There is no denying that machine learning and artificial intelligence (AI) are very much in vogue across the healthcare landscape. AI was a key topic in the president’s address by Mark Okusa, MD, FASN, at last year’s ASN Kidney Week in San Diego. As more healthcare information becomes digital, it is tempting to get excited about the potential for data-backed tools despite the limited deployment of AI in the clinic. Creating risk models in healthcare takes more than just computing power and advanced algorithms; it requires a deep knowledge of the underlying medical problems and a tight integration with clinical teams and their workflows. Clinicians must understand the models to effectively and seamlessly integrate them into their everyday practice.
The Rogosin Institute is affiliated with New York-Presbyterian Weill Cornell Medical Center and specializes in the care of chronic kidney disease (CKD). In 2015, Rogosin created the Program for Education in Advanced Kidney Disease (PEAK), a multidisciplinary care team that assists patients in making a smooth transition to renal replacement therapy (RRT). The PEAK program educates patients about all their options for dialysis and encourages a higher adoption of home dialysis modalities.
Healthcare AI startup pulseData specializes in creating predictive models that provide insight into the clinical domain. Rogosin and pulseData have been collaborating for over a year to effectively deploy machine learning models in a clinical setting. Through this partnership, we discovered that a deep integration of human intelligence and AI methodology matched to customized workflows is a powerful aid to delivering preventive care.
Rogosin referred patients into the PEAK program when they were at CKD stage 4, but with an increasing number of patients and limited resources, PEAK sought a better way to identify high-risk patients who would best benefit from the transition program. Perhaps machine learning was the answer.
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