of errors. Only articles that both reviewers agreed upon were included in the review, as displayed in Fig. 1 .

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PRISMA (preferred reporting items for systematic reviews and meta-analyses)

Quality assessment of literature extracted

The QI Minimum Quality Criteria Set (QI-MQCS) (16) was used to assess this study. The QI-MQCS is employed in the evaluation of QI interventions in healthcare. The QI-MQCS comprises 16 operational and psychometrically dimensions being assessed to present a reliable and accurate assessment of different QI intervention evaluations. Two of the three reviewers in our study individually reviewed the publications. We allocated a score of 1 to each domain with the minimal criterion and a score of 0 to each area that was not satisfied; hence, each article was allocated a score between 0 and 16. The full review committee handled any score disagreements until a consensus was agreed upon. Although the QI-MQCS does not have a set threshold at which the quality of the articles is determined acceptable, “high quality” was defined in this study as a score between 14 and 16. ( 21 )

A total of 776 results were collected from PubMed and WOS bibliographic databases. Of these, 50 were duplicates, and 726 were screened based on their titles and abstracts. After an additional assessment, 224 of the remaining articles were deemed eligible for the QI study, and 53 met the inclusion criteria, as depicted in Fig. 1 . The selected papers were classified into QI ( n = 19) and QC ( n = 33), as presented in Table 1 . Most QI studies were performed in university hospital laboratories ( n = 34), while some of the QC studies were conducted in general community hospital laboratories ( n = 9). There was a great difference in the types of errors detected in these two categories of examinations. Preanalytical errors ( n = 12) were the most prevalent in the QI studies. In contrast, analytical errors ( n = 28) were the most prevalent error in QC studies.

Table 1.

Characteristics of selected papers

		QI	QC
Number of papers		19	33
Institution type	Hospital	6	3
	University hospital	10	24
	Research center	2	0
	University research center	0	1
	Company laboratory	0	2
	Routine clinical laboratories	1	3
Laboratory type	Tertiary care hospital laboratory	1	0
	University hospital laboratory	10	24
	Hospital clinical laboratory	5	3
	Research laboratory	2	0
	Routine clinical laboratory	1	2
	Public and private laboratories	0	1
	University research laboratory	0	1
	Company laboratory	0	2
Focused error type	Preanalytical	12	4
	Analytical	7	28
	Postanalytical	0	1

QI in the clinical laboratory focuses on preserving quality standards. The 19 extracted papers on QI were classified based on their themes, goals, methods, and interventions. The major theme among these papers was the improvement of clinical quality standards lab practice and training in the laboratory ( n = 8), followed by the improvement of problems in the reception area ( n = 5), the improvement of TTP ( n = 4), the management of preanalytical errors ( n = 4), and the evaluation and evolution of quality indicators ( n = 2). Accreditation ( n = 6) was the most prevalent method employed in these QI approaches. In contrast, training and education ( n = 17) were the most common interventions employed to achieve these goals, as highlighted in Table 2 .

Table 2.

Characteristics of quality improvement papers

	Number of papers	19
Theme	Clinical quality standard lab practice and training	8
	Improving the reception area problem	5
	Improvement of TTP	4
	Management of preanalytical errors	4
	Utilization and evolution of quality indicator	2
	Lab workspace initiative	1
	Financial and work volume problems	1
	Ratification of errors	1
Aim	Quality indicators utilization evaluation and evolution	2
	reduction of preanalytical errors	2
	Reduce TAT	1
	Utilization of GCLP guidelines	1
	cost reduction approach	1
	work and workspace improvement techniques	1
	Errors evaluation in terms of sigma metrics	1
	Assessing the level of physician satisfaction with clinical lab reports	1
	Reliability of quality control standards	1
	The method validation process for the new lab setup	1
	Intra and inter-laboratory reproducibility of an ELISA to facilitate Lyme disease diagnosis	1
Methods	Accreditation	6
	Six Sigma/PDSA/DMAIC	10
	QI standards and TQM	3
Intervention	Training/Education	17
	Task force	4
	Observation	1
	Reducing waste	1

PDSA, plan, do, study, act; DMAIC, define, measure, analyze, improve, control; TQM, total quality management.

