Avoiding a Med-Wreck: a structured medication reconciliation framework and standardized auditing tool utilized to optimize patient safety and reallocate hospital resources
Bibliographic record
Abstract
BACKGROUND: The incidence of preventable adverse drug events (ADE) is approximately one medication error per patient per hospital-day. A quality medication reconciliation (MedRec) process is a crucial intervention used to reduce ADE in the hospital and community setting. Amid the coronavirus disease 2019 (COVID-19) pandemic, preventing medication errors is vital to avoid patient readmission, reduce disease complications, and reduce cost and patient burden on the healthcare system. OBJECTIVES: To develop a standardized MedRec framework that can be implemented in all healthcare settings to reduce patient and staff harm during COVID-19. Also, to create a standardized auditing tool used to assess the quality of the MedRec process and allow for continuous quality improvement. METHODS: A multi-site gap analysis (MGA) was performed to collect observational data that were collected from four different healthcare sites (two hospitals, a long-term care facility, and a community pharmacy). MGA consists of collecting data across several sites which answer a standardized questionnaire. A standardized MedRec framework and auditing tool were developed based on the gaps observed in each site and literature reviews. RESULTS: A standardized MedRec process was not implemented in any of the observed sites. The healthcare sites lacked a designated MedRec team and training related to the MedRec process leading to multiple discrepancies at discharge. Patients were not counselled on changes to home medications, and a discharge report was often not provided upon discharge. Communication mechanisms between community pharmacies and hospital physicians are not available or easily accessible. CONCLUSION: The proposed structured MedRec framework is vital to reduce medication errors and patient harm amid COVID-19. Moreover, the comprehensive auditing tool developed in this study allows for continuous quality improvement resulting in superior quality care, reduction of workflow inefficiencies, cost savings on hospital readmissions, and overall enhanced healthcare system performance.
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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 | 0.089 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".