Risk Management in the Clinical Study

With ICH E6(r3) in draft, I think it is important to look at the current state of risk management expectations in a clinical study.

Risk management is an essential part of any clinical study, and is a critical component of the ICH E6 and E8 guidelines for Good Clinical Practice (GCP). These guidelines provide a framework for ensuring the safety and well-being of study participants, as well as the integrity and reliability of the study data. By following the principles outlined in these guidelines, researchers can help to ensure that their study results are reliable and can be used to inform clinical practice.

Through risk management we ensure the four main goals of the GCPs are obtained.

The ICH E6 guideline provides recommendations for the conduct of clinical trials, emphasizing the importance of risk management, specifying that a risk management plan should be developed and implemented for each study. The guideline also provides recommendations for the content of the risk management plan, including the identification of potential risks, the assessment of their likelihood and potential impact, and the development of strategies for managing or mitigating those risks.

Risk management is a key enabler and result of the quality management system.

The ICH E8 guideline, which focuses on the conduct of clinical trials also emphasizes the importance of risk management. It specifies that the risk management plan should include a comprehensive evaluation of the risks associated with the study interventions, as well as a plan for managing or mitigating those risks. The guideline also recommends that the risk management plan be regularly reviewed and updated as needed, to ensure that it continues to effectively address the risks facing the study.

When planning a clinical study, sponsors must carefully consider the potential risks involved and take steps to minimize them. Sources of the risk assessment include performing a thorough literature review to identify any known risks associated with the study interventions, as well as conducting pre-study assessments to identify potential risks specific to the study population. E8 also state sthe importance of a wide variety of stakeholders, including the patient population.

Once the study is underway, it’s important to closely monitor for potential risks and have a plan in place for managing them.

In addition to protecting the safety of study participants, effective risk management is also essential for maintaining the integrity of the data being collected. Risks to the study data might include things like errors in data entry or missing data, which can compromise the validity of the study results. To address these risks, sponsors must have robust quality control measures in place, such as regular data audits and checks for missing or inconsistent data.

Overall, the role of risk management in a clinical study is to ensure the safety and well-being of study participants, while also protecting the integrity of the data being collected. By carefully considering and managing potential risks, researchers can help to ensure that their study results are reliable and can be used to inform clinical practice.

Risk Based Monitoring

Risk-based monitoring is a approach to monitoring the quality of a clinical study that focuses on identifying and addressing potential risks to the study. This approach involves regularly assessing the risks associated with a study and implementing strategies to manage or mitigate those risks.

In a risk-based monitoring approach, the study team typically uses a risk register to identify and assess potential risks to the study, such as the potential for errors in data collection or analysis, or the potential for adverse events in study participants. The team then develops a plan for addressing these risks, which might involve implementing additional quality control measures or training for study staff.

During the study, the team regularly monitors for potential risks and takes action to address them as needed. This might involve conducting regular audits or reviews of the study data to identify potential errors, or monitoring the health and well-being of study participants to identify and address any adverse events.

Overall, the goal of risk-based monitoring is to ensure the quality and integrity of a clinical study by proactively identifying and addressing potential risks. By using a risk-based approach, the study team can help to ensure that the study results are reliable and can be used to inform clinical practice.

Risk Register

A risk register is a document that is used to identify, assess, and track potential risks in a clinical study. It typically includes a list of identified risks, along with information about their likelihood and potential impact, as well as the actions that are being taken to manage or mitigate the risks.

In a clinical study, a risk register might include risks such as the potential for errors in data collection or analysis, the potential for adverse events in study participants, or the potential for the study to be impacted by external factors, such as changes in regulatory requirements.

The purpose of a risk register in a clinical study is to help the study team identify and prioritize potential risks, and to develop strategies for addressing them. By having a clear and comprehensive overview of the risks that a study is facing, the team can take proactive steps to manage or mitigate those risks, and can monitor their progress over time.

Overall, a risk register is an essential tool for managing risks in a clinical study. By providing a clear and comprehensive overview of potential risks, it helps the study team identify and address risks in a proactive and effective way.

