Chartis Center Research: More Than 450 Rural Hospitals Vulnerable to Closure

Feb. 24, 2020
Many of the states with high levels of vulnerability (Texas, Tennessee, Alabama) have not expanded Medicaid, report finds

Over the last 10 years, more than 120 rural hospitals have ceased operations, and research from the Chartis Center for Rural Health finds that more than 450 rural hospitals are vulnerable to closure.

 The Chartis Center for Rural Health is an arm of consulting firm The Chartis Group, which provides advisory services and analytics to the healthcare industry. Using a multilevel logistic regression model, the center compared the performance levels of nearly 2,000 open rural hospitals to those of rural hospitals that have closed. The model identified 453 rural facilities that can be considered “vulnerable” to closure based on performance levels. Within this group, two distinct cohorts emerged; a group of 216 that can be considered “most vulnerable” and a second group of 237 which are defined as “at risk.”

Some states are working to address this crisis in rural health. For instance, nearly half of all rural hospitals in Pennsylvania are operating with negative margins and are at risk of closure. The Commonwealth of Pennsylvania recently announced it has added eight more hospitals to its new Rural Health Model, designed to ensure the financial viability of hospitals in rural areas across Pennsylvania. The model is the first of its kind in the nation.

The Rural Health Model is an alternative payment model, transitioning hospitals from a fee-for-service model to a global budget payment. Payment for the global budget comes from multiple payers, including private and public insurers. Instead of hospitals getting paid when someone is admitted to the hospital, they will receive a predictable amount of money at a specified time to provide services in the community.

The model developed by the Chartis Center for Rural Health identified nine variables of statistical significance in predicting the likelihood of closure. Variables showing the greatest potential to decrease the likelihood of closure on average are Government Control Status, Medicaid Expansion Status and Health System Affiliation. For Medicaid Expansion Status, for example, this variable decreases the likelihood of closure by 62 percent on average. Many of the states with high levels of vulnerability (Texas, Tennessee, Alabama) are states that have not expanded Medicaid.

Data from the Chartis analysis shows that the greatest areas of rural hospital vulnerability are in the Southeast and lower Great Plains, two regions also hit hard by the closure crisis. States such as Mississippi, Missouri, Oklahoma, Tennessee and Texas feature prominently across the three study categories (vulnerable, most vulnerable, at risk) both in terms of total number of rural hospitals in a category as well as the percentage of the state’s rural hospitals in a category. The study also found that states which have thus far avoided any rural hospital closures, or have experienced two or fewer, such as Nebraska, Wisconsin and Wyoming, all have facilities that can be considered vulnerable to closure.

“Our model provided us with the opportunity to conduct a more nuanced examination of the path toward closure and better understand the breadth of vulnerability across the nation,” said Michael Topchik, national leader of the Chartis Center for Rural Health, in a statement. “None of the metrics we track to measure the stability of the rural health safety net are improving, and this research allows us to quantify just how severe the implications could be if the current situation worsens.”

 “The fact that states hit hardest by the closure crisis also see the highest levels of vulnerability threatens to further erode the delivery of healthcare services at the local level,” added Topchik. “The ability of our model to identify indicators with the greatest impact on predicting closure has the potential to better inform rural health stakeholders in their efforts to devise new policies and guidelines aimed at reducing downward pressure on rural provider revenues and improving care.”

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