Ensuring patients adhere to their prescribed medication is not only important for their health but can also significantly impact the financial strain on the healthcare system. According to the Centers for Disease Control and Prevention (CDC), nonadherence to medication can cost thousands of dollars annually. For instance, one study found that better adherence to prescribed medications lowered healthcare costs among patients with congestive heart failure by an estimated $7,800 per person yearly.
Similarly, patients taking blood pressure medication could save approximately $3,900 annually, while those taking medication to manage high cholesterol could save $1,250 every year.
The issue of medication adherence is even more critical in underserved areas, where public health diseases like tuberculosis (TB) are prevalent. Patients with TB require Directly Observed Therapy (DOT), where healthcare professionals oversee medication intake and monitor the response to treatment. Unfortunately, TB disproportionately affects underserved communities, with CDC data indicating that ethnic and racial minority groups account for around 88% of reported TB cases in America.
Improving medication adherence in underserved communities, particularly for diseases like TB, is crucial not only for individual patient outcomes but also for reducing healthcare costs and promoting health equity. By addressing the underlying social determinants of health, we can create a more equitable healthcare system that ensures every patient receives the care they deserve.
To address the problem of medication nonadherence, the CDC recommends using telehealth technology with video-enabled devices that allow remote interactions between patients and healthcare professionals. The University of Georgia has conducted a new study that shows that artificial intelligence can further reduce the burden on healthcare workers by evaluating patient-submitted videos for medication adherence with accuracy.
The study looked at TB treatment in low-resource communities in Uganda, where an AI-enabled program reviewed thousands of videos of people taking their medication, identifying patients taking their medication 85% of the time, a percentage comparable to humans performing the same task. While the technology can reduce the workload of healthcare workers, they are not left out entirely as they can watch only a few that need verification. The technology could be applied to various treatments that may benefit from observed medication adherence.
Though humans aren’t left out of the process entirely. University of Georgia researcher Juliet Sekandi says, “AI is really an accelerator of that process because then a nurse will not be worried that they have to watch all the 10,000 videos, but maybe watch only a few that need verification, say 100 out of 10,000.” While the study only considered AI-assisted DOT among TB patients, it’s easy to see how this technology could apply to various treatments that may benefit from observed medication adherence.
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