If present trends continue, the healthcare AI revolution may leave 56 million Americans behind

Research into the design and development of AI models for rural healthcare isn’t hard to come by. However, that’s about as far as most of the investigations go. What’s missing—and sorely needed—are studies uncovering how best to validate, deploy and sustain healthcare AI models that would benefit people living at a far remove from urban centers. 

The scale of the problem becomes clear in a study conducted by biomedical informaticists at Vanderbilt University and posted ahead of peer review on medRxiv.

With approximately 18% of the U.S. population residing in areas designated as rural or borderline rural by the Centers for Medicare and Medicaid Services, “a more thorough understanding of the current state and barriers to use of AI in rural care facilities is essential for the medical and public health communities to advance the health of rural populations and reduce geographic health disparities,” write co-authors Katherine Brown, PhD, and Sharon Davis, PhD.

To close in on such understanding, the researchers reviewed 14 papers discussing predictive models and 12 papers concentrating on data or research infrastructure. Here’s more from their as-yet unpublished study. 

1. For predictive AI models in rural healthcare, applications have most commonly targeted resource allocation and distribution. 

This makes sense, the authors remark, as smaller medical centers “could quickly be overwhelmed by surging case loads, especially given limited staffing, making models to predict where public health agencies could efficiently direct resources in rural communities imperative.” 

‘However, we noted few AI solutions for acute medical events faced by rural patients, such as trauma and stroke. Outcomes are worse for rural patients suffering from an acute neurological event or trauma. As such, these conditions pose an opportunity for AI to improve care for rural patients.’ 

2. In rural areas, patient-level EHR data is often limited to specific medical centers, which can only provide small sample sizes in rural communities. 

“While existing patient-level EHR databases such as All of Us or electronic ICU (eICU) contain proxies for rurality such as most frequent ZIP-3 codes per site or site size, these sources are not widely used for research in AI for the rural U.S.,” Brown and Davis write. “Moreover, these databases may not reflect demographic or medical event prevalence of a specific rural area, a widely noted concern with model development and evaluation.” More: 

‘Synthetic data generation and federated learning are two technical approaches that could help mitigate these sample size and data representativeness concerns, but such approaches have yet to be applied to support AI in rural health and may require additional computational and analytic staff support.’

3. There has been limited exploration of deep learning and advanced neural network models—including generative AI iterations such as large language models—in rural healthcare settings. 

One reason for the lack of deep learning use cases in rural healthcare may be that specialized deep learning models require intensive and expensive computational power, Brown and Davis write. Obtaining access to such compute power, they add, “may be infeasible for small, rural medical centers—many of which are financially tenuous and lack the ability to invest in computational resources.” They add: 

‘This lack of research into deep learning for rural U.S. healthcare has introduced a rural-urban divide in AI technologies, widening the existing rural-urban healthcare divide. Unfortunately, this divide is likely to expand if research into generative AI does not include evaluating performance for rural U.S. healthcare and improving accessibility to underserved communities.’

Read the full paper.

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