Quality Governance in Biomedical Research

The term governance is derived from the Greek verb kubernaein [kubernáo] (meaning to steer), and its first metaphorical use (to steer people) is attributed to Plato. Subsequently the term gave rise to the Latin verb gubernare, and from there it was taken over in different languages. It is only since the 1990s that the term governance is used in a broad sense encompassing different types of activities in a wide range of public and private institutions and at different levels (European Commission, http://ec.europa.eu/governance/docs/doc5_fr.pdf).

As a result of the broad application of the term governance, there are multiple definitions, among which the following is a good example (http://www.businessdictionary.com/definition/governance.html): “Establishment of policies, and continuous monitoring of their proper implementation, by the members of the governing body of an organization. It includes the mechanisms required to balance the powers of the members (with the associated accountability), and their primary duty of enhancing the prosperity and viability of the organization.”

In simple terms, governance is (a) a means to monitor whether you are on a good path to achieve the intended outcomes and (b) a means to steer in the right direction so that you proactively prevent risks turning into issues. It is almost like looking in the mirror every morning and making sure you look ok for whatever your plans are that day, or like watching your diet and getting regular exercise in order to keep your cholesterol levels under control.

How does this translate into quality governance? Quality simply means fitness for purpose; in other words the end product of your work should be fit for the purpose it is meant to serve. In experimental pharmacology, this means that your experimental outcomes should be adequate for supporting conclusions and decisions such as on the validity of a molecular target for a novel treatment approach, on the generation of a mode of action hypothesis for a new drug, or on the safety profile of a pharmaceutical ingredient. The different activities you undertake from planning of your experiment, generating the raw data, processing these data to ultimately reporting experimental outcomes and conclusions should be free from bias and should be documented in a way that allows full reconstruction of the experiment.

Quality governance in the context of this book means potential ways in which institutions can monitor research data quality over time and have a mechanism in place to detect and to deal with signals of drift. The purpose and concept of quality governance can be visualized such as in Fig. 1.

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The definition of quality governance implies that, although very important, having policies or guidelines for good research practices is not sufficient. Such documents can be seen as an important building block for good quality research data. However, policies or guidelines will not reach their full impact potential and will not be sustainable over time when the monitoring component of governance is missing. Three aspects of governance are equally important: (1) there needs to be a mechanism to check whether people are applying the guidelines, (2) there needs to be a mechanism to make sure the guidelines remain adequate over time, and (3) there needs to be a mechanism to take the right actions when deviations are seen in (1) and (2). Having these three aspects of governance in place is expected to increase the likelihood of long-term sustainability and full engagement by those who are expected to apply the guidelines.

Common to all effective quality governance systems is the attention to both cultural (acceptance, engagement, sense of responsibility, etc.) and technical (guidelines, procedures, equipment, research data storage, etc.) aspects of quality. It is definitely worth investing time in effective quality governance system as it will help achieve quality research data (data fit for their purpose). Quality data lead to quality decision-making (for acceptance of the best publications or for grants to be given to the best project proposals or for investing in the best active pharmaceutical ingredient). All together quality research data will ultimately lead to unlocking the best possible innovation potential in order to help address unmet medical needs (Fig. 2).

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The size and type of organization (university, biotech, pharma, contract research, etc.) and the type of work that is being conducted (exploratory, confirmatory, in vivo, in vitro, etc.) will determine the more specific quality governance needs. The next parts of this chapter are meant to offer a stepwise and practical approach to install an effective and tailor-made quality governance approach.

How do you know where your lab or your institution is positioned respective to the different data quality zones schematically introduced in Fig. 1? The first thing to do is to define what success means in your specific situation. In quality management terms, success is about making sure you have controlled the risks that matter to your organization. Therefore, one may start with clearly defining the risks that matter. “What is at stake?” is a good question to trigger this thinking.

After having looked in the quality mirror, you can ask yourself: “Do I like what I see?”

Like many things in life, quality is to be considered a journey rather than a destination and requires a continued level of attention. Therefore, it is very likely that your assessment outcome indicates that small or large adjustments are advisable.

It is important to realize that quality cannot be the responsibility of one single person or an isolated group of quality professionals in your organization. Quality is really everyone’s responsibility and should be embedded in the DNA of both people and processes. The right people need to be involved in order to have true impact.

Therefore, after analyzing your quality image, it is important to articulate very clearly what you see, why improvement is needed, and who needs to get involved. Also, one should not forget to bring a well-balanced message, adapted to the target audience, and remember to emphasize what is already good.

