The world of healthcare analytics — in fact, all analytics — is not monolithic. It ranges from solutions that admire problems to those that prescribe actions to create value.
The further healthcare insitutions move to the right of this spectrum (i.e. using analytics that create valuable prescriptions rather than just describing them), the more they will experience the true benefits of analytics.
The most common forms of analytics are descriptive. These merely admire the problem. Think of getting on a scale or using a thermometer. Before doing so, most people have a defined hypothesis they want to confirm about their weight or their temperature. The answer they receive from these tools confirms or denies their hypothesis. It does not tell them the underlying cause for the output nor what they should do with that information moving forward.
Similar in healthcare are the multitudes of dashboards and reports that committees and leaders receive from EHRs, Excel, Tableau and other “analytics.” For example in the OR, historical case volumes, times, turnovers, or delays merely underlines issues that were already known. This is not enough to enact meaningful change.
Diagnostic analytics are more powerful but still insufficient. If you look at your thermometer and see you have a high fever, you can surmise something like “I have a fever because I was out in the rain last night and got soaking wet.” But this doesn’t help cure your fever at all.
The slicing, dicing, and deep diving that healthcare diagnostic dashboards provide are also like explanations of yesterday’s weather. They may be wonderful for understanding where and when last night’s storm occurred, but not help you decide whether to take an umbrella when you go out today.
Specifically in the OR world, diagnostic analytics might help you understand that Dr. Jones’ cases always run late because he habitually requests too little time and doesn’t think “wheels in to wheels out.” These analytics don’t actually solve anything, however, unless they can help Dr. Jones learn to request the right amount of time and let him easily do so.
The real power of analytics starts to come into play when we gain the ability to forecast meaningful future events. These predictive analytics can help you start to plan.
Google Maps predicts you’ll take 54 minutes to get home from the airport when you land at 5pm three days from now. This helps you plan the rest of your evening. How does Google Maps work such magic? By mining historical data from millions of trips drivers have taken over the years. The app has no way of knowing exactly who will be on the road when you take your trip, nor can it rate their driving skills, but it can access a lot of data upon which it can build predictive models and your likely drive time from point A to point B.
Timestamp data can be mined in the same way to help an OR scheduler predict the surgeons/block owners who will not use their time well in the future. Analytics start truly adding value when they offer specific information about the future you can use to solve a problem.
Prescriptive Analytics Create Value
The most useful analytics drive high-value action. A tool like Waze can show an alternate route to shorten the length of your journey, and companies like FedEx and UPS use forecasting algorithms to predict the volume and mix of packages they will receive and put the right number of planes, trucks, and drivers in the right places at the right times to handle the demand.
In the healthcare analytics world, a surgical department can alter its staffing patterns for a future day on which a surge in case volumes is predicted, to “flatten the peak” and fit more patients in. It can also predict if, say, Dr. Smith is unlikely to utilize his assigned block three weeks from today, and proactively (and automatically) request he consider releasing it so one of his colleagues could use it. This improves the utilization of the assigned operating room.
The Future of Healthcare Analytics
Hospitals need better tools, with better math and more predictive and prescriptive capability than EHR reports and dashboards can provide. Operational teams will need to go beyond describing or diagnosing problems to actually predicting what’s likely to happen and making action adjustments in anticipation—as illustrated by Waze, UPS and FedEx, and so many other real-world examples we all encounter in our day to day lives.
To learn how healthcare institutions have used tools to employ predictive and prescriptive analytics, in ORs and in managing infusion centers and inpatient beds, visit our knowledge center.