Using electronic knowledge resources for person-centered medicine - II: The Number Needed to Benefit from Information (NNBI)
Bibliographic record
Abstract
Rationale: Electronic knowledge resources are routinely searched by physicians for clinical information in practice. With respect to searching, no studies have systematically assessed the relevance of clinical information, its cognitive impact, the use of information for specific patients and information-related patient health benefits. In our companion paper (Part 1) we critically reviewed the literature and proposed a model of the value of clinical information for health professionals, which includes types of information use and subsequent patient health benefits.Aims and objectives: The purpose of the present paper (Part 2) is to systematically examine patient health benefits associated with physicians’ use of information retrieved from one electronic knowledge resource in routine clinical practice. Methods: Longitudinal mixed methods study. Cases were 84 critical searches for information for patients conducted by 16 family medicine residents over two months. Using the Information Assessment Method (IAM), each ‘opened’ information object was linked to a questionnaire (n = 309). IAM permitted residents to systematically document the cognitive impact of information. Guided by reports of quantitative data, residents were interviewed to explore the search context, the cognitive impact, the use of information and information-related patient health benefits.Results and conclusion: Our results suggest patterns of information use and show that 14.3% of residents’ searches were associated with health benefits. This suggests a new concept we refer to as the Number Needed to Benefit from Information (NNBI). In this study, the number of patients for whom information was retrieved to observe health benefits for one patient, was seven.
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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 | 0.016 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".