Simulating Telemedicine, Medication Reconciliation, and Social Determinants: A Novel Instructional Approach to Health Systems Competencies
Bibliographic record
Abstract
While medication reconciliation is necessary to reduce errors, it is often challenging to gather an accurate history in the clinic. Telemedicine offers a relative advantage over clinic and hospital-based interviews by enabling the clinician to inspect the home environment, review pill bottles, and identify social determinants affecting adherence, such as financial instability. To be effective, however, clinicians must be trained in best-practice interview methods and the proper use of telemedicine. There is very little information in the literature describing the best strategies for teaching students or measuring competencies in telemedicine. Therefore, we created an educational module with a telemedicine simulation and an evaluation rubric. We piloted this module with 48 medical and physician assistant students. Most students could complete a virtual interview and gather a medication history. However, only half identified an over-the-counter medication missing from the list. Most students were either entrustable or approaching entrustment in the six telemedicine competencies measured in this simulation. This simulation is valuable for teaching students about medication reconciliation, using telemedicine to close gaps in access to care, and identifying health-related social needs affecting medication adherence.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".