Disclaimer: I have had the privilege of being a former colleague of Jayet’s, and hold him in immense regard.
Mastering Safety Risk Management for Medical and In Vitro Devices by Jayet Moon and Arun Mathew is a comprehensive guide that addresses the critical aspects of risk management in medical and in vitro devices. This book is an essential resource for professionals involved in medical device design, production, and post-market phases, providing a structured approach to ensure product safety and regulatory compliance.
Starting with a solid overview of risk management principles that apply not only to medical devices under ISO13485 but will also teach pharmaceutical folks following ICH Q9 white a bit, this book delivers a heavy dose of knowledge and the benefit of wisdom in applying it.
The book then goes deep into the design assurance process, which is crucial for identifying, understanding, analyzing, and mitigating risks associated with healthcare product design. This foundational approach ensures that practitioners can perform a favorable benefit-risk assessment, which is vital for the safety and efficacy of medical devices.
Strengths
Regulatory Compliance: The authors provide detailed guidance on conforming to major international standards such as ISO 13485:2016, ISO 14971:2019, the European Union Medical Device Regulation (MDR), In Vitro Diagnostic Regulation (IVDR), and the US FDA regulations, including the new FDA Quality Management System Regulation (QMSR).
Risk Management Tools: The book offers a variety of tools and methodologies for effective risk management. These include risk analysis techniques, risk evaluation methods, and risk control measures, which are explained clearly and practically.
Lifecycle Approach: One of the standout features of this book is its lifecycle approach to risk management. It emphasizes that risk management does not end with product design but continues through production and into the post-market phase, ensuring ongoing safety and performance.
The authors, Jayet Moon and Arun Mathew, bring their extensive experience in the field to bear, providing real-world examples and case studies that illustrate the application of risk management principles in various scenarios. This practical approach helps readers to understand how to implement the theoretical concepts discussed in the book. This book is essential for anyone working in medical devices and a good read for other quality life sciences professionals as there is much to draw on here.
I’ve seen my fair share of risk assessments listing data quality or bias as hazards. I tend to think that is pretty sloppy. I especially see this a lot in conversations around AI/ML. Data quality is not a risk. It is a causal factor in the failure or severity.
Data Quality and Data Bias
Data Quality
Data quality refers to how well a dataset meets certain criteria that make it fit for its intended use. The key dimensions of data quality include:
Accuracy – The data correctly represents the real-world entities or events it’s supposed to describe.
Completeness – The dataset contains all the necessary information without missing values.
Consistency – The data is uniform and coherent across different systems or datasets.
Timeliness – The data is up-to-date and available when needed.
Validity – The data conforms to defined business rules and parameters.
Uniqueness – There are no duplicate records in the dataset.
High-quality data is crucial for making informed quality decisions, conducting accurate analyses, and developing reliable AI/ML models. Poor data quality can lead to operational issues, inaccurate insights, and flawed strategies.
Data Bias
Data bias refers to systematic errors or prejudices present in the data that can lead to inaccurate or unfair outcomes, especially in machine learning and AI applications. Some common types of data bias include:
Sampling bias – When the data sample doesn’t accurately represent the entire population.
Selection bias – When certain groups are over- or under-represented in the dataset.
Reporting bias – When the frequency of events in the data doesn’t reflect real-world frequencies.
Measurement bias – When the data collection method systematically skews the results.
Algorithmic bias – When the algorithms or models introduce biases in the results.
Data bias can lead to discriminatory outcomes and produce inaccurate predictions or classifications.
Relationship between Data Quality and Bias
While data quality and bias are distinct concepts, they are closely related:
Poor data quality can introduce or exacerbate biases. For example, incomplete or inaccurate data may disproportionately affect certain groups.
High-quality data doesn’t necessarily mean unbiased data. A dataset can be accurate, complete, and consistent but still contain inherent biases.
Addressing data bias often involves improving certain aspects of data quality, such as completeness and representativeness.
Organizations must implement robust data governance practices to ensure high-quality and unbiased data, regularly assess their data for quality issues and potential biases, and use techniques like data cleansing, resampling, and algorithmic debiasing.
Identifying the Hazards and the Risks
It is critical to remember the difference between a hazard and a risk. Data quality is a causal factor in the hazard, not a harm.
Think of it like a fever. An open wound is a causal factor for the fever, which has a root cause of poor wound hygiene. I can have the factor (the wound), but without the presence of the root cause (poor wound hygiene), the event (fever) would not develop (okay, there may be other root causes in play as well; remember there is never really just one root cause).
