Chang said she’s not surprised by the influence numbers have on behavioral decision-making, but what stands out to her is the robustness of the effect, which was replicated across 21 experiments involving 23,000 randomly selected participants. Despite the significant sample size, the predilection for numbers never wavered, except when numbers were presented in ways that were harder to process. Chang and her co-authors describe the mechanism underlying quantification fixation as “comparison fluency,” or the ease of judging numerical values compared with non-numbers, such as words and pictures.
Katherine Milkman, one of the coauthors, is a scholar I follow pretty closely, and this article seems pretty insightful and I’ll be reading the research this week. Our field has some difficulty here, none no more so in the mixed legacy of Deming on the subject, mostly misinterpretations if you ask me. Mark Graban wrote a great post on that last year.
In complex industries such as aviation and biotechnology, effective communication is crucial for ensuring safety, quality, and efficiency. However, the presence of communication loops and silos can significantly hinder these efforts. The concept of the “Tower of Babel” problem, as explored in the aviation sector by Follet, Lasa, and Mieusset in HS36, highlights how different professional groups develop their own languages and operate within isolated loops, leading to misunderstandings and disconnections. This article has really got me thinking about similar issues in my own industry.
The Tower of Babel Problem: A Thought-Provoking Perspective
The HS36 article provides a thought-provoking perspective on the “Tower of Babel” problem, where each aviation professional feels in control of their work but operates within their own loop. This phenomenon is reminiscent of the biblical story where a common language becomes fragmented, causing confusion and separation among people. In modern industries, this translates into different groups using their own jargon and working in isolation, making it difficult for them to understand each other’s perspectives and challenges.
For instance, in aviation, air traffic controllers (ATCOs), pilots, and managers each have their own “loop,” believing they are in control of their work. However, when these loops are disconnected, it can lead to miscommunication, especially when each group uses different terminology and operates under different assumptions about how work should be done (work-as-prescribed vs. work-as-done). This issue is equally pertinent in the biotech industry, where scientists, quality assurance teams, and regulatory affairs specialists often work in silos, which can impede the development and approval of new products.
Tower of Babel by Joos de Momper, Old Masters Museum
Impact on Decision Making
Decision making in biotech is heavily influenced by Good Practice (GxP) guidelines, which emphasize quality, safety, and compliance – and I often find that the aviation industry, as a fellow highly regulated industry, is a great place to draw perspective.
When communication loops are disconnected, decisions may not fully consider all relevant perspectives. For example, in GMP (Good Manufacturing Practice) environments, quality control teams might focus on compliance with regulatory standards, while research and development teams prioritize innovation and efficiency. If these groups do not effectively communicate, decisions might overlook critical aspects, such as the practicality of implementing new manufacturing processes or the impact on product quality.
Furthermore, ICH Q9(R1) guideline emphasizes the importance of reducing subjectivity in Quality Risk Management (QRM) processes. Subjectivity can arise from personal opinions, biases, or inconsistent interpretations of risks by stakeholders, impacting every stage of QRM. To combat this, organizations must adopt structured approaches that prioritize scientific knowledge and data-driven decision-making. Effective knowledge management is crucial in this context, as it involves systematically capturing, organizing, and applying internal and external knowledge to inform QRM activities.
Academic Research on Communication Loops
Research in organizational behavior and communication highlights the importance of bridging these silos. Studies have shown that informal interactions and social events can significantly improve relationships and understanding among different professional groups (Katz & Fodor, 1963). In the biotech industry, fostering a culture of open communication can help ensure that GxP decisions are well-rounded and effective.
Moreover, the concept of “work-as-done” versus “work-as-prescribed” is relevant in biotech as well. Operators may adapt procedures to fit practical realities, which can lead to discrepancies between intended and actual practices. This gap can be bridged by encouraging feedback and continuous improvement processes, ensuring that decisions reflect both regulatory compliance and operational feasibility.
Case Studies and Examples
Aviation Example: The HS36 article provides a compelling example of how disconnected loops can hinder effective decision making in aviation. For instance, when a standardized phraseology was introduced, frontline operators felt that this change did not account for their operational needs, leading to resistance and potential safety issues. This illustrates how disconnected loops can hinder effective decision making.
Product Development: In the development of a new biopharmaceutical, different teams might have varying priorities. If the quality assurance team focuses solely on regulatory compliance without fully understanding the manufacturing challenges faced by production teams, this could lead to delays or quality issues. By fostering cross-functional communication, these teams can align their efforts to ensure both compliance and operational efficiency.