The retrieved papers were classified based on their objectives, goals, and methods to examine the QC characteristics in the clinical laboratory. The core QC analytical processes in these papers included performance evaluation ( n = 10), QC assessment ( n = 7), improvement of laboratory practices ( n = 3), improvement of quality through the use of the sigma metric ( n = 8), and the QC criteria for susceptibility testing ( n = 7). These processes highlighted the objectives of QC standards in the clinical laboratory. They were implemented using various methods, including accreditation ( n = 22), six sigma ( n = 12), QC practices ( n = 4), statistical approaches ( n = 4), external quality assessment (EQA) ( n = 2), and EQC ( n = 1), as expressed in Table 3 .

Table 3.

Characteristics of quality control papers

	Number of papers	33
Objective	Performance evaluation	10
	Quality control assessment	7
	Laboratory practice improvement	3
	Analytical quality assessment	2
	Execution of training and QC program	1
	Design and implementation of IQC	1
	Evaluated the reliability of serological point-of-care	1
	Evaluation of QC practice	1
	Implementation of QC method	1
	Examines the effects of blood-collection tube additives	1
	QC evaluation of ESR	1
	Periodic analysis of quality control	1
	Standard statistical approach	1
	Identification of biomarker for preanalytical QC	1
	Evaluate the validity of blood lead analysis	1
Aim	Quality improvement through sigma metric	8
	QC criteria of susceptibility testing	7
	Examination of training and QC programs	2
	QC specimens Evaluation	2
	Siemens Dimensions Rxl execution	1
	Calculation of CV and bias	1
	Establishment of IQC based on sigma metric	1
	Validation of Z score indicator	1
	IQC system specification	1
	Evaluate POC tests for EBV	1
	Execution of QC method	1
	CUSUM-Logistic Regression for rapid detection of error	1
	Quality control of Median monitoring	1
	Identification of unsatisfactory scores in the CAP PT surveys	1
	Suggestions Potential biomarker for blood sample quality	1
	Estimation of QC material	1
	Assessment of total testing errors	1
	Identification of disparities	1
Method	Accreditation	22
	Six Sigma	12
	QC Practice	4
	Statistical approach	4
	EQA	2
	EQC/IQC/GQC	3

EQA, external quality assessment; EQC, external quality control; POC, point of care; EBV, Epstein-Barr virus; IQC, internal quality control.

In this systematic review, we evaluated the present state of QI interventions, the frequency of errors in clinical laboratories, and the prevalence of issues in QI reporting by systematically examining QI articles in clinical laboratory contexts. As the number of QI publications in healthcare has elevated, so is the number of QI publications in clinical laboratories. ( 22 ) Laboratory errors can occur at any stage of the TTP and can promote increased healthcare costs, decreased patient satisfaction, delayed diagnosis, misdiagnosis, and adverse risks to patient health. ( 23 ) Despite the increasing automation of laboratory diagnostics, our research discovered that laboratories remain a source of errors that can influence patient care decisions.

Distribution of errors among QI and QC papers

Overall, errors in the preanalytical and postanalytical phases are more prevalent, accounting for most errors. ( 24 ) Errors within the analytical stage are generally fewer. ( 25 , 26 ) Our findings indicate that the frequency of errors within the analytical phase has declined in recent years. We categorized the papers into QI and QC to identify the prevalence of errors in each setting. Our findings revealed that preanalytical errors were most predominant in QI papers, comprising 12 out of 19 papers.

In contrast, analytical errors were mostly observed in QC papers, comprising 28 out of 33 papers, as presented in Table 1 . This disparity may be due to the focus of the papers in each category. QI papers often address training, education on safety teams, and other interventions that involve direct human interaction, such as phlebotomy, which may elucidate the higher prevalence of preanalytical errors in these papers. However, QC papers often assess methods or processes for improvement, such as six sigma, accreditation, QC practices, statistical approaches, and other related methods, which involve more analysis in the context.

GCLP is a potential source for QI

To prevent errors, the clinical laboratory must be accurate and precise in its testing. A quality assurance system based on GCLP guidelines can help with this, but it necessitates the commitment of both management and technical staff. A study executed by Horace Gumba et al. ( 27 ) has revealed that improving the workflow, increasing patient satisfaction, evaluating performance, and improving the test-treatment process can all contribute to QI in the clinical laboratory. Implementing GCLP guidelines also requires effective management, a solid foundation of best practices and a focus on quality culture, and training and education. Another study by Horace Gumba et al. 2018 ( 28 ) indicated that on-site training and education have been found to enhance the implementation of quality management systems considerably. Our previously reported data linked to QI supplement these ideas and propose that writing standard operating procedures, improving documentation practices, implementing GCLP guidelines, conducting improvement projects, and providing training on quality indicators can all be efficient interventions for improving the quality in the clinical laboratory, as expressed in Table 2 .

