• Politics
  • Diversity, equity and inclusion
  • Financial Decision Making
  • Telehealth
  • Patient Experience
  • Leadership
  • Point of Care Tools
  • Product Solutions
  • Management
  • Technology
  • Healthcare Transformation
  • Data + Technology
  • Safer Hospitals
  • Business
  • Providers in Practice
  • Mergers and Acquisitions
  • AI & Data Analytics
  • Cybersecurity
  • Interoperability & EHRs
  • Medical Devices
  • Pop Health Tech
  • Precision Medicine
  • Virtual Care
  • Health equity

Lost in directory: How bad data deepens rural health disparities | Viewpoint


The answer to the complex issues around rural health providers is to get more data.

In rural healthcare, timely access to crucial mental healthcare and other specialized services presents a significant challenge. Over the last decade, numerous rural hospitals have shuttered, with more at risk of closure due to staffing shortages, declining reimbursement rates, diminished patient volume, and challenges attracting talent.

With very few options for specialty and subspecialty providers, rural patients often endure long journeys for necessary care. According to a Pew Research Center report, the average drive to a hospital in a rural community is approximately 17 minutes, nearly 65 percent longer than the average drive time in urban areas. Such systemic failures not only exacerbate disparities but also challenge the very foundation of patient care.

A functioning rural health system relies on legions of specialty care doctors conducting outreach visits across vast geographic areas. In principle, this approach presents an efficient means to provide rural patients with access to specialty care, eliminating the need for extensive travel to major urban centers. However, the persistence of inaccurate data poses a significant barrier to achieving comprehensive access to specialty care in rural regions.

This “hub and spoke” model of specialty care, where a provider is based in one area but may serve a very wide geography, presents unique data challenges that require a different approach to analyzing information and modeling data. In this care model, a provider may go long periods without seeing patients at a given location simply because their services are only needed intermittently. A data model that doesn’t understand that will show that that provider is no longer serving that location.

Without taking this business model into account, a payer may underestimate the amount of network availability to serve members in a specific region. That’s a problem because you’d be underestimating the network when the region is already underserved in terms of provider network. The last thing we want to do is make it appear that there are network adequacy gaps where they may not exist.

Part of the problem is that legacy manual attestation practices can’t easily track all of the locations these rural providers can serve. Current data practices would show a provider at his or her primary location and would likely not show them as a referral option in the rural areas he or she serves. The system (and the data feeding that system) has to know a provider could practice at a location and is taking appointments.

Even using typical AI models, the location information isn’t accurate because it isn’t reflective of a health insurance plan’s whole network or a provider’s ability to see patients. Once appointments in an area stop, AI may say “this provider isn’t at this location anymore, do not offer this provider or location” when really, they’ll be back in a month and have appointments available for patients to book.

The answer to the issues around rural health providers is simple yet complicated: get more data. This isn’t the “easy” data to deal with. These are the unruly problems no one wants to address. Providers share all sorts of ambient data all the time from state and federal licensures, registrations, claims patterns, and more. These data points all can be triangulated to create a more accurate picture of the entire geography that a provider serves—not just their home base or frequently visited locations.

The good news is that all of this data can be gleaned from existing sources—there is no need for time-consuming and error-prone manual calls.

We’ll shine a spotlight on the challenges facing rural healthcare during this year’s VIVE conference, where I'll be joined by experts from Texas A&M, Walmart and the National Institute of Rural & Minority Health. Growing up in rural Wisconsin, healthcare is "personal" for me. Nearly 20 percent of the U.S. — about 46 million Americans — live in rural areas, yet only 12 percent of doctors practice in these communities, most of whom are primary care physicians.

With the growing importance of telehealth, traveling nurse practitioners, and funding programs playing crucial roles in improving rural access and outcomes, the foundation of these efforts lies in the data. Ensuring high-quality data will not only expedite local and federal initiatives but also maximizes their effectiveness, leading to better outcomes and experiences for rural populations.

Dr. Bob Lindner is the chief science and technology officer of Veda. He will be speaking on the VIVE “Rural Health’s Pilot Season” panel on Monday, Feb. 26 at 3pm.

Related Videos
Image: Ron Southwick, Chief Healthcare Executive
Image credit: HIMSS
Related Content
© 2024 MJH Life Sciences

All rights reserved.