In this special guest feature, Harshith Ramesh, President for Episource, discusses how the application of big data and predicative analytics combined with mobile/app data has the potential to be a dramatic game changer in the delivery of healthcare. Harshith is President of Episource, a Los Angeles based provider of services and software for Healthcare Payers and Provider Organizations. Prior to joining Episource, he was in the investment banking industry, focusing on M&A, restructuring, and capital markets assignments. He has worked for a range of global institutions, including Moelis & Co., Bain & Co., and Bear, Stearns & Co. Harshith earned his Bachelors at Tulane University, and an MBA from the Wharton School.
Nowadays, the digitization of healthcare has created a deluge of data that can be overwhelming. Some of the leading contributors are mobile devices such as FitBit and Jawbone, and smartphone apps like Apple Health. To date, these kinds of solutions have been mostly focused on capturing diet and exercise information, but they have tremendous potential to contribute to a Big Data solution that could transform how health care information is captured and applied.
In 2015, according to iMedicalApps, there were approximately 165,000 health & medical apps available for smartphones and tablets. Of these, about 60% address general wellness issues like fitness, lifestyle and diet while the rest focus on specific health conditions (9%), medication info & reminders (6%), and women’s health & pregnancy (7%).
While this is certainly an impressive number of options, the fact of the matter is that only a handful of these apps have really made it into the mainstream and fewer still are being used with any kind of regularity. The result? Not much real benefit or impact on patient health being delivered based on information provided by mobile devices.
Of course, for any kind of app to be successful, whether it is PeaPod or Uber, it has to drive an ongoing interaction and connection between the user, the application and the device it’s on. People use apps that deliver meaningful, personalized data and support, though a simple, secure and intuitive user experience.
So why do people stop using health-related apps? A recent survey by Research Now Group revealed that 96% of mobile health app users think the apps improve their quality of life. By contrast, only 37% of health professionals believe that these kinds of tools will improve their patients’ lives. This striking discrepancy may reflect a more common disconnect between healthcare professionals and their patients – while we are often focused on the control of disease, patients are particularly interested in the downstream effects on their day to day lives.
But the application of Big Data and predicative analytics combined with mobile/app data has the potential to be a dramatic game changer in the delivery of healthcare.
According to Statista, the average US citizen visits a doctor four times a year. That means a doctor has only four chances every twelve months to collect and analyze data and make decisions and recommendations about a patient’s health based on this comparatively scant amount of information.
Imagine if a mobile health app was able to provide a steady stream of real time data – with access managed by the user of course – to his or her ecosystem of caregivers – family, friends, physicians, other stakeholders. At the receiving end, a solution leveraging Big Data would collect, analyze and rationalize that information.
A physician could not only view a patient’s historical data – test results, blood work, vitals, X-rays, surgical history – but also factor in real-time data about stress, exercise, diet, lifestyle. The Big Data instance could even map this information to other anonymized patient data to get a sense of their patient’s overall health trending, relative to family members, close friends, work colleagues or even the general population.
A patient supplying this treasure trove of valuable information combined with caregivers leveraging Big Data has the potential to dramatically improve strategic healthcare decision making. This “connecting the dots” functionality has exciting potential to improve accuracy of diagnoses and suggested treatments, resulting ultimately in improved patient outcomes.
Mobile health app users, somewhat predictably, tend to use the apps for weight loss and exercise. For consumers, just as for health care professionals, the power of mobile health apps seems to be as a tool for prevention with the greatest focus on health behaviors. But the advent of mobile Big Data represents a tremendous opportunity for innovative healthcare companies to design apps that impact downstream health outcomes in patients’ day to day lives, rather than merely controlling disease or current conditions.
We are in an exciting time. I look forward to seeing the next generation of powerful Big Data-driven health care apps. The time is right for innovative mobile health care apps to take advantage of the next generation of Big Data and predictive analytics and rethink how health care data is collected and leveraged.
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