Performance evaluation

Performance evaluation in clinical laboratories is crucial for guaranteeing test results’ accuracy, precision, and reproducibility. This is typically accomplished through QC materials. These materials, which have prominent values, are used to validate the performance of the laboratory’s test systems. QC materials can be classified into internal and external types. Internal quality control (IQC) materials are used for consistent monitoring of the laboratory’s test systems, while EQC materials are used for comparison to those of other laboratories. A study was carried out by Loh et al. , ( 29 ) analyzed several methods used to assess clinical laboratories’ performance, including QC materials and inter-laboratory comparisons. The study highlighted the importance of constant improvement in the QC of clinical laboratories. Our QC paper intentionally highlights this concept in Table 3 .

Importance of accreditation in clinical laboratory

Accreditation of clinical laboratories is essential for promoting the quality of clinical laboratory practices. Our findings in Table 3 highlight the significance of accreditation in clinical laboratories, which conforms with the findings of research by Alkhenizan et al. ( 30 ) One of the main restrictions to implementing accreditation programs is the skepticism of healthcare professionals, particularly physicians, concerning the impact of accreditation on the quality of healthcare services. ( 31 , 32 ) In healthcare, QI activities are often promoted as part of a total quality management (TQM) strategy, including Kaizen/QI activities in nursing care, medical quality, logistics, administrative work, and patient services. In clinical laboratories, however, the influencing force behind the QI is often linked to accreditation, as it presents formal recognition and certification from a regulatory body that the laboratory is competent and operates effectively. ( 33 )

Influence of accreditation in QI and QC studies

To assess the trend of QI in clinical laboratories, we analyzed papers from 1981 to 2021 and made some intriguing findings. There was relatively minimal research on QI or control from the 1980s to 2000s, possibly due to insufficient quality infrastructure, barriers to globalization, and limited access to modern knowledge. Data categorization revealed that QI and QC trends increased considerably after 2000, suggesting a significant improvement in the laboratory sector. Several possible explanations abound for this trend, including increased awareness of the importance of quality healthcare and developing quality management systems. The most substantial factor is the establishment of accreditation agencies such as ISO 15189 and CAP. CAP and ISO 15189 have greatly impacted the clinical laboratory sector through several initiatives and guidelines. ( 34 ) CAP has had multiple changes from 1994 to 2020, including implementing training and unannounced inspection programs for pathology laboratories, establishing a multiyear initiative to promote the pathology specialty, and introducing CAP 15189 as a voluntary program. ISO 15189 was first published in 2003, offering information on the medical laboratory sector and outlining guidelines for sample procedures, results interpretation, reasonable turnaround times, patient sample collection, and the role of the laboratory in training and educating healthcare staff. It was revised in 2007 to conform with ISO/IEC 17205. A third edition was published in 2012, as depicted in Table 4 , which revised the prior layout and added a section on laboratory information management. ( 35 ) The effects of these changes on QI in clinical laboratories can be seen in our results in Fig. 2 from 2000 onwards, indicating a clear QI trend in medical laboratories.

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Object name is jcbn23-22f02.jpg

Number of QI and QC papers per 5 years from 1981–2020. QC papers were the most published from 2001, indicating the gradual change of quality in clinical laboratory settings.

Table 4.

Introduction of accreditation agencies for the improvement of clinical laboratory

Accreditation agencies	Time frame	Introduction of quality techniques
College of American Pathologists	1946–1996	Certification of hemoglobin standards.
		The professional component in the laboratory.
		Laboratory management index program.
		Cytology policy statement.
		The legal status of pathology.
		Surgical pathology policy.
	1997–2000	Implementation and further advancement of advocating improvement.
	2001–2005	Unannounced inspection programs.
		Several trainings.
	2007–2009	CAP 15189 is a voluntary and non-regulated accreditation to ISO 15189.
		Multiyear initiative.
	2011–2020	Biorepository accreditation program.
		Pathologist quality registry.
		SARS-CoV-2PT.
ISO 15189	First published in 2003	Role of the laboratory in the training and education of health staff.
		Turnaround times.
	Revised in 2007	To align more closely with ISO/IEC 17205.
	Third edition in 2012	Revised the previously published layout and added a new section on laboratory information management.
Joint Commission	2010	Evidence bases lab standards.
		Address the patient safety and quality.
		Survey methodology.

