Engineering Resilient Community Pharmacies for Chronic Care Management: Protocol for the Development of a Medication Safety Map
Bibliographic record
Abstract
BACKGROUND: The increase in people with complex chronic health conditions is stressing the US health care delivery system. Community pharmacies play a role in ensuring patients' safe medication use for chronic care management (CCM), but their efforts are undermined by volatile work demands and other system barriers. Medication safety in community pharmacies is a multidimensional issue shaped by the work system and interactions among pharmacists, primary care providers, and patients. OBJECTIVE: The objective is to create and evaluate a system of CCM that supports safe medication use. The CCM system design will focus on creating and evaluating a Medication Safety Map (MedSafeMap) for patients with complex chronic health conditions. This study has three aims: (1) identify and define community pharmacy work system design requirements for safe medication practices, enabling resilient performance; (2) design and develop MedSafeMap, a feasible and sustainable solution, to facilitate safe medication practices through resilient performance; and (3) implement MedSafeMap in community pharmacies and pilot-test its impact on pharmacy staff attitudes, behaviors, and performance. METHODS: This study will leverage participatory design and human factors engineering methods throughout the 3 aims. For aim 1, four rounds of qualitative observations within 6 pharmacy sites will be conducted to parse areas MedSafeMap could address. Two rounds of interviews with pharmacists and technicians from each of the sites will be used to expand upon areas of interest identified during the observations. Observational and interview data will be used to construct functional resonance analysis method models and resilience narratives to map both risks and best practices within the system based on daily workplace factors. For aim 2, focus groups with pharmacist and technician stakeholders will be guided by participatory stakeholder engagement to inform prototyping for MedSafeMap. Simulation-based research involving standardized patients in CCM scenarios will be used to test and refine MedSafeMap components. Finally, for aim 3, MedSafeMap will be implemented in pharmacies. Observations using the work observation method by activity timing (WOMBAT) for the time and motion study will aid in understanding how MedSafeMap impacts pharmacy staff workflow. We will assess adoption challenges and resilience-focused attitudes, behaviors, and performance to support CCM. RESULTS: As of August 2025, all 6 pharmacy sites have been recruited. Three of the 4 rounds of observations, 2 rounds of interviews with 12 pharmacists and 12 technicians from the study sites, and the 6 focus groups have been conducted. Preparations for the simulations are ongoing. CONCLUSIONS: MedSafeMap is an innovative approach that will guide pharmacists and technicians in safely providing care to patients with complex chronic health conditions. It will help them navigate the complex tasks and communications between the pharmacy, patient, and primary care provider arising with this type of complex care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/69011.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.059 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.064 | 0.017 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".