Identifying potential risks: The first step in implementing a clinical risk management program is to identify potential risks to the study, such as the potential for errors in data collection or analysis, or the potential for adverse events in study participants. This might involve reviewing the study protocol and data collection tools, consulting with the study team and other stakeholders, and conducting a thorough assessment of the study environment.
Assessing risks: Once potential risks have been identified, the next step is to assess their likelihood and potential impact. This will help to prioritize the risks and determine the appropriate level of response. For example, a risk with a high likelihood and a high potential impact might require more immediate action, while a risk with a low likelihood and a low potential impact might not require as much attention.
Developing strategies for managing risks: Based on the assessment of risks, the next step is to develop strategies for managing or mitigating those risks. This might involve implementing additional quality control measures, providing training to study staff, or conducting regular audits or reviews of the study data. The goal is to develop a comprehensive and effective plan for addressing the identified risks.
Monitoring for potential risks: Once the risk management plan is in place, it’s important to regularly monitor for potential risks and take action to address them as needed. This might involve conducting regular audits or reviews of the study data, or monitoring the health and well-being of study participants. By proactively monitoring for potential risks, the study team can help to ensure the safety and well-being of study participants, as well as the integrity and reliability of the study data.
Follow-up and corrective action: If potential risks are identified during the study, it’s important to take prompt action to address them. This might involve implementing corrective action plans, such as retraining study staff or revising the study protocol. It’s also important to track the progress of these plans and ensure that they are effective in addressing the identified risks. By taking timely and effective action to address potential risks, the study team can help to ensure the safety and well-being of study participants, as well as the integrity and reliability of the study data.

Risk Management in the Clinical Study Process

To summarize, each clinical study should:

Identify Risks

Before the study begins, the sponsor should perform a thorough review of the study protocol, data collection tools, and other study-related documents to identify potential risks to the study.
The cross-functional study team, CROs and other relevant stakeholders, such as the sponsor and regulatory authorities, to identify additional potential risks.
All identified risks should be documented in the study’s risk register.

2. Assess Risks

For each identified risk, assess its likelihood and potential impact on the study.
The risks should be prioritized based on their likelihood and potential impact, with a focus on the highest-priority risks.

3. Manage Risks

For each identified risk, the sponsor should develop a plan for managing or mitigating the risk. This plan should be documented in the study’s risk register.
The plan for managing or mitigating each risk should include specific actions to be taken, as well as the individuals or groups responsible for implementing those actions.

4. Monitor Risks

Regularly monitor key risk indicators and the study for success of the study risk plan and to identify new potential risks and take action to address them as needed. This might involve conducting regular audits or reviews of the study data, or monitoring the health and well-being of study participants.
Any significant risks that arise during the study should be reported to the sponsor and relevant regulatory authorities.

4th GxP Cloud Compliance Summit – September 5-7

I am looking forward to speaking at the GxP Cloud Compliance Summit in Boston in September on Implementing a Lifecycle Risk Management Approach to the Cloud. I’ll be discussing some of my favorite topics:

Best practices to harness a life cycle risk management approach to protect product quality and patient data
What does a living risk assessment look like when key parts of your IT infrastructure is maintained by cloud service providers
How does Q9 R1 impact functional and usage assessments around cloud applications

I am looking forward to meeting and discussing some of the critical questions in our heady embrace of the cloud.

Cloud based GxP systems have shifted in the last few years from “Something I guess we should figure out” to “Well guess we have it now” to “Well that is all I seem to have now.” And where 5 years ago it seemed we were obsessed about the fine details of Open vs Closed systems and what cloud-based applications are, we are now looking at much more mature questions around a risk based strategy that evaluates and ensures appropriate controls around Data Integrity, Privacy, and Security. Through a risk-based approach, we drive activities such as auditing, change control, qualification/validation, and oversight.

I am looking forward to having this discussion with my peers and sharing best practices and experiences. It is only though this type of event that we can grow as a professional.

I hope to see you there.

Build Your Knowledge Base

Engaging with knowledge and Knowledge Management are critical parts of development. The ability to navigate the flood of available data to find accurate information is tied directly to individuals’ existing knowledge and their skills at distinguishing credible information from misleading content.

There is ample evidence that many individuals lack the ability to accurately judge their understanding or the quality and accuracy of their performance (i.e., calibration). To truly develop our knowledge, we need to be engaged in deliberative practice. But to truly calibrate requires feedback, guidance, and coaching that you may not have access to within our organizations. This requires effort and deliberate building of a system and processes.