QI-MQCS as a psychometrically tool for quality publication

To determine the QI of clinical laboratory literature, we used the 16 domains of QI-MQCS. ( 21 ) Each paper was evaluated on these domains and scored on a scale of 0 to 16, with a score of 1 given if at least one reason was outlined. The QI papers generally followed the most domains. These papers were then classified by year of publication, and the average QI-MQCS score was determined. A substantial difference in QI-MQCS scores was detected in articles published between 2000 and 2020, as depicted in Fig. 3 . This disparity may be due to the implementation of laboratory QI standards and the accreditation of clinical laboratory facilities, which have been previously outlined.

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Object name is jcbn23-22f03.jpg

This figure illustrates the scoring pattern of QI-MQCS concerning years of publication. The average score of QI-MQCS from 1981–2000 is 2.5, whereas, from 2001–2020, it is 6.8, which reveals the high quality of continuous enhancement in the clinical laboratory sector.

Limitations and strengths

One of the strengths of this analysis is its thorough analysis of all QI-related clinical laboratory papers. The clinical laboratory field is extensive and includes various subfields, but to our knowledge, only 12 reviews have previously addressed QI in the clinical laboratory. This research is the first to thoroughly evaluate all QI-related clinical laboratory papers in one review. There are some limitations to this research. Firstly, the lack of reporting or evaluation of clinical laboratory studies using QI-MQCS limits our comprehension of the QI process. Second, we assessed and scored all papers based on the 16 domains of QI-MQCS, even though some domains may not have been significant to medical laboratories ( Supplemental Table 2 * ). For example, spread (7%), sustainability (3%), penetration (3%), adherence/fidelity (7%), organizational readiness (11%), and intervention description (11%). This is because clinical laboratories do not typically entail delivering interventions or implementing evidence-based interventions in practice and do not usually require the analysis of performance measurements or process systems or developing connections between people.

The major function of the clinical laboratory is to offer diagnostic support to physicians, which can aid in the treatment process and contribute to further progress. However, the QI-MQCS was developed to help stakeholders determine high-quality studies in their field. QI techniques are diverse and distinct from clinical interventions, and the QI-MQCS is a psychometrically tested tool for evaluating the QI-specific characteristics of QI publications. This analysis has possible bias as it did not include other significant databases like Embase and EBSCOhost and only included articles in English.

This study investigated the trend and scope of QI and QC papers in clinical laboratory practice. Our findings revealed that the trend of QI and QC increased markedly after 2000, possibly due to the implementation of laboratory QI standards and the accreditation of clinical laboratory facilities. Our study emphasizes the importance of compliance with good clinical laboratory practice standards and the potential for collaboration between accredited and non-accredited organizations to enhance the quality management system and influence consistent improvement in the clinical laboratory sector.

Author Contributions

This research paper is the culmination of a joint effort between the author, the co-author YI, and the supervisor EN-Y. The study was conceptualized and designed through collaborative discussions between the author and the supervisor. The data collection process was a collaborative effort with significant contributions from YI, who provided valuable data visualization and analysis guidance. The supervisor was crucial in developing and refining the research framework, offering valuable insights that improved study conceptualization. The co-authors reviewed and revised the manuscript and provided critical feedback on presenting findings, including figures and tables.

Acknowledgments

We extend our heartfelt gratitude to the following colleagues for their invaluable contributions and support: Dr. Kaoru Nakatani, Mr. Nozomi Kamamemoto, Ms. Tomoko Honjo, and Mr. Atsushi Tokuwame. Additionally, we would like to acknowledge all those who have been a source of inspiration and motivation throughout the research process.

Abbreviations

DMAIC	define, measure, analyze, improve, control
EQA	external quality assessment
EQC	external quality control
IQC	internal quality control
PDSA	plan, do, study, act
QI	quality improvement
QC	quality control
QI-MQCS	quality improvement Minimum Quality Criteria Set
TQM	total quality management

Conflict of Interest

No potential conflicts of interest were disclosed.

