Rural Health and AI
How AI is revolutionizing rural health
With high costs and healthcare staffing gaps, it’s no question that healthcare access in the U.S. is in crisis.
And nowhere is that crisis more acute than in rural America.
Here’s a snapshot: Between 2010 and 2021, at least 136 rural hospitals closed. And almost half of rural hospitals operate with negative profit margins. With these tight budgets, access to more advanced technologies is limited for the providers who do work in these rural hospitals and clinics.
And not many providers do. Just take a look at this provider shortage map and check out the distribution.
And of course, this rural health crisis is not just limited to the U.S.
Rural areas are overrepresented in global poverty. Rural health clinics around the world are understaffed—when they even exist.
So how do we go about solving this problem?
If you've been reading our newsletter for some time, you’re probably thinking healthcare AI has something to do with it. And we agree.
As we think of potential medical applications of advancements in AI, we need to be thinking of where there’s the greatest need (and room for improvement).
Rural health is possibly the best candidate.
First things first: We must build up rural health data
Before AI can become an effective tool in rural healthcare, we need to increase connectivity—and collect data.
In our discussion of the use of AI in health information exchange (HIE), we noted how predictive analytics and alerts can help providers more efficiently intervene and provide preventive services to the most vulnerable patients.
For more disconnected rural communities, that kind of intel can help resource-strapped rural providers make a more targeted impact.
But first, these tools need data to understand local health contexts, trends, behaviors, outcomes, etcetera.
Here are the best strategies we see for building up rural health data:
Increasing the use of remote monitoring tools (e.g., wearable devices) and reporting of OTC diagnostic testing. Making patient-driven data a piece of cake.
Creating telehealth connections to urban health centers for more specialist coverage of rural patients. So that patients are actually getting the specialized care they need—and adding to the data pool.
Bolstering HIE, so that health records travel with patients if they move from rural area to city (or other rural area). People are mobile! Their health data shouldn’t vanish into the ether when they travel and relocate.
Then, you pair these tools with AI and 5G connectivity, and their power to enhance care amongst provider shortages really takes off. We’ll dive into that topic more in a future feature (stay tuned).
But overall, beyond improving the availability of rural health data, these strategies also serve to improve access to quality healthcare in themselves. We call that a win-win.
Putting AI to work in the rural health system
Once more data is collected on these populations and their health conditions, predictive analytics can get to work.
Applications of AI-enabled rural healthcare enhancements can range from predictive acute illness triaging and diagnostics to population health alerts.
But AI can also help us better understand not just what individual rural patients need. It can also help us better pinpoint systemic weaknesses. And get to work addressing them more efficiently.
Using AI to analyze rural healthcare data, we can tackle:
Rural hospitals’ and clinics’ operational inefficiencies and inadequacies. That’s a great path to data-driven corporate and government resource & funding allocation.
Provider shortages on the hyper-local level.
Bringing rural health data into the fold of overall healthcare big data, improving overall population data for everyone.
Take another look at that last point.
Even if we ourselves don’t live in rural areas, rural healthcare still impacts us. Rural health doesn’t happen in a vacuum—like we said, people travel and relocate. And having more people represented in our data on different conditions and health environments ultimately makes our responses more accurate.
Plus, increasing data diversity improves the accuracy of our healthcare AI tools.
Look at that. Another win-in.
These projects are all locally-led initiatives in low- and middle-income countries, where access to advanced technology overall is limited—including in the healthcare context.
The Gates Foundation’s focus on this issue is a big deal.
Having such a big name in global health bring attention to this issue bolsters our firm belief that AI is poised to transform access to quality healthcare—even in the most remote areas of our country and world.
That being said, here’s our advice for healthtech innovators: Don’t forget the “little guy.”
Lower population areas may give user-hungry healthtech leaders and developers a smaller audience. But these areas are often high-need. And isn’t that why we innovate in healthcare? To help those who need it most?
Plus, with more attention being brought to servicing these high need areas (by the likes of the Gates Foundation, no less), we’d bet that being on the frontlines of the rural health revolution is likely to pay off for those daring to tackle the challenge.