Health risk appraisal for older people in general practice using an expert system: a pilot study
Bibliographic record
Abstract
The prevention of disability in later life is a major challenge facing industrialised societies. Primary care practitioners are well positioned to maintain and promote health in older people, but the British experience of population-wide preventive interventions has been disappointing. Health risk appraisal (HRA), an emergent information-technology-based approach from the USA, has the potential for fulfilling some of the objectives of the National Service Framework for Older People. Information technology and expert systems allow the perspectives of older people on their health and health risk behaviours to be collated, analysed and converted into tailored health promotion advice without adding to the workload of primary care practitioners. The present paper describes a preliminary study of the portability of HRA to British settings. Cultural adaptation and feasibility testing of a comprehensive health risk assessment questionnaire was carried out in a single group practice with 12,500 patients, in which 58% of the registered population aged 65 years and over participated in the study. Eight out of 10 respondents at all ages found the questionnaire easy or very easy to understand and complete, although more than one-third had or would have liked assistance. More than half felt that the length of the questionnaire was about right, and one respondent in 10 disliked some questions. Of those who completed the questionnaire and received tailored, written health promotion advice, 39% provided feedback on this with comments that can be used for increasing the acceptability of tailored advice. These findings have informed a wider exploratory study in general practice.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".