Another common mistake is to think that research quality principles can be installed just by writing a policy, generating procedures and guidelines, and organizing training sessions, so everyone knows what to do. This may look good on paper and may even be designed to be lean and fit-for-purpose. However, building quality into the everyday activities and at all levels requires to go beyond work instructions and policies. It is the emotional connection that is the basis of success (Fig. 3) since this will make sure that quality is built into the thinking and actions of everyone involved. One can never foresee all potential situations and exceptions in a procedure, but having people’s mindset right will trigger the right behavior in any situation.

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As an example, a campaign appealing to scientist’s creativity can result in very engaging visuals that can be displayed in labs and corridors (example in Fig. 4) and will become the talk of the town. Positivity, creativity, and fun are key elements in building a research quality culture.

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It is highly likely that during your initial analysis, you have met individuals who were well aware of (parts of) the gaps and have shown interest and energy to participate in finding solutions. Chances are high that they have already started taking steps toward improvement. It is key to involve these people as you move on.

It is also likely that your initial analysis showed that, in certain parts of the organization, people have already put in place solutions for gaps that still exist in other parts of the organization. These can serve as best practice examples going forward.

Also, you may have come across some very critical people, especially those who are afraid of additional workload and bureaucracy. Although this may sound counterintuitive, it is of high value to also include this type of people in your team or to reach out to them at regular intervals with proposed (draft) solutions and ask for their input.

The gaps, best practices, and route causes defined during the initial analysis will be the starting point for change. Remember that route causes are not necessarily always of technical nature. Cultural habits or assumptions may be equally or even more important and will often take more time and effort to resolve.

It is also important to try not to change everything at once. It is better to take small incremental steps and focus on implementing some quick wins first.

Apart from prioritizing the topics that need to be tackled, it is good practice to define roles and responsibilities as well as a communication plan. For a large organization, it may be helpful to agree on a governance model for which an example is given in Fig. 5.

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Change is never an easy journey, and awareness on change management principles will be helpful to achieve the ultimate goal. On the web there is a lot of useful material on change management, such as the Kotter’s 8-step change model (https://www.mindtools.com/pages/article/newPPM_82.htm) that explains the hard work required to change an organization successfully. Other useful references are the RQA (Research Quality Association) booklet “Quality in research: guidelines for working in non-regulated research” that can be purchased via the RQA website (https://www.therqa.com/resources/publications/booklets/quality-in-research-booklet/) and the RQA quality systems guide that can be downloaded for free (https://www.therqa.com/resources/publications/booklets/Quality_Systems_Guide/). Careful planning and building the proper foundations is key. Equally important is creating a sense of urgency and effective communication (real examples, storytelling). Having some quick wins will help to build on the momentum and increase enthusiasm.

Interactive and fun activities during training sessions and hands-on workshops are generally well appreciated by the scientists. As an example, a quiz can be built into the training session during which scientists can work in small teams and get a small prize at the end of the session.

After having led your organization through the change curve, you can sit back and enjoy the change you had envisioned. However, don’t stay in this mode for too long. As mentioned before, quality is a journey, not a destination.

Today, it may look as if your solutions are embedded in the way people work and have become the new norm. However, 2 years from now, your initial solutions may no longer work for new technologies, newly recruited staff may not have gone through the initial roll out and training, or new needs may show up for which new solutions are required.

For a quality management system to be sustainable over time, it needs to have a built-in continuous improvement mechanism, such as described by the PDCA (plan–do–check–act), also known as the Deming cycle (Deming 1986) and visualized in Fig. 6.

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First, the quality solutions need to be sustainably integrated in the way of working with clear roles and responsibilities (DO). In accordance with what has been established in the previous section (Fixing your quality image), clear quality expectations in the form of policies, best practices, or guidelines should be available to the organization, and mechanism for training of new employees and for refresher trainings of existing employees should be in place.

Ideally, all scientists advocate and apply these best practice solutions and also communicate expectations and monitor their application in their external collaborations.

Secondly, there needs to be a mechanism to monitor adherence to the green zone of Fig. 1 (CHECK). When having expectations in place, it is not sufficient to just assume that everyone will now follow them. There may be strong quantitative pressures (time) that tend to make people act differently and may lead to quality being compromised. If people know that there is a certain likelihood that their work will be checked, this will provide a counterbalance for such quantitative pressures. Recent simulations support the idea that a detailed scrutiny of a relatively small proportion of research output could help to affordably maintain overall research quality in a competitive research world where quantity is highly prized (Barnett et al. 2018).

