Timely access to critical mental health and other specialized services is a major challenge in rural health care. Over the past decade, many rural hospitals have closed, and many more are at risk of closure due to staffing shortages, declining reimbursement rates, declining patient volumes, and staffing challenges.
With so few options for specialty and subspecialty providers, rural patients often endure long distance travel to get the care they need. The average drive time to a hospital in rural areas is about 17 minutes, which is nearly 65% longer than the average drive time in urban areas, according to a Pew Research Center report. These systemic failures not only exacerbate disparities, but challenge the very foundations of patient care.
The functioning of rural health systems relies on the presence of large numbers of specialized physicians who travel across vast geographic areas. In principle, this approach provides an efficient means of providing rural patients with access to specialized treatment, eliminating the need for long-distance travel to metropolitan centers. However, persistent inaccurate data remains a major barrier to achieving comprehensive access to specialty care in rural areas.
In this “hub-and-spoke” model of specialty care, providers may be based in one region but serve a very large area, requiring different approaches to analyzing information and modeling data. It presents unique data challenges. In this model of care, providers may not see patients at a particular location for long periods of time simply because the services are only needed intermittently. A data model that does not understand this indicates that the provider does not serve that location.
Without considering this business model, payers may underestimate the amount of network available to serve members in a given region. This is a problem. That’s because you’re underestimating your provider’s network, even though the area is already underserved by it. The last thing we want is to create the appearance that there is a network adequacy gap when there may not be one.
Part of the problem is that traditional manual verification methods cannot easily track all the locations of these rural providers. can serve. Under current data practices, providers are likely to be visible at their primary location and not as a referral option in the rural areas they serve.The system (and the data it feeds) must be aware of the provider did it We are practicing on-site and are accepting reservations.
Even with common AI models, location information is not accurate because it does not reflect the health plan’s entire network or the provider’s ability to see the patient. When appointments run out in an area, the AI says, “This provider is no longer in this location,” when in fact he will be back in a month and has appointments available for patients to book. So please don’t offer this provider or location.”
The answer to the question of rural health care providers is simple but complex. It’s about getting more data. This is not “easy” data to work with. These are intractable problems that no one wants to deal with. Providers constantly share all kinds of surrounding data, including state and federal licenses, registrations, and billing patterns. All of these data points can be triangulated to create a more accurate picture of the entire geography the provider serves, not just the provider’s home base or frequently visited locations.
Fortunately, all of this data can be collected from existing sources, eliminating the need for time-consuming and error-prone manual calls.
This year’s VIVE conference will spotlight the challenges facing rural healthcare. Experts from Texas A&M, Walmart, and the National Institute for Rural and Minority Health will also participate. Growing up in rural Wisconsin, healthcare is personal to me. Although nearly 20 percent of the United States (approximately 46 million Americans) live in rural areas, only 12 percent of physicians practice in these areas, and most are primary care physicians.
With the increasing importance of telemedicine, traveling nurses, and funding programs that play a critical role in improving rural access and outcomes, the foundation of these efforts is data. Ensuring high-quality data not only accelerates local and federal efforts, but also maximizes their effectiveness and delivers better outcomes and experiences for rural people.
Dr. Bob Lindner is Veda’s Chief Science and Technology Officer. He will be speaking on the VIVE “Rural Health’s Pilot Season” panel on Monday, February 26th at 3pm.
