Mobile Health Data for Clinical and Health Systems Research
Ida Sim, MD, PhD, Co-director, Biomedical Informatics at the UCSF Clinical Translational Science Institute, and Co-founder, Open mHealth
Mobile health; mHealth; Big data; Data-driven research; Biomarker data; Wearable sensors
Mobile health, or mHealth, involves the use of mobile apps, wearables, and sensors for preventive and medical care—and research. Mobile health data are a potentially powerful source of insight into physiology, environment, and behavior.
There is growing interest in accessing EHR data for protocol-driven and data-driven research. But EHR data and mHealth data have different perspectives, usage, and considerations.
Today’s commercially available sensors provide a lot of functionality, but most were not designed to be research-grade. To track disease-specific digital biomarkers (eg, gait in Parkinson’s disease or gaze in autistic children), new sensors need to be developed and validated.
When clinicians, researchers, and sensor companies jointly explore and validate useful digital biomarkers for healthcare and research, the companies learn what’s clinically useful and academics learn what sensors are possible.
Should mHealth data flow first to the EHR database and then back out for research—or be independent of the EHR?
There are data alignment challenges when working with different sensor devices. mHealth data standards are needed. Developing common data schemas, tools, and visualizations are part of this effort.
Are EHR companies and professional medical societies actively engaged in defining the mHealth standards?
Does the integration of mHealth data and EHR data into a workflow system require clinicians to access two care management applications simultaneously?
What about the regulatory oversight of mobile health devices?
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