The International Diabetes Federation estimates that around 463 million people globally are presently living with diabetes. By 2030, this figure is projected to rise to 578 million, and by 2045, to a staggering 700 million. Although these numbers are concerning, diabetes is a disease that can often be properly managed with consistent monitoring, proactive treatment, and adherence to prescribed anti-diabetic medication.
One of the challenges, however, is that nearly half of all diabetes patients do not meet their glycemic goals due to non-adherence to medication. This non-adherence can arise from a variety of reasons such as the exorbitant prices of anti-diabetic medications, adverse side effects, and even a fear of injections. Other common factors contributing to non-adherence include younger age, female gender, belonging to racial minorities, a diagnosis of cancer, fewer comorbidities and a smaller pill burden.
Non-adherence to diabetes treatment places a significant burden on individual patients and the healthcare system. Negative impacts include inadequate glycemic control, an increase in morbidity and mortality, a rise in the costs of outpatient care, more emergency room visits and hospitalizations, and difficulty in managing complications. According to research, diabetes accounts for around $24.6 billion in avoidable costs excluding complications and co-morbidities. In addition, non-adherent diabetes patients are twice as likely to be hospitalized, often with 24% longer stays.
In a bid to circumvent the severe consequences of non-adherence, healthcare organizations are exploring more effective and innovative ways to promote adherence. One such approach is the use of artificial intelligence (AI). This technology is now utilized in both type 1 and type 2 diabetes management to educate patients, encourage self-management, monitor potential complications, and intervene promptly when necessary.
AllazoHealth’s AI uses an impressive array of data inputs like prescription data, medical claims data, patient demographics, historical engagement data, and consumer behaviors to predict each patient’s risk level. It then prioritizes those most likely to be influenced and identifies the best engagement methods for each individual’s unique needs.
For instance, some non-adherent diabetes patients respond better to in-person appointments with a healthcare representative while others benefit from easy, cost-effective solutions like text reminders or emails. A one-size-fits-all intervention strategy is largely ineffective and expensive. Instead, by leveraging AI, healthcare organizations can devise personalized intervention strategies that have a better chance of influencing patients’ behavior positively.
This technology has the potential to fill gaps in therapy, personalize patient support programs, increase brand ROI, and ultimately boost health outcomes. To understand how AI can influence anti-diabetic medication adherence, it is possible to request a demo from AI platforms like AllazoHealth. Artificial intelligence has the potential to revolutionize how we address the ongoing global issue of diabetes by assisting in improving medication adherence.
