Background
We analyze a sample of 2,816 Medicare-certified acute care hospitals across all US states, using January to December 2019 CMS Hospital Compare datasets merged with county-level socio-demographic data. These data allow us to identify how features of the community in which a hospital serves differentially relate to its patients’ experiences based on the quality of that hospital.
Methods
A Finite Mixture Model (FMM) is used to uncover a mix of latent groups of hospitals that differ in quality. In the FMM, a multinomial logistic equation relates hospital-level factors to the odds of being in a particular group. And a multiple linear regression relates the characteristics of communities served by hospitals to the patients’ expected ratings of their experiences at hospitals in each group. Thus, this association potentially varies with hospital quality. We conducted the analysis via Stata.
Results
We provide evidence that relatively low-quality hospitals have much more variability in patient experience ratings than relatively high-quality ones. Moreover, the experiences at low-quality hospitals are more sensitive to county demographic factors, which means exogenous shocks, like COVID-19, will likely affect these hospitals differently, as such shocks are known to disproportionately affect their communities.
Conclusion
Our results imply that low-quality hospitals with more variability in their HCAHPS responses are more likely to face adverse patient experiences due to COVID-19 than relatively high-quality hospitals. Pandemics like COVID-19 create conditions that intensify the already high demands placed on hospitals and make it even more challenging to deliver quality care.