ICH Q9(R1) Example: The revised ICH Q9(R1) guideline emphasizes the need to manage and minimize subjectivity in QRM. For instance, in assessing the risk of a new manufacturing process, a structured approach using historical data and scientific evidence can help reduce subjective biases. This ensures that decisions are based on comprehensive data rather than personal opinions.
Technology Deployment: . A recent FDA Warning Letter to Sanofi highlighted the importance of timely technological upgrades to equipment and facility infrastructure. This emphasizes that staying current with technological advancements is essential for maintaining regulatory compliance and ensuring product quality. However the individual loops of decision making amongst the development teams, operations and quality can lead to major mis-steps.
To overcome the challenges posed by communication loops and silos, organizations can implement several strategies:
Promote Cross-Functional Training: Encourage professionals to explore other roles and challenges within their organization. This can help build empathy and understanding across different departments.
Foster Informal Interactions: Organize social events and informal meetings where professionals from different backgrounds can share experiences and perspectives. This can help bridge gaps between silos and improve overall communication.
Define Core Knowledge: Establish a minimum level of core knowledge that all stakeholders should possess. This can help ensure that everyone has a basic understanding of each other’s roles and challenges.
Implement Feedback Loops: Encourage continuous feedback and improvement processes. This allows organizations to adapt procedures to better reflect both regulatory requirements and operational realities.
Leverage Knowledge Management: Implement robust knowledge management systems to reduce subjectivity in decision-making processes. This involves capturing, organizing, and applying internal and external knowledge to inform QRM activities.
Combating Subjectivity in Decision Making
In addition to bridging communication loops, reducing subjectivity in decision making is crucial for ensuring quality and safety. The revised ICH Q9(R1) guideline provides several strategies for this:
Structured Approaches: Use structured risk assessment tools and methodologies to minimize personal biases and ensure that decisions are based on scientific evidence.
Data-Driven Decision Making: Prioritize data-driven decision making by leveraging historical data and real-time information to assess risks and opportunities.
Cognitive Bias Awareness: Train stakeholders to recognize and mitigate cognitive biases that can influence risk assessments and decision-making processes.
Conclusion
In complex industries effective communication is essential for ensuring safety, quality, and efficiency. The presence of communication loops and silos can lead to misunderstandings and poor decision making. By promoting cross-functional understanding, fostering informal interactions, and implementing feedback mechanisms, organizations can bridge these gaps and improve overall performance. Additionally, reducing subjectivity in decision making through structured approaches and data-driven decision making is critical for ensuring compliance with GxP guidelines and maintaining product quality. As industries continue to evolve, addressing these communication challenges will be crucial for achieving success in an increasingly interconnected world.
Katz, D., & Fodor, J. (1963). The Structure of a Semantic Theory. Language, 39(2), 170–210.
Dekker, S. W. A. (2014). The Field Guide to Understanding Human Error. Ashgate Publishing.
Shorrock, S. (2023). Editorial. Who are we to judge? From work-as-done to work-as-judged. HindSight, 35, Just Culture…Revisited. Brussels: EUROCONTROL.
Great thought piece on the use of “reputation” in purpose statement, which should include quality policies.
Writing a quality policy is a crucial step in establishing a quality management system within an organization. Here are some best practices to consider when crafting an effective quality policy:
Key Components of a Quality Policy
Management’s Quality Commitment
The Quality Policy reflects top management’s dedication to quality standards. It includes clear quality objectives, resource allocation, regular policy reviews, active participation in quality initiatives, and support for quality-focused training. The quality policy is a lynchpin artifact to quality culture.
Customer-Centric Approach
Identify customer requirements.
Meet customer expectations.
Handle customer feedback.
Improve customer satisfaction.
Track customer experience metrics
Drive for Continuous Improvement
Regularly evaluate process effectiveness, product quality metrics, service delivery standards, employee performance, and quality management systems. Document specific improvement methods and set measurable targets.
Steps to Write a Quality Policy
Define the Quality Vision
Develop a concise and inspiring statement that describes what quality means to your organization and how it supports your mission and values.
Identify Quality Objectives
Align these objectives with your strategic goals and customer needs.
Develop the Quality Policy
Focus on clear, actionable statements that reflect your organization’s quality commitments. Include specific quality objectives, measurement criteria, and implementation strategies.
