To examine system integration with physician specialties across markets and the association between local system characteristics and their patterns of physician integration.
Data come from the AHRQ Compendium of US Health Systems and IQVIA OneKey database.
We examined the change from 2016 to 2018 in the percentage of physicians in systems, focusing on primary care and the 10 most numerous nonhospital‐based specialties across the 382 metropolitan statistical areas (MSAs) in the US. We also categorized systems by ownership, mission, and payment program participation and examined how those characteristics were related to their patterns of physician integration in 2018.
We examined local healthcare markets (MSAs) and the hospitals and physicians that are part of integrated systems that operate in these markets. We characterized markets by hospital and insurer concentration and systems by type of ownership and by whether they have an academic medical center (AMC), a 340B hospital, or accountable care organization.
Between 2016 and 2018, system participation increased for primary care and the 10 other physician specialties we examined. In 2018, physicians in specialties associated with lucrative hospital services were the most commonly integrated with systems including hematology‐oncology (57%), cardiology (55%), and general surgery (44%); however, rates varied substantially across markets. For most specialties, high market concentration by insurers and hospital‐systems was associated with lower rates of physician integration. In addition, systems with AMCs and publicly owned systems more commonly affiliated with specialties unrelated to the physicians’ potential contribution to hospital revenue, and investor‐owned systems demonstrated more limited physician integration.
Variation in physician integration across markets and system characteristics reflects physician and systems’ motivations. These integration strategies are associated with the financial interests of systems and other strategic goals (eg, medical education, and serving low‐income populations).
Data Collection/Extraction Methods