Hospital-based health systems 20 years later: A taxonomy for policy research and analysis.

OBJECTIVE: Building on the original taxonomy of hospital-based health systems from 20 years ago, we develop a new taxonomy to inform emerging public policy and practice developments. DATA SOURCES: The 2016 American Hospital Association's (AHA) Annual Survey; the 2016 IQVIA Healthcare Organizations and Systems (HCOS) database; and the 2017-2018 National Survey of Healthcare Organizations and Systems (NSHOS). STUDY DESIGN: Cluster analysis of the 2016 AHA Annual Survey data to derive measures of differentiation, centralization, and integration to create categories or types of hospital-based health systems. DATA COLLECTION: Principal components factor analysis with varimax rotation generating the factors used in the cluster algorithms. PRINCIPAL FINDINGS: Among the four cluster types, 54% (N = 202) of systems are decentralized (-0.35) and relatively less differentiated (-0.37); 23% of systems (N = 85) are highly differentiated (1.28) but relatively decentralized (-0.29); 15% (N = 57) are highly centralized (2.04) and highly differentiated (0.65); and approximately 9 percent (N = 33) are least differentiated (-1.35) and most decentralized (-0.64). Despite differences in calculation, the Highly Centralized, Highly Differentiated System Cluster and the Undifferentiated, Decentralized System Cluster were similar to those identified 20 years ago. The other two system clusters contained similarities as well as differences from those 20 years ago. Overall, 82 percent of the systems remain relatively decentralized suggesting they operate largely as holding companies allowing autonomy to individual hospitals operating within the system. CONCLUSIONS: The new taxonomy of hospital-based health systems bears similarities as well as differences from 20 years ago. Important applications of the taxonomy for addressing current challenges facing the healthcare system, such as the transition to value-based payment models, continued consolidation, and the growing importance of the social determinants of health, are highlighted.

Landscape of Health Systems in the United States.

Despite the prevalence of vertical integration, data and research focused on identifying and describing health systems are sparse. Until recently, we lacked an enumeration of health systems and an understanding of how systems vary by key structural attributes. To fill this gap, the Agency for Healthcare Research and Quality developed the Compendium of U.S. Health Systems, a data resource to support research on comparative health system performance. In this article, we describe the methods used to create the Compendium and present a picture of vertical integration in the United States. We identified 626 health systems in 2016, which accounted for 70% of nonfederal general acute care hospitals. These systems varied by key structural attributes, including size, ownership, and geographic presence. The Compendium can be used to study the characteristics of the U.S. health care system and address policy issues related to provider organizations.

Evaluating Breast Cancer Care Coordination at a Rural National Cancer Institute Comprehensive Cancer Center Using Network Analysis and Geospatial Methods.

BACKGROUND: Variation in cancer care coordination may affect care quality and patient outcomes. We sought to characterize the impact of geographic access to and dispersion of cancer care providers on variation in care coordination. METHODS: Using electronic health record data from 2,507 women diagnosed with breast cancer at a National Cancer Institute Comprehensive Cancer Center from April 2011 to September 2015, a breast cancer patient-sharing physician network was constructed. Patient "care networks" represent the subnetworks of physicians with whom the focal patient had a clinical encounter. Patient care networks were analyzed to generate two measures of care coordination, care density (ratio of observed vs. potential connections between physicians), and clustering (extent to which physicians form connected triangles). RESULTS: The breast cancer physician network included 667 physicians. On average, the physicians shared patients with 12 other physicians. Patients saw an average of 8 physicians during active treatment. In multivariable models adjusting for patient sociodemographic and clinical characteristics, we observed that greater travel burden (>2 hours) and lower geographic dispersion were associated with higher care density (P < 0.05 and P < 0.001, respectively) but lower care network clustering (P < 0.05). CONCLUSIONS: Variation in network-based measures of care coordination is partially explained by patient travel burden and geographic dispersion of care. IMPACT: Improved understanding of factors driving variation in patient care networks may identify patients at risk of receiving poorly coordinated cancer care.