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.

BACKGROUND: Community health center (CHC) patients experience a disproportionately high prevalence of chronic conditions and barriers to accessing technologies that might support the management of these conditions. One such technology includes tools used for remote patient monitoring (RPM), the use of which surged during the COVID-19 pandemic.
OBJECTIVE: The aim of this study was to assess how a CHC implemented an RPM program during the COVID-19 pandemic.
METHODS: This retrospective case study used a mixed methods explanatory sequential design to evaluate a CHC\'s implementation of a suite of RPM tools during the COVID-19 pandemic. Analyses used electronic health record-extracted health outcomes data and semistructured interviews with the CHC\'s staff and patients participating in the RPM program.
RESULTS: The CHC enrolled 147 patients in a hypertension RPM program. After 6 months of RPM use, mean systolic blood pressure (BP) was 13.4 mm Hg lower and mean diastolic BP 6.4 mm Hg lower, corresponding with an increase in hypertension control (BP<140/90 mm Hg) from 33.3% of patients to 81.5%. Considerable effort was dedicated to standing up the program, reinforced by organizational prioritization of chronic disease management, and by a clinician who championed program implementation. Noted barriers to implementation of the RPM program were limited initial training, lack of sustained support, and complexities related to the RPM device technology.
CONCLUSIONS: While RPM technology holds promise for addressing chronic disease management, successful RPM program requires substantial investment in implementation support and technical assistance.