AI-Enabled Predictive Analytics

AI-enabled predictive analytics uses machine learning to look at large amounts of data and identify patterns that help forecast future outcomes and trends, such as hospital readmissions. These tools can help healthcare teams better understand a person's health and may support both individual-level care and population-level planning. Predictive analytics often work alongside other AI technologies, such as triage tools and remote patient monitoring.

By analyzing large datasets such as electronic health records, medical images, and other health data, predictive analytics can be used to identify early signs of disease, predict disease progression, recognize patients who may be at higher risk, and support more personalized care. This can help healthcare providers act sooner, tailor treatment plans to patients' individual needs, and prevent problems from becoming more serious.

Predictive AI models are used in rural healthcare settings to identify high-risk patients. A review of gaps in AI research in rural healthcare found that more than half of the 26 studies included discussed predictive AI models. These studies looked at the use of AI to predict outcomes such as stroke, COVID-19 test results, diabetes, breast cancer, and hospital readmissions for various conditions. Despite this growing body of research — and that most U.S. hospitals now use AI predictive analytics tools — small, rural, and independent hospitals and Critical Access Hospitals (CAHs) are still less likely to have access to these tools compared to non-CAHs. Lower adoption is often due to structural factors, including limited staff and a lack of technology infrastructure.

AI-enabled predictive analytics is also used to support healthcare operations, such as automated billing and appointment scheduling (See AI-Assisted Appointment Scheduling). National data shows that use of AI for automated billing has increased substantially. Predictive AI can also be used to review claims. One of the biggest challenges is ensuring that these tools are well integrated into clinical and administrative workflows.

Examples of Rural Programs Using AI-Enabled Predictive Analytics

Sanford Health, the largest rural health system in the U.S., uses AI-enabled predictive analytics to manage chronic diseases. Sanford Health's AI-assisted chronic kidney disease model identifies patients at higher risk for chronic kidney disease, prompting providers to order timely tests. The program has doubled the number of diabetes patients receiving recommended kidney tests and tripled early diagnoses, helping patients to get treatment sooner and avoid dialysis. Sanford also uses AI to support colon cancer screening by helping clinicians determine the most appropriate screening option for each patient, such as prioritizing colonoscopy or identifying patients eligible for non-invasive screening.
Atrium Health Carolinas Medical Center uses AI to predict surgical complexity by reviewing CT scans taken before surgery. The tool helps identify patients who may need a more complex procedure or who are more likely to develop complications, such as wound infections. The tool is highly accurate and can help physicians and patients make more informed decisions ahead of time — potentially reducing complications, follow-up treatments, and the need for additional hernia surgeries.
Geisinger Health System, serving counties in central, south-central, and northeast Pennsylvania, created an AI-enabled predictive model to identify patients who are at a high risk of having a stroke. Geisinger care teams then proactively manage these patients to close care gaps and reduce stroke risk.

Considerations for Implementation

AI-predictive analytics can help providers spend more time on direct patient care instead of administrative tasks. These tools can also help healthcare systems to improve workflows, quality of care, and patient outcomes. To be effective, AI predictive analytics tools must fit naturally into existing workflows. When AI is built into existing systems and tools already used in practice, it is more likely to be trusted and used consistently.

Provider involvement in the design and implementation of AI predictive tools is another key factor for success. Provider input helps ensure the tools address real needs, produce useful alerts, and support clinical decision-making. At the same time, challenges remain. Providers may lack confidence in the accuracy and reliability of AI predictive analytics tools.

Resources to Learn More

Four Actions to Close Hospitals' Predictive AI Gap
Document
Describes national trends in the adoption of AI‑enabled predictive analytics in hospitals and identifies gaps between large, system‑affiliated hospitals and smaller, rural, independent, and critical access hospitals. Outlines four actions health system leaders can take to close the predictive AI gap and improve operational efficiency and patient care.
Organization(s): American Hospital Association, Center for Health Innovation
Date: 11/2025

More Hospitals Using Predictive AI, but Disparities Persist: ASTP
Document
Summarizes federal survey data showing increased use of AI-enabled predictive analytics in U.S. hospitals, while highlighting disparities in adoption among rural, independent, and critical access hospitals.
Organization(s): Healthcare Dive
Author(s): Olsen, E.
Date: 9/2025

Rural Hospitals and the AI Advantage: Turning Constraints into Catalysts
Document
Highlights how rural hospitals are using targeted AI applications such as predictive analytics to address workforce shortages, financial pressures, and access challenges. Includes real‑world examples and lessons learned from rural health systems implementing AI in resource‑constrained settings.
Organization(s): American Hospital Association, Homeward Health
Date: 1/2026