Communicate the Quality Policy
Ensure all employees understand the policy and their roles in implementing it. Use various channels such as publishing on the company website or displaying in premises.
Implement and Review:
Create a structured implementation timeline with clear milestones. Establish communication channels for ongoing feedback and questions. Make sure employees at all levels are involved. Regularly review and refine the policy to ensure it remains relevant and effective.
Additional Best Practices
Keep it Simple and Relevant: Ensure the policy is easy to understand and aligns with your organization’s strategic direction.
Top Management Involvement: Top management should actively participate in creating and endorsing the policy to demonstrate leadership commitment.
ISO Compliance: If applicable, ensure the policy meets ISO standards such as ISO 9001:2015, which requires the policy to be documented, communicated, and enforced by top management.
By following these guidelines, you can create a quality policy that effectively guides your organization towards achieving its quality goals and maintaining a culture of excellence.
Quality escalation is a critical process in maintaining the integrity of products, particularly in industries governed by Good Practices (GxP) such as pharmaceuticals and biotechnology. Effective escalation ensures that issues are addressed promptly, preventing potential risks to product quality and patient safety. This blog post will explore best practices for quality escalation, focusing on GxP compliance and the implications for regulatory notifications.
Understanding Quality Escalation
Quality escalation involves raising unresolved issues to higher management levels for timely resolution. This process is essential in environments where compliance with GxP regulations is mandatory. The primary goal is to ensure that products are manufactured, tested, and distributed in a manner that maintains their quality and safety.
This is a requirement across all the regulations, including clinical. ICH E6(r3) emphasizes the importance of effective monitoring and oversight to ensure that clinical trials are conducted in compliance with GCP and regulatory requirements. This includes identifying and addressing issues promptly.
Key Triggers for Escalation
Identifying triggers for escalation is crucial. Common triggers include:
Regulatory Compliance Issues: Non-compliance with regulatory requirements can lead to product quality issues and necessitate escalation.
Quality Control Failures: Failures in quality control processes, such as testing or inspection, can impact product safety and quality.
Data Integrity: Significant concerns and failures in quality of data.
Supply Chain Disruptions: Disruptions in the supply chain can affect the availability of critical components or materials, potentially impacting product quality.
Patient Safety Concerns: Any issues related to patient safety, such as adverse events or potential safety risks, should be escalated immediately.
Escalation Criteria
Examples of Quality Events for Escalation
Potential to adversely affect quality, safety, efficacy, performance or compliance of product (commercial or clinical)
•Contamination (product, raw material, equipment, micro; environmental) •Product defect/deviation from process parameters or specification (on file with agencies, e.g. CQAs and CPPs) •Significant GMP deviations •Incorrect/deficient labeling •Product complaints (significant PC, trends in PCs) •OOS/OOT (e.g.; stability)
Product counterfeiting, tampering, theft
•Product counterfeiting, tampering, theft reportable to Health Authority (HA) •Lost/stolen IMP •Fraud or misconduct associated with counterfeiting, tampering, theft •Potential to impact product supply (e.g.; removal, correction, recall)
Product shortage likely to disrupt patient care and/or reportable to HA
•Disruption of product supply due to product quality events, natural disasters (business continuity disruption), OOS impact, capacity constraints
Potential to cause patient harm associated with a product quality event
•Urgent Safety Measure, Serious Breach, Significant Product Compliant, Safety Signal that are determined associated with a product quality event
Significant GMP non-compliance/event
•Non-compliance or non-conformance event with potential to impact product performance meeting specification, safety efficacy or regulatory requirements
Regulatory Compliance Event
•Significant (critical, repeat) regulatory inspection findings; lack of commitment adherence •Notification of directed/for cause inspection •Notification of Health Authority correspondence indicating potential regulatory action
Best Practices for Quality Escalation
Proactive Identification: Encourage a culture where team members proactively identify potential issues. Early detection can prevent minor problems from escalating into major crises.
Clear Communication Channels: Establish clear communication channels and protocols for escalating issues. This ensures that the right people are informed promptly and can take appropriate action.
Documentation and Tracking: Use a central repository to document and track issues. This helps in identifying trends, implementing corrective actions, and ensuring compliance with regulatory requirements.
Collaborative Resolution: Foster collaboration between different departments and stakeholders to resolve issues efficiently. This includes involving quality assurance, quality control, and regulatory affairs teams as necessary.