Information can be found with little mental effort but without critical analysis of its legitimacy or validity, the ease of information can actually work against the development of deeper-processing strategies. It is really easy to go-online and get an answer, but unless learners put themselves in positions to struggle cognitively with an issue, and unless they have occasions to transform or reframe problems, their likelihood of progressing into competence is jeopardized.

Creative teams

The more learners forge principled knowledge in a professional domain, the greater their reported interest in and identity with that field. Therefore, without the active pursuit of knowledge, these individuals’ interest in professional development may wane and their progress toward expertise may stall. This is why I find professional societies so critical, and why I am always pushing people to step up.

My constant goal as a mentor is to help people do the following:

Refuse to be lulled into accepting a role as passive consumers of information, striving instead to be active producers of knowledge
Probe and critically analyze the information they encounter, rather
than accepting quick, simple answers
Forge a meaningful interest in the profession and personal connections to members
of professional communities, instead of relying on moment-by-moment stimulation and superficial relationships

Competence

If we are going to step up to the challenges ahead of us, to address the skill gaps we are seeing, we each need to be deliberate in how we develop and deliberate in how we build our organizations to support development.

Expert Intuition and Risk Management

Saturday Morning Breakfast Cereal source http://smbc-comics.com/comic/horrible

Risk management is a crucial aspect of any organization or project. However, it is often subject to human errors in subjective risk judgments. This is because most risk assessment methods rely on subjective inputs from experts. Without certain precautions, experts can make consistent errors in judgment about uncertainty and risk.

There are methods that can correct the systemic errors that people make, but very few organizations implement them. As a result, there is often an almost universal understatement of risk. We need to keep in mind a few rules about experience and expertise.

Experience is a nonrandom, nonscientific sample of events throughout our lifetime.
Experience is memory-based and we are very selective regarding what we choose to remember,
What we conclude from our experience can be full of logical errors
Unless we get reliable feedback on past decisions, there is no reason to believe our experience will tell us much.

No matter how much experience we accumulate, we seem to be very inconsistent in its application.

Experts have unconscious heuristics and biases that impact their judgment, some important ones include:

Misconceptions of chance: If you flip a coin six times, which result is more likely (H= heads, T= tails): HHHTTT or HTHTTH? They are both equal, but many people assume that because the first series looks “less random” than the second, it must be less likely. This is an example of representativeness bias. We appear to judge odds based on what we assume to be representative scenarios. Human beings easily confuse patterns and randomness.
The conjunction fallacy: We often see specific events as more likely than broader categories of events.
Irrational belief in small samples
Disregarding variance in small samples. Small samples have more random variance that large samples is considered less than it should be.
Insensitivity to prior probabilities: People tend to ignore the past and focus on new information when making subjective estimates.

This is all about overconfidence as an expert, which will consistently underestimate risks.

What are some ways to overcome this? I recommend the following be built into your risk management system.

Pretend you are in the future looking back at failure. Start with the assumption that a major disaster did happen and describe how it happened.
Look to risks from others. Gather a list of related failures, for example, regulatory agency observations, and think of risks in relation to those.
Include Everyone. Your organization has numerous experts on all sorts of specific risks. Make the effort to survey representatives of just about every job level.
Do peer reviews. Check assumptions by showing them to peers who are not immersed in the assessment.
Implement metrics for performance. The Brier score is a way to evaluate the result of predictions both by how often the team was right and by the probability the estimated for getting a correct answer.

The Challenges Ahead for Quality

Discussions about Industry 4.0 and Quality 4.0 often focus on technology. However, technology is just one of the challenges that Quality organizations face. Many trends are converging to create constant disruption for businesses, and the Quality unit must be ready for these changes. Rapid changes in technology, work, business models, customer expectations, and regulations present opportunities to improve quality management but also bring new risks.

The widespread use of digital technology has raised the expectations of stakeholders beyond what traditional quality management can offer. As the lines between companies, suppliers, and customers become less distinct, the scope of quality management must expand beyond the traditional value chain. New work practices, such as agile teams and remote work, are creating challenges for traditional quality management governance and implementation strategies. To remain relevant, Quality leaders must adapt to these changes..

