Do you rely on a home-brewed health analytics platform? More importantly, have you outgrown it? I hadn’t thought much about this issue, but a recent article reminded me that we should.
Because I write about health IT, I’m often sucked into the vortex of big vendors’ marketing messages. I’m not saying that there’s anything wrong with a vendor having a large marketing presence, mind you – I’m merely pointing out that given how immersive these messages can be, I don’t tend to think about legacy systems already in place very often.
But as a recent column notes, healthcare organizations can’t afford this luxury, and might need to focus on whether their legacy health data analytics platform serves them anymore. The column, by Ryan Smith, SVP of professional services for HealthCatalyst, argues that there are a number of problems involved in keeping them in place.
“Many health systems are finding their data management analytics solutions are falling behind,” Smith writes. “Most traditional data warehouses are overwhelmed by analytic requests and lack the ability to support today’s increased and rapidly changing demands.”
It’s easy to understand why health systems might prefer to keep their existing health analytics systems in place, given that many have invested heavily in their platform. However, there are problems with this approach nonetheless, he says, including:
- The aforementioned problem keeping pace with analytics demands affects for many organizations. The typical turnaround time for such requests is 4 to 6 months, and by the time the IT team get started the operational teams have already moved on or business needs have changed.
- With mergers and acquisitions becoming common, healthcare organizations often need to integrate data rapidly and consolidate analytics platforms. Doing this is very tough when you’re working with a home-grown infrastructure, as the chances of it being plugged into another organization’s platform smoothly are slim.
- It’s difficult to support and scale a home-grown analytics platform over time. The initial development goals might apply only to the needs that existed at the time, making additional buildups difficult or even impossible depending on what’s needed.
- Supporting a home-grown product requires a large number of analytics and technical staffers. Unfortunately, it can be tough to find and retain great analysts, as they are needed across the industry.
- In some cases, healthcare organizations have kept their home-grown analytics platforms working by using point solutions to plug gaps in the infrastructure. These point solutions may come from third-party vendors, which increases the demands on IT and analytics staffers.
You won’t be surprised to learn that Ryan goes on to pitch his company’s solution to the problem. I won’t review his take here, but if you’d like to learn more you can always click on the link above.
Regardless, if you maintain a proprietary health analyst platform, it would probably make sense to give it a checkup and see if’s capable of meeting the fast-expanding demands of a data-centric world. Despite his objections, your platform may be just fine for you at the moment, but given the growing importance of health analytics, it’d be smart to be sure that your solution still gets the work done right.