Potential Issues of Poor Data Quality and Inadequate Data Governance
The risks associated with poor data quality and inadequate data governance can significantly impact organizations. Here are the key areas where risks can develop:
Decreased Data Quality
Inaccurate, incomplete, or inconsistent data leads to flawed decision-making
Errors in customer information, product details, or financial data can cause operational issues
Poor quality data hinders effective analysis and forecasting
Compliance Failures:
Non-compliance with regulations can result in regulatory actions
Legal complications and reputational damage from failing to meet regulatory requirements
Increased scrutiny from regulatory bodies
Security Breaches
Inadequate data protection increases vulnerability to cyberattacks and data breaches
Financial costs associated with breach remediation, legal fees, and potential fines
Loss of customer trust and long-term reputational damage
Operational Inefficiencies
Time wasted on manual data cleaning and correction
Reduced productivity due to employees working with unreliable data
Inefficient processes resulting from poor data integration or inconsistent data formats
Missed Opportunities
Failure to identify market trends or customer insights due to unreliable data
Missed sales leads or potential customers because of inaccurate contact information
Inability to capitalize on business opportunities due to lack of trustworthy data
Poor Decision-Making
Decisions based on inaccurate or incomplete data leading to suboptimal outcomes, including deviations and product/study impact
Misallocation of resources due to flawed insights from poor quality data
Inability to effectively measure and improve performance
Potential Issues of Data Bias
Data bias presents significant risks across various domains, particularly when integrated into machine learning (ML) and artificial intelligence (AI) systems. These risks can manifest in several ways, impacting both individuals and organizations.
Discrimination and Inequality
Data bias can lead to discriminatory outcomes, systematically disadvantaging certain groups based on race, gender, age, or socioeconomic status. For example:
Judicial Systems: Biased algorithms used in risk assessments for bail and sentencing can result in harsher penalties for people of color compared to their white counterparts, even when controlling for similar circumstances.
Healthcare: AI systems trained on biased medical data may provide suboptimal care recommendations for minority groups, potentially exacerbating health disparities.
Erosion of Trust and Reputation
Organizations that rely on biased data for decision-making risk losing the trust of their customers and stakeholders. This can have severe reputational consequences:
Customer Trust: If customers perceive that an organization’s AI systems are biased, they may lose trust in the brand, leading to a decline in customer loyalty and revenue.
Reputation Damage: High-profile cases of AI bias, such as discriminatory hiring practices or unfair loan approvals, can attract negative media attention and public backlash.
Legal and Regulatory Risks
There are significant legal and regulatory risks associated with data bias:
Compliance Issues: Organizations may face legal challenges and fines if their AI systems violate anti-discrimination laws.
Regulatory Scrutiny: Increasing awareness of AI bias has led to calls for stricter regulations to ensure fairness and accountability in AI systems.
Poor Decision-Making
Biased data can lead to erroneous decisions that negatively impact business operations:
Operational Inefficiencies: AI models trained on biased data may make poor predictions, leading to inefficient resource allocation and operational mishaps.
Financial Losses: Incorrect decisions based on biased data can result in financial losses, such as extending credit to high-risk individuals or mismanaging inventory.
Amplification of Existing Biases
AI systems can perpetuate and even amplify existing biases if not properly managed:
Feedback Loops: Biased AI systems can create feedback loops where biased outcomes reinforce the biased data, leading to increasingly skewed results over time.
Entrenched Inequities: Over time, biased AI systems can entrench societal inequities, making it harder to address underlying issues of discrimination and inequality.
Ethical and Moral Implications
The ethical implications of data bias are profound:
Fairness and Justice: Biased AI systems challenge the principles of fairness and justice, raising moral questions about using such technologies in critical decision-making processes.
Human Rights: There are concerns that biased AI systems could infringe on human rights, particularly in areas like surveillance, law enforcement, and social services.
Perform the Risk Assessment
ICH Q9 (r1) Risk Management Process
Risk Management happens at the system/process level, where an AI/ML solution will be used. As appropriate, it drills down to the technology level. Never start with the technology level.
Hazard Identification
It is important to identify product quality hazards that may ultimately lead to patient harm. What is the hazard of that bad decision? What is the hazard of bad quality data? Those are not hazards; they are causes.
Hazard identification, the first step of a risk assessment, begins with a well-defined question defining why the risk assessment is being performed. It helps define the system and the appropriate scope of what will be studied. It addresses the “What might go wrong?” question, including identifying the possible consequences of hazards. The output of the hazard identification step is the identification of the possibilities (i.e., hazards) that the risk event (e.g., impact to product quality) happens.
The risk question takes the form of “What is the risk of using AI/ML solution for <Process/System> to <purpose of AI/MIL solution.” For example, “What is the risk of using AI/ML to identify deviation recurrence and help prioritize CAPAs?” or “What is the risk of using AI/ML to monitor real-time continuous manufacturing to determine the need to evaluate for a potential diversion?”
