Cost-effectiveness analysis of medical documentation alternatives
Bibliographic record
Abstract
OBJECTIVES: The delay between patient discharge and the completion of the final discharge note have prompted hospitals to consider new information technologies. This study compared the relative cost-effectiveness of an automated medical documentation system to the current system in place at a Canadian hospital. There are significant expenditures associated with the choice of medical documentation system, yet the benefit to the patient population has not been studied. METHODS: A systematic review of the literature was carried out. Cost data for the current documentation system were obtained from the study hospital. The costs of purchasing the automated system were obtained from the manufacturer. Other resource cost implications of the automated system were estimated based on information obtained from the Centre for Applied Health Informatics at the study hospital. The outcome was determined to be the average time (days) between patient discharge and note completion. A cost-effectiveness analysis was conducted. Sensitivity analyses were used to determine the robustness of the results. RESULTS: The automated documentation system was associated with higher costs but better outcomes than the current system. The incremental cost-effectiveness ratio used for comparing the automated medical documentation system with the traditional system indicated that the incremental daily cost for decreasing a day in average note completion time per discharge note was 0.331 Canadian $/day over the study period (4 years). CONCLUSIONS: Although the automated documentation system was more expensive than the current system, it also provided qualitative benefits that were not considered in the cost-effectiveness analysis.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".