The Ludwig Boltzmann Institute for Digital Health and Prevention, Salzburg, Austria.
Institute of eHealth, University of Applied Sciences - FH JOANNEUM, Graz, Austria.
Stud Health Technol Inform. 2024 Aug 22;316:437-441. doi: 10.3233/SHTI240442.
In recent years, the adoption of wearable gadgets such as Fitbit has revolutionized the way individuals track and monitor their personal activity data. These devices provide valuable in-sights into an individual's physical activity levels, sleep patterns, and overall health metrics. Integrating this data into healthcare informatics systems can offer significant benefits in terms of personalized healthcare delivery and improved patient outcomes. This paper explores the synergistic integration of Fitbit-generated personal activity data using the openEHR Reference Model in healthcare informatics as a practical case study in patient-generated health data (PGHD) integration based on health informatics standards as a framework for the representation and exchange of Electronic Health Records (EHRs). The synergistic integration of Fitbit-generated personal activity data through openEHR and FHIR standards models also covers the way for advanced analytics and population health management. By linking and analyzing data from various sources, including sensors and wearable devices, healthcare organizations can identify trends, patterns, and insights that can guide population health strategies, preventive care initiatives, and personalized treatment plans, in addition to aiding physicians in follow-up care.
近年来,可穿戴设备(如 Fitbit)的普及彻底改变了个人追踪和监测个人活动数据的方式。这些设备为个人的身体活动水平、睡眠模式和整体健康指标提供了有价值的见解。将这些数据整合到医疗保健信息学系统中,在个性化医疗服务的提供和改善患者预后方面具有重要意义。本文通过使用 openEHR 参考模型探索 Fitbit 生成的个人活动数据与医疗保健信息学的协同整合,将基于健康信息学标准的患者生成健康数据(PGHD)集成作为电子健康记录(EHR)表示和交换的框架,作为患者生成健康数据(PGHD)集成的实际案例研究。通过 openEHR 和 FHIR 标准模型的协同整合,Fitbit 生成的个人活动数据还可以为高级分析和人群健康管理提供途径。通过链接和分析来自各种来源(包括传感器和可穿戴设备)的数据,医疗保健组织可以识别趋势、模式和见解,从而指导人群健康策略、预防保健举措和个性化治疗计划,此外还可以帮助医生进行后续护理。