J Korean Diabetes.  2020 Sep;21(3):149-155. 10.4093/jkd.2020.21.3.149.

Out-of-Hospital Data: Patient Generated Health Data

Affiliations
  • 1Department of Nursing, College of Life & Health Sciences, Hoseo University, Asan, Korea
  • 2The Research Institute for Basic Sciences, Hoseo University, Asan, Korea
  • 3Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
  • 4Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
  • 5College of Nursing, Seoul National University, Seoul, Korea
  • 6Interdisciplinary Program of Medical Informatics, Seoul National University, Seoul, Korea
  • 7Research Institute of Nursing Science, Seoul National University, Seoul, Korea

Abstract

Patient-generated health data (PGHD) are health-related data generated, recorded, and collected by patients or caregivers. Its main advantage is that patients can actively participate in their own health care, since the data-generating agents are patients and caregivers, not hospitals. Due to the development and popularization of information and communications technology and digital devices, the number of studies using PGHD for better health care is increasing. When PGHD was used in the outpatient setting, healthcare providers were better able to understand each patients’ condition using more accurate data, and to monitor patient health status between visits. In particular, to manage chronic diseases such as diabetes, it is essential to monitor daily blood sugar and change nutrient intake in the context of medication, overall diet, and exercise. However, problems associated with data quality, data extraction, and insufficient evidence and research to guide use of this kind of data in clinical setting are yet to be solved. Further, the gap between patient and healthcare providers’ perceptions of PGHD persists. We suggest that PGHD, electronic medical record data in hospitals, and claims and genome data could be combined to good effect. This combination can help patients and healthcare providers make better decisions with respect to patient health and to maintain patient engagement. In addition, the collection of PGHD through sophisticated sensors, and data analysis through advanced portals could combine medical big data with daily big data. Eventually, a personalized healthcare automation system through PGHD-based algorithms could provide healthcare artificial intelligence services.

Keyword

Artificial intelligence; Big data; Consumer health informatics; Diabetes mellitus; Mobile health; Patient generated health data
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