Improving Completion Rates of Orthopaedic Surgery Discharge Medication Reconciliation: A Quality Improvement Initiative
Bibliographic record
Abstract
Aim Discharge Medication Reconciliation (DMR) is critical in the transition from inpatient to outpatient care. Incomplete or inaccurate DMR results in medication errors, polypharmacy, or missed medications. Our high-volume tertiary care orthopaedic division was identified as an underperformer in DMR. Therefore, this Quality Improvement initiative aimed to achieve >85% DMR completion at hospital discharge by June 30, 2023. Methods An interrupted time series study design, following the “Model for improvement” of the Institute for Healthcare Improvement, was used with a committee formed with CTU representatives, a resident, and two facilitators. Diagnostic tools, including root cause analysis, stakeholder interviews, process mapping, and driver diagrams, were employed. Multiple Plan-Do-Study-Act cycles were executed, along with interventions, such as audit and feedback and involvement of medical residents and nurse practitioners. Electronic medical record functionality was enhanced to facilitate medication reconciliation, with 'Continue all Remaining Home Medications'. In addition, for monthly data tracking, statistical process control charts were employed. Results Initial analysis showed a 38% DMR completion rate pre-implementation. Post-intervention, completion rates rose to 90%. The 'Continue all Remaining Home Medications' feature saw near-universal adoption among orthopaedic residents. Conclusion The initiative boosted the timely completion rates of DMR, achieving project-specific and institutional objectives. Key factors contributing to this success included active stakeholder engagement, representation from CTUs, timely analysis of performance data, and thorough root cause analysis. Resident involvement was pivotal in identifying and implementing workflow improvements. The audit and feedback system fostered a competitive environment, driving enhancements. Overall, this project improved DMR timeliness, enhancing the safety of patient transitions from hospital to outpatient care.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".