Critical thinking may be one of the most overused terms out there. It can mean just about anything anyone wants it to. We keep seeing it popping up in ICH Q9(r1), guidance on data integrity, and many other places. It has really become shorthand for “think better.” So, let us go to the basics and discuss what critical thinking is.
Critical thinking is a multifaceted cognitive process that involves the active and skillful conceptualization, application, analysis, synthesis, and evaluation of information gathered from observation, experience, reflection, reasoning, or communication. It aims to form a judgment or make a decision based on reliable information and rational analysis.
Questioning and Clarifying: Critical thinking begins with questioning the information presented and clarifying the purpose and context of the thinking process. This involves identifying the problem or question and understanding the assumptions and concepts.
Analyzing and Evaluating: This involves breaking down information into constituent parts to understand its structure and meaning. It also includes evaluating the credibility of sources, the validity of arguments, and the relevance and accuracy of the information.
Synthesizing and Interpreting: Critical thinkers synthesize information from various sources to form a coherent understanding. They interpret data and evidence to draw conclusions and make informed judgments.
Awareness of Biases: It is crucial to be aware of one’s own biases and assumptions. Critical thinkers strive to minimize personal biases’ influence and objectively consider alternative viewpoints.
Intellectual Standards: Critical thinking is guided by intellectual standards such as clarity, accuracy, precision, consistency, relevance, sound evidence, good reasons, depth, breadth, and fairness.
Application and Communication: It involves applying the insights gained through critical thinking to real-world problems and effectively communicating the reasoning behind decisions and judgments.
Critical thinking is a vital skill that involves a disciplined approach to analyzing and evaluating information. It is characterized by a commitment to intellectual rigor and a systematic method of questioning, analyzing, and synthesizing information to make well-informed decisions. Developing critical thinking skills is a lifelong endeavor that enhances one’s ability to reason and make judgments.
Critical thinking is something we educate on, not train. We cannot proceduralize critical thinking; we can only create tools to drive the behaviors.
We tend to jumble forms of accountability in an organization, often confusing between a people manager and a technical manager. I think its very important to differentiate between the two.
People managers deal with human resources and team dynamics, while technical managers deal with managing design, execution, and improvement. They can be the same person, but we need to recognize the differences and resource appropriately. Too often we blur the two roles and as a result neither is done well.
Technical understanding of manufacturing systems and equipment
Leadership and communication skills
Certification, degree, or other professional recognition
Several years of experience in pharmaceutical manufacturing
Analytical and validation skills
Risk management and verification skills
Analytical and problem-solving skills
Ability to solve technical problems
Registered with the competent authority in the EU member state
Training and support skills
Continuous improvement and change management skills
Ability to define and monitor KPIs
Authority
Authority to design and install safety systems
Authority to certify batches and ensure compliance
Authority over knowledge management processes and content
Authority to define and verify critical aspects of systems
Authority to make decisions and implement changes in the process
Interaction with Others
Collaborates with production and quality control teams
Works with quality control, assurance, and regulatory teams
Works with various departments to ensure knowledge is shared and utilized
Collaborates with project stakeholders and engineering teams
Communicates with project leaders, process users, and other stakeholders
Examples of Activities
Reviewing batch documentation and certifying products
Certifying each batch of medicinal products before release
Validating new knowledge submissions
Conducting quality risk analyses and verification tests
Defining process objectives and mission statements
Ensuring compliance with GMP and regulatory standards
Ensuring compliance with GMP and regulatory standards
Providing training on knowledge management systems
Reviewing system designs and managing changes
Monitoring process performance and compliance
Overseeing investigations related to quality issues
Overseeing quality control and assurance processes
Updating and maintaining knowledge databases
Leading continuous improvement efforts
Identifying and implementing process improvements
Industry Context
Primarily in construction, manufacturing, and safety-critical industries
Pharmaceutical and biotechnology industries within the EU
Applicable across various industries, especially information-heavy sectors
Primarily in pharmaceutical and biotechnology industries
Applicable in any industry with defined business processes
Comparison table
Qualified Person (OSHA Definition): Focuses on ensuring compliance with safety standards and solving technical problems. They possess technical expertise and professional recognition and are responsible for designing and installing safety systems.
Qualified Person (EU): Ensures that each batch of medicinal products meets all required provisions before release. They are responsible for compliance with GMP and regulatory standards and must be registered with the competent authority in the EU member state.
Knowledge Owner: Manages and disseminates knowledge within an organization. They ensure that knowledge is accurate, up-to-date, and accessible, and they provide training and support to facilitate knowledge sharing.
ASTM E2500 SME: Ensures that manufacturing systems meet quality and safety standards. They define system needs, develop verification strategies, manage risks, and lead continuous improvement efforts.
Process Owner: Manages and optimizes specific business processes. They define process goals, monitor performance, ensure compliance with standards, and implement improvements to enhance efficiency and effectiveness.
All roles require a high level of subject matter expertise in their respective domains, whether it’s technical knowledge, regulatory compliance, manufacturing processes, or business processes.
