Developing a medication communication framework across continuums of care using the Circle of Care Modeling approach
Bibliographic record
Abstract
BACKGROUND: Medication errors are a common type of preventable errors in health care causing unnecessary patient harm, hospitalization, and even fatality. Improving communication between providers and between providers and patients is a key aspect of decreasing medication errors and improving patient safety. Medication management requires extensive collaboration and communication across roles and care settings, which can reduce (or contribute to) medication-related errors. Medication management involves key recurrent activities (determine need, prescribe, dispense, administer, and monitor/evaluate) with information communicated within and between each. Despite its importance, there is a lack of conceptual models that explore medication communication specifically across roles and settings. This research seeks to address that gap. METHODS: The Circle of Care Modeling (CCM) approach was used to build a model of medication communication activities across the circle of care. CCM positions the patient in the centre of his or her own healthcare system; providers and other roles are then modeled around the patient as a web of relationships. Recurrent medication communication activities were mapped to the medication management framework. The research occurred in three iterations, to test and revise the model: Iteration 1 consisted of a literature review and internal team discussion, Iteration 2 consisted of interviews, observation, and a discussion group at a Community Health Centre, and Iteration 3 consisted of interviews and a discussion group in the larger community. RESULTS: Each iteration provided further detail to the Circle of Care medication communication model. Specific medication communication activities were mapped along each communication pathway between roles and to the medication management framework. We could not map all medication communication activities to the medication management framework; we added Coordinate as a separate and distinct recurrent activity. We saw many examples of coordination activities, for instance, Medical Office Assistants acting as a liaison between pharmacists and family physicians to clarify prescription details. CONCLUSIONS: Through the use of CCM we were able to unearth tacitly held knowledge to expand our understanding of medication communication. Drawing out the coordination activities could be a missing piece for us to better understand how to streamline and improve multi-step communication processes with a goal of improving patient safety.
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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 | 0.000 |
| 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 | 0.001 |
| 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".