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Micro hospitals are a new form of for-profit health-care facility with rapid expansion in some parts of the country. They continue to grow in Texas without in-depth public understanding or explicit policy guidance on their role in the health-care system. Our project aims to define socioeconomic and demographic characteristics of areas served by micro and regular hospitals, and by doing so help assess micro hospitals' impact in expanding health-care access for disadvantaged populations in Texas.

We (1) estimated hospital service areas (catchment areas) with a spatial model based on advanced Geographic Information System (GIS) methods using a proprietary ESRI traffic network; (2) assigned population socioeconomic measures to the catchment areas from the 2014–2018 American Community Survey 5-Year Estimates, weighted with an empirically tested Gaussian distribution; (3) used two-tailed t-tests to compare means of population characteristics between micro and regular hospital catchment areas; and (4) conducted logistic regressions to examine relationships between selected population variables and the associated odds of micro hospital presence.

We found micro hospitals in Texas tend to serve a population less stressed in health-care access compared to those who are more in need as measured by various dimensions of disadvantages.

Our analysis takes a cross sectional look at the population characteristics of micro hospital service areas. Even though the initial geographic choices of micro hospitals may not reflect the long-term population changes in specific neighborhoods, our analysis can provide policy makers a tool to examine health-care access for disadvantaged populations at given point in time. As the population socioeconomic characteristics have long been associated with health-care inequality, we hope our analysis will help foster structural policy considerations that balance growing health-care delivery innovations and their social accountability.

We used GIS based spatial modeling to dynamically capture the potential patient basis by travel time calculated with a street network dataset, rather than using the traditional static census tract to define hospital service areas. By integrating both spatial and nonspatial dimensions of healthcare access, we demonstrated that the policy considerations on the implications of equal opportunity for health-care access need to take into account the social realities and lived experiences of those experiencing the most vulnerability in our society, rather than a conceptual “equality” existing in the spatial and market abstraction.