Costs Associated with Developing and Implementing a Computerized Clinical Decision Support System for Medication Dosing for Patients with Renal Insufficiency in the Long-term Care Setting
Bibliographic record
Abstract
A team of physicians, pharmacists, and informatics professionals developed a CDSS added to a commercial electronic medical record system to provide prescribers with patient-specific maximum dosing recommendations based on renal function. We tracked the time spent by team members and used US national averages of relevant hourly wages to estimate costs. The team required 924.5 hours and $48,668.57 in estimated costs to develop 94 alerts for 62 drugs. The most time intensive phase of the project was preparing the contents of the CDSS (482.25 hours, $27,455.61). Physicians were the team members with the highest time commitment (414.25 hours, $25,902.04). Estimates under alternative scenarios found lower total cost estimates with the existence of a valid renal dosing database ($34,200.71) or an existing decision support add-on for renal dosing ($23,694.51). Development of a CDSS for a commercial computerized prescriber order entry system requires extensive commitment of personnel, particularly among clinical staff.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".