Physician Subspecialization and the Health and Health Care of Older Americans - PROJECT SUMMARY Over the past 40 years, specialist physicians have supplanted primary care as the most frequently seen clinicians for older adults in the US. This shift towards specialty care is driven by advancing medical technology and increased “subspecialization,” whereby specialist physicians focus on narrower and narrower clinical areas. Subspecialization has grown markedly: in 1980, the American Board of Medical Specialties had 28 specialty boards, with an additional 28 certified subspecialties. By 2020, 40 specialty boards encompassed 147 separate subspecialties. While subspecialists bring greater clinical expertise, too much subspecialization could lead to inequitable access, overtreatment, overdiagnosis or fragmentation of care. There is little empirical evidence on the implications of growing subspecialization for the health of older Americans. A major obstacle to filling these evidence gaps is the lack of meaningful measures of subspecialization at the physician level. Existing physician directories, like the one used by Medicare, contain in-depth specialty data, but are also highly inaccurate. For example, Medicare data identify only 17% of board-certified advanced heart failure specialists in the US. Other specialties have similar data gaps. To understand how access to subspecialists influences access to specific advanced treatments and clinical outcomes, it is necessary to better define the hundreds of types of subspecialty care being provided to patients. We propose to characterize subspecialization in the US and assess its implications for the health and health care of older adults. Using comprehensive data from Medicare, we will develop novel methods to classify physician subspecialists by their observed practice patterns, focusing on 3 key specialties in the care of older adults (cardiology, medical oncology and general surgery) as “tracer” disciplines to fill evidence gaps in subspecialty care that can inform policy. Specifically, we will: 1) Use community detection algorithms, a common tool in network science, to identify subspecialists based on their practice patterns (as measured by services provided, drug treatments, and patient diagnoses). 2) Identify patient, health system and geographic factors associated with subspecialty supply and access. 3) Using quasi-experimental methods, measure the impact of access to subspecialist care on health outcomes and utilization in the three key specialties. These Aims will provide novel evidence to guide health policy, including improved methods to accurately measure subspecialist supply, guide health insurers and policymakers for applications such as determining adequacy of specialist coverage in insurance design (e.g., Medicare Advantage), identify populations with shortages in subspecialist access, and guide telemedicine development. Without this evidence, clinical advances may not reach older adults who could benefit the most.
