Assessing the impact of clinical information‐retrieval technology in a family practice residency
Bibliographic record
Abstract
RATIONALE AND OBJECTIVE: Evidence-based sources of information do not integrate self-assessment tools to assess the impact of a users' search for clinical information. We present a method to evaluate evidence-based sources of information, by systematically assessing the impact of searches for clinical information in everyday practice. METHODS: We integrated an information management tool (InfoRetriever 2003) with an educational intervention in a cohort of 26 family medicine residents. An electronic impact assessment scale was used by these doctors to report the perceived impact of each item of information (each hit) retrieved on hand-held computer. We compared the types of impact associated with hits in two distinct categories: clinical decision support systems (CDSS) vs. clinical information-retrieval technology (CIRT). Information hits in CDSS were defined as any hit in the following InfoRetriever databases: Clinical Prediction Rules, History and Physical Exam diagnostic calculator and Diagnostic Test calculator. CIRT information hits were defined as any hit in: Abstracts of Cochrane Reviews, InfoPOEMs, evidence-based practice guideline summaries and the Griffith's 5 Minute Clinical Consult. RESULTS: The impact assessment questionnaire was linked to 5160 information hits. 4946 impact assessment questionnaires were answered (95.9%), and 2495 contained reports of impact (48.4%). Reports of positive impact on doctors were most frequently in the areas of learning and practice improvement. In comparison to CDSS, CIRT hits were more frequently associated with learning and recall. CDSS hits were more frequently associated with reports of practice improvement. CONCLUSIONS: Our new method permits systematic and comparative assessment of impact associated with distinct categories of information.
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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 | 0.226 | 0.664 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.009 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| 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".