Unravelling the determinants of medical practice variation in referrals among primary care physicians: insights from a retrospective cohort study in Southern Israel

Abstract

ObjectivesReducing 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.MethodsA 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 R2, computing the marginal r2.ResultsThe 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).ConclusionOver 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.

Population Health Management: Leveraging it for Better Patient Outcome’s

“…Preprocessing in the context of Population Management for Health using Python entails normalising, cleaning, or encoding categorical variables. The pandas package in Python makes it possible to handle missing information as well as outliers effectively while maintaining the integrity of the dataset [9]. Using scikit-learn to standardise numerical characteristics makes analysis more accurate by facilitating uniformity.…”