Predictive Analytics in Healthcare: It's not happening | 7wData

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Blasphemy I know, but Healthcare does not need another dashboard. And there is something else.  We need to stop believing healthcare dashboards and healthcare analytics are the same thing.  They are not. As I recall from time spent in endless sales presentations, dash boards were to be only the first step (some marketer’s PowerPoints label dashboards as only the First Plateau) toward a world where real data would provide real answers to real problems so we could help real patients.

To state the obvious, ‘we are not there yet’. Not only are we not ‘There’ yet, we remain a long, long way from ‘There’ and we don’t seem to be getting any closer to ‘There’. Dashboards and their hybrids have become synonyms for Healthcare Analytics, and when we buy Healthcare Analytics we know we are buying dash boards or their kin.  It’s time to move to that Next Plateau – predictive analytics.

The Rise and Stall of the dashboard

Dashboards are flashy. Dashboards are cool. Dashboards can integrate data from all those disparate systems we need to operate our hospitals and clinics. Dashboards can scoop data up in real time. Dashboards can even use “Big Data”. There is no doubt. Dashboards are an invaluable management tool, and today we don’t know how we ever got along without them. But like everything else, dashboards have strengths and weaknesses.

 ​predictive analytics, using sound statistical and sampling techniques for minimizing risk, must form the foundation of an intervention 

A well-designed dashboard can certainly highlight problems, but it is up to an experienced manager to evaluate dashboard information, then formulate solutions and take action. So, as adept as dashboards have become at presenting data– real time, disparate, big, and otherwise – most dash boards remain sophisticated management reports. I make this observation because the majority of dashboards I see are not designed to uncover the root cause of a problem; only the symptoms of cause. Root cause and solutions are someone else’s job. Uncovering cause and causal relationships, on the other hand, is the prime commodity of predictive analytics.

I have two jobs. I’m an academic, and I am a healthcare CIO. In my role as an academic, my colleagues and I publish predictive analytic models based on healthcare data. Publishing helps my academic career. Published articles look good on my resume. Sure, it would be nice if someone someday would use this work to improve healthcare, but chances are slim to none this will happen.

On the other hand, as a healthcare CIO I am interested in action. I want to help devise interventions leading to higher quality healthcare, lower costs, and improved patient outcomes. I want these interventions to be evidence based - derived from sound predictive models calculated using reliable data.

Consider this proposition as an example: “The use of a patient portal by congestive heart failure patients (CHF) will lead to improved outcomes”. We (vendors and healthcare CIOs) have been peddling this rhetorical morsel since Day One. But in truth this intuitive assumption, though widely accepted, has never been proven. We have no idea if using a patient portal really has any effect whatsoever on patient outcomes. Think of the possibilities if we could prove a positive causal effect between portal use and improved outcomes. We could create interventions designed to foster more effective use of portals by patients. We could design targeted training programs. We could mount patient recruitment programs. We might even determine giving computers and mobile devices to CHF patients is cost effective because portal use reduces emergency room visits.

Before getting too excited about interventions there something you should know. Interventions can be dangerous and expensive.