Pharmacotherapy Selection System Supports Shared Clinician-Patient Decision-Making in Diabetes Treatment

In the United States, the number of patients with diabetes has risen to 23.1 million, and one in four persons aged 65 years or older is diagnosed with the condition. Unfortunately, about half of these patients fail to achieve the treatment target (<7% HbA1c) set by medical guidelines. Further, some patients forego certain treatments because they cannot afford them due to insufficient health insurance coverage.

Within the last few years, health care providers have been emphasizing a shared decision-making approach to medical care – where patients are involved in deciding their own course of treatment rather than simply following a physician’s or pharmacist’s treatment plan. The new system helps clinicians and patients to discuss different options available to them. It not only provides information on the efficacy of each drug but also takes into consideration other information important to patients, such as side effects and cost.

The machine learning-based pharmacotherapy outcome prediction and comparison technology, developed in collaboration between Hitachi and U of U Health, and announced last November^*2, was integrated into a standards-based clinical decision support system developed at U of U Health (OpenCDS^*3). By employing HL7® FHIR®^*4, a next generation standards platform that facilitates interoperability between electronic health record systems, the new drug selection support system can be linked to HL7 FHIR-compatible electronic health records. During development of the new system, the number of data sets and items were increased, improving the predictive accuracy of the technology.^*5 This system can also be utilized as a platform to connect electronic health records with various machine learning based models.