According to the International Diabetes Federation (IDF), 463 million people around the world are currently living with diabetes. IDF estimates that the number will jump to 578 million by 2030, and 700 million by 2045. Although these numbers are staggering, the disease can often be effectively managed with ongoing monitoring, proactive treatment, and proper adherence to anti-diabetic prescription medications.
With that said, nearly 50 percent of patients with diabetes fail to reach their glycemic goals. People with diabetes fail to take their medications as prescribed for a myriad of reasons, ranging from the high cost of anti-diabetic drugs to unwelcome side effects, fear of injections, and beyond.
A study revealed that younger age, female gender, racial minorities, cancer diagnosis, fewer comorbidities, and a smaller pill burden were also common factors associated with medication non-adherence in patients using non-insulin anti-diabetic medication.
Whatever the reason behind poor anti-diabetic adherence, finding ways to effectively intervene and influence behavioral change is essential to improve health outcomes. Fortunately, artificial intelligence (AI) technology can identify and address anti-diabetic non-adherence with unprecedented accuracy. Here’s how:
Understanding the Consequences of Anti-Diabetic Medication Non-Adherence
Non-adherence to diabetes treatment can place a significant burden on both individual patients and the healthcare system as a whole. Research conducted by the Behavioral Diabetes Institute found that poor medication adherence in diabetes patients is very common and can have substantial consequences, such as:
- Inadequate glycemic control
- Increase in morbidity and mortality
- Inflation of costs of outpatient care
- More ER visits and hospitalizations
- Difficulty managing complications
To put the negative impact of non-adherence into perspective: Diabetes is proven to account for $24.6 billion in avoidable costs, which doesn’t factor in diabetes-related complications and comorbidities, or related conditions such as cardiovascular disease. What’s more, non-adherent patients with diabetes have twice the likelihood of being hospitalized than adherent patients, and those hospital stays are around24 percent longer.
To avoid the dangerous (and potentially even deadly) consequences of non-adherence to anti-diabetic medication, healthcare organizations are tasked with finding more effective ways to intervene and boost adherence in patients with diabetes.
Influencing Anti-Diabetic Medication Adherence with AI Technology
Today, AI tools are widely used for both type 1 and type 2 diabetes to educate patients, encourage self-management, monitor potential complications, and intervene as needed. AI technology learns from a number of large, varied data inputs, including prescription data, medical claims data, patient demographics, the social determinants of health, historical engagement data, and consumer behaviors.
The AI then uses what it learns from various data sources to predict each patient’s risk level, prioritize patients who are most likely to be influenced, and target the optimal engagement and channel methods to achieve the best possible results for each patient.
The beauty of AI-powered interventions is the ability to personalize each one to meet every patient’s unique needs. Some non-adherent patients with diabetes are more likely to respond to high-touch interventions, such as in-person appointments with a healthcare provider. Alternatively, other non-adherent patients may benefit from easier, more cost-effective efforts, such as text message reminders or smart pills.
The point is, leveraging artificial intelligence enables data-driven, individualized intervention strategies that are more likely to be effective because they’re tailored to each patient. So rather than a cookie-cutter attempt to intervene—which can be as expensive as it is ineffective—your organization can develop engagement programs that have a real, positive impact on individual patient behavior.
Curious about how AI can influence anti-diabetic medication adherence? Request a demo to learn how AllazoHealth’s AI engine can boost adherence, close gaps in care, and ultimately drive positive quality outcomes for your organization.
About the Author
William is passionate about helping people get the most from their medications. As CEO of AllazoHealth, he is driving the organization’s growth to improve adherence and patient support programs. William originally joined AllazoHealth as the Chief Operating Officer and as the organization expanded, moved into the CEO role. Prior to joining AllazoHealth, he served in a variety of leadership positions at CVS Health, including time as Vice President of Strategy and Vice President of Product Management. While at CVS Health, he delivered industry-leading clinical programs, including the award-winning Pharmacy Advisor program, which drove CVS Health clients’ Medicare Star ratings to their highest levels ever. Earlier, William served in the US Navy before getting an MBA from Northwestern University’s Kellogg Graduate School of Management; William also worked for Bain and Company and EMC before he moved to CVS Health.