Trends in Healthcare Predictive Analytics

Pramod Sridharamurthy
Thursday, September 28, 2017

Predicting patient demand is common place tech now. Healthcare providers are amassing people and clinical data to reveal surprising associations. Insights our human brains never suspected existed.

Here’s an example of predictive analytics (clinical data) in action. University of Mississippi Medical Center (UMMC) is scanning 1500 relevant data points out of 100,000 out there. They have an app that predicts pollen counts and correlates it with asthmatic patients. This means, better facilities planning and far greater contingencies measures in place. It also means better staff planning.

Hospitals networks have full information on both patients and staff. That information gets passed on with context, metadata, and classification. Meaning, nurse managers or technicians know what it means and how to act on that information. Remarkable technology used to its full potential.

Attention Please! There are Machines in Hospital Networks

We are getting the customer side of things right. For instance, predicting foot falls, staffing requirements, and clinical data accuracy right. Having the ability to predict those factors is only part of the equation. You may, however, be failing to factor machine downtime and thus making your operational plans incomplete. You need the best case scenario of 99.9% uptime, for example, on your diagnostic equipment. Diagnostics labs are a notorious source of inefficiencies. Some areas that need a closer look are maximizing capacity, unscheduled downtime, and parts breakdown.

What we trying to avoid is having the ability to predict patients coming through the hospital doors but are choking in queues at the imaging labs. Not quite ok, right?

Here’s how you can do it.

The missing piece is having machines tell us when and at what capacity it can operate to its fullest potential. Have a look at the big picture on machine data healthcare providers here.

Glassbeam Analytics powered diagnostics equipment checks can help you to:

  • Forecast capacity usage using our Workbench app. You can use what-if scenarios to tweak your imaging center’s readiness to accommodate demand
  • Predict parts and system failures for imaging modalities, thus improving machine uptime leading to better patient care per facility
  • Make informed procurement decisions by understanding actual utilization metrics for the diagnostic equipment based on real-time machine data

Take the next step in cultivating a machine data analytics rigor at your imaging centers and networks. Download our solution brief, Machine Log Analytics for Healthcare Providers.