Who performs these checks may depend on the type of organization. For smaller organizations, there can be a self-assessment checklist, or a person within the lab can be assigned to do spot checks for a percentage of their time. In larger organizations, there can be an independent person or group of people (e.g., belonging to the quality department) that can perform spot checks. The benefit of a person performing these checks across different labs can be that certain practices that work well within one lab can be identified and shared with another lab that is in need of a practical solution. Such a person can also serve as go-to person for advice (e.g., on how to deal with specific situation). Whoever is performing checks, it is important to build in an element of positive reinforcement, making sure that people are recognized for applying best practices and not feel blamed when something went wrong. Also, it is important to make people “part of” the exercise and discuss any unclarities with them. This will maximize the learning opportunity as it is much more powerful to say “I am trying to reconstruct this experiment, but I am having some difficulties for which I would like to ask for your help” than to say “I noticed you forgot to document your 96-well plate layout.” When performed in the right way, these checks can be a learning exercise and provide a counter-incentive for time pressure. To clarify the latter point, in contrast to quantitative metrics (such as the number of publications or the number of projects completed within business timelines), most institutions are missing metrics on data quality. When checks are performed on agreed upon quality standards (such as timely recording of data or data exclusion criteria setting before experimental conduct), the outcomes of these checks can be used as a metric for data quality. This way, the common quantitative metrics will no longer be the only drivers within a research lab, and as such, quality metrics can provide a platform to discuss route causes for poor quality data such as time pressure.

It is also worth to consider measuring the “quality culture” or “research climate” in your institution. For this purpose, the SOuRCE (Survey of Organizational Research Climate) (Baker 2015) may be useful. This is a validated 32-question survey for which responses have been shown to correlate with self-reported behavior in the conduct of research. As such, it can provide a snapshot of the research climate in an organization through the aggregated perspectives of its members.

Other applicable indicators of quality that can be considered are, for example, the degree to which (electronic) lab notebooks are used and reviewed within preset timelines or the attendance of trainings.

Besides checking whether expectations are being followed, it is equally important to make it clear to people that the quality expectations are not all carved in stone and, for good reasons, some expectations may need to be refined, altered, deleted, or added over time. Everyone should feel empowered and know how to suggest a justified change to the quality expectations. Changes to quality expectation should not only be considered to prevent drift into the “not acceptable” zone but also to avoid or course-correct when drifting into the “overengineering zone.” The latter is often forgotten. Overengineering can have different causes: overinterpretation of guidelines, mitigations staying in place while the risks have disappeared (e.g., QC checks still occurring long after automation took place), solutions that have been put in place being so complex that no one is following them, etc. Conversations are the best way to detect overengineering. Such conversations can be triggered by people voicing their frustration or coming with critical questions or suggestions on certain expectations. Root-cause conversations can also be started when finding multiple times the same issues during audits or a survey can be sent out from time to time to get feedback on feasibility of quality expectations.

Third, whenever drift is seen, or when a suggestion comes up to change a certain quality expectation, there must be a mechanism to react and make decisions (ACT).

Monitoring outcomes, be it from research data spot checks or from a cultural survey, should be communicated and analyzed. If needed, follow-up actions should be defined and implemented by responsible person(s).

Last but not least, the culture of quality needs to be kept alive, and expectations need to be updated as required (PLAN). For this purpose, regular communication on the importance of quality is crucial. This can be achieved in various ways, for example, by putting posters next to copy or coffee machines, by highlighting best practices during group meetings, by e-mailing relevant publications, or by inviting external speakers. Messages coming from mentors and leaders are most impactful in this respect as well as having people recognize that these leaders themselves are “walking the walk and talking the talk.”

Quality governance, when installed successfully, can be a way to provide simple and sustainable solutions that facilitate data quality and promote innovation. The basic principles of quality governance are very similar across different disciplines; however, the practical application of quality governance is dependent on multiple variables. Currently, existing guidance on quality governance for research is limited and fragmented. As a result, institutions may have policies or guidelines in place, but there is often no mechanism to monitor their application. An exception is the animal care and use aspect of research where there is legislation as well as internal and external oversight bodies (see chapter “Good Research Practice: Lessons from Animal Care and Use”). Recently, the IMI project EQIPD (European Quality in Preclinical Data, https://quality-preclinical-data.eu/) has assembled a team of both industrial and academic researchers and quality professionals to work on practical solutions to improve preclinical data quality. One of their deliverables is a tool to help institutions set up a fit-for-purpose quality system including governance aspects, aligned with the information in this chapter. Until the team has delivered their tool, we hope the guidance provided above can be of help for institutions for bringing their research data quality to the right level.