Evaluation of a Web-Based Medication Reconciliation Application Within a Primary Care Setting: Cluster-Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Despite routine review of medication lists during patient encounters, patients' medication lists are often incomplete and not reflective of actual medication use. Contributing to this situation is the challenge of reconciling medication information from existing health records, along with external locations (eg, pharmacies, other provider/hospital records, and care facilities) and patient-reported use. Advances in the interoperability and digital collection of information provides a foundation for integration of these once disparate information sources. OBJECTIVE: We aim to evaluate the effectiveness of and satisfaction with an electronic health record (EHR)-integrated web-based medication reconciliation application, MedTrue (MT). METHODS: We conducted a cluster-randomized controlled trial of MT in 6 primary care clinics within an integrated health care delivery system. Our primary outcome was medication list accuracy, as determined by a pharmacist-collected best-possible medication history (BPMH). Patient and staff perspectives were evaluated through surveys and semistructured interviews. RESULTS: Overall, 224 patients were recruited and underwent a BPMH with the pharmacist (n=118 [52.7%] usual care [UC], n=106 [47.3%] MT). For our primary outcome of medication list accuracy, 8 (7.5%) patients in the MT arm and 9 (7.6%) in the UC arm had 0 discrepancies (odds ratio=1.01, 95% CI 0.38-2.72, P=.98). The most common discrepancy identified was patients reporting no longer taking a medication (UC mean 2.48 vs MT mean 2.58, P=.21). Patients found MT easy to use and on average would highly recommend MT (average net promoter score=8/10). Staff found MT beneficial but difficult to implement. CONCLUSIONS: The use of a web-based application integrated into the EHR which combines EHR, patient-reported data, and pharmacy-dispensed data did not improve medication list accuracy among a population of primary care patients compared to UC but was well received by patients. Future studies should address the limitations of the current application and assess whether improved implementation strategies would impact the effectiveness of the application.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".