Telemedicine, a branch of medicine that aims to connect patients and providers in alternative ways rather than face-to-face communication, is a rapidly expanding field that seeks to improve the quality and accessibility of patient health. With the establishment of this kind of technology in medicine, health informatics will be taking a more consequential role.

The benefit of telemedicine is that underserved and isolated populations are able to access the care they need. Due to the digitization of health records, now information can be easily passed on and patients can receive faster care of the same quality. A current need is for systems to be configured for the accessibility of patient information internationally. The result from telemedicine connecting hospitals and their constituents is that global systems will become more efficient in dealing with world-wide epidemics as well as providing care to remote areas.

The data gathered from remote patient monitoring, a discipline of telemedicine where providers use heart rate sensors, pressure gauges, and other medical devices to gather information on patient health, is used to identify high-risk patients and then consequently provide a personalized treatment plan.

Because it’s hard to share and analyze healthcare information due to HIPAA regulations, an NIH sponsored project allows datasets to be anonymous so that they can be more accessible to researchers. The project strips any identifying information and leaves the treatment plan and how the patient responded. This allows researchers to provide health analysis and improve patient care.

Dr. Saif Khairat, Assistant Professor in the School of Nursing and CHiP core faculty member, is the Principle Investigator of Carolina Applied Informatics Research. The focus for this research team is on EHR usability, teleconsent, and telemedicine. More specifically in telemedicine, Dr. Khairat’s research focuses on improving clinical management of chronic diseases. Based on existing data, this study aims to find a systematic approach to classifying potential patients who would benefit from telemedicine, whether it be due to geographic distance, lack of distance, or costs.