Physicians in pioneering organizations have been teaming up to apply evidence and clinical consensus to medical practice in new, innovative ways. One of the organizations in which very promising activity in this area has been taking place is Hackensack Meridian Health, an integrated health system based in Edison, New Jersey that encompasses 13 hospitals, including two academic medical centers, two children's hospitals and nine community hospitals, physician practices, more than 120 ambulatory care centers, surgery centers, home health services, long-term care and assisted living communities, and other services, and 28,000 team members, and more than 6,000 physicians.
At Hackensack Meridian Health, physician leaders have been developing a broad strategy to leverage data analytics to support the ordering process at the point of diagnosis and care, beginning with the hematology-oncology service line. The physician who initiated this initiative, Andrew Pecora, M.D., is a hematologist-oncologist who has been practicing at the Hackensack organization since 1989. Since July 1, 2016, Dr. Pecora has been president of the Physician Enterprise at the Hackensack Meridian organization, and chief innovation officer for oncology for the entire system. He has received numerous awards and recognitions, and has published many articles (a summary of which can be viewed here).
At the core of the initiative that Dr. Pecora initiated with fellow specialist physicians at Hackensack Meridian Health, and whose methodology is being replicated now across numerous specialties, is a precision analytics tool called a gene expression profiling (GEP) tool, which is helping hematologists and oncologists as a clinical decision support (CDS) tool that streamlines the use of large data sets and enhances decision-making and ordering around cancer care—and which is beginning to be applied to other areas of medical care as well. Pecora and some of his colleagues, after developing and expanding the use of this tool at Hackensack Meridian Health, have commercialized this methodology, through the New York City-based Cota, Inc., where he serves as co-founder and chairman. Pecora and his colleagues continue to expand and refine the methodology at Hackensack Meridian Health.
Dr. Pecora spoke recently with Healthcare Informatics Editor-in-Chief Mark Hagland about the leveraging of analytics for clinical decision support in diagnosis. Below are excerpts from that interview.
Tell me a bit about the core initiative that you’ve helped to create and lead.
We’ve created a precision analytics tool that enables physicians, nurses, and patients to identify adverse variance, to prevent it, for all healthcare, not just oncology, though we’re starting in oncology. Adverse variance means when too much or too little care is provided, and results in adverse outcomes. About a third of our healthcare expenditure is wasted, according to current statistics. We’ve created a methodology that allows all stakeholders to see where adverse variance is occurring, and you can reduce variance and reduce adverse outcomes and total cost of care at the enterprise level.
Andrew Pecora, M.D.
What was the origin of this program?
About eight or nine years, ago, I took part in a think tank with the McKinsey Corporation, looking at how we control the rising costs of cancer care. And at the end of a three-day work session with a lot of well-known people in the country, the conclusion was that we had to ration care, though we couldn’t call it that. I left that meeting quite discouraged, because I’ve taken part in so many clinical innovation efforts. I believe our job is to cure cancer, and here we are on the precipice of curing more and more cases of cancer, and that didn’t sit well with me. And I’m an inventor, with over 50 patents in stem cell science, and I’m an administrator as well as an entrepreneur. So I sat down and spent the better part of six months trying to solve this problem, and realized it wasn’t a biology problem, but a mass statistics problem and a variance problem.
So I started to consult with biostatisticians and others, and came to the conclusion that we weren’t going to solve the problem of wasting one-third of our resources using traditional methods. And clearly, using ICD-9-based and claims data, just wouldn’t get us there. So I came to the conclusion that only the expression of numbers would allow us to identify, codify, stratify, and analyze data sets in a timeframe appropriate for the codification of care. That will allow you to intervene at the point of care, because you want to know that there’s the risk of adverse variance so that you can prevent it, because once it happens, it’s too late. We had to create a way to compress data. So I looked at how the television industry digitizes pictures and large data sets are compressed into a numeric format. And combining that with the concept of GPS, which is numbers-based, and a node, and we came up with the idea of a Coda Nodal Address. It takes all your demographic information as well as behavioral information like smoking, drinking, your family history, and all the attributes of your disease, histology, stage, level of risk, etc., and the intent of the treatment of the doctor, and where you are in the progression of your disease—first, second, third, fourth—in other words, its presentation; and we took all of that data and put it into a CNA.
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