Co-Founder & Chief Strategy Officer, Percipio Health
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Health risks in rural areas are substantial. Residents of rural areas tend to be older and face higher health risks, such as increased rates of smoking, elevated blood pressure, and higher obesity rates. These health challenges, compounded with limited access to healthcare, are contributing to a growing rural mortality rate, which is already 20% higher than that of urban populations.
Although health systems with large rural populations like the Oklahoma State University Center for Health Sciences (OSU-CHS) have put numerous, multi-faceted programs in place to address these rural population health risks, challenges remain. Leaders are turning to digital health technologies and AI for patient/member health monitoring that can be deployed quickly, is widely accessible and usable, low cost, and engaging.
Here are four ways device-free patient/member monitoring can improve rural healthcare access and outcomes.
1. Addressing healthcare professional shortage areas and access
Care access issues in rural America are expansive.
Population vs. healthcare providers: While 15% of the U.S. population, equating to 46 million people, live in rural areas, only 9% of physicians choose to practice there.
Even though health systems like OSU-CHS have been and can be highly successful in recruiting and supporting providers who practice in those HPSAs, personnel alone is not going to solve this dilemma and access to care. The aim should be to utilize technology AI better in order to support rural communities.
Hear how OSU-CHS is tackling HPSAs through a combination of programs, personnel, and technology.
The No. 1 challenge of traditional remote patient monitoring (RPM) is lack of scale due to high costs and complex logistical processes required. RPM has been limited to the highest 1% of risk within a population. There is not enough proactive attention, assessment and intervention outside of that 1% of highest risk when it comes to RPM.
Hear how OSU-CHS is overcoming RPM scalability by deploying device-free monitoring to collect multiple health signals directly from patients via their smartphones.
3. Driving earlier detection of diseases and conditions
The ability to get in front of health risks before they become reality makes a substantial positive impact on outcomes and cost savings. With device-free monitoring, this is now possible across broad and hard to reach populations.
For example, a patient’s 45-second voice recording via smartphone provides voice biomarker data for assessing behavioral health and potentially detecting anxiety, depression, and other mental health conditions. Earlier detection and treatment of anxiety and depression can reduce hospital admissions by up to 30%, saving more than $3,100 in annual health costs per patient.
Percipio’s mobile app can collect not only vocal AI biomarkers for brain health assessments, but also vision-based AI biomarkers for vitals and medication monitoring, and SDoH data, to provide a whole-person view. Percipio’s clinical portal enables predictive, proactive, and personalized care, and provides clinicians with predictive insights to assist in earlier diagnosis and inform next best actions.
4. Providing usable data for clinicians and education for patients
The amount of data that device-free monitoring provides can be aggregated and analyzed with AI to provide digestible and usable information for clinicians and care teams – enabling faster intervention. For example, if data shows higher levels of specific conditions like hypertension, care teams can not only push out communication from nurses but also educational materials for patients.
To hear more about how OSU-CHS is addressing and closing rural healthcare gaps to improve population health, watch the full discussion.
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