Supplementary Material

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Article Details

Main article content, quality management in clinical and public health research: a panacea for minimising and eliminating protocol deviations in research operations, elvis efe isere, nosa eniye omorogbe.

A quality management system for clinical and public health research operations is indispensable because it ensures the integrity and reliability of research outcomes. By implementing a robust quality management practice in research implementation and operation, research teams can uphold the highest standard of research conduct, thereby enhancing the credibility and trustworthiness of research findings. This paper elucidates the significance and role of a quality management system in clinical and public health research operations and its efficacy in minimising and eliminating protocol deviations and highlights the key steps in setting up a quality management system for research operations.

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Driving Success: The Role of a Quality Management System

Featured image by summit art creations.

What is a quality management system (QMS)? More to the point, how will it benefit your business? Read here to find out.

RELATED ARTICLE: THE SIGNIFICANCE OF PRECISION IN BIOANALYTICAL CHEMISTRY

In today’s competitive landscape, achieving and maintaining success hinges on one crucial factor: the quality of your products or services. Whether you’re running a small business in the initial stages of its operation or managing a large business conglomerate with facilities all over the world, it is crucial to continuously deliver quality products and services. Otherwise, you will fail to earn consumers’ confidence and they will not continue to be your customers. Emphatically, repeat customers are key to long-term business success.

However, the fact that businesses merely aim for quality is inadequate. If you want quality to become part of the fabric of your organization’s culture and gain from all the advantages this implies, you need to integrate your production processes with a quality management system (QMS) .

What Is a QMS?

A quality management system is a systematic approach in the form of a structure of policies, processes, and resources. Such a system consistently directs and manages an organization to fulfill the requirements of its stakeholders by delivering quality goods and services. This model is priceless as it serves as a compass in a company. Simply put, it provides a framework for all aspects of the organization’s work, including product design and production, customer relations, and optimization.

What Are the Benefits of Implementing a QMS?

There are myriad advantages when it comes to QMS that are a plus for any organization:

Enhanced customer satisfaction: Organizations use QMS to identify improvement plans that can prevent the company from producing defective products. This enables every organization to deliver quality products and services. Therefore, companies get to enjoy the benefits of enhanced customer satisfaction and loyalty, as well as improved brand image.
Improved operational efficiency: A clear definition of QMS emphasizes the organization and improvement of the various processes. This also increases understanding of which procedures should change and why they should, suporting the goal of consistency. This brings a better flow to the organization’s activities and avoids the wasting of resources, hence cutting costs.
Reduced risk and error: Strict adherence to the guidelines and procedures offered by a QMS noticeably reduces the likelihood of occasional errors or non-conformity. This helps decrease product recalls, safety hazards, and legal issues, thus improving the overall efficiency of the business.
Enhanced employee management: A QMS encourages responsibility and accountability among employees. Every employee is responsible for quality in such a system. They get to embrace active participation in the recognition and definition of quality, hence enhancing general efficiency and motivation.
Competitive advantage: Standardization is a key strategic tool in a world filled with choices. Emphatically, every organization aims to differentiate itself from its competitors. A strong QMS indicates a healthy sign. It assures customers that the company delivers only the best products.

RELATED ARTICLE: ENHANCE PRODUCTIVITY BY IMPROVING EMPLOYEE WELL-BEING

What Are the Key Elements of a QMS?

Several key elements contribute to the effectiveness of a QMS:

Customer focus: This is perhaps the key principle on which any QMS depends. This refers, of course, to the ability to understand and, arguably more importantly, meet customer’s expectations.
Leadership commitment: To ensure effectiveness, top management must be willing and involved in the process to fully support, contribute, and follow.
Process approach: QMS focuses on business processes, meaning it is about the proper organization, management, and optimization of all the processes within the organization.
Continuous improvement: Ideally, a QMS framework promotes ongoing improvement.
Data-driven decision-making: With a QMS in place, managers have access to data and metrics they can use to assess performance, track variations, and make smart decisions that foster improvement.

What Are Some Key Requirements for Implementation?

