Q&A: AI Helps Healthcare Organizations Reduce Avoidable Patient Harm

As the healthcare industry shifts to more preventive, value-based care, AI provides guidance for early interventions that can prevent adverse outcomes, such as readmission or death. A study published by the Mayo Clinic found that an AI-powered decision support tool helped reduce readmissions at a Wisconsin hospital by 25 percent. The tool uses AI to mine data on social determinants of health and clinical risk factors to predict which patients are at risk, then recommends evidence-based interventions.

The AI in the study, developed by Jvion, has been demonstrated to reduce avoidable patient harm events, ranging from sepsis to pressure injuries, by 20 to 30 percent on average. Dr. John Frownfelter, Jvion’s chief medical officer, spoke with HealthTech about how AI can help providers prevent avoidable patient harm and where AI is delivering value today compared with expectations for 2030.

One example is a diabetic who’s being admitted repeatedly to the hospital. How does a health system with all these processes in place help this patient who keeps getting admitted to the hospital? A hospital will have a good process in place to explore a dozen possibilities at once. That’s reactive medicine, and it’s good process at times, but it’s not precise for that patient. Artificial intelligence in this context will help to identify the underlying drivers for that patient that might be less visible and bring them to the surface so they can be addressed to help the patient on their journey toward better health.

I’m referring to a specific case where we identified a diabetic patient as being at risk for depression when screened more carefully. At first, she wasn’t open to discussing it but after the provider gently dug a little deeper, they not only helped uncover some depression that could be treated but also some struggles with her daily living activities and taking her insulin. They were able to address those things and give her support. She was able to lose 20 pounds, got treated for depression and stopped being admitted to the hospital. It wasn’t magic; it was because we helped to identify that which was unseen and under-recognized. That’s the role AI has to play today in the clinical space.