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.
By creating step-by-step clinical pathways based on a patient’s anticipated disease development, big data analytics techniques could help providers “achieve accurate predictions of anticipated future events and costs following different clinical and cost pathways for improved shared decision making, and, subsequently, identify appropriate ranges of cost for targeted clinical pathways within a patient population,” says the article.
Using a sample of 288 patients from Western Pennsylvania with multiple chronic diseases, Zhang and Padman extracted medication information from the electronic health records system at a nephrology practice specializing in chronic kidney disease.
The patients all had diagnoses of CKD stage 3, diabetes, and hypertension. They had an average age of 73.4 years, were primarily Caucasian, and experienced an average of 5.5 office visits and 0.4 hospitalizations per year.
First, the researchers used machine learning algorithms to group the patients into three clinically-focused subgroups identified through the classes of drugs present in the EHR data.
They also divided patients into four spending cohorts based on the costs of their medications using prescription copay prices tailored to regional pricing trends.
The data revealed a few expected trends: the highest complexity patients tended to spend the most on medications, while less complex chronic disease patients incurred fewer costs.
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