In January 2015, the U.S. Department of Health and Human Services (HHS) boldly announced a plan to tie 30 percent of traditional fee-for-service, Medicare payments to quality or value through alternative payment models such as accountable care organizations (ACOs) and bundled payments by 2016, and tying 50 percent of payments to these models by the end of 2018. HHS also set a goal of tying 85 percent of all traditional Medicare payments to quality or value by 2016 and 90 percent by 2018 through initiatives such as the Hospital Value Based Purchasing and the Hospital Readmissions Reduction Programs.
But, earlier this year, a survey from Salt Lake City, Utah-based analytics vendor Health Catalyst revealed findings that many expected: most industry stakeholders seem to think the government was quite ambitious with these projected numbers. The survey, at the time of its publication, found that just 3 percent of health systems have already met the target set by HHS. Only 23 percent expect to meet it by 2019, just a year after the feds had hoped that half of all Medicare reimbursements would be value-based. What’s more, the majority of health systems—a full 62 percent—had either zero or less than 10 percent of their care tied to the type of risk-based contracts identified by HHS as “value-based,” including Medicare ACOs and bundled payments, the survey revealed.
The healthcare executives surveyed did say that they intend to steadily increase value-based care and at-risk contracts, and they said the most important organizational element needed for success with risk-based contracting is analytics. This is where Leonard D’Avolio, Ph.D., an assistant professor in the Brigham and Women’s division of general internal medicine and primary care, says change is needed. Dr. D’Avolio is also the CEO and co-founder of Cyft, a company based on years of his research optimizing machine learning and natural language processing to improve healthcare. He previously led informatics for the Department of Veterans Affairs’ (VA) precision medicine initiative (the Million Veteran Program) and the first clinical trial embedded within an electronic medical record (EMR) system.
D’Avolio fully understands that the success of value-based care is dependent on healthcare stakeholders understanding and predicting what will happen based on the information they have. Thus, he recommends a different approach to analytics from what has traditionally been practiced in healthcare. He says, “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.”
As such, he says that most clinically relevant information is ignored by traditional analytics. To this end, as part of Healthcare Informatics’ Special Report on data analytics in this issue, D’Avolio recently spoke to Managing Editor Rajiv Leventhal about what needs to change in approaches to leveraging analytics in healthcare’s value-based future. Below are excerpts of that discussion.
Can you tell me a little about your company, as it relates to the future of healthcare, and healthcare analytics?
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. Frankly, relatively little of care provided at hospitals is at true financial risk today, though that number is increasing. Most companies are incentivized to still invest in technology to help them see folks more quickly. We are happy to see that changing though.
Sure you can talk about readmissions, but when you are at full financial risk, what you really care about is preventable utilization. Our customers will sometimes start the conversation asking about readmissions, but we ask them, what interventions do you have at your disposal? They might say that they hired a nurse to focus on COPD [chronic obstructive pulmonary disease]. So we say to them, what if we build a model to identify exactly who in your COPD population will end up in the ER in the near future? It’s a different approach from today’s risk scores, which is limited to claims data and is too one-sized-fits-all, with a focus on only a few problems. These approaches treat the geriatric patient with heart disease the same way as the high-risk pregnant patient. So we are trying to move away from one-sized-fits-all approaches.
Leonard D’Avolio, Ph.D.
How do you view the overall landscape in terms of analytics being leveraged by payers and providers as they move into risk-based contracting and reimbursing for value rather than volume?
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