This expertise is typically gained through formal education, certifications, extensive training, and practical experience.
Ensuring Compliance and Quality
A key responsibility across these roles is ensuring compliance with relevant laws, regulations, standards, and quality requirements.
Risk Identification and Management
These roles are all responsible for identifying potential risks, hazards, or process inefficiencies.
They are expected to develop and implement strategies to mitigate or eliminate these risks, ensuring the safety of operations and the quality of products or processes.
Continuous Improvement and Change Management
They are involved in continuous improvement efforts, identifying areas for optimization and implementing changes to enhance efficiency, quality, and knowledge sharing.
They are responsible for managing change processes, ensuring smooth transitions, and minimizing disruptions.
Authority and Decision-Making
Most of these roles have a certain level of authority and decision-making power within their respective domains.
Collaboration and Knowledge Sharing
Effective collaboration and knowledge sharing are essential for these roles to succeed.
While these roles have distinct responsibilities and focus areas, they share common goals of ensuring compliance, managing risks, driving continuous improvement, and leveraging subject matter expertise to achieve organizational objectives and maintain high standards of quality and safety. They are more similar than dissimilar and should be looked at holistically within the organization.
When we solve problems in the wrong way, we end up creating bigger problems. One of the biggest of these stems from the differences between education and training and how we try to address education deficiencies (real or perceived) in the procedure.
Training: The primary goal of training is to develop specific skills and behaviors that improve performance and productivity in a particular job or task. It is practical and hands-on, focusing on applying knowledge to perform specific tasks effectively. For example, training might involve learning how to use a particular software or operate machinery.
Education: Education aims to provide a broader understanding of concepts, theories, and principles. It is more about acquiring knowledge and developing critical thinking, reasoning, and judgment. Education prepares individuals for future roles and helps them understand the broader context of their work.
For example, in writing a procedure on good documentation practices (GDocP), we might include a requirement to show the work on all calculations except simple. Knowledge of the broader principles of mathematics is education, and a simple calculation is a fundamental building block of mathematics. We now have two choices. We can proceduralize a definition and provide examples of simple calculations, or a basic understanding of mathematics is a prerequisite for doing the work, part of the core competencies.
This example may seem minor, but it quickly builds up. Every time we add an item that should be education to a procedure, we increase the difficulty of using and training on the document. Good documentation practices are a great example because we take some basic ALCOA+ concepts and then give possible permutations, many of which rely on education premises.
To be honest, too often, we perform a risk assessment not to make decisions but to justify an already existing risk assessment. The risk assessment may help define a few additional action items and determine how rigorous to be about a few things. It actually didn’t make much of an impact on the already-decided path forward. This is some pretty bad risk management and decision-making.
For highly important decisions with high uncertainty or complexity, it is useful to consider the options/alternatives that exist and assess the benefits and risks of each before deciding on a path forward. Thoroughly identifying options/alternatives and assessing the benefits and risks of each can help the decision-making process and ultimately reduce risk.
An effective, highly structured decision-making process can help answer the question, ‘How can we compare the consequences of the various options before deciding?
The most challenging risk decisions are characterized by having several different, important things to consider in an environment where there are often multiple stakeholders and, often, multiple decision-makers.
In Multi-Criteria Decision-Making (MCDM), the primary objective is the structured consideration of the available alternatives (options) for achieving the objectives in order to make the most informed decision, leading to the best outcome.
In a Quality Risk Management context, the decision-making concerns making informed decisions in the face of uncertainty about risks related to the quality (and/or availability) of medicines.
Key Concepts of MCDM
Conflicting Criteria: MCDM deals with situations where criteria conflict. For example, when purchasing a car, one might need to balance cost, comfort, safety, and fuel economy, which often do not align perfectly.
Explicit Evaluation: Unlike intuitive decision-making, MCDM involves a structured approach to explicitly evaluate multiple criteria, which is crucial when the stakes are high, such as deciding whether to build additional manufacturing capacity for a product under development.
Multiple-Criteria Evaluation Problems: These involve a finite number of alternatives known at the beginning. The goal is to find the best alternative or a set of good alternatives based on their performance across multiple criteria.
Multiple-Criteria Design Problems: In these problems, alternatives are not explicitly known and must be found by solving a mathematical model. The number of alternatives can be very large, often exponentially.
Preference Information: The methods used in MCDM often require preference information from decision-makers (DMs) to differentiate between solutions. This can be done at various stages of the decision-making process, such as prior articulation of preferences, which transforms the problem into a single-criterion problem.
MCDM focuses on risk and uncertainty by explicitly weighing criteria and trade-offs between them. Multi-criteria decision-making (MCDM) differs from traditional decision-making methods in several key ways:
Explicit Consideration of Multiple Criteria: Traditional decision-making often focuses on a single criterion like cost or profit. MCDM explicitly considers multiple criteria simultaneously, which may be conflicting, such as cost, quality, safety, and environmental impact[1]. This allows for a more comprehensive evaluation of alternatives.
