Section 17.2: Continuous Quality Improvement (CQI) Methodologies

17.2.1 The “Why”: Moving Beyond “Let’s Try This” to a System of Improvement

As a pharmacist, you are a natural problem-solver. Every day, you engage in informal quality improvement. When a patient’s blood pressure isn’t at goal, you don’t simply refill the same prescription; you investigate. You ask about adherence, side effects, and diet. You might recommend a dose increase, the addition of a new agent, or a change in therapy altogether. You then schedule a follow-up to see if your change worked. This iterative process of assessment, intervention, and reassessment is the scientific method applied to a single patient. It is an informal, small-scale cycle of improvement.

While this approach is perfect for individual patient care, it falls short when you need to improve a process, a workflow, or the outcomes of an entire patient panel. Simply having a “good idea” and implementing it broadly—the “let’s try this and see what happens” method—is fraught with peril. What if the idea doesn’t work? How do you know it was your idea that caused the change, and not something else? What unintended consequences might arise? How do you convince others to adopt your change? To address these challenges, we must move from informal, individual problem-solving to a formal, systematic methodology for improvement. This is the world of Continuous Quality Improvement (CQI).

CQI is not a new or radical concept; it is the rigorous application of the scientific method to how we work. It provides a structured framework for identifying problems, testing potential solutions on a small scale, studying the results, and then deciding whether to adopt, adapt, or abandon the change. It replaces guesswork with data, and opinion with evidence. For the collaborative practice pharmacist, mastering a CQI methodology like the Plan-Do-Study-Act (PDSA) cycle is a superpower. It allows you to take your clinical insights and transform them into durable, scalable, and measurable improvements in care. It gives you a common language to work with nurses, physicians, administrators, and other stakeholders to effect meaningful change. This section will teach you how to become a scientist of your own practice.

17.2.2 The Engine of Improvement: A Deep Dive into the Plan-Do-Study-Act (PDSA) Cycle

The Plan-Do-Study-Act (PDSA) cycle, also known as the Deming Cycle, is the fundamental engine of most modern quality improvement initiatives. It is an elegant, four-stage scientific method for testing changes in complex systems. Its power lies in its iterative nature and its emphasis on starting small. Rather than betting the farm on a large, untested change, the PDSA cycle allows you to run small, rapid experiments, learn from them, and gradually build toward a robust, evidence-based solution. We will now dissect each phase of the cycle in the detail required for a clinical professional.

Phase 1: PLAN – The Blueprint for Change

This is the most critical and often most time-consuming phase. A well-constructed plan is the foundation for a successful test of change. A rushed plan almost guarantees a failed cycle. This phase is about being a meticulous architect before becoming a builder.

Masterclass Table: Deconstructing the “PLAN” Phase

Phase 2: DO – Executing the Test

This phase is about action. The goal is to carry out the test of change as designed in the planning phase. The key here is adherence to the plan while also being a keen observer of reality. No plan survives first contact with the real world, and the deviations and unexpected events are often where the most valuable learning occurs.

During the “Do” phase, your primary responsibilities are:

• Carry out the change: Follow the protocol you designed in the “Plan” phase as closely as possible.
• Collect the data: Use the data collection sheet you created. Record the quantitative measures (the numbers) diligently.
• Be an anthropologist – observe and document: This is just as important as collecting the numbers. Keep a log. What worked well? What was awkward? What problems did you encounter that you didn’t anticipate? For our no-show example, you might note: “Three out of ten patients didn’t answer the phone. Had to leave a voicemail. Is that as effective?” or “Dr. Smith’s MA was confused about the new process and forgot to flag a referral for me.” This qualitative data is gold.

Phase 3: STUDY – Analyzing the Results

In this phase, you put on your data analyst hat. The goal is to turn the raw data and observations from the “Do” phase into knowledge. This involves analyzing the data, comparing it to your predictions, and summarizing what you’ve learned.

Key activities in the “Study” phase:

• Aggregate your data: Compile the numbers from your data collection sheet. For our example:
  • Outcome: “Of the 10 patients in the test group, only 1 was a no-show. This is a 10% no-show rate, compared to our baseline of 25%.”
  • Process: “We successfully contacted 7 of the 10 patients (70%) within 24 hours. The other 3 received voicemails.”
  • Balancing: “The calls took an average of 6 minutes per patient, for a total of one hour of pharmacist time.”
• Compare results to predictions: Did what happened match what you expected to happen? “Our hypothesis was that the calls would reduce no-shows, and they did. The rate dropped from 25% to 10% in our small sample.”
• Analyze the qualitative data: Review your observation log. “The learning that voicemails may be less effective and that the MA workflow needs to be clearer are critical findings.”
• Summarize the learnings: Synthesize everything into a few key takeaways. “The personal phone call appears to be a highly effective intervention. However, we need a better process for patients we can’t reach directly, and we must provide better training for the MAs to ensure the workflow is reliable.”

Phase 4: ACT – Deciding What’s Next

Based on what you learned in the “Study” phase, the team must now make a decision. This is not simply a “pass/fail” judgment. There are three distinct paths you can take, and each one represents progress.

17.2.3 Foundational QI Tools: Seeing the Problem Clearly

To effectively “Plan” a test of change, you first need a deep understanding of your current state. Two foundational QI tools are invaluable for this: Process Mapping, to visualize your workflow, and Cause-and-Effect Diagrams, to brainstorm the root causes of a problem.

Visualizing the Workflow: Process Mapping (Flowcharts)

You can’t fix a process you don’t understand. A process map, or flowchart, is a simple visual tool that lays out all the steps, decision points, and handoffs in a workflow. The act of creating the map with your team is often enlightening, revealing hidden complexities, redundancies, and bottlenecks that no single person was aware of. It’s the equivalent of drawing out a complex biochemical pathway to understand where a drug exerts its effect.

Example: Process Map for New Patient Scheduling

By mapping this out, the team can immediately see a major source of delay: the back-and-forth communication to get missing information. This becomes a prime target for a PDSA cycle (e.g., “Let’s test a new, standardized referral template that requires all necessary information upfront”).

Brainstorming Root Causes: The Fishbone Diagram

Once you’ve identified a problem, you need to understand its root causes. The Cause-and-Effect Diagram, also known as a Fishbone or Ishikawa Diagram, is a structured brainstorming tool that helps teams think through all the potential reasons for a problem. The “head” of the fish is the problem statement, and the “bones” represent categories of potential causes.

Masterclass Table: Fishbone Diagram Categories & Pharmacy Examples

Problem: High Patient No-Show Rate

By brainstorming causes in these categories, the team can identify multiple potential areas for intervention and prioritize which ones to test first with a PDSA cycle.