Establishing a QMS is not one-size-fits-all solution. Instead, the precise requirements of any organization will define which of the frameworks mentioned here will be more appropriate. Additionally, each company will determine how that particular business will utilize that framework. However, here are some general steps you can follow:

Define quality objectives: Repeatedly state the organization’s standard for quality and consumer expectations.
Select a QMS framework: You can select an established framework such as ISO 9001. Alternatively, you could also establish a company-wide system unique to your needs and circumstances.
Develop documentation: Codify the organization’s policies and procedures to align with your chosen framework. Create policies and develop work instructions to support the framework of your choice.
Implement and train: Initiate the QMS and ensure that every employee is trained on the new procedures.
Monitor and improve: Ongoing activities will include monitoring the QMS and its outputs as well as being alert for any necessary changes based on results and feedback.

The End Result of Your Quality Journey Will Be Success

The strategies of establishing a strong QMS are meant not only for compliance but also for the success of an organization. A good QMS creates the foundation for sustainable growth and added competitiveness by nurturing quality, improving organizational capability, and satisfying the company’s customers. Moreover, it is important to note that quality is not fate. It is a business decision based on an intention for the long-term welfare of the organization.

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Full article: Quality 2030: quality management for the future
6.1. General conclusions. Quality 2030 consists of five collectively designed themes for future QM research and practice: (a) systems perspectives applied, (b) stability in change, (c) models for smart self-organising, (d) integrating sustainable development, and (e) higher purpose as QM booster.
Increasing the value of quality management systems
Introduction. Today, more than one million companies and organisations globally are certified in accordance with ISO 9001 (ISO - International Organization for Standardization, 2018 Survey).In organisations' quality management work, a substantial amount of time and focus is given to the quality management systems (QMS) (Elg et al., 2011).Thus, it is important that QMS adds value to the ...
An inexpensive and easy-to-implement approach to a Quality Management
Due to its immense documentation load, the establishment of a quality management system (QMS) certified with ISO 9001 standard 1 in biomedical research labs, like ours (state-funded lab with less than 20 employees, only two of them being permanent positions, one being the lab head, the other a technician), is challenging. This is particularly ...
Quality management
Manage Your Human Sigma. Organizational Development Magazine Article. John H. Fleming. Curt Coffman. James K. Harter. If sales and service organizations are to improve, they must learn to measure ...
Full article: Four decades of research on quality: summarising
The purpose of this paper is to identify and depict the key areas around which research on quality has orbited during the past 37 years. Additionally, this paper aims to explore longitudinal patterns and trends in the identified key areas. ... Impact of quality management systems on firm performance. International Journal of Quality ...
From total quality management to Quality 4.0: A systematic literature
Quality 4.0 is an emerging concept that has been increasingly appreciated because of the intensification of competition, continually changing customer requirements and technological evolution. It deals with aligning quality management practices with the emergent capabilities of Industry 4.0 to improve cost, time, and efficiency and increase product quality. This article aims to comprehensively ...
Quality Management Journal
Quality Management Journal (QMJ) is the first journal to link the efforts of academic researchers and quality management practitioners. QMJ provides a forum for communicating and discussing research findings. New research that scientifically explores the principles of quality management is actively sought for the journal. Suitable Articles.
Quality Management Journal
Description. The Quality Management Journal (QMJ) is a peer-reviewed journal, published by ASQ. It aims to link the efforts of academic researchers and quality management practitioners by publishing significant research relevant to quality management practice and provide a forum for discussion of such research by academics and practitioners.
Management at the service of research: ReOmicS, a quality management
A Quality Management System (QMS) could represent a valid tool for these purposes, improving the quality of the research. ... Management at the service of research: ReOmicS, a quality management ...
10000 PDFs
Explore the latest full-text research PDFs, articles, conference papers, preprints and more on QUALITY MANAGEMENT SYSTEMS. Find methods information, sources, references or conduct a literature ...
Sustainable quality management in hospitals: The experiences of
Moving towards sustainable quality management systems in hospitals requires a good interaction between a bottom-up approach and leadership to ensure continuous support from healthcare stakeholders. Conclusions: This study contributes to existing conceptual and theoretical foundations with practical insights into sustainable quality management.
Implementing quality management systems to close the AI ...
The integration of Quality Management System (QMS) principles into the life cycle of development, deployment, and utilization of machine learning (ML) and artificial intelligence (AI) technologies ...