Structured Approach: MCDM provides a structured framework for evaluating alternatives against multiple criteria rather than relying solely on intuition or experience. It involves techniques like weighting criteria, scoring alternatives, and aggregating scores to rank or choose the best option.
Transparency and Consistency: MCDM methods aim to make decision-making more transparent, consistent, and less susceptible to individual biases. The criteria, weights, and evaluation process are explicitly defined, allowing for better justification and reproducibility of decisions.
Quantitative Analysis: Many MCDM methods employ quantitative techniques, such as mathematical models, optimization algorithms, and decision support systems. This enables a more rigorous and analytical approach compared to traditional qualitative methods.
Handling Complexity: MCDM is particularly useful for complex decision problems involving many alternatives, conflicting objectives, and multiple stakeholders. Traditional methods may struggle to handle such complexity effectively.
Stakeholder Involvement: Some MCDM methods, like the Analytic Hierarchy Process (AHP), facilitate the involvement of multiple stakeholders and the incorporation of their preferences and judgments. This can lead to more inclusive and accepted decisions.
Trade-off Analysis: MCDM techniques often involve analyzing trade-offs between criteria, helping decision-makers understand the implications of prioritizing certain criteria over others. This can lead to more informed and balanced decisions.
While traditional decision-making methods rely heavily on experience, intuition, and qualitative assessments, MCDM provides a more structured, analytical, and comprehensive approach, particularly in complex situations with conflicting criteria.
Multi-Criteria Decision-Making (MCDM) is typically performed following these steps:
Define the Decision Problem: Clearly state the problem or decision to be made, identify the stakeholders involved, and determine the desired outcome or objective.
Establish Criteria: Identify the relevant criteria that will be used to evaluate the alternatives. These criteria should be measurable, independent, and aligned with the objectives. Involve stakeholders in selecting and validating the criteria.
Generate Alternatives: Develop a comprehensive list of potential alternatives or options that could solve the problem. Use techniques like brainstorming, benchmarking, or scenario analysis to generate diverse alternatives.
Gather Performance Data: Assess how each alternative performs against each criterion. This may involve quantitative data, expert judgments, or qualitative assessments.
Assign Criteria Weights: By assigning weights, determine each criterion’s relative importance or priority. This can be done through methods like pairwise comparisons, swing weighting, or direct rating. Stakeholder input is crucial here.
Apply MCDM Method: Choose an appropriate MCDM technique based on the problem’s nature and the available data. Some popular methods include: Analytic Hierarchy Process (AHP); Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS); ELimination and Choice Expressing REality (ELECTRE); Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE); and, Multi-Attribute Utility Theory (MAUT).
Evaluate and Rank Alternatives: Apply the chosen MCDM method to evaluate and rank the alternatives based on their performance against the weighted criteria. This may involve mathematical models, software tools, or decision support systems.
Sensitivity Analysis: Perform sensitivity analysis to assess the robustness of the results and understand how changes in criteria weights or performance scores might affect the ranking or choice of alternatives.
Make the Decision: Based on the MCDM analysis, select the most preferred alternative or develop an action plan based on the ranking of alternatives. Involve stakeholders in the final decision-making process.
Monitor and Review: Implement the chosen alternative and monitor its performance. Review the decision periodically, and if necessary, repeat the MCDM process to adapt to changing circumstances or new information.
MCDM is an iterative process; stakeholder involvement, transparency, and clear communication are crucial. Additionally, the specific steps and techniques may vary depending on the problem’s complexity, the data’s availability, and the decision-maker’s preferences.
MCDM Technique
Description
Application
Key Features
Analytic Hierarchy Process (AHP)
A structured technique for organizing and analyzing complex decisions, using mathematics and psychology.
Widely used in business, government, and healthcare for prioritizing and decision-making.
Pairwise comparisons, consistency checks, and hierarchical structuring of criteria and alternatives.
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)
Based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution and the longest geometric distance from the negative ideal solution.
Frequently used in engineering, management, and human resource management for ranking and selection problems.
Compensatory aggregation, normalization of criteria, and calculation of geometric distances.
Elimination and Choice Expressing Reality (ELECTRE)
An outranking method that compares alternatives by considering both qualitative and quantitative criteria. It uses a pairwise comparison approach to eliminate less favorable alternatives.
Commonly used in project selection, resource allocation, and environmental management.
Use of concordance and discordance indices, handling of both qualitative and quantitative data, and ability to deal with incomplete rankings.
Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE)
An outranking method that uses preference functions to compare alternatives based on multiple criteria. It provides a complete ranking of alternatives.
Applied in various fields such as logistics, finance, and environmental management.
Preference functions, visual interactive modules (GAIA), and sensitivity analysis.
Multi-Attribute Utility Theory (MAUT)
Involves converting multiple criteria into a single utility function, which is then used to evaluate and rank alternatives. It takes into account the decision-maker’s risk preferences and uncertainties.
Used in complex decision-making scenarios involving risk and uncertainty, such as policy analysis and strategic planning.
Utility functions, probabilistic weights, and handling of uncertainty.
Investigations of a Dog 