A medication reconciliation form and its impact on the medical record in a paediatric hospital
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to evaluate the quality of medication information available in medical charts before and after the implementation of a medication reconciliation form. PATIENTS AND METHODS: This study is a retrospective chart review of patients under 18 years who were taking two medications or more at home and were admitted to a paediatric hospital for more than 24 hours and discharged from a general paediatrics, infectious disease, gastroenterology or pneumology ward over two 20-week periods (pre- and post-implementation). Each week, 10 medical records were randomly chosen and reviewed. The quality of the medication information was measured on admission (dose, route of administration and frequency) and on discharge (dose, route of administration, frequency and duration of treatment). The proportion of medications that fully met these criteria was compared between the groups using the chi-squared test. RESULTS: Information was analysed for a total of 3275 medications in the pre-implementation group, vs. 3240 medications in the post-implementation group. Baseline characteristics were similar in both groups. On admission, the quality of medication information was comparable between the pre- and post-implementation groups (29.1 vs. 29.3%, respectively; P = 0.86). However, on discharge, an improvement in the quality of information was observed in the post-implementation group (51.7 vs. 65.2%; P < 0.001). CONCLUSION: Our study demonstrated that the forms used in the reconciliation process, in particular the discharge prescription, could increase the quality of the information related to drug use in medical charts. We believe that medication reconciliation forms should be widely used by all the health care professional teams involved in the drug history or prescription process.
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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 | 0.020 | 0.116 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".