Can amputation save the hospital? The impact of the Medicare Rural Flexibility Program on demand and welfare

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract This paper seeks to understand the impact of the Medicare Rural Hospital Flexibility (Flex) Program on rural resident hospital choice and welfare. The Flex program created a new class of hospital, the Critical Access Hospital (CAH), which receives more generous reimbursement in return for limits on capacity and length of stay. We find that conversion to CAH status reduced inpatient admissions by a mean of 5.4%, of which almost all was driven by factors other than capacity. The program increased consumer welfare if it reduced the closure rate by at least 4 percentage points. Terms of use: Documents in EconStor may

Modeling Interregional Patient Mobility: Theory and Evidence From Spatially Explicit Data

“…Ideally, we could draw on patient‐level data and model the individual patients' choices of their physicians, similar to Kessler and McClellan (2000) and Gowrisankaran et al. (2018) in modeling patients' hospital choices, and derive the parameter β from these estimates. Lacking these data, we estimate Equation () with the spatial accessibility measures $$A_{od}^{r1}$$ and $$A_{od}$$, respectively, as well as regional fixed effects $$\tau _{o}$$ and $$\mu _{d}$$ as regressors.…”

“…In order to specify the distance decay function f (dist lk ) = dist −β lk for calculating the accessibility measure A od , we have to determine the appropriate parameter β. Ideally, we could draw on patient-level data and model the individual patients' choices of their physicians, similar to Kessler and McClellan (2000) and Gowrisankaran et al (2018) in modeling patients' hospital choices, and derive the parameter β from these estimates. Lacking these data, we estimate Equation ( 12) with the spatial accessibility measures A r1 od and A od , respectively, as well as regional fixed effects τ o and μ d as regressors.…”

Why is Distance Important for Hospital Choice? Separating Home Bias From Transport Costs*

“…Gowrisankaran et al . [2017] measure patient's valuation of Critical Access Hospitals in Medicare's Rural Hospital Flexibility Program in terms of distance. In addition, previous work has used distance to determine patients’ valuation of quality (Romley and Goldman [2011], Chandra et al .…”