The traditional Value Proposition of quality management is no longer sufficient to meet the expectations of stakeholders. With the rise of a digitally native workforce, there are new expectations for how work is done and managed. Business leaders expect quality leaders to have full command of operational data, diagnosing and anticipating quality problems. Regulators also expect high data transparency and traceability.

The value proposition of quality management lies in predicting problems rather than reacting to them. The primary objective of quality management should be to find hidden value by addressing the root causes of quality issues before they manifest. Quality organizations who can anticipate and prevent operational problems will meet or exceed stakeholder expectations.

Our organizations are on a journey towards utilizing predictive capabilities to unlock value, rather than one that retroactively solves problems. Our scope needs to be based on quality being predictive, connected, flexible, and embedded. For me this is the heart of Qualty 4.0.

Quality management should be applied across a multitude of systems, devices, products, and partners to create a seamless experience. This entails transforming quality from a function into an interdisciplinary, participatory process. The expanded scope will reach new risks in an increasingly complex ecosystem. The Quality unit cannot do this on its own; it’s all about breaking down silos and building autonomy within the organization.

To achieve this transformation, we need to challenge ourselves to move beyond top-down and regimented Governance Models and Implementation Strategies. We need to balance our core quality processes and workflows to achieve repeatability and consistency while continually adjusting as situations evolve. We need to build autonomy, critical thinking, and risk-based thinking into our organizational structures.

One way to achieve this is by empowering end-users to solve their own quality challenges through participatory quality management. This encourages personal buy-in and enables quality governance to adapt in real-time to different ways of working. By involving end-users in the process of identifying and solving quality issues, we can build a culture of continuous improvement and foster a sense of ownership over the quality of our products and services.

The future of quality management lies in being predictive, connected, flexible, and embedded.

Predictive: The value proposition of quality management needs to be predicting problems over problem-solving.
Connected: The scope of quality management needs to extend beyond the value chain and connect across the ecosystem
Flexible: The governance model needs to be based on an open-source model, rather than top-down.
Embedded: The implementation strategy needs to shift from viewing quality as a role to quality as a skill.

By embracing these principles and involving all stakeholders in the process of continuous improvement, we can unlock hidden value and exceed stakeholder expectations.

Deaing with these challenges and implications requires the Quality organization to treat transformation like a Program. This program should have four main initiative areas:

Build the capacity for targeted prevention through targeted data insights. This includes building alliances with IT and other teams to have the right data available in flexible ways but it also includes the building of capacity to actually use the data.
Expand quality management to cover the entire value network.
Localize Risk Management to Make Quality Governance Flexible and Open Source.
Distribute Tasks and Knowledge to Embed Quality Management in the Business.

Across these pillars the program approach will:

Assess the current state: Identify areas requiring attention and improvement by examining existing People, Processes, Policies and Platforms. This comprehensive assessment will provide a clear understanding of the organization’s current situation and help pinpoint areas where projects can have the most significant impact
Establish clear objectives: Establish clear objectives to h provide a clear roadmap for success.
Prioritize foundational elements: Prioritize building foundational elements. Avoid bells-and-whistles for their own sake.
Develop a phased approach: This is not an overnight process. Develop a phased approach that allows for gradual implementation, with clear milestones and measurable outcomes. This ensures that the organization can adapt and adjust as needed while maintaining ongoing operations and minimizing disruptions.
Collaborate with stakeholders: Engage stakeholders from across the organization,to ensure alignment and buy-in. Create a shared vision for the initiative to ensure that everyone is working towards the same goals. Regular communication and collaboration among stakeholders will foster a sense of ownership and commitment to the transformation process.
Continuously monitor progress: Regularly review the progress, measuring outcomes against predefined objectives. This enables organizations to identify any potential issues or roadblocks and make adjustments as necessary to stay on track. Establishing key performance indicators (KPIs) will help track progress and determine the effectiveness of the Program.
Embrace a culture of innovation: Encourage a culture that embraces innovation and continuous improvement. This helps ensure that the organization remains agile and adaptive, making it better equipped to take advantage of new technologies and approaches as they emerge. Fostering a culture of innovation will empower employees to seek out new ideas and solutions, driving long-term success.
Invest in employee training and development: It is crucial to provide employees with the necessary training and development opportunities to adapt to new technologies and processes. This will ensure that employees are well-equipped to handle the changes brought about by these challenges and contribute to the organization’s overall success.
Evaluate and iterate: As the Program unfolds, it is essential to evaluate the results of each phase and make adjustments as needed. This iterative approach allows organizations to learn from their experiences and continuously improve their efforts, ultimately leading to greater success.

