Determinants of Medical Practice Variation Among Primary Care Physicians: Protocol for a Three Phase Study
Bibliographic record
Abstract
BACKGROUND: One of the greatest challenges of modern health systems is the choice and use of resources needed to diagnose and treat patients. Medical practice variation (MPV) is a broad term which entails the differences between health care providers inclusive of both the overuse and underuse. In this paper, we describe a 3-phase research protocol examining MPV in primary care. OBJECTIVE: We aim to identify the potential targets for behavioral modification interventions to reduce the variation in practice patterns and thus improve health care, decrease costs, and prevent disparities in care. METHODS: The first phase will delineate the variation in primary care practice over a wide range of services and long follow-up period (2003-2017), the second will examine the 3 determinants of variation (ie, patient, physician, and clinic characteristics), and attempt to derive the unexplained variance. In the third phase, we will assess a novel component that might contribute to the previously unexplained variance - the physicians' personal behavioral characteristics (such as risk aversion, fear of malpractice, stress from uncertainty, empathy, and burnout). RESULTS: This work was supported by the research grant from Israel National Institute for Health Policy Research (Grant No. 2014/134). Soroka University Medical Center Institutional Ethics Committee has approved the updated version of the study protocol (SOR-14-0063) in February 2019. All relevant data for phases 1 and 2, including patient, physician, and clinic, were collected from the Clalit Health Services data set in 2019 and are currently being analyzed. The evaluation of the individual physician characteristics (eg, risk aversion) by the face-to-face questionnaires was started on 2018 and remains in progress. We intend to publish the results during 2020-2021. CONCLUSIONS: Based on the results of our study, we aim to propose a list of potential targets for focused behavioral intervention. Identifying new targets for such an intervention can potentially lead to a decrease in the unwarranted variation in the medical practice. We suggest that such an intervention will result in optimization of the health system, improvement of health outcomes, reduction of disparities in care and savings in cost. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/18673.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".