The economic effects of rural hospital closures.

OBJECTIVE: To provide an updated analysis of the economic effects of rural hospital closures. STUDY SETTING: Our study sample was national in scope and consisted of nonmetro counties from 2001 to 2018. STUDY DESIGN: We used a difference-in-differences study design to estimate the effect of a hospital closure on county income, population, unemployment, and size of the labor force. Specifically, we compared economic changes over time in nonmetro counties experiencing a hospital closure to changes in a control group of nonmetro counties over the same time period. We also leveraged insight from recent research to control for estimation bias due to heterogeneity in the closure effect over time or across groups defined by when closure was experienced. DATA EXTRACTION: Data on (adjusted gross) annual income (in real dollars), annual population size, and monthly unemployment rate and labor force size were sourced from the Internal Revenue Service, Census Bureau, and Bureau of Labor Statistics, respectively. We used data from the North Carolina Rural Health Research Program to identify counties that experienced a hospital closure. PRINCIPAL FINDINGS: Of the 1759 nonmetro counties in our study sample, 109 experienced a hospital closure during the study period. Relative to the nonclosure counterfactual, closures significantly decreased labor force size, on average, by 1.4% (95% CI: [-2.1%, -0.8%]). Results also suggest that Prospective Payment System (PPS) hospital closures significantly decreased population size, on average, by 1.1% (95% CI: [-1.7%, -0.5%]), relative to the nonclosure counterfactual. CONCLUSIONS: Our analysis suggests that rural hospital closures often have adverse effects on local economic outcomes. Importantly, the negative economic effects of closure appear to be strongest following Prospective Payment System hospital closures and attenuated when the closed hospital is converted to another type of health care facility, allowing for the continued provision of services other than inpatient care.

The Rising Rate of Rural Hospital Closures.

PURPOSE: Since 2010, the rate of rural hospital closures has increased significantly. This study is a preliminary look at recent closures and a formative step in research to understand the causes and the impact on rural communities. METHODS: The 2009 financial performance and market characteristics of rural hospitals that closed from 2010 through 2014 were compared to rural hospitals that remained open during the same period, stratified by critical access hospitals (CAHs) and other rural hospitals (ORHs). Differences were tested using Pearson's chi-square (categorical variables) and Wilcoxon rank test of medians. The relationships between negative operating margin and (1) market factors and (2) utilization/staffing factors were explored using logistic regression. FINDINGS: In 2009, CAHs that subsequently closed from 2010 through 2014 had, in general, lower levels of profitability, liquidity, equity, patient volume, and staffing. In addition, ORHs that closed had smaller market shares and operated in markets with smaller populations compared to ORHs that remained open. Odds of unprofitability were associated with both market and utilization factors. Although half of the closed hospitals ceased providing health services altogether, the remainder have since converted to an alternative health care delivery model. CONCLUSIONS: Financial and market characteristics appear to be associated with closure of rural hospitals from 2010 through 2014, suggesting that it is possible to identify hospitals at risk of closure. As closure rates show no sign of abating, it is important to study the drivers of distress in rural hospitals, as well as the potential for alternative health care delivery models.

Satisfaction with electronic health records is associated with job satisfaction among primary care physicians.

OBJECTIVE: To evaluate the association between electronic health record (EHR) satisfaction and job satisfaction in primary care physicians (PCPs). METHOD: Cross-sectional survey of PCPs at 825 primary care practices in North Carolina. RESULTS: Surveys were returned from 283 individuals across 214 practices (26% response rate for practices), of whom 122 were physicians with EHRs and no missing information. We found that for each point increase in EHR satisfaction, job satisfaction increased by ∼0.36 points both in an unadjusted and an adjusted model (ß 0.359 unadjusted, 0.361 adjusted; p < 0.001 for both models). CONCLUSION: We found that EHR satisfaction was associated with job satisfaction in a cross-sectional survey of PCPs. Our conclusions are limited by suboptimum survey response rate, but if confirmed may have substantial implications for how EHR vendors develop their product to support the needs of PCPs.