The Perceived Effectiveness of Secure Messaging for Medication Reconciliation During Transitions of Care: Semistructured Interviews With Patients
Bibliographic record
Abstract
BACKGROUND: Medication discrepancies can lead to adverse drug events and patient harm. Medication reconciliation is a process intended to reduce medication discrepancies. We developed a Secure Messaging for Medication Reconciliation Tool (SMMRT), integrated into a web-based patient portal, to identify and reconcile medication discrepancies during transitions from hospital to home. OBJECTIVE: We aimed to characterize patients' perceptions of the ease of use and effectiveness of SMMRT. METHODS: We recruited 20 participants for semistructured interviews from a sample of patients who had participated in a randomized controlled trial of SMMRT. Interview transcripts were transcribed and then qualitatively analyzed to identify emergent themes. RESULTS: Although most patients found SMMRT easy to view at home, many patients struggled to return SMMRT through secure messaging to clinicians due to technology-related barriers. Patients who did use SMMRT indicated that it was time-saving and liked that they could review it at their own pace and in the comfort of their own home. Patients reported SMMRT was effective at clarifying issues related to medication directions or dosages and that SMMRT helped remove medications erroneously listed as active in the patient's electronic health record. CONCLUSIONS: Patients viewed SMMRT utilization as a positive experience and endorsed future use of the tool. Veterans reported SMMRT is an effective tool to aid patients with medication reconciliation. Adoption of SMMRT into regular clinical practice could reduce medication discrepancies while increasing accessibility for patients to help manage their medications. TRIAL REGISTRATION: ClinicalTrials.gov NCT02482025; https://clinicaltrials.gov/ct2/show/NCT02482025.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".