Regulatory Awareness: Be aware of regulatory requirements and ensure that all escalations are handled in a manner that complies with these regulations. This includes maintaining confidentiality when necessary and ensuring transparency with regulatory bodies.
GxP Impact and Regulatory Notifications
In industries governed by GxP, any significant quality issues may require notification to regulatory bodies. This includes situations where product quality or patient safety is compromised. Best practices for handling such scenarios include:
Prompt Notification: Notify regulatory bodies promptly if there is a risk to public health or if regulatory requirements are not met.
Comprehensive Reporting: Ensure that all reports to regulatory bodies are comprehensive, including details of the issue, actions taken, and corrective measures implemented.
Continuous Improvement: Use escalations as opportunities to improve processes and prevent future occurrences. This includes conducting root cause analyses and implementing preventive actions.
Fit with Quality Management Review
This fits within the Quality Management Review band, being an ad hoc triggered review of significant issues, ensuring appropriate leadership attention, and allowing key decisions to be made in a timely manner.
Conclusion
Quality escalation is a vital component of maintaining product quality and ensuring patient safety in GxP environments. By implementing best practices such as proactive issue identification, clear communication, and collaborative resolution, organizations can effectively manage risks and comply with regulatory requirements. Understanding when and how to escalate issues is crucial for preventing potential crises and ensuring that products meet the highest standards of quality and safety.
Effectiveness checks are a critical component of a robust change management system, as outlined in ICH Q10 and emphasized in the PIC/S guidance on risk-based change control. These checks serve to verify that implemented changes have achieved their intended objectives without introducing unintended consequences. The importance of effectiveness checks cannot be overstated, as they provide assurance that changes have been successful and that product quality and patient safety have been maintained or improved.
When designing effectiveness checks, organizations should consider the complexity and potential impact of the change. For low-risk changes, a simple review of relevant quality data may suffice. However, for more complex or high-risk changes, a comprehensive evaluation plan may be necessary, potentially including enhanced monitoring, additional testing, or even focused stability studies. The duration and scope of effectiveness checks should be commensurate with the nature of the change and the associated risks.
The PIC/S guidance emphasizes the need for a risk-based approach to change management, including effectiveness checks. This aligns well with the principles of ICH Q9 on quality risk management. By applying risk assessment techniques, companies can determine the appropriate level of scrutiny for each change and tailor their effectiveness checks accordingly. This risk-based approach ensures that resources are allocated efficiently while maintaining a high level of quality assurance.
An interesting question arises when considering the relationship between effectiveness checks and continuous process verification (CPV) as described in the FDA’s guidance on process validation. CPV involves ongoing monitoring and analysis of process performance and product quality data to ensure that a state of control is maintained over time. This approach provides a wealth of data that could potentially be leveraged for change control effectiveness checks.
While CPV does not eliminate the need for effectiveness checks in change control, it can certainly complement and enhance them. The robust data collection and analysis inherent in CPV can provide valuable insights into the impact of changes on process performance and product quality. This continuous stream of data can be particularly useful for detecting subtle shifts or trends that might not be apparent in short-term, targeted effectiveness checks.
To leverage CPV mechanisms for change control effectiveness checks, organizations should consider integrating change-specific monitoring parameters into their CPV plans when implementing significant changes. This could involve temporarily increasing the frequency of data collection for relevant parameters, adding new monitoring points, or implementing statistical tools specifically designed to detect the expected impacts of the change.
For example, if a change is made to improve the consistency of a critical quality attribute, the CPV plan could be updated to include more frequent testing of that attribute, along with statistical process control charts designed to detect the anticipated improvement. This approach allows for a seamless integration of change effectiveness monitoring into the ongoing CPV activities.
It’s important to note, however, that while CPV can provide valuable data for effectiveness checks, it should not completely replace targeted assessments. Some changes may require specific, time-bound evaluations that go beyond the scope of routine CPV. Additionally, the formal documentation of effectiveness check conclusions remains a crucial part of the change management process, even when leveraging CPV data.
In conclusion, while continuous process verification offers a powerful tool for monitoring process performance and product quality, it should be seen as complementary to, rather than a replacement for, traditional effectiveness checks in change control. By thoughtfully integrating CPV mechanisms into the change management process, organizations can create a more robust and data-driven approach to ensuring the effectiveness of changes while maintaining compliance with regulatory expectations. This integrated approach represents a best practice in modern pharmaceutical quality management, aligning with the principles of ICH Q10 and the latest regulatory guidance on risk-based change management.