To do this leverage the eight accelerators to change.

Challenge	Means	Impact to Quality Management	How to Prepare
Advanced Analytics	The increase in data sources and improved data processing has led to higher expectations from customers, regulators, business leaders, and employees. They expect companies to use data analytics to provide advanced insights and improve decision-making.	Requires a holistic approach that allows quality professionals to access, analyze and apply insights from structured and unstructured data Quality excellence will be determined by how quickly data can be captured, analyzed, shared and applied	Develop a talent strategy to recruit, develop, rent or borrow individuals with data analytics capabilities, such as data science, coding and data visualization
Hyper-Automation	To become more efficient and agile in a competitive market, companies will increasingly use technologies like RPA, AI, and ML. These technologies will automate or enhance tasks that were previously done by humans. In other words, if a task can be automated, it will be.	How to ensure these systems meet intended use and all requirements Algorithm-error-generated root causes	Develop a hyperautomation vision for quality management that highlights business outcomes and reflects the use cases of relevant digital technology Perform a risk-based assessment with appropriate experts to identify critical failure points in machine and algorithm decision making
Virtualization of Work	The shift to remote work due to COVID-19, combined with advancements in cloud computing and AR/VR technology, will make work increasingly digital.	Rethink how quality is executed and governed in a digital environment.	Evaluate current quality processes for flexibility and compatibility with virtual work and create an action plan. Uncover barriers to driving a culture of quality in a virtual working environment and incorporate virtual work-relevant objectives, metrics and activities into your strategy.
Shift to Resilient Operations	Prioritizing capabilities that improve resilience and agility.	Adapt in real-time to changing and simultaneously varying levels of risk without sacrificing the core purpose of Quality	Enable employees to make faster decisions without sacrificing quality by developing training to build quality-informed judgment and embedding quality guidance in employee workflows. Identify quality processes that may prevent operational resilience and reinvent them by starting from scratch, ruthlessly challenging the necessity of every step and requirement. Ensure employees and new hires have the right skill sets to design, build and operate a responsive network environment.
Rise of Inter-connected Ecosystems	The growth of interconnected networks of people, businesses, and devices allows companies to create value by expanding their systems to include customers, suppliers, partners, and other organizations.	Greater connectivity between customers, suppliers, and partners provides more visibility into the value chain. However, it also increases risk because it can be difficult to understand and manage different views of quality within the ecosystem.	Map out the entire quality management ecosystem model and its participants, as well as their interactions with customers. Co-develop critical-to-quality behaviors with strategic partners. Strengthen relationships with partners across the ecosystem to capture and leverage relevant information and data, while at the same time addressing data privacy concerns.
Digitally Native Workforce	Shift from digital immigrants (my generation and older) to digital natives who are those people who have grown up and are comfortable with computers and the internet. Unlike other generations, digital natives are so used to using technology in all areas of their lives that it is (and always has been) an integral, necessary part of their day-to-day.	Increased flexibility leads to a need to rethink the way we monitor, train, and incentivize quality. Connecting the 4 Ps: People, Processes, Policies and Platforms	Identify and target existing quality processes to digitize to offer desired flexibility. Adjust messages about the importance of quality to connect with values employees care about (e.g., autonomy, innovation, social issues).
Customer Expectation Multiplicity	Customer expectations evolve quickly and expand into new-in-kind areas as access to information and global connectedness increases.	Develop product portfolios, internal processes and company cultures that can quickly adapt to rapidly changing customer expectations for quality.	Identify where hyperautomation and predictive capabilities of quality management can enhance customer experience and prevent issues before they occur.
Increasing Regulatory Complexity	The global regulatory landscape is becoming more complex as countries introduce new regulations at different rates. Increased push for localization.	Need strong system to efficiently implement changes across different systems, locations, and regions while maintaining consistent quality management throughout the ecosystem.	Coordinate a structured regulatory tracking approach to monitor changing regulatory developments — highly regulated industries require a more comprehensive approach compared to organizations in a moderate regulatory environment

Category: system lifecycle