Agentic AI could transform treatment of chronic conditions

By Boris Kheyn-Kheyfets, senior manager, Deloitte Consulting LLP

Artificial intelligence (AI) is helping to transform the medtech industry by enabling more accurate diagnostics and personalized treatments. In Part 1 of this two-part series, we looked at how medtech companies could use agentic AI to streamline business functions and improve margins. Here, in part 2, we look at how agentic AI could help patients with chronic conditions understand and comply with prescribed treatments.

Part 2: Seven questions for Richard Mackey, chief technology officer at CCS

Artificial intelligence—specifically agentic AI—was a hot topic of conversation at the annual HLTH conference in Las Vegas earlier this fall. Wearable devices and home monitoring systems powered by AI can be used to track vital signs (like heart rate, blood pressure, and blood-sugar levels) in real-time. AI algorithms can then analyze this data for patterns and alert patients or caregivers to potential issues before they become emergencies.

At the conference, I had the opportunity to share a stage with Richard Mackey, chief technology officer at CCS, a provider of collaborative-care programs and home-delivered medical supplies for people who have diabetes and other chronic conditions. The title of the session was "AI advances patient-first diabetes care." While there are thousands of use-cases in health care for generative AI, few organizations have started to use agentic AI.¹

For the roughly 30 million Americans who have been diagnosed with Type 1 or Type 2 diabetes,² wearable tools such as continuous glucose monitors (CGMs) are increasingly using predictive analytics and AI to track glucose levels, alert patients when adjustments are needed, and to support real-time decision-making.³ By analyzing streams of data, these systems can improve adherence, personalize care, and strengthen patient engagement. However, the effectiveness of the device also depends on the user’s confidence in using it. Without adequate education, patients may misinterpret readings or misuse the device, leading to poorer outcomes and higher medical costs.

I caught up with Richard a few weeks after the conference to talk a little bit more about the use of agentic AI in health care. Here is an excerpt from our conversation:

Boris: What role does a CGM play in the management of diabetes?

Richard: Continuous glucose monitoring devices offer critical real-time monitoring of glucose levels and fluctuations throughout the day, allowing for more timely decisions about treatment and necessary lifestyle modifications than would be possible with traditional intermittent glucose-monitoring techniques. A CGM helps patients achieve tighter glycemic control without the fear of hypoglycemia and is associated with reduced medical costs and better health care utilization. However, CGMs can only improve outcomes if patients use them. Previous studies have shown that nonadherence to a CGM is associated with adverse health outcomes and increased costs due to less consistent glycemic control.⁴ CGMs have transformed diabetes management and changed it from a reactive to a proactive approach to care.

Boris: How can predictive analytics and AI determine which patients are in danger of noncompliance?

Richard: Understanding why some people don’t comply with prescribed treatments is a serious issue. Up to 40% of people who receive a prescription for a CGM might not use that device as prescribed…for a variety of reasons.⁵ Some people might decide not to use the device at all. In other cases, they might stop using it after several months or after a year or two. Some people might not have received enough education about using the device. Others might find that wearing the device is uncomfortable. Even people who have experience with a CGM might stop due to the various ups and downs of life. In addition, changes in health coverage or the loss of a job could make it difficult to schedule appointments with a doctor or cover the costs. Keeping people on therapy can require a wide range of messages and reinforcement based on their situation.

Boris: How can an organization keep patients on CGM therapy?

Richard: Health care organizations that have access to patient behavior data—and have invested in predictive capabilities—might be better positioned to keep patients on CGM therapy. A patient might not be at risk today, but could be at risk months from now. The real-time data generated by the devices are fresher than claims data, and that is part of what makes it possible to have more accurate predictions. The fresher the data, the more actionable it is. That can be a significant differentiator.

Boris: What type of savings could AI generate?

Richard: Depending on the population, as many as 40% of people will not stay compliant in the use of their CGM device. If organizations can ensure people maintain adherence, they could see savings of $2,200 per patient per year.⁶ A big part of that savings is making sure that device isn’t just sitting on a shelf. Organizations could also see savings from helping patients maintain glycemic control and the benefits associated with better regulated glucose levels.

Boris: How can organizations calculate the ROI?

Richard: There are three factors to consider. First, the use of AI means employees don’t need to spend time on repetitive administrative tasks. There is also a market-expansion opportunity. And there is a patient-experience component where patients see a higher value from services because employees are able to spend more time with them. A company can look at efficiencies, improved employee or patient experience, or revenue growth options to develop ROI in these areas.

Boris: How do you see agentic AI coming into play?

Richard: There’s been a lot of traction in health care with ambient listening systems that record conversations and summarize resultant insights in a useful way. This reduces the amount of time staff spends taking notes and documenting. Staff then has more time to devote to patient care.

Boris: What are some considerations for organizations when they begin to scale AI?

Richard: To scale AI, organizations should focus on embedding AI into core platforms and workflows, starting with high-impact areas like operational automation and customer engagement. They should prioritize cross-functional collaboration and ensure that business, clinical, and technology leaders align on outcomes.

Conclusion
Agentic AI has the potential to transform the management of chronic diseases by shifting from simple data-tracking to proactive, patient-centered engagement. By integrating advanced analytics and agentic AI, health care organizations can both predict and address barriers to device adherence—delivering timely education and support that is tailored to an individual’s needs. This can empower patients to manage their chronic conditions more effectively, while freeing up clinical staff to focus on the human aspects of care. As the health care landscape continues to evolve, agentic AI is likely to become a differentiator—not only by helping to prevent costly complications and improve outcomes, but by also enhancing the overall patient experience.

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Endnotes:

¹AI agents shone on the HLTH stage, Newsweek, October 23, 2025
²National Diabetes Statistics Report, Center for Disease Control and Prevention
³Integration of artificial intelligence and wearable technology, Springer Nature, November 18, 2025
⁴The ongoing need to address cost-related nonadherence, Diabetes Care, October 29, 2025.
⁵The use of diabetes technology to address inequity in outcomes, Springer Nature, June 1, 2022
⁶Health and economic benefits of diabetes interventions, National Center for Chronic Disease Prevention and Health Promotion, July 11, 2024

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