Medical care today is episodic. We’re assessed by our doctors at our annual physical exams, and we’re treated by our doctors when symptoms of ill-health become manifest. In the periods in between, we’re unmoored and untended, perhaps engaging in behaviors that will sabotage our health. And because medical care today is driven by data derived from physician encounters, it becomes an explicitly physician-focused dynamic. If health care data were to be continually collected and assessed independent of physician encounters, medical care might finally be transformed to become a patient-focused dynamic instead, yielding efficiencies, improved outcomes, and lower costs.
Going forward, our cell phones and wearables will be by far our biggest source of health-related data, providing a continuous stream of information that promises, for the first time, to enable the practice of continuous healthcare. This data is collected non-invasively and will specifically help us understand how patient behaviors affect patient outcomes. For example, for those who are pre-symptomatic, what behaviors will lead to the prevention of the onset of chronic disease? What insights can we gain by correlating the new continuous data with our existing EMR and genomic data? What can Google Maps tell us about the toxins – as implied by vehicle traffic patterns or location of nearby industrial sites – affecting our health in our day-to-day lives?
For individuals who are post-symptomatic (think chronic diseases), continuous healthcare allows for real-time disease management at the largest possible scale. Patients who are experiencing depressive disorders can, for example, receive early interventions, either digitally or physically, when warning signs such as changes to mobility, social communication, or sleep patterns are detected. Similarly, for patients who are post-operative or undergoing drug therapy, continuous healthcare allows the real-time monitoring of treatment efficacy (via patient mobility, communication and sleep), as well as remote triage to target which patients merit additional intervention.
Continuous healthcare brings a brand new class of fine-grained behavioral data into the clinical assessment process. Further, the provision of this type of real-time data at population scale allows strong correlations to be made between best practices in health and best outcomes. Continuous data, when supplemented with other data sources, promises to revolutionize the current methods of population health, including the monitoring of disease and disease vectors in real time.
The benefits of big data-driven continuous healthcare might be most profound for communities that are under-served today by the traditional apparatus of the medical care system. For populations that cannot access or afford proper healthcare, digital healthcare can be ubiquitous and relatively free; digital healthcare is essentially a function of the right information provided at the right time to the right person. Of course, supplying continuous healthcare to under-served populations is explicitly predicated on ready access to the Internet, pointing out the tremendous societal necessity of providing universal connectivity.
Once upon a time, brands bought ad inventory on actual print media months in advance and targeted their consumers at the population level. Big data and the Internet revolutionized the media industry through 40-millisecond-latency real-time bidding and micro-targeting at the individual level. Big data and the Internet will enable a similar transformation to real-time and micro-targeted healthcare.
With medicine having progressed from herbs and leeches to pharmaceuticals and robot-assisted laparoscopic surgery, big data now provides the latest tool in the spectrum of healthcare delivery. It’s important to note, though, that the promise of continuous healthcare will only be realized if technology is able to completely address the attendant issues of privacy and security; progress in healthcare practice needs to always be fully balanced with ethics.
Fans of the 1960s TV series Star Trek will remember that the starship’s chief medical officer, Dr. Leonard McCoy, wielded an omniscient handheld medical “tricorder” device that enabled caregivers to diagnose patient health through the non-intrusive collection of vital medical data. Thanks to recent advances in ubiquitous computing, communication, and most importantly big data, Star Trek’s medical tricorder is now within reach. It just happens to look like a combination of a Hadoop cluster with your smartphone, your Fitbit, and your Levi’s.
Shomit Ghose is a partner at ONSET Ventures, where he invests in early-stage software startups, with a particular focus on data-centric business models. He represents ONSET on the boards of Adara, Gridstore, Talena, Vidder and Vindicia and also led ONSET’s investments in AdsNative, Netseer, Pancetera (acquired by Quantum), Sentilla and Truviso (acquired by Cisco). He is active in mentoring health-technology startups at UCSF and also serves on the advisory boards of UC Berkeley’s Sutardja Center and the Lundbeck Foundation Clinical Research Fellowship program. Prior to entering venture capital, he spent 19 years as a startup entrepreneur and was part of three successful IPOs.
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