Process mapping evaluation of medication reconciliation in academic teaching hospitals: a critical step in quality improvement
Bibliographic record
Abstract
BACKGROUND: Medication reconciliation (MedRec) has been a mandated or recommended activity in Canada, the USA and the UK for nearly 10 years. Accreditation bodies in North America will soon require MedRec for every admission, transfer and discharge of every patient. Studies of MedRec have revealed unintentional discrepancies in prescriptions but no clear evidence that clinically important outcomes are improved, leading to widely variable practices. Our objective was to apply process mapping methodology to MedRec to clarify current processes and resource usage, identify potential efficiencies and gaps in care, and make recommendations for improvement in the light of current literature evidence of effectiveness. METHODS: Process engineers observed and recorded all MedRec activities at 3 academic teaching hospitals, from initial emergency department triage to patient discharge, for general internal medicine patients. Process maps were validated with frontline staff, then with the study team, managers and patient safety leads to summarise current problems and discuss solutions. RESULTS: Across all of the 3 hospitals, 5 general problem themes were identified: lack of use of all available medication sources, duplication of effort creating inefficiency, lack of timeliness of completion of the Best Possible Medication History, lack of standardisation of the MedRec process, and suboptimal communication of MedRec issues between physicians, pharmacists and nurses. DISCUSSION: MedRec as practised in this environment requires improvements in quality, timeliness, consistency and dissemination. Further research exploring efficient use of resources, in terms of personnel and costs, is required.
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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 | 0.174 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 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".