What You Should Know:
– Hitachi, U of U Health, and Regenstrief researchers partnered to develop and test a new AI method to improve personalized care for diabetes patients needing complex drug treatment.
– The new AI method can now be used to help determine an optimal drug regimen for a specific patient.
Hitachi, University of Utah Health, and Regenstrief Institute today announced the development of an artificial intelligence (AI) method to improve care for patients with type 2 diabetes mellitus who need complex treatment. Some of the results of this study are published in the peer-reviewed medical journal, Journal of Biomedical Informatics, in the article, “Predicting pharmacotherapeutic outcomes for type 2 diabetes: An evaluation of three approaches to leveraging electronic health record data from multiple sources”.
Personalized Care for Diabetes Patients Needing Complex Drug Treatment
One in 10 adults worldwide has been diagnosed with type 2 diabetes, but a smaller number require multiple medications to control blood glucose levels and avoid serious complications, such as loss of vision and kidney disease. Hitachi had been working with U of U Health for several years on the development of a pharmacotherapy selection system for diabetes treatment. However, the system was not always able to accurately predict more complex and less prevalent treatment patterns because it did not have enough data. In addition, it was not easy to use data from multiple facilities, as it was necessary to account for differences in patient disease states and therapeutic drugs prescribed among facilities and regions. To address these challenges, the project partnered with Regenstrief to enrich the data it was working with.
AI Method & Approach
The new AI method initially groups patients with similar disease states and then analyzes their treatment patterns and clinical outcomes. It then matches the patient of interest to the disease state groups and predicts the range of potential outcomes for the patient depending on various treatment options. The researchers evaluated how well the method worked in predicting successful outcomes given drug regimens administered to patients with diabetes in Utah and Indiana. The algorithm was able to support medication selection for more than 83 percent of patients, even when two or more medications were used together.
In the future, the research team expects to help patients with diabetes who require complex treatment in checking the efficacy of various drug combinations and then, with their doctors, decide on a treatment plan that is right for them. This will lead not only to better management of diabetes but increased patient engagement, compliance, and quality of life. The three parties will continue to evaluate and improve the effectiveness of the new AI method and contribute to future patient care through further research in healthcare informatics.