A computer decision aid for medical prevention: a pilot qualitative study of the Personalized Estimate of Risks (EsPeR) system
Bibliographic record
Abstract
BACKGROUND: Many preventable diseases such as ischemic heart diseases and breast cancer prevail at a large scale in the general population. Computerized decision support systems are one of the solutions for improving the quality of prevention strategies. METHODS: The system called EsPeR (Personalized Estimate of Risks) combines calculation of several risks with computerisation of guidelines (cardiovascular prevention, screening for breast cancer, colorectal cancer, uterine cervix cancer, and prostate cancer, diagnosis of depression and suicide risk). We present a qualitative evaluation of its ergonomics, as well as it's understanding and acceptance by a group of general practitioners. We organised four focus groups each including 6-11 general practitioners. Physicians worked on several structured clinical scenarios with the help of EsPeR, and three senior investigators leaded structured discussion sessions. RESULTS: The initial sessions identified several ergonomic flaws of the system that were easily corrected. Both clinical scenarios and discussion sessions identified several problems related to the insufficient comprehension (expression of risks, definition of familial history of disease), and difficulty for the physicians to accept some of the recommendations. CONCLUSION: Educational, socio-professional and organisational components (i.e. time constraints for training and use of the EsPeR system during consultation) as well as acceptance of evidence-based decision-making should be taken into account before launching computerised decision support systems, or their application in randomised trials.
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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 | 0.034 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".