Chronic illnesses are among the most common, costly, and avoidable of all health problems today. Half of all adults in the U.S. suffer from at least one chronic condition and nearly 1.7 million people die from their illness each year. Non-adherence to restorative health regimens is one of the greatest barriers to improving chronic health issues in the U.S. Timothy Bickmore, PhD, and Thomas Morrow, MD, believe it’s time for pharmacies to consider using modern devices to optimize treatments.
Come medication time, patients rely primarily on pamphlets and pill bottle labels to remind them of their dosage, how to perform proper administration, and what side effects to look out for. All too often, the combination of exhaustive fine-print and personal carelessness can lead to non-adherence, in turn causing preventable health complications.
Bickmore and Morrow want doctors to imagine a world in which every patient with a chronic condition has a virtual personal assistant capable of emulating face-to-face interaction when patients type or tap on their smartphone screens. These virtual assistants would have an advanced voice recognition feature in order to engage in a social relationship with patients while telling them what to do with their medication and when to do it.
In the words of Bickmore, Associate Professor at Northeastern University, College of Computer and Information Science, “Conversational computer agents can play a major role in the chronic disease management process by providing patients with an additional source of information about their disease, treatment regimen and adherence level, as well as the motivational support for taking care of themselves.”
Morrow follows suit, saying it’s time to think beyond the pill reminder and shift our approach from “nagging to coaching.” The two ultimately argue that technology capable of facilitating personalized adherence procedures would reduce the chance of human error with much more efficacy than label or loose-leaf instructions.
Posted on February 16, 2022 by Pablo Bullian
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