Targeting Chronic Disease at the Point of Care

Gerard L. Coté, Texas A&M University

Of all chronic diseases, cardiovascular diseases are now the leading cause of death worldwide, with cancer, pulmonary diseases, and diabetes close behind. It will be important for a range of stakeholders engaged in the use of optical point-of-care (POC) devices to help ameliorate this situation.

Chronic diseases are particularly devastating in underserved communities in the U.S. According to Families USA, an African American adult is 72% more likely to have diabetes and 25% more likely to die of cardiovascular disease while a Latino/Hispanic adult is 63% more likely to have diabetes. Further, chronic diseases are an increasing cause of disability and lead to poor quality of life and high health care costs. The burden of chronic disease requires the purposeful design of cost-effective technologies to reduce mortality rates and hospitalizations.

It is critical to not only design transformative POC systems but also ensure that the devices are designed to provide sustainable solutions that can be used with minimal disruption.

POC devices, such as optoelectronic wearable devices or hand-held systems, can help, but designing them requires a team of engineers, computer scientists, behavioral psychologists, ethicists, and clinicians who can engage effectively with multiple stakeholders, including patients, health care providers, caregivers, industry, insurance providers, and government agencies. It is critical to not only design transformative POC systems but also ensure that the devices can be used with minimal disruption in the health care provider workflow.

Such engagement is already helping in the design of certain wearable optical technologies, such as photoplethysmography devices or pulse oximeters, to mitigate accuracy concerns by accounting for differences in the data regarding levels of fat content and skin tone. Further, paper fluidic designs, such as lateral flow assays, are being developed for these communities to provide quantifiable biomarker information more economically and in a more environmentally friendly manner. Such paper fluidic assays can be read with very cost-effective colorimetric readers for some of the higher-concentration biomarkers and even such readers as Raman systems are being designed into hand-held form factors with size and cost in mind.

Also, machine learning can enhance the sensitivity and reduce false positives for POC devices. Generative artificial intelligence, verified for accuracy, can be built into apps for smart devices to provide more digestible and actionable information to the patient. These computational approaches can combine the data that provides alerts with contextual information about the patient, such as heart rate, along with whether the person is moving, to understand whether their heart rate is normal. Multidisciplinary design using AI approaches combined with stakeholder and community engagement could, and should, be used to provide the right information to the right person at the right time.

Meet the author

Gerard L. Coté, Ph.D., is a Texas A&M Regents Professor, director of the TEES Center for Remote Health Technologies and Systems, director of the NSF-funded PATHS-UP Engineering Research Center, and holder of the James J. Cain Professor I in Biomedical Engineering at Texas A&M University. He has cofounded three medical device companies; email: gcote@tamu.edu.

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