463 million. That’s the number of people around the world currently living with diabetes, according to the International Diabetes Federation (IDF). And it’s estimated that the figure will jump further to 578 million by 2030, and 700 million by 2045.
Although these statistics are staggering, the disease can often be effectively managed with ongoing monitoring, proactive treatment, and proper adherence to anti-diabetic prescription medication.
With that said, nearly 50 percent of patients with diabetes do not reach their glycemic goals. This is partly due to patients’ medication non-adherence. People 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 and even fear of injections.
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 medication adherence, finding ways to effectively intervene and influence medication behavior 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 patients with diabetes 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 shown 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.
To avoid the dangerous (and potentially even deadly) consequences of non-adherence to anti-diabetic medication, healthcare organizations are tasked with finding more effective and creative ways to intervene and boost adherence in patients with diabetes.
Influencing anti-diabetic medication adherence with AI
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. AllazoHealth’s AI learns from a large number of 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 applies what it learns from these sources to predict each patient’s risk level, prioritize those who are most likely to be influenced, and target the optimal engagement and channel methods to achieve the best possible results for individuals.
The value of AI-powered interventions is its ability to personalize interventions 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, whereas others may benefit from easier, more cost-effective efforts, like text message reminders or email.
The point is, leveraging AI enables data-driven, personalized intervention strategies that are more likely to be effective because they’re tailored to each patient. Rather than a one-size-fits-all attempt to intervene – which can be as expensive as it is ineffective –organizations can develop more nuanced patient engagement programs that have a real, positive impact on individual patient behavior.
Unlock the potential of AI in diabetes medication adherence
Curious about how AI can influence anti-diabetic medication adherence? Request a demo to learn how AllazoHealth’s AI platform can predict and close gaps in therapy, personalize patient support programs, and ultimately maximize brand ROI while improving health outcomes.