Medication Discrepancies Associated With a Medication Reconciliation Program and Clinical Outcomes After Hospital Discharge
Bibliographic record
Abstract
STUDY OBJECTIVE: To identify the frequency of unintended medication discrepancies 30 days postdischarge from medicine wards with interprofessional medication reconciliation processes and clinical import. METHODS: Prospective cohort study of adults discharged between October 2013 and November 2014 from two teaching hospitals in Edmonton, Canada. The Best Possible Medication Discharge Plan (BPMDP) was prepared for all patients. Patients were called 30 days postdischarge to determine the medication discrepancy rate from the BPMDP and whether this was intentional or unintentional; three clinicians used standardized criteria to determine if the discrepancy was inconsequential. Electronic health records and patient contact were used to ascertain death, hospital readmissions, and emergency department (ED) visits at 90 days. RESULTS: Of 433 patients (mean age 64 yrs, 52% female, median discharge prescriptions 6 [interquartile range 4-9]), 168 (38.8%) had at least one unintentional medication discrepancy at 30 days (325 total discrepancies; median one [interquartile range 1-2 discrepancies per patient]). Patients with unintentional medication discrepancies were older (65.9 vs 61.9 yrs, p=0.03) with more discharge medications (7 vs 6, p=0.03). Most unintentional discrepancies (91.1%) were judged inconsequential. The presence of an unintentional medication discrepancy was not associated with 90-day readmission or death (42/167 [25.1%] vs 64/263 [24.3%], adjusted odds ratio 0.96 [95% confidence interval 0.60-1.54]) or ED visits (69 [41.3%] vs 101 [38.4%], adjusted odds ratio 1.11 [95% confidence interval 0.74-1.67]. CONCLUSION: Despite the presence of an interprofessional medication reconciliation process, over one-third of patients had a medication discrepancy within 30 days of discharge, although most were inconsequential and there was no association between unintended medication discrepancies and risk of readmission, ED visit, or death 3 months after discharge.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".