Tell us a little bit about yourself and Gray Matter.
I am the founder and CEO of Gray Matter Analytics, the leader in healthcare Analytics as a Service. I’ve been in technology for 35 years, with extensive experience in application and systems software and consulting services. What led me to found Gray Matter seven years ago was the void that I saw in the application of analytics in healthcare. I saw an opportunity to apply analytics for the improvement of quality and costs in care delivery and support providers and payors across the value-based care continuum.
How has COVID-19 impacted the healthcare analytics space?
I would estimate that the pandemic caused at least a six-month delay in healthcare organizations’ investments in analytics capabilities. Rightfully so, providers were focusing their energy and resources on caring for patients. Although payors weren’t as financially impacted by COVID-19, the uncertainty caused them to essentially freeze any decisions on forward-looking investments.
Ultimately, COVID exposed a widespread lack of analytics capabilities among both providers and payors. If the majority of these organizations had a more sophisticated level of data analysis, I believe some of the pandemic-related pitfalls could have been anticipated and mitigated. For instance, advanced analytics could have helped providers predict and avoid PPE shortages. Further, analytics could have been leveraged by payors to identify members at higher risk of COVID-19 complications and conduct proactive, targeted outreach to prevent unnecessary ED utilization.
What are some examples of healthcare analytics really making a difference?
The ability to utilize analytics to integrate social determinants of health (SDoH) data into patient/member care and engagement is making a difference, but we still have a long way to go. COVID-19 has exposed health disparities, particularly in black and brown communities, whether caused by food deserts, isolation, or lack of access to care or medication. There is massive potential for harnessing SDoH advanced analytics to address these types of disparities.
We’ve heard about predictive analytics for a long time. Where do you think we are at in the development of predictive analytics? How effective are predictive analytics today?
Most providers and payors are still at the foundational level of analytics capabilities. They are still reporting what happened rather than aggregating and analyzing their data to develop insights on what will happen. For those who are ahead of the curve and leveraging predictive analytics, we’ve seen extremely promising results. An example of this would be in virtual care analytics. Providers are able to manage utilization of virtual services with a predictive model that looks at the best candidates for virtual visits and determines those with a higher probability of compliance. By forecasting both the patient panel, utilization and compliance rates, health systems can better manage resources and improve healthcare delivery.
What are the biggest challenges with healthcare analytics today?
It all comes back to the data. Because healthcare has underinvested in technology and lacks enterprise-wide data strategies, a lot of the data resides in siloes. Also, the data that exists in these siloes is not analytics ready. For example, providers often have multiple EMRs or they have multiple instances of the same EMR. The EMR(s) may not be able to interface with other data sources, which is a significant obstacle for those in value-based care arrangements. Some performance metrics may lie outside of the EMR(s).
On the payor side, they are now recognizing the value of sharing data with providers, but it’s still challenging. Most of the time, their data lies in a claims platform that’s not readily available to be integrated with EMR data. There’s no current system in place to make this data integration easily doable or universal across all systems and networks.
Overcoming these challenges should be a priority for every organization. Developing analytics capabilities is not something that should be pushed down the road in a three-year strategic plan. Reducing the cost of care and improving outcomes cannot wait any longer.
Which part of healthcare analytics gets you most excited?
Without question, predictive analytics. The opportunity to use predictive analytics to identify SDOH patterns and conduct targeted interventions to prevent chronic or acute conditions is huge. Further, predictive analytics presents a new opportunity for providers and payors to collaborate on this targeted intervention to optimize quality outcomes.
What can the health IT community do to help you and Gray Matter?
For those who’ve not yet embraced the cloud, it’s imperative that our health IT peers adapt to cloud-based solutions in order for the industry to progress to more secure, agile, and efficient processes. Further, everyone throughout the health IT community should be asking themselves, “Does my solution/product support value-based care and the future of analytics?” Of course, we can’t all offer fully comprehensive, enterprise-wide solutions, but we should each be doing our part to improve processes and technology so providers and payors can ultimately improve outcomes for patients/members at reduced costs.
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