Unravelling the determinants of medical practice variation in referrals among primary care physicians: insights from a retrospective cohort study in Southern Israel
Bibliographic record
Abstract
Objectives Reducing medical practice variation (MPV) is a central theme of system improvement because it is associated with poor health outcomes, increased costs and disparities in care. This study aimed to estimate the extent to which each determinant (patient, physician, clinic) explains MPV among primary care physicians and to identify the characteristics of health services with a greater explained variance. Methods A retrospective cohort study of primary care physicians practising in non-private clinics of Clalit Health Services in Southern Israel, for longer than a year between 2011 and 2017 and with more than 100 adult patients per practice. We assessed the variation in referral rates among 17 health services and the proportion explained by each domain (patient, physician and clinic). We used generalised linear negative binomial mixed models and the Nakagawa’s R 2 , computing the marginal r 2 . Results The study included 243 physicians working in 295 practices and 139 clinics. The mean-explained variance was 28.5%±10.0%, where physician characteristics explained 4.5% of the variation. The intrapractice variation (within a single physician between the years) was explained better than the interphysician (between physicians). Health services with high explained variation were blood tests characterised by both low intrapractice variation (Rs=−0.65, p value=0.005) and high referral rates (Rs=0.46, p value=0.06). Conclusion Over 70% of MPV is not explained by the patient, clinic and physician demographic and professional characteristics. Future research should focus on the fraction of MPV that is explained by the physicians’ psychological characteristics, and thus potentially identify psychological targets for behavioural modifications aimed at reducing MPV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".