No abstract

The application of social network analysis to the organization of healthcare delivery is a relatively new area of research that may not be familiar to health services statisticians and other methodologists. We present a methodological introduction to social network analysis with a case study of physicians’ adherence to clinical guidelines regarding use of implantable cardioverter defibrillators (ICDs) for the prevention of sudden cardiac death. We focus on two hospital referral regions (HRRs) in Indiana, Gary and South Bend, characterized by different rates of evidence-based ICD use (86% and 66%, respectively). Using Medicare Part B claims, we construct a network of physicians who care for cardiovascular disease patients based on patient-sharing relationships. Approaches for weighting physician dyads and aggregating physician dyads by hospital are discussed. Then, we obtain a set of weighted network statistics for the positions of hospitals in their referral region, global statistics for the physician network within each hospital, and of the network positions of individual physicians within hospitals, providing the mathematical specification and sociological intuition underlying each measure. We find that adjusting for network measures can reduce the observed differences between referral regions for evidence-based ICD therapy. This study supports previous reports on how variation in physician network structure relates to utilization of care, and motivates future work using physician network measures to examine variation in evidence-based medicine.

Most women with incident urinary incontinence continued to experience symptoms over 10 years; few had complete remission. Identification of risk factors for urinary incontinence progression, such as body mass index and physical activity, could be important for reducing symptoms over time.

Our findings suggest claims-based algorithms identify incident cancer with variable reliability when measured against an observational cohort study reference standard. Self-reported baseline information available in cohort studies is more effective in removing prevalent cancer cases than are claims data algorithms. Use of claims-based algorithms should be tailored to the research question at hand and the nature of available observational cohort data.

Theoretical models of competition with fixed prices suggest that hospitals should compete by increasing quality of care for diseases with the greatest profitability and demand elasticity. Most empirical evidence regarding hospital competition is limited to heart attacks, which in the U.S. generate positive profit margins but exhibit very low demand elasticity -ambulances usually take patients to the closest (or affiliated) hospital. In this paper, we derive a theoretically appropriate measure of market concentration in a fixed-price model, and use differential travel-time to hospitals in each of the 306 U.S. regional hospital markets to instrument for market concentration. We then estimate the model using risk-adjusted Medicare data for several different population cohorts: heart attacks (low demand elasticity), hip and knee replacements (high demand elasticity) and dementia patients (low demand elasticity, low or negative profitability). First, we find little correlation within hospitals across quality measures. And second, while we replicate the standard result that greater competition leads to higher quality in some (but not all) measures of heart attack quality, we find essentially no association between competition and quality for what should be the most competitive markets -elective hip and knee replacements. Consistent with the model, competition is associated with lower quality care among dementia patients, suggesting that competition could induce hospitals to discourage unprofitable patients.