The 5 Things Your Hospital’s Predictive Analytics Platform Must Do

Photo by Ifness/iStock / Getty Images
Photo by Ifness/iStock / Getty Images

A predictive analytics platform is an indispensable tool for improving hospital efficiency.  Whether you’re executing dozens of improvement initiatives, contemplating an expansion, or just trying to get a handle on what’s happening today, the right platform can provide the actionable intelligence to make faster and smarter decisions.  In fact, hospitals are finding they can’t afford not to leverage their data in this way. 

A useful predictive analytics platform is not “standard issue” in EHR offerings, unfortunately, and building one from scratch requires extensive expertise in engineering and data science.  Even the most advanced business analytics groups struggle to keep their home-grown models running with fresh data. 

As hospital leaders turn to the market for solutions, they should be aware that some platforms have limitations that lead to poor results, like ineffective reports, micro-optimizations that make the overall system worse, and “alert fatigue” that wears down staff.  To avoid these pitfalls, ensure your platform of choice can offer these things:

  1. A holistic, up-to-date view.  The platform bridges together disparate data silos, like ED systems, bed management systems and EHRs, and keeps that data timely.  Hospitals are complex operations, and without the complete picture the analysis falls short.  A comprehensive data set also provides a good baseline; only when you can truly see what’s happening today do you have the context to effectively evaluate potential changes.
  2. Historical accuracy.  It’s one thing to collect data, but another to trust it.  A proper platform collects and cross-references data across IT systems, and continually checks for discrepancies across data sources. Confidence in data integrity is a must, as there is too much data to rely on manual checks alone.
  3. Predictive accuracy.  The platform provides forecasts, such as predicted occupancy rates, surgical block fill rates, and appointment demand, in a responsible way, by providing clear prediction ranges (not just absolute numbers), measuring the accuracy of past forecasts, and using machine learning to improve.  Otherwise, how are the forecasts to be trusted?
  4. Actionable insights.  The platform’s analytics answers the “so what?” question by providing specific recommendations, like an early warning system for initiating a surge plan, a prioritized list of patients to check on for likely discharge, or an optimized staffing plan to implement.  And the reports are designed to identify outliers from normal activity.
  5. What-if capabilities.  The platform provides a simple and accessible way of running simulations to prioritize improvement projects based on anticipated clinical, operational, and financial measures.  With actual vs. expected reports, it also supports the plan-do-check-act cycle to verify initiatives are tracking to goals.

We’ve entered an exciting time in healthcare, where digitized activity is just waiting to be turned into actionable insights. With the right predictive analytics platform, you can drive both operation efficiency and better care.