Impact of adverse drug reactions on the coding of a hospital stay in Quebec: exploratory descriptive study by simulation
Bibliographic record
Abstract
The discharge summary sheet's coding allows calculation of the severity index (SI), mortality index (MI), and resource intensity weight (RIW). These indicators help to describe the burden of care for individual cases and could potentially influence patient-based funding. This study was undertaken to simulate the impact of different adverse drug reactions (ADRs) on the hospital length of stay, thus allowing calculation of the effect of ADRs on the SI, MI, and RIW. This exploratory descriptive study was based on computer simulations. We created, by simulation, seven patient profiles of various complexities representative of our patients. Fifteen types of combination of drug and ADR manifestation comprising 15 ADR caused by eight different drug classes were identified based on the most frequently coded ADR in fiscal years 2016-2017 and 2017-2018. Those 15 combinations were applied to the patient profile to simulate the impact on the SI, MI, and RIW in eight scenarios. From these data, we measured the impact of the ADRs on these indicators. A total of 1,571 simulations were run. In general, the addition of a couple of drug and ADR manifestation contributed to increases in all three of the indicators. More specifically, the SI and RIW both increased in 30.7% (n = 482), whereas the MI increased in 14.6% (n = 229). For a same scenario, the impact on the three indicators could vary depending on the patient profile to which it was applied. This study has presented simulation data on the impact of the coding of ADRs on the hospital stay of a patient in Quebec.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".