Healthcare is at an analytics scalability crossroad
When it comes to healthcare, data analytics isn’t just a ‘nice to have’, it can dramatically improve patient outcomes. Back in 2014, a data infrastructure solution called health data interchanges (HIE) was accessed only 2.4% of the time but those patients who had their providers examine their previous health data were 30% less likely to end up in the hospital. What’s more, the healthcare network in question saw an annual savings of $357,000. The Future Value of that savings, in 2014, from 2014 to 2020, is a remarkable $2.8M.
But even with that financial ROI, fast forward to February, 2020, six years later — it still took the COVID-19 coronavirus to cause HIE services use to triple as providers now understood the importance of patient data sharing during COVID-19.
There are other examples of technical successes in data infrastructure projects, but it can be difficult to get people to use these data systems or build the infrastructure for the right business problem. Let’s say you are committed to data sharing, analytics and deriving benefits from your health data gold mine; what solution challenges can occur and how do you avoid them?
A division of one of our customers, one the largest, national, commercial health insurance companies, has a mission to reimagine health insurance as a digital business. The insights it can gain, though, from patient outcomes, clinic visits, treatment regimens, pharmaceutical courses and doses, and a myriad other clinical data sources, depends upon its data infrastructure. Unfortunately, like many other large businesses, it has big data headaches preventing it from achieving its vision. Recognize any?
- Fail to Bootstrap - There were at least three failed attempts at building a data platform — a design that took centralized approaches of ingesting data, processing and then serving it within a monolithic data solution (e.g. data lake)
- Fail to Scale Sources and Consumers - Our client had a decades old data warehouse that was aging and difficult to work with. They initially just wanted to put it on the cloud
- Fail to Materialize Value - Our customer was exhausted from the large goals set by the digital vision to support and promote the health of every healthcare customer. This was a typical ‘boil the ocean’ requirement.
- Fail to Materialize Value - The IT analytics unit, like many others pursuing ‘big data projects’, spent an enormous amount of time on architecture. They were stuck in the how of architecture, and not the what it should be, the why it should be, or the who it should be for.