THE MULTI-LEVEL IMPACT OF CLINICAL DECISION SUPPORT SYSTEM: A FRAMEWORK AND A CALL FOR MIXED METHODS EVALUATION
Bibliographic record
Abstract
Clinical decision support systems are important healthcare systems that help in improving healthcare quality and in reducing cost. These systems have multiple levels of outcomes on the individual, group, organization, and society levels. However, despite the abundant studies conducted to evaluate CDSS, most of these studies failed to recognize these multiple levels of impact and mostly focused on the clinical efficacy of these systems using randomized controlled trials (RCT) designs. In this paper, we propose that CDSS evaluation is a complex task that cannot be accomplished using a single research methodology. We propose a framework that identifies the multiple levels impacted by CDSS and the appropriate methodologies that can be used to evaluate these impacts. We also provide an example of using this framework to evaluate a pain management CDSS. This study informs future evaluation studies on what and how to evaluate at different levels of CDSS interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.111 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".