With 30 percent of Medicare payments expected to be tied to alternative payment models and bundled payments by the end of 2016, the pressure is on for healthcare leaders still struggling to align their organizations with the idea of value-based care (VBC).

This struggle is compounded further when one considers the push to utilize data analytics, machine learning, natural language processing (NLP), and other health IT buzzwords that promise to deliver better outcomes.

To get to the crux of the issue and discern what really matters in our value-centric future, Healthcare Informatics’ Rajiv Leventhal interviewed Cyft CEO Len D’Avolio on the true use of data analytics to achieve value and help both patients and providers thrive.

Here are several key takeaways from Rajiv’s piece that we thought were of particular note:

1) Traditional analytics are failing healthcare’s push for value: As value-based care organizations are now discovering, these multi-million dollar investments in traditional analytics are useful for understanding what happened—how many beds were filled, drugs prescribed, surgeries performed. However, they are incapable of answering the fundamental questions of value-based care: what should happen, to whom, when, and how, in order to prevent future events.

2) Cyft is taking a new role in the future of VBC: Our company is focused on making technologies—such as machine learning and natural language processing—available to data analysts so they can harness the power of predictions in ways they haven’t been able to. We try to find organizations where the chief financial officer and the chief medical officer have the same incentive, meaning the organization is at financial risk for delivering high quality care. 

3) Healthcare must emulate other industries using predictive analytics: When other industries became digital, they had agreed upon outcomes, but then the competitive advantage came when they used all of their data to discover the best way to get to those outcomes. Amazon and Netflix, for example, did this by learning everything about the consumers they were serving. That’s the competitive advantage—taking all of the data and then becoming very personalized towards the recommendation and an agreed upon outcome.

4) Machine Learning and NLP are not cure-all fixes: Analytics is not a tool; it’s a process. Clinicians understand where to focus, but you need to come up with the processes, tools, and support staff that will help and empower them to identify the highest priorities. Also, measure them on where it’s working and not working with ongoing feedback loops. It’s a problem to think of analytics as a product that you buy that will lead to behavior change, workflow change, and process change.  

5) We must help CIOs tailor interventions for specific patient populations: No CIO should settle for a vendor’s insistence about what’s good enough when it comes to predictions. If you are building models based on other people’s data and other people’s priorities and populations, you cannot presume that can be brought into your shop and will perform at the same level. CIOs need to own the evaluation with their own data on their own problems. 

To read the full article, click here: “Tailoring Healthcare Analytics to a Value-Based Future.”