Immediate financial impact of computerized clinical decision support for long-term care residents with renal insufficiency: a case study
Bibliographic record
Abstract
In a randomized trial of a clinical decision support system for drug prescribing for residents with renal insufficiency in a large long-term care facility, analyses were conducted to estimate the system's immediate, direct financial impact. We determined the costs that would have been incurred if drug orders that triggered the alert system had actually been completed compared to the costs of the final submitted orders and then compared intervention units to control units. The costs incurred by additional laboratory testing that resulted from alerts were also estimated. Drug orders were conservatively assigned a duration of 30 days of use for a chronic drug and 10 days for antibiotics. It was determined that there were modest reductions in drug costs, partially offset by an increase in laboratory-related costs. Overall, there was a reduction in direct costs (US$1391.43, net 7.6% reduction). However, sensitivity analyses based on alternative estimates of duration of drug use suggested a reduction as high as US$7998.33 if orders for non-antibiotic drugs were assumed to be continued for 180 days. The authors conclude that the immediate and direct financial impact of a clinical decision support system for medication ordering for residents with renal insufficiency is modest and that the primary motivation for such efforts must be to improve the quality and safety of medication ordering.
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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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| 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 | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".