From Hospital to Home: A Resident-Driven Quality Improvement Project to Overcome Discharge Prescription Barriers
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Inability to obtain timely medications is a patient safety concern that can lead to delayed or incomplete treatment of illness. While there are many patient and system factors contributing to postdischarge medication nonadherence, availability and insurance-related barriers are preventable. PURPOSE: To implement a systematic process ensuring review of discharge prescriptions to ensure availability and resolve insurance barriers before patient discharge. METHODS: A prospective single-arm quality improvement intervention study to identify and address insurance-related prescription barriers using nonclinical staff. Intervention was pilot tested with sequential spread across general medicine resident teams. The primary outcome was successful obtainment of postdischarge prescriptions confirmed by phone calls to patients or their pharmacies. RESULTS: From April to August 2015, 59 of 161 patients included in the improvement process (36.6%) had one or more insurance or availability-related barriers with their prescriptions, totaling 89 issues. Forty-three of the 59 patients (72.9%) responded to postdischarge phone calls, 39 of whom (39/43, 90.7%) successfully filled their prescriptions on the first pharmacy visit. CONCLUSIONS: In our study, we preemptively identified that over a third of patients discharged would have encountered barriers filling their prescriptions. This interdisciplinary quality improvement project using nonclinical team members removed barriers for over 90% of our patients to ensure continuation of medical therapy without disruption and a safer postdischarge plan.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".