Quality management in healthcare: The pivotal desideratum
IT-Integrated Health Management Information System and committed leadership facilitates the implementation. The four-step quality model, the plan-do-check-act (PDCA) cycle, also known as the Deming Cycle, is the most widely used tool for continuous quality improvement (CQI). (Fig. 1) Other methods are Six Sigma, Lean and total quality ...
Quality Risk Management Framework: Guidance for Successful
In 2012, the Clinical Trials Transformation Initiative introduced Quality by Design to the industry. 1 Then, in 2013, the European Medicines Agency (EMA) issued its Reflection Paper on Risk-Based Quality Management in Clinical Trials. 2 In 2016, TransCelerate's Clinical Quality Management System: From a Vision to a Conceptual Framework ...
(PDF) Quality Management System and Practices
A quality system comes on top of an organization and is defined as the organizational structure, responsibilities, processes, procedures, and resources for implementing quality management.
Quality Management Journal: Vol 31, No 2 (Current issue)
Explore the current issue of Quality Management Journal, Volume 31, Issue 2, 2024. Browse; Search. Close search. Publish. ... Integrated management systems: Barrier assessment through Grey Incidence Analysis and contributions to quality management ... ISO 9001 research mapping: Trends, findings, and future issues. Tri Rakhmawati et al.
ISO
And achieving this goal requires a foundation of sound quality management systems. An effective quality management system (QMS) provides the means to consistently meet consumer expectations and deliver products and services with minimal waste. In today's highly competitive global economy, having a QMS in place is the prerequisite for ...
A proposal of model for a quality management system in research testing
There is a broad consensus on the importance and advisability of testing laboratories adopting a Quality Management System (QMS) to support their work, no matter they are industrial or research oriented. However, laboratories involved in R&D have specific difficulties to implement a QMS due to the peculiar nature of their activity. This paper analyzes the main challenges and difficulties found ...
What Is a Quality Management System (QMS)?
A quality management system (QMS) is defined as a formalized system that documents processes, procedures, and responsibilities for achieving quality policies and objectives. A QMS helps coordinate and direct an organization's activities to meet customer and regulatory requirements and improve its effectiveness and efficiency on a continuous ...
An overview of Quality Management System implementation in a research
The aim of this paper is to show the advantages of implementing a Quality Management System (QMS) in a research laboratory in order to improve the management of risks specific to research programmes and to increase the reliability of results. This paper also presents experience gained from feedback following the implementation of the Quality ...
The Ever-Changing Life of a Quality Management System?
Quality management systems are indispensable for organizations striving to deliver excellence in their products and services when implemented from the start of the process and not after the fact. Bringing in more work to the shop leads to more jobs, new products, new equipment, and improved employee satisfaction from knowing customers seek ...
Full article: Does quality management system help organizations in
H2a: Quality management system possesses a significant positive effect on corporate environmental sustainability. ... Lean manufacturing and the environment: research on advanced manufacturing systems and the environment and recommendations for leveraging better environmental performance. United States Environmental Protection Agency.
Quality analysis of the clinical laboratory literature and its
(18,19) In this research, the Web of Science (WOS) core collection and PubMed were chosen for their significance to management and medical research. Three keywords were used to determine relevant articles: "quality control" in any of its forms, terms linked to quality processes such as "quality systems," "quality improvement," or ...
Quality management in clinical and public health research: A panacea
A quality management system for clinical and public health research operations is indispensable because it ensures the integrity and reliability of research outcomes. By implementing a robust quality management practice in research implementation and operation, research teams can uphold the highest standard of research conduct, thereby enhancing the credibility and trustworthiness of research ...
Full article: Quantitative assessment of quality management systems
The creation of a quality management system (QMS) based on the ISO 9000 series standards contributes a lot to an enterprise's competitiveness and so to an increase of activity efficiency. Countries of the European Union (EU), as well as those that took the decision to join the EU, are especially interested in providing high-quality products ...
Driving Success: The Role of a Quality Management System
A quality management system is a systematic approach in the form of a structure of policies, processes, and resources. Such a system consistently directs and manages an organization to fulfill the requirements of its stakeholders by delivering quality goods and services. This model is priceless as it serves as a compass in a company.

Increasing the value of quality management systems

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Quality management systems as a tool for daily management

Quality management systems as a support for developing the quality of the offering

Quality management systems as a tool for documentation and standardization

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