Developing Physician Migration Estimates for Workforce Models

OBJECTIVE: To understand factors affecting specialty heterogeneity in physician migration. DATA SOURCES/STUDY SETTING: Physicians in the 2009 American Medical Association Masterfile data were matched to those in the 2013 file. Office locations were geocoded in both years to one of 293 areas of the country. Estimated utilization, calculated for each specialty, was used as the primary predictor of migration. Physician characteristics (e.g., specialty, age, sex) were obtained from the 2009 file. Area characteristics and other factors influencing physician migration (e.g., rurality, presence of teaching hospital) were obtained from various sources. STUDY DESIGN: We modeled physician location decisions as a two-part process: First, the physician decides whether to move. Second, conditional on moving, a conditional logit model estimates the probability a physician moved to a particular area. Separate models were estimated by specialty and whether the physician was a resident. PRINCIPAL FINDINGS: Results differed between specialties and according to whether the physician was a resident in 2009, indicating heterogeneity in responsiveness to policies. Physician migration was higher between geographically proximate states with higher utilization for that specialty. CONCLUSIONS: Models can be used to estimate specialty-specific migration patterns for more accurate workforce modeling, including simulations to model the effect of policy changes

Physician Careers in Rural Areas: Transitions and Trajectories

There is general consensus among health care policy makers on the need to reform and strengthen the primary care infrastructure. This is especially true in rural and underserved areas. Despite significant investment of federal and state dollars in programs to address physician maldistribution, policy interventions have had only limited success. One reason may be that current policies are based on research that does not investigate how the geographic preferences of male and female physicians in different birth cohorts may vary. This dissertation applies the conceptual framework of life course theory from sociology to explore whether the choice of rural practice location diverged for male and female physicians of the same age in different birth cohorts. Licensure data from the North Carolina Board of Medical Examiners were linked at two-year intervals between 1980 and 2005 to form 13 waves of physician-level location histories. Descriptive statistics, logistic regression and event history analyses were employed to compare the timing of transitions into rural practice in North Carolina for physicians in the Boomer 1 (born 1946-1954), Boomer 2 (born 1955-1964) and Generation X (born 1965-1979) birth cohorts. The most compelling finding was that while female physicians in earlier birth cohorts were significantly less likely than their male colleagues to choose rural practice settings, this gender effect was much smaller in the Generation X cohort. The study also found that both male and female physicians in the Generation X cohort were less likely than an earlier cohort to practice in rural counties and that physicians over the age of 50 were more likely to choose rural settings than younger physicians. Existing rural workforce polices are based on research which implicitly assumes that the effect of age and gender on physicians' decisions to enter rural areas is equivalent and fixed across birth cohorts. Findings from this dissertation demonstrate the presence of inter- and intra-cohort differences in rural location behaviors for physicians. The study suggests the need for more dynamic policy levers that are differentially targeted toward male and female physicians in different birth cohorts and are specifically designed to work across physicians' career trajectories

A Methodology for Using Workforce Data to Decide Which Specialties and States to Target for Graduate Medical Education Expansion

OBJECTIVE: To outline a methodology for allocating graduate medical education (GME) training positions based on data from a workforce projection model. DATA SOURCES: Demand for visits is derived from the Medical Expenditure Panel Survey and Census data. Physician supply, retirements, and geographic mobility are estimated using concatenated AMA Masterfiles and ABMS certification data. The number and specialization behaviors of residents are derived from the AAMC's GMETrack survey. DESIGN: We show how the methodology could be used to allocate 3,000 new GME slots over 5 years-15,000 total positions-by state and specialty to address workforce shortages in 2026. EXTRACTION METHODS: We use the model to identify shortages for 19 types of health care services provided by 35 specialties in 50 states. PRINCIPAL FINDINGS: The new GME slots are allocated to nearly all specialties, but nine states and the District of Columbia do not receive any new positions. CONCLUSIONS: This analysis illustrates an objective, evidence-based methodology for allocating GME positions that could be used as the starting point for discussions about GME expansion or redistribution

Modernizing Scope-of-Practice Regulations - Time to Prioritize Patients.

Ongoing payment reforms are pressing health systems to reorganize delivery of care to achieve greater value, improve access, integrate patient care among settings, advance population health, and address social determinants of health. Many organizations are experimenting with new ways of unleashing their workforce’s potential by using telehealth and various forms of digital technology and developing team- and community-based delivery models. Such approaches require reconfiguring of provider roles, but states and health care organizations often place restrictions on health professionals’ scope of practice that limit their flexibilit