Evaluation of a New Integrated Discharge Prescription Form
Bibliographic record
Abstract
OBJECTIVE: To determine whether a new discharge prescription form which integrates admission medications, in-hospital changes, and discharge medications could enhance the accuracy of information in patient profiles in community pharmacies after hospital discharge. DESIGN: Nonrandomized, prospective, multi-site study. SETTINGS: Internal medicine wards of the three teaching hospitals (1200 beds) of the Centre Hospitalier de l'Université de Montréal. SUBJECTS: Patients admitted to the internal medicine wards between January 4 and 31, 1999, at St.-Luc and Notre-Dame Hospitals formed the control group and received a usual discharge form (UD). Those admitted between February 1 and 28,1999, received the new discharge prescription form (DPF) capturing the list of admission medications and revisions during hospitalization; they served as the experimental group. METHODS: Patient profiles were reviewed to calculate conformity rates of community pharmacy patient profiles after discharge and the rate of overall conformity for each group in the study. Each drug in the patient profile was assessed according to six criteria. Healthcare providers' satisfaction with the DPF was assessed via a written questionnaire. RESULTS: Eighty-nine patients and 669 discharge medications were studied. The patient profiles had a higher conformity rate in the DPF group than in the UD group (82% vs. 40%; p < 0.001); improvement could be attributed to higher conformity rates, particularly for two criteria (medications stopped in hospital and dose changes in hospital). CONCLUSIONS: Integration of admission medications, in-hospital changes, and discharge medications on a single form increases the conformity rates of community pharmacy patient profiles after hospitalization. This tool is well accepted by both pharmacists and physicians and may lead to a major decrease in drug-related problems.
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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 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".