We can now identify the specific failure modes associated with AI/ML. This may involve deeep dive risk assessments. A failure mode is the specific way a failure occurs. So in this case, the specific way that bad data or bad decision making can happen. Multiple failure modes can, and usually do, lead to the same hazardous situation.
Make sure you drill down on failure causes. If more than 5 potential causes can be identified for a proposed failure mode, it is too broad and probably written at a high level in the process or item being risk assessed. It should be broken down into several specific failure modes with fewer potential causes and more manageable.
Start with an outline of how the process works and a description of the AI/ML (special technology) used in the process. Then, interrogate the following for potential failure modes:
The steps in the process or item under study in which AI/ML interventions occur;
The process/procedure documentation for example, master batch records, SOPs, protocols, etc.
Current and proposed process/procedure in sufficient detail to facilitate failure mode identification;
These experiments show some preliminary evidence that the color assignment in risk matrices might influence people’s perception of risk gravity, and therefore their decisionmaking with regards to risk mitigation. We found that individuals might be tempted to cross color boundaries when reducing risks even if this option is not advantageous (i.e., the boundary crossing effect). However, this effect was not consistently found when we included exploratory analyses of risk mitigations at different impact levels.
Pending future research replicating these results, the cautious recommendation is that the potential biasing effects of color should be considered alongside the goal of communication. If the purpose of communication is informing individuals in an unbiased way, these findings suggest it might be worth eliminating colors from risk matrices in order to reduce the risk of the boundary-crossing effect. On the other hand, if the goal of communication is to persuade individuals to implement certain risk mitigation actions, it might be that assigning colors so as to elicit the boundary-crossing effect would facilitate this. This could be the case, for example, when designing risk matrices that communicate action standards (i.e., severity level at which risk mitigation should be implemented) (Keller et al., 2009). This advice might be particularly relevant in the case of semiqualitative risk matrices, where color assignment might be arbitrary due to the absence of clear numeric cut-off points separating risk severity categories, and to situations where the users of the risk matrix are expected to be of higher numeracy and not have prior training in the design and use of risk matrices.
Proto, R., Recchia, G., Dryhurst, S., & Freeman, A. L. J. (2023). Do colored cells in risk matrices affect decision-making and risk perception? Insights from randomized controlled studies. Risk Analysis, 43, 2114–2128. https://doi.org/10.1111/risa.14091
Well, that is thought-provoking. I guess I need to start evaluating the removal of a lot of color from SOPs, work instructions, and templates.
I have a little trick when reviewing a Quality Risk Management SOP. I go to the process/procedure map section, and if I see only the illustration from ICH Q9, I know I am looking at an organization that hasn’t actually thought about risk management.
A risk management process needs more than the methodology behind individual risk management (assess, control, review). It needs to include the following:
Risk Plan: How do you manage risk management holistically? Which systems/processes have living risk assessments? What are your planned reviews? What significant initiatives around quality risk management are included?
Risk Register: How do you manage your entire portfolio of risks? Link to quality management review.
Selection of tools, and even more importantly, development of tools.
Mechanisms and tools for risk treatment
Improvement strategy for the quality risk management program. How do we know if the program is working as intended?
How to define, select, and train risk owners
How to engage the appropriate stakeholders in the risk process
Too many quality risk management SOPs do not read like process or procedure. They read like a regurgitation of ICH Q9 or the ISO31000 documents. Neither is a good thing. You must go deeper and create an executable process to govern the system.
The revised ICH Q9 (R1) guideline shifts from “Risk Identification” to “Hazard Identification” to reflect a more precise approach to identifying potential sources of harm (hazards) rather than broadly identifying risks.
Alignment with Risk Assessment Definition: The term “Hazard Identification” is more consistent with the established definition of Risk Assessment, which involves identifying hazards and analyzing and evaluating the associated risks.
Clarity and Precision: By focusing on hazards, the guideline aims to improve the clarity and precision of the risk management process. This helps better understand and assess the potential harms associated with identified hazards, leading to more effective risk management.
Improved Perception and Assessment: The change is expected to enhance how hazards are perceived and assessed, making the risk management process more robust and scientifically grounded. This is particularly important for ensuring patient safety and product quality.
Consistency in Terminology: The revision aims to standardize the terminology used in quality risk management, reducing confusion and ensuring all stakeholders understand the terms and processes involved.
ICH Q9 (r1) Figure 1: Overview of a typical quality risk management process
This small change in terminology can lead to better risk-based decisions by highlighting the need to identify hazards and not risks during the first step of the risk assessment process to remove any distractions about risks that may interfere with the hazard identification activity. When a Risk Assessment team focuses only on identifying hazards, they do not have to think about any related probabilities of occurrence – they only have to consider the potential hazards concerning the risk question under consideration. This is also the case of the severity of harm during hazard identification. There is no need to work to estimate the severity of the harm that may be presented by a hazard that comes later after the hazards have been identified.
