Tool development to improve medication information transfer to patients during transitions of care: A participatory action research and design thinking methodology approach
Bibliographic record
Abstract
BACKGROUND: Medication changes during transitions of care is a recognized challenge that has been linked to adverse events. The delivery of medication instructions during transition from hospital to home is a priority area for improvement. OBJECTIVE: The goals of this work were to 1) understand the current experiences of patients and families; and 2) co-design tools to improve medication information transfer during transitions of care together with patients, families, and providers. METHODS: A participatory action approach, using mixed methods within a design thinking framework was used. Participants were chosen from patient groups at higher risk of adverse events, guided by extreme user design, which posits that needs of extreme users can also fit the majority. Providers, patients and family (users) were interviewed to understand current experiences with medication information transfer during transitions of care and to solicit input on potential elements to inform tool design. Users were engaged in iterative creation of prototypes. RESULTS: A total of 116 patients, family caregivers, and providers were engaged throughout this project. Findings highlighted challenges currently experienced, strengthening the case for tools that engage the patient and family in medication information transfer. Important information included why medications were prescribed, how to take them, side effects, and an explanation of the role of community pharmacists. Displaying information in a grid format was preferred. Two tools were prototyped: (1) A Medication Whiteboard for engaging patients and families in creating their medication routine, and (2) A Patient Oriented Medication List for providing medication instructions and as a reference once patients are home. CONCLUSIONS: Through the use of mixed methods within a design thinking framework, the team was able to understand the challenges and design prototypes of tools that both engage patients and families in developing their medication routine and improve medication information transfer during transitions of care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".