Self-Learning Analytics and Making Analytics Useful

Posted on April 2, 2018 I Written By

John Lynn is the Founder of the HealthcareScene.com blog network which currently consists of 10 blogs containing over 8000 articles with John having written over 4000 of the articles himself. These EMR and Healthcare IT related articles have been viewed over 16 million times. John also manages Healthcare IT Central and Healthcare IT Today, the leading career Health IT job board and blog. John is co-founder of InfluentialNetworks.com and Physia.com. John is highly involved in social media, and in addition to his blogs can also be found on Twitter: @techguy and @ehrandhit and LinkedIn.

One of the shocks to me at HIMSS 2018 was that there wasn’t nearly as much discussion around healthcare analytics as I thought there would be. I thought for sure we’d see an explosion of proven analytics that healthcare organizations could start to take advantage of. Maybe I just missed it, but I certainly didn’t see anything all that new.

It’s too bad because that’s one of the huge opportunities I see for healthcare. I was looking through some old notes from conferences and saw a note where I wrote: “What you do with the data is the competitive differentiator, not the data.

Certainly, you need access to the data to be successful, but there are a lot of organizations out there which have access to health data and they’re not making any sort of dent. Many of the now defunct HIEs had access to the data, but they didn’t know what to do with all that data. I’m still on the search for more analytics which are useful.

One other idea I found in my notes was the concept of a self-learning analytic. Related to this was the discussion we had about black box analytics in a recent #HITsm Twitter chat. I don’t think they have to be the same, but I do think that the key to successful healthcare analytics is going to require some component of self-learning.

The concept is simple. The analytic should look at its past recommendations and then based on the results of past recommendations, the analytic should adjust future recommendations. Notice that I still call it recommendations which I think is still the right approach for most analytics. This approach to constantly learning and evolving analytics is why it’s so hard to regulate healthcare analytics. It’s hard to regulate moving targets and a self-learning analytic needs to be moving to be most effective.

This is possibly why we haven’t seen an explosion of healthcare analytics. It’s hard to get them right and to prove their effectiveness. Plus, they need to continually evolve and improve. That’s the opposite of what researchers want to hear.

This is why the future of healthcare analytics is going to require deep collaboration between healthcare analytics vendors and provider organizations. It’s not a black box that you can buy and implement. At least not yet.

What’s been your experience with healthcare analytics? Where are you seeing success? We’d love to hear your thoughts in the comments.