This is a K99/R00 Pathway to Independence Award submitted to the National Institute on Aging by Yongkang Zhang, a Research Associate in the Department of Healthcare Policy and Research at Weill Cornell Medical College (WCMC). Dr. Zhang?s career goal is to become an independent researcher on improving care for the elderly through developing and applying effective prediction tools to identify older adults with complex conditions and high healthcare needs. This K99/R00 application will provide Dr. Zhang with the necessary training 1) to understand the complexity and characteristics of high-need, high-cost (HNHC) older adults; 2) to develop a prediction model for HNHC adults using machine learning methods; and 3) to test the performance of the machine learning-based model with three commonly used, patient-risk prediction tools. Dr. Zhang has assembled a mentor team of accomplished researchers across multiple divisions and departments at Weill Cornell Medical College: Dr. Rainu Kaushal (primary mentor) who is the Nanette Laitman Distinguished Professor and an expert on the HNHC patients and health data science; Dr. Lawrence Casalino (co-mentor) who is the Livingston Farrand Professor of Public Health and an expert on characteristics of and healthcare delivery for HNHC patients; Dr. Mark Lachs (co-mentor) who is a Professor of Geriatrics and prac- ticing geriatrician and an expert on the complexity and healthcare needs of older adults; Dr. Yuhua Bao (co- mentor) who is an Associate Professor of Healthcare Policy and Research and expert on behavioral health conditions and prescription data; Dr. Fei Wang (co-mentor) who is an Associate Professor of Health Informat- ics and an expert on machine learning methods; and Dr. James Flory (consultant) who is an Assistant Profes- sor of Healthcare Policy and Research and practicing clinician and an expert on medication use. HNHC older adults are small group of patients representing a disproportionate share of healthcare utili- zation. These patients are more likely to experience preventable quality and safety problems due to their fre- quent interactions with health systems. Caring for HNHC older adults provides great potential benefits for qual- ity improvement and cost reduction. However, the benefits are unlikely to be realized unless these patients can be correctly identified and targeted. Building on his previous research and training on developing a claims data-based taxonomy for HNHC Medicare patients, Dr. Zhang?s research will understand the characteristics of HNHC older adults and develop predictors for these patients using clinical and prescription data (Aim 1), de- velop a machine learning-based prediction model for HNHC older adults (Aim 2), and compare the perfor- mance of the prediction model with three commonly used, patient risk prediction tools (Aim 3). This research will be the foundation for an R01 grant application that will incorporate this prediction model into healthcare de- livery process and identify the optimal ways to inform opportunities for improvement in healthcare delivery.

Public Health Relevance

High-need, high-cost (HNHC) older adults are a small group of individuals representing a disproportionate share of healthcare utilization and experiencing preventable quality and safety problems. Improving care for HNHC older adults is challenging because of their complex medical, behavioral, and social conditions and the instability of their healthcare utilization. Developing a prediction model for HNHC older adults could better inform patient-centered clinical decision-making, help target quality improvement interventions, and prioritize the allocation of scarce healthcare resources.

National Institute of Health (NIH)
National Institute on Aging (NIA)
Career Transition Award (K99)
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Neuroscience of Aging Review Committee (NIA)
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Bhattacharyya, Partha
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Weill Medical College of Cornell University
Other Health Professions
Schools of Medicine
New York
United States
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