EHR Data, Machine Learning Create Cost-Based Clinical Pathways 7wData

Healthcare organizations may be able to better identify variations in best practices for chronic disease management by utilizing EHR data and machine learning analytics to combine clinical and cost information, says a new article from Weill Cornell Medical School and Carnegie Mellon University. The study, published in the American Journal of Managed Care, details the process of creating clinical pathways for chronic disease care using statistical machine learning algorithms, which can divide patients into risk-based sub-groups based on spending patterns and the evolution of their clinical complexity. The resulting data may be able to foster patient engagement and care coordination by giving patients and providers more insight into how to best manage – and pay for – multiple chronic conditions. "With medical cost being such an opaque subject, providers may not have the best guidance strategy for the treatments that they offer to their patients," wrote authors Yiye Zhang, PhD, and Rema Padman, PhD. Value-based care and innovative payment models for chronic disease management are prompting providers to take a more patient-centered approach to treatment, Zhang and Padman said, and require more patient involvement in their own care.