Determinants of Medical Practice Variation Among Primary Care Physicians: Protocol for a Three Phase Study

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

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 R2, computing the marginal r2.

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.

Metabolic syndrome in primary care: A latent variable analysis of electronic medical records

BACKGROUND

The best definition of the metabolic syndrome (MetSyn) remains controversial. The aim was to develop a latent variable measurement model for the MetSyn using primary care data.

METHODS

This retrospective case-control study was based on the data from the IQVIA Disease Analyzer (DA) database including a representative panel of general and internal medicine practices in Germany. 23,020 adult patients, who were newly diagnosed with ≥1 component of the MetSyn (cases; median age [IQR]: 60 [50-72] years; 54 % males) between January 2016 to December 2019, and 2,144 patients without any MetSyn component (controls; 48 [38-61] years; 45 % males) were included. MetSyn was modeled by ICD-10 codes (E66: obesity, I10: hypertension, E11: type 2 diabetes, E78: lipid disorders) and physiological measures (BMI, HbA1c, blood pressure, HDL-cholesterol, LDL-cholesterol, triglycerides).

RESULTS

The covariance structure of the ICD codes and physiological measures could not be accounted for by a single MetSyn factor (root mean square error of approximation, RMSEA=0.085). Enrichment by another factor "Likelihood to Diagnose" significantly increased model fit with the observed data (RMSEA=0.054). "Likelihood to Diagnose" hardly correlated with MetSyn (r = 0.06), and was differentially associated with male sex, older age, and corresponding cardio- and cerebrovascular diseases. MetSyn showed the highest loading on four subordinate factors "hyperlipidemia" (0.70), "obesity" (0.66), "type 2 diabetes" (0.60), followed by "hypertension" (0.42). The ICD codes E66 and E78 were less indicative of these factors relative to BMI and triglycerides/HDL-cholesterol, respectively, whereas E11 and I10 were similarly indicative as their corresponding physiological measures.

CONCLUSIONS

MetSyn was best indicated by BMI, triglycerides (an indirect marker of insulin sensitivity), HbA1c, and ICD-10 diagnosis E11 (type 2 diabetes). The factor "Likelihood to diagnose" probably constitutes a method factor of differential diagnosis systematically contributing to the common consistency of the MetSyn diagnosis in primary care.

Successful Implementation of Single Urine Polymerase Chain Reaction Test for Evaluating Suspected Cytomegalovirus Infection in Neonates

Overuse of laboratory investigations is viewed as medical waste. In the past, to diagnose congenital cytomegalovirus (CMV) infection, consecutive urine culture samples were obtained. With the advent of polymerase chain reaction (PCR) technology, 1 urine specimen should be enough. We conducted this quality improvement study to look at the effect of a practice change from 3 to 1 urine specimen for PCR testing.

A Clinical Decision Support System (KNOWBED) to Integrate Scientific Knowledge at the Bedside: Development and Evaluation Study

BACKGROUND

The evidence-based medicine (EBM) paradigm requires the development of health care professionals' skills in the efficient search of evidence in the literature, and in the application of formal rules to evaluate this evidence. Incorporating this methodology into the decision-making routine of clinical practice will improve the patients' health care, increase patient safety, and optimize resources use.

OBJECTIVE

The aim of this study is to develop and evaluate a new tool (KNOWBED system) as a clinical decision support system to support scientific knowledge, enabling health care professionals to quickly carry out decision-making processes based on EBM during their routine clinical practice.

METHODS

Two components integrate the KNOWBED system: a web-based knowledge station and a mobile app. A use case (bronchiolitis pathology) was selected to validate the KNOWBED system in the context of the Paediatrics Unit of the Virgen Macarena University Hospital (Seville, Spain). The validation was covered in a 3-month pilot using 2 indicators: usability and efficacy.

RESULTS

The KNOWBED system has been designed, developed, and validated to support clinical decision making in mobility based on standards that have been incorporated into the routine clinical practice of health care professionals. Using this tool, health care professionals can consult existing scientific knowledge at the bedside, and access recommendations of clinical protocols established based on EBM. During the pilot project, 15 health care professionals participated and accessed the system for a total of 59 times.

CONCLUSIONS

The KNOWBED system is a useful and innovative tool for health care professionals. The usability surveys filled in by the system users highlight that it is easy to access the knowledge base. This paper also sets out some improvements to be made in the future.