An estimated seven in ten U.S. adults say they track at least one health indicator, and 21 percent of this group use some form of technology to track their health data, according to data from the Pew Research Center. But these figures are likely to skyrocket thanks to health platforms such as Google Fit, Apple’s HealthKit and AT&T ForHealth, which use sensors built into smartphones and wireless fitness devices to record physical activity.
This data deluge is a goldmine for biomedical research and drug development, particularly with the introduction of Apple’s ResearchKit. The software, which powers the Stanford-developed MyHeart Counts app, allows users to better understand their health data while providing researchers the opportunity to access it for future studies.
In a recent Huffington Post article, Ida Sim, MD, PhD, professor of medicine at University of California, San Francisco, noted that such technologies hold the potential to encourage the general public to participate in medical studies and make the research community more collaborative and open. “There’s a new movement in academic research called participatory research, where patients are part of the groups that should be asking: ‘What questions are interesting? What should we test?’” Sim said in the piece. “The public could start seeing research as something that isn’t imposed on [them], but as an activity that we all do together so that we can learn together.”
This May, Sim, who co-directs of Biomedical Informatics at UCSF’s Clinical and Translational Sciences Institute, will speak at Stanford’s Big Data in Biomedicine Conference on how health information collected on mobile devices holds the potential to inform clinical decisions and transform health care. As a co-founder of non-profit Open mHealth, she and colleagues are leading the charge to build open source software that facilitates sharing and integration of digital health data.
Below she outlines how leveraging mobile health data can improve how physicians diagnose, treat and prevent disease and the challenges in facilitating the sharing and integration of this vast treasure trove of data.
What are the large-scale opportunities to harness the rapidly growing reservoir of information to improve biomedical research and human health?
We can use this data to do a variety of things like combining genomic information and behavior data from wearables to discover new insights into health and disease.
We can also move from what works on average to more tailored programs focused on the idea of what works for me. For example, if we employ A/B-like testing with digital health, genomics, and other data combined, we can understand which interventions work for an individual and under what contexts, allowing for more tailored healthcare.
Finally, we can learn about a person beyond their clinical visit – which is only a small slice of their “health pie.” By getting multiple health snapshots, doctors will be able to provide patients with better medical support and preventative strategies that support overall physical and mental well-being.
What are the major challenges in unlocking the potential of digital health data?
When we write a sentence, we construct the sentence with grammar. We use vocabulary to fill in the blanks to give meaning to the sentence. Meaning is lost when either the grammar or the vocabulary is ambiguous or not shared between parties. In a similar way, making sense of data from various digital health devices is challenging when the devices don’t represent data the same way.
Currently, wearable devices and other healthcare tools describe the data they collect using their own languages that are not shared or integrated with other devices. For example, a Wi-Fi enabled weight scale might represent data as “weight: 88” but we have no clue if that means 88 kg, femptograms, lbs, or stones. A calorie counter might represent calories as “calories: 400” but we have no clue if this was calories expended or calories consumed. For clinicians, these kinds of ambiguities are show stoppers that lock up the potential of digital health data.
In addition, data from the devices themselves are stored in silos, meaning that it is not easy for patients or clinicians to combine and view multiple data streams together. Blood pressure from one device isn’t syncing with weight data from another, which can lead to an incomplete picture of a patient’s health over time.
If we strive for greater interoperability with a common language and structure for both understanding and integrating digital health data, we can help to bring clinical and patient needs together for better health-care outcomes.
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