A Two-Phase Framework Leveraging User Feedback and Systemic Validation to Improve Post-Live Clinical Decision Support
Bibliographic record
Abstract
Despite the benefits of clinical decision support (CDS), concerns of potential risks arise amidst increasing reports of CDS malfunctions. Without objective and standardized methods to evaluate CDS in the post-live stage, CDS performance in a dynamic healthcare environment remains a black box from the user's perspective. In this study, we proposed a comprehensive framework to identify and evaluate post-live CDS malfunctions from the perspective of healthcare settings.We developed a two-phase framework to identify and evaluate post-live CDS system malfunctions: (1) real-time feedback from users in healthcare settings; (2) systematic validation through the use of databases that involve fundamental data flow validation and knowledge and rules validation. Identity, completeness, plausibility, and consistency across locations and time patterns were included as measures for systematic validation. We applied this framework to a commercial CDS system in 14 acute care facilities in Canada in a 2-year period.During this study, seven types of malfunctions were identified. The general rate of malfunctions was below 2%. In addition, an increase in CDS malfunctions was found during the electronic health record upgrade and implementation periods.This framework can be used to comprehensively evaluate CDS performance for healthcare settings. It provides objective insights into the extent of CDS issues, with the ability to capture low-prevalence malfunctions. Applying this framework to CDS evaluation can help improve CDS performance from the perspective of healthcare settings.
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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 | 0.152 | 0.160 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".