For years, the wearable and mobile health industry has been working to convince health systems that their data will be game changing, and so far not much has changed.
Industry titans like Apple, Google, and Samsung have created paved roads for consumer data collection and sharing where dirt paths existed just two years ago. Fitbit, Under Armour, Garmin, and Jawbone have built large networks of users who are generating complex and important health data.
Experts say that the IoT (Internet of Things) health care market will reach $136 Billion by 2021†, with devices and remote patient monitoring dominating the category.
Every health conference, news outlet, and industry pundit is pumping out content about mHealth and digital health. They say it will be one of the keys to saving providers in the transition to value based care. Talk to any leader in the care coordination space and they'll tell you they want to integrate mHealth data in the next 18 months.
Yet how many of these organizations actually know what to do with the data once they get it?
There is no shortage of platforms that normalize digital health data. However, without teams of data scientists, engineers, and analysts, this data is far from insightful and actionable. Buying access to raw data is like piping crude oil straight into your car. Wouldn't it be great if you could tap into data from the refinery instead?
Recently I was having a conversation with one of our customers. He's a leading physician at a nationally ranked health system. "When I open Epic," he told me, "the last thing I want is another set of data points that I have to spend time digging through. I don't want my patients' raw step counts. I want to know how their recent activity trends impact their specific condition, and ultimately have my treatment decisions informed by those insights."
This customer had the opportunity to use one of the existing "crude oil" mHealth platforms, but they immediately saw the value in Strap's out of the box "refinery": analytics, data science, and triggers. We estimate that choosing Strap's data analytics platform will save them upwards of $1M in year one technology and development costs alone.
Let's break down those cost savings a bit further.
Labor Costs (USD)
Basic Trigger Engine
Implementation & Maintenance
These are up front costs based on an in-house team of full time technology specialists, and assumes the team builds a basic platform capable of powering one or two rudimentary use cases. We are also assuming the organization has hired the team, integrated them into the organization, and has a development plan.
The reason mHealth data has not impacted health care is because health care is having to work too hard to make it impactful.
Instead of wasting time and money converting data into insights, IT leadership should allocate resources to developing patient engagement experiences that are powered by data insights. If the industry is going to be transformed any time soon, it must take advantage of the ever-present and growing ability to take action on patient generated data.
So far, I've presented the case for ROI related to IT burden. In the next post, I present some of the use cases for clean data. After all, fixing the waste due to improper management of chronic conditions and comorbidities is a top priority. These issues cost the industry billions of dollars each year, a number that we can reduce significantly.
I would love to connect and learn more about your mHealth challenges. Shoot me an email to steve -at- straphq.com, follow me @stevecaldwell on Twitter, or come chat with us on Slack. Be sure to subscribe to our blog if you'd like to follow this thread.
Ready to learn more about how Strap's analytics can add value?