Patient satisfaction with a pharmacist-led best possible medication discharge plan via tele-robot in a remote and rural community hospital
Bibliographic record
Abstract
INTRODUCTION: Medication reconciliation (MedRec) reduces the risk of preventable medication-related adverse events (ADEs). A best possible medication discharge plan (BPMDP) is a revised list of medications a patient will take when discharged from hospital; a pharmacist review ensures accuracy. For many hospitals, on-site pharmacists are non-existent. Extension of a visual presence via a mobile robotic platform with real-time audiovisual communication by pharmacists to conduct MedRec remains unstudied. This study explored patient perceptions of a pharmacist-led BPMDP using a telepresence robot. Time requirements, unintentional discharge medication discrepancies (UMD), programme inefficiencies/barriers and facilitators involved in pharmacist review of the discharge medication list and patient interviews were also described. METHODS: This prospective cohort study enrolled adult patients admitted to a 12-bed community hospital at high risk of an ADE. Remote pharmacists reviewed the discharge prescription list, identified/resolved UMDs, and interviewed/counselled patients using a telepresence robot. Thereafter, patients completed an anonymous satisfaction questionnaire. Prescriber discharge UMDs were classified, and barriers/inefficiencies and facilitators were documented. RESULTS: Nine patients completed an interview, with a 75% interview agreement rate. All patients were comfortable with the robot and 76% felt their care was better. With a median of 11 discharge medications/patient, the UMD rate was 78%; 71% had omitted medications, 43% involved a cardiovascular medication, 88% were due to a hospital system cause, and 43% were specifically due to an inaccurate best possible admission medication history. Median times for interview preparation, interview and UMD/drug therapy problem resolution were 45, 15 and 10 min, respectively. CONCLUSION: Using a telepresence robot to provide pharmacist-led BPMDPs is acceptable to patients and an innovative, effective solution to identify/resolve UMDs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".