Improved Disease Surveillance, Population Health Management, and Patient Engagement through Innovative AI Approaches

In this webinar, Dr. Guy Hembroff discusses his work in developing a trusted framework architecture designed to improve population health management and patient engagement. He demonstrates how his team’s work in the development of accurate geo-tagged pandemic prediction algorithms is used to help coordinate medical supply chains to care for patients most vulnerable to COVID-19 and can extend to help improve general population health management. The framework consists of a holistic and secure mHealth community model for residents to overcome significant barriers of care and provide healthcare agencies improved coordinated population management through the aggregation of longitudinal patient health data, including patient generated health data and social determinants of health. The architecture’s security includes the development of touchless biometrics, capable of large-scale identity management through the establishment of unique health identifiers. Machine learning algorithms can strategically connect residents to community resources and provide customized health education aimed at increasing the health literacy, empowerment and self-management of patients. Users are able to securely share their health data with others (e.g., physicians, caregivers), while clinicians can better track patients, offering improved preventative measures and care management.

Speaker

Dr. Guy Hembroff, Associate Professor in the College of Computing at Michigan Technological University

Dr, Hembroff is the founding director of the Health Informatics graduate program. He has expertise in medical AI, cybersecurity and application development. He also serves as director of the Institute of Computing and Cybersystems’s (ICC) Center for Cybersecurity and a member of the ICC’s Center for Biocomputing and Digital Health. Dr. Hembroff's research is focused on machine learning, deep learning and image analysis for the purpose of medical diagnosis, treatment, population health and quality control. He also has research projects in large-scale cybersecurity architectures and the development of medical applications and devices, such as the Internet of Medical Things.

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