MT Health System Implements Artificial Intelligence Tool for Diabetes Care

Montana-based Billings Clinic has partnered with DreaMed Diabetes to deploy an artificial intelligence (AI)-based clinical decision support tool, which will allow its providers to remotely treat patients across the state.

Remote patient monitoring (RPM) and telehealth have grown in the wake of the COVID-19 pandemic, with multiple use cases for these technologies showing significant potential to enhance chronic disease management, including diabetes care.

Like many health systems, Billings Clinic has turned to RPM and telehealth to improve access to care and support patient engagement for diabetes patients. The health system serves a large rural population, which can create additional healthcare access challenges. The implementation of DreaMed’s tool aims to address this issue.

The press release states that the platform, known as endo.digital, is designed to support clinicians tasked with optimizing insulin dosing for type 1 and type 2 diabetes patients. Insulin optimization generally requires specialized care or access to an endocrinologist, which is a challenge for those living in rural areas. The lack of access can lead to significant delays in care and symptom management, which are associated with adverse patient outcomes.

The endo.digital platform allows non-specialist clinicians to analyze patient data and create personalized treatment plans in real-time within the care setting or remotely, according to the press release. Billings Clinic will implement the platform within its endocrinology clinic before expanding its deployment to its primary care and other clinical settings.

"Billings Clinic recognizes the importance of ensuring people with diabetes are reaching their glucose control goals in order to improve outcomes. Achieving this requires that all people with diabetes across Montana can access personalized expert-level care, and we are excited to begin using the endo.digital technology to help us make this a reality," said Lisa Ranes, manager of diabetes, endocrinology, and metabolism, part of Diabetes Research and Allergy, at Billings Clinic, in a press release.

These efforts highlight how healthcare organizations are applying AI to enhance chronic disease management, particularly in treating conditions like heart disease, diabetes, and cancer.

In February, Community Health Systems announced it would partner with Candence to use AI to enhance its RPM practices to bolster chronic disease management for patients with hypertension, heart failure, diabetes, and chronic obstructive pulmonary disease.

Further, research presented at the 82nd Scientific Sessions of the American Diabetes Association in June found that an AI-based intervention offered significant rates of remission compared to standard care alone for type 2 diabetes patients.

This month, researchers showed that a machine-learning algorithm trained to analyze electrocardiogram readings could accurately predict pre-diabetes and type 2 diabetes in at-risk populations, which has the potential to advance diabetes screening in the future.