This is the age of advanced analytics in healthcare: population management, predictive analytics, personalized medicine, and clinical decision support. Laboratory analytics are a dominant — in fact, disruptive — solution well-suited for many health systems.
Health systems are adopting advanced analytics
A lot of data exists showing that health systems are adopting advanced analytics rapidly. In a recent survey by Deloitte, more than four in five indicated value-based care was the driver for adopting analytics. More than half identified population health analytics in particular as a top investment focus. And three in five reported that they were going to invest in advanced analytics capability for clinical and population health focus.
- Triage tools: triage patients by risk of disease
- Chronic care: identify providers in terms of their chronic care gaps
- Care coordination: good analytics are required to coordinate across settings and providers
- Predict readmissions, adverse events, mortality, and ER visits
- Predict costs for a cohort or for an episode of care
- Optimizing therapeutic or other decisions
Clinical decision support
- Putting it all together for the clinician at the point of care to help make the right decisions
In another recent survey, 82% of health systems saw an improvement in care by using these analytics, and 63% saw reduced readmission rates. They’re seeing results, which is why they’re continuing to invest and adopt. 65% of providers and 60% of payers responded that they planned to increase their spend in 2015.
Also, in mid-2014, research found that virtually every major healthcare delivery system in the country was either planning, or in the early stages of implementing, predictive analytics. All the signs exist that this is in the early to high growth stages of taking off.
Virtually every major healthcare delivery system in the country is either planning, or in the early stages of implementing, predictive analytics.
Challenges to implementing advanced analytics
However, there are challenges. Not surprisingly, many studies say that data integration and interoperability is the number one challenge. It’s expensive, lengthy, and there are issues with terminology variations, data structures, etc. To top it off, the vendors don’t want to play fair with the other systems.
IT resources are overwhelmed, in part due to these interoperability problems. But there’s also the issue — even for large health systems — of a huge lack of analytics experts. What do you do once you’ve created a huge enterprise data warehouse, or once you’ve integrated? Organizations don’t have the expertise or capabilities to create the advanced analytics on top of the Big Data. A McKinsey & Company report predicted that by 2018 there would be a shortage of 190,000 skilled data scientists and 1.5 million advanced Big Data analysts. The report covered multiple industries, but they were certainly talking about this being a major problem for healthcare.
All of this is contributing to an urgency problem, because Medicare and others are tying reimbursements to value-based care within the next year or two. Some of these enterprise data systems take years to assemble and implement. This is a real mismatch, and there are some real concerns around what to do about it.
For more of Dr. Herriman’s insights into the new era of analytical tools, download “Advanced Laboratory Analytics — A Disruptive Solution for Health Systems“.