Building a Good Effectiveness Check
To build a good effectiveness check for a change control, consider the following key elements:
Define clear objectives: Clearly state what the change is intended to achieve. The effectiveness check should measure whether these specific objectives were met.
Establish measurable criteria: Develop quantitative and/or qualitative criteria that can be objectively assessed to determine if the change was effective. These could include metrics like reduced defect rates, improved yields, decreased cycle times, etc.
Set an appropriate timeframe: Allow sufficient time after implementation for the change to take effect and for meaningful data to be collected. This may range from a few weeks to several months depending on the nature of the change.
Use multiple data sources: Incorporate various relevant data sources to get a comprehensive view of effectiveness. This could include process data, quality metrics, customer feedback, employee input, etc.
Data collection and data source selection. When collecting data to assess change effectiveness, it’s important to consider multiple relevant data sources that can provide objective evidence. This may include process data, quality metrics, customer feedback, employee input, and other key performance indicators related to the specific change. The data sources should be carefully selected to ensure they can meaningfully demonstrate whether the change objectives were achieved. Both quantitative and qualitative data should be considered. Quantitative data like process parameters, defect rates, or cycle times can provide concrete metrics, while qualitative data from stakeholder feedback can offer valuable context. The timeframe for data collection should be appropriate to allow the change to take effect and for meaningful trends to emerge. Where possible, comparing pre-change and post-change data can help illustrate the impact. Overall, a thoughtful approach to data collection and source selection is essential for conducting a comprehensive evaluation of change effectiveness.
Determine the ideal timeframe. The appropriate duration should allow sufficient time for the change to be fully implemented and for its impacts to be observed, while still being timely enough to detect and address any issues. Generally, organizations should allow relatively more time for changes that have a lower frequency of occurrence, lower probability of detection, involve behavioral or cultural shifts, or require more observations to reach a high degree of confidence. Conversely, less time may be needed for changes with higher frequency, higher detectability, engineering-based solutions, or where fewer observations can provide sufficient confidence. As a best practice, many organizations aim to perform effectiveness checks within 3 months of implementing a change. However, the specific timeframe should be tailored to the nature and complexity of each individual change. The key is to strike a balance – allowing enough time to gather meaningful data on the change’s impact, while still enabling timely corrective actions if needed.
Compare pre- and post-change data: Analyze data from before and after the change implementation to demonstrate improvement.
Consider unintended consequences: Look for any negative impacts or unintended effects of the change, not just the intended benefits.
Involve relevant stakeholders: Get input from operators, quality personnel, and other impacted parties when designing and executing the effectiveness check.
Document the plan: Clearly document the effectiveness check plan, including what will be measured, how, when, and by whom. This should be approved with the change plan.
Define review and approval: Establish who will review the effectiveness check results and approve closure of the change.
Link to continuous improvement: Use the results to drive further improvements and inform future changes.
By incorporating these elements, you can build a robust effectiveness check that provides meaningful data on whether the change achieved its intended purpose without introducing new issues. The key is to make the effectiveness check specific to the change being implemented while keeping it practical to execute.
Determining the effectiveness of a change involves several key steps, as outlined in the provided document and aligned with best practices in change management:
What to Do If the Change Is Not Effective
If the effectiveness check reveals that the change did not meet its objectives or introduced unintended consequences, several steps can be taken:
Re-evaluate the Change Plan: Consider whether the change was executed as planned. Were there any discrepancies or modifications during execution that might have impacted the outcome?
Assess Success Criteria: Reflect on whether the success criteria were realistic. Were they too ambitious or not aligned with the change’s potential impact?
Consider Additional Data Collection: Determine if the sample size was adequate or if the timeframe for data collection was sufficient. Sometimes, more data or a longer observation period may be needed to accurately assess effectiveness.
Identify New Problems: If the change introduced new issues, these should be documented and addressed. This might involve initiating new corrective actions or revising the change to mitigate these effects.
Develop a New Effectiveness Check or Change Control: If the initial effectiveness check was incomplete or inadequate, consider developing a new plan. This might involve revising the metrics, data collection methods, or acceptance criteria to better assess the change’s impact.
Document Lessons Learned: Regardless of the outcome, document the findings and any lessons learned. This information can be invaluable for improving future change management processes and ensuring that changes are more effective.
By following these steps, organizations can ensure that changes are thoroughly evaluated and that any issues are promptly addressed, ultimately leading to continuous improvement in their processes and products.
Investigations of a Dog 