Preparing for Vanessa’s Law: Collaboration between the Medical Records and Pharmacy Departments at a Canadian Hospital Center
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: In the context of Vanessa's Law, the medical records department and the pharmacy team of a mother-child hospital collaborated to create a system for coding adverse drug reactions (ADRs). This study was conducted to validate the coding of ADRs by the medical records team. MATERIAL AND METHODS: This retrospective descriptive study covered 12 months of coding of hospitalization data by the medical records team (November 1, 2017, to October 31, 2018). The pharmacy team performed twice-monthly analysis to validate the ADR data, based on coded information for drugs and associated clinical manifestations. RESULTS: Over the 12-month study period, a total of 755 ADRs were coded by the medical records department (i.e., 2.1 ADRs per day, corresponding to 7.1% of admissions). For 34 (4.5%) of these ADRs, the pharmacy team made a change to the code originally assigned by the medical records department. Eighty-five (11.5%) of the coded ADRs were deemed serious, as defined by Health Canada, but only 13 (15%) of these serious ADRs were reported to the regulatory authority. The new process allowed clinical manifestation codes to be associated with individual drugs in the pharmacy's Med-Echo-Plus® software, which facilitated interpretation of the data. Following this study, coding practices were reviewed, a coding algorithm was developed, and the codes for 18 drugs were clarified. CONCLUSION: This study highlights the feasibility of establishing a link between the medical records and pharmacy departments to validate the coding of ADRs. At the study hospital, this linkage has identified serious ADRs, for which reporting will soon be required by Health Canada.
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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 | 0.031 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".