Top Things to Know: Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease
Prepared by Katherine A. Sheehan, PhD Bernadette Okwu-Gage, PhD, MPH, MBBS
- A growing segment of the population use wearable devices to track health and illnesses that generate data that can potentially transform health care.
- Consequently, there is a need for an interoperability framework that can support the deployment of platforms, sensors, devices, and software applications in the various health systems that are looking to facilitate innovation in prevention and treatment of cardiovascular disease (CVD).
- The goal of this scientific statement is to address the best practices, gaps, and challenges related to data interoperability in this area with a focus on (i) data integration and the scope of measures, (ii) application of these data into clinical approaches/strategies, and (iii) regulatory/ethical/legal issues.
- However, there is limited efficacy in the current strategies for data quality assurance and the appropriateness of clinical content which would help guide the design of clinically acceptable ambulatory monitoring systems, and the approach toward accessing and incorporating these data into clinical workflows.
- Several key physiological metrics for CVD are readily sensed, quantified, and monitored by ambulatory devices. These devices are useful for diseases such as arrhythmias, hypertension, heart failure, ischemic heart diseases, stroke, and pulmonary diseases.
- Wearable devices can track physical activity continuously and passively during daily activities and this can inform care in those with or at risk for CVD in controlled settings more objectively than by subjective recall.
- A useful application of interoperability would be fall detection and alert generation using devices that use movement (e.g., gyroscope, accelerometer), vision (surveillance cameras), or other sensors in the ambient environment.
- Data interoperability involves information being communicated in a structure that can be identified by both sender and receiver (syntactic interoperability) and requires both sender and receiver to understand the meaning of the information (semantic interoperability).
- Best practices for data formats include use of tools such as Consolidated Clinical Data Architecture (CCDA), Fast Healthcare Interoperability Resources (FHIR), Observational Medical Outcomes Partnership (OMOP), Canonical Data Models (CDM), and Open mHealth. However, there are some challenges with these formats such as (a) lack of structure that can be recognized by sender and receiver, (b) lack of clear meaning of the data, and (c) lack of data digestibility.
- It is crucial to integrate patient-generated health data (PGHD) into electronic health records (EHRs) and personal health records to leverage these data for clinical care and quality improvement and continue to identify best practices in PGHD integration.
Citation
Armoundas AA, Ahmad FS, Bennett DA, Chung MK, Davis LL, Dunn J, Narayan SM, Slotwiner DJ, Wiley KK Jr, Khera R; on behalf of the American Heart Association Data Science and Precision Medicine Committee of the Council on Genomic and Precision Medicine and Council on Clinical Cardiology; Council on Cardiopulmonary, Critical Care, Perioperative and Resuscitation; Council on Cardiovascular and Stroke Nursing; Council on Lifestyle and Cardiometabolic Health; Council on Peripheral Vascular Disease; Council on Quality of Care and Outcomes Research; and Stroke Council. Data interoperability for ambulatory monitoring of cardiovascular disease: ascientific statement from the American Heart Association. Circ Genom Precis Med. 2024;16:e000095. doi: 10.1161/HCG.0000000000000095