While clinician burnout is widely known, “patient burnout” among patients with chronic conditions is also a significant but silent problem. Managing chronic conditions, especially diabetes, is complex and overwhelming. Noncompliance affects 12% of the US population has type 2 diabetes; 42% of chronic disease patients are treated for at least two or more diseases.
Dr. Tejaswi Kompala, director of clinical strategy for cardiometabolic medicine at Teladoc Health, says artificial intelligence is rethinking the healthcare industry’s approach to chronic conditions like diabetes. She believes artificial intelligence can help by:
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Personalize interventions to improve patient engagement and reduce A1c levels in diabetes management.
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Predict and identify patients at risk for uncontrolled outcomes one year in advance.
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Work closely with healthcare providers and coaches to enhance the patient experience.
We interviewed Kompala to get her insights on these topics and examples from her own work as an endocrinologist of how AI helps treat patients holistically, focusing on the key pillars of cardiometabolic health for sustainable results – nutrition, activity, sleep and stress.
Q. Please describe what you call patient burnout in the chronic illness population. And as you say, it’s a “silent problem.”
ONE. Clinician burnout is a serious and well-documented problem, but patient burnout is often overlooked. Although rarely talked about, patient burnout has serious health consequences – and from my own work, I can see that it is on the rise.
One contributing factor is the increased incidence of chronic disease. Approximately 42% of Americans have at least two chronic conditions, adding to the complexity each patient faces in their healthcare journey.
Managing these conditions is an ongoing challenge, requiring significant commitment, engagement and resources. and time to fully adhere to their treatment regimen and medications. Juggling the demands of managing a chronic condition is stressful and often takes a toll on a person’s mental health. In fact, people with diabetes are two to three times more likely to struggle with mental health than those without the disease.
These conditions can make it more difficult to successfully adhere to a treatment plan. Non-adherence – when patients fail to take their medications or fail to follow prescribed instructions – is a major problem and can stem from the everyday stresses of life, exacerbated by complex medication regimens or treatment schedules.
There’s a lot to keep track of: remembering to take your medications, making lifestyle and behavioral changes, and keeping up with medical appointments. And remember, patients often have to manage these conditions for their entire lives, which can also contribute to feelings of overwhelm and burnout.
In my practice, we see chronic conditions increasing in younger populations, which can exacerbate these challenges. We ask patients to be involved in their health for decades. – not just for a few weeks or months.
Q. In general, how can artificial intelligence intervene and help address patient burnout?
ONE. Managing chronic conditions is a burden, and it’s normal for patients to have periods of lack of motivation and burnout. Artificial intelligence can play a powerful role in keeping people busy during those ups and downs.
Over the past few years, we have begun leveraging new AI applications to proactively identify patients at risk for uncontrolled diabetes. Our predictive models use data such as blood sugar levels, medication intake, food logs, and other health signals to identify those at risk and drive greater engagement in the program. Engagement is a key factor in driving health outcomes and can help address patient burnout.
Additionally, AI models like these help identify patterns in patient behavior, highlighting potential signs of burnout or non-adherence when patients miss multiple doses or fail to keep a record of their activities.
These insights help keep patients on track and avoid being lost in the healthcare system. Ultimately, it helps clinicians allocate resources and time more effectively to provide additional support to and where patients need it most.
Q. How can AI predict and identify patients at risk of developing uncontrolled complications one year in advance?
ONE. Predictive modeling has the potential to transform diabetes care by proactively identifying patients or populations at risk for uncontrolled outcomes up to a year in advance. This is especially important because long-term uncontrolled diabetes can lead to serious and devastating complications, such as nerve damage.
With predictive modeling, we can analyze over 100 ongoing personal health data points to identify people at risk and focus on attributes that may influence their risk status, based on their individual health journey.
The data is analyzed regularly, taking into account a variety of inputs, from glucose reading frequency to medication dosage and even how engaged a person is with educational materials in the app. Traditionally, medicine has taken a reactive approach. – react after complications occur. But new technological applications give us the opportunity to react and intervene before complications occur.
Once you identify who is at risk, clinicians can act to help patients get back on track. Moving from a reactive to a proactive approach allows for more effective, appropriate care. These tools enable more timely and personalized interventions to avoid complications, improve outcomes, and better control costs for employers and health plans.
Q. How can AI work together with healthcare providers and coaches to enhance the patient experience?
ONE. I like to think of it as collaborative intelligence versus “artificial” intelligence. As clinicians, these tools help us harness data and insights in ways that make us smarter, more efficient, and more effective. By using data in, on, and around each person, AI can enable more personalized care.
Emerging technologies will continue to play a larger role in the future when it comes to drawing relevant insights from data, but ultimately it is up to humans – coaches and clinicians – to leverage the trusted relationships they have built with patients to help put those insights into action and deliver better outcomes.
Throughout the care continuum, these insights identify which engagement methods are effective for certain populations and where additional interventions are needed to target and engage patients.
Targeted interventions through outreach to healthcare providers or coaches can make a difference in promoting sustainable behavior changes such as helping patients adhere to medication, exercise regularly, and eat healthy. Ultimately, these tools actually help us become more humane and more caring and attentive.
Q. Please share some insights and examples from your experience as an endocrinologist on how AI helps treat patients holistically.
ONE. I see the greatest potential for AI in my practice as providing insights that allow me and my colleagues to provide more personalized care, supporting individual behavior change on a day-to-day basis.
For example, I spend a lot of time working with patients to implement behavior change, but I often have only a few touchpoints with them throughout the year. Digital tools help us execute our care plans and can help us intervene in a timely manner if a patient is getting off track.
Managing diabetes is a marathon, not a sprint. It requires a lot of lifestyle and behavioral changes that must be sustainable over time to achieve better results. AI plays a role in connecting and engaging with my patients beyond regular appointments to ensure they are compliant with their medications, exercising regularly, and eating healthy to lower their A1c, lose weight, and improve their mental health.
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