Impact of Knowledge Resources Linked to an Electronic Health Record on Frequency of Unnecessary Tests and Treatments
Bibliographic record
Abstract
INTRODUCTION: Electronic knowledge resources have the potential to rapidly provide answers to clinicians' questions. We sought to determine clinicians' reasons for searching these resources, the rate of finding relevant information, and the perceived clinical impact of the information they retrieved. METHODS: We asked general internists, family physicians, and clinical nurse practitioners to complete the Information Assessment Method (IAM) survey after searching 1 of 2 electronic knowledge resources linked in the electronic health record. IAM stimulates reflection on the relevance, cognitive impact, use, and potential health outcomes of retrieved clinical information. RESULTS: Forty-two clinicians rated 502 searches (mean 12, range 1-48) and reported finding information 75% (n = 375) of the time. The most common reasons for searching were to address a clinical question (411, 82%) and for curiosity (75, 15%). In 68% of the rated searches (341), participants indicated they would use the retrieved information for at least 1 patient. In 31% (157) of rated searches, clinicians expected the retrieved information to benefit the patient by avoiding an unnecessary or inappropriate treatment, diagnostic procedure, or preventive intervention. CONCLUSIONS: Searches in electronic knowledge resources frequently yield relevant information that may benefit the patient by, for example, avoiding an inappropriate diagnostic procedure or treatment. Knowing that searches for answers to clinical questions can result in patient health benefits should intensify efforts to encourage clinicians to pursue answers to their questions.
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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 | 0.011 | 0.195 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".