Baseball season has officially started and many of us are checking players’ projections and hoping for a great season for our team. When the Houston Astros famously became World Series Champions only a few years after being ranked one of the worst teams in baseball, they did so in part by embracing the latest science in predictive analytics, including the concept of Wins Above Replacement (WAR). In simple terms, this metric calculates the value of a player relative to others who play the same position. If you replaced me, how many wins would you get?
Could predictive analytics be used in the same way when it comes to healthcare? Would it be possible to assess the ‘value’ of any physician relative to others? And if so, could this improve care delivery?
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The answer is a clear yes. Bringing the same analytics technology used by leading banks and retailers like JPMorgan and Amazon, together with data from the Centers for Medicare Services (CMS), and electronic health records, reveals a fascinating new picture on the quality, resource effectiveness, and practice patterns of individual physicians and hospitals. Those who embrace these new analytics will have a clear advantage as the healthcare industry increasingly shifts from payments based on fee-for-service to those based on performance, also known as fee-for-value.
Choosing your doctor wisely
Which doctor is best for me or my loved one? Which care team can handle my complex needs most effectively? Which hospital should we choose? Thousands of patients, parents, daughters, sons, caregivers, and friends face these questions each day. Coming up with answers remains a process fraught with anxiety caused by a lack of transparency and objective measures. We end up relying on the opinion of our family doctors, friends, and reputational surveys, much in the same way that sports teams once relied on scouts to assess players. But what if we did have reliable measures to augment human opinion?
It’s also important to explore a popular myth that doctors and nurses are genetically resistant to greater objectivity and transparency on their performance. Clinicians have in fact had solid reasons for objecting to the prior generation of comparative metrics. Their chief complaint has been, “my patients are different.” By that, they mean unless you can precisely account for the level of difficulty associated with caring for their specific patients, you will likely arrive at the wrong conclusion about which doctor is the best performer. A classic example is the surgeon who accepts the cases that all others have refused. It stands to reason that the toughest cases have a mortality rate that would be higher than average. The question is, exactly how much higher than average should we expect this rate to be?
The reality is that, historically, the methodologies used to adjust for the difficulty of a doctor’s or hospital’s patients, also known as the case mix index, have not been precise enough to provide a truly fair assessment. This has understandably created resistance to both publishing performance metrics and using these to differentially reimburse doctors and hospitals.
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The path forward is clear: employers must evaluate their health plan the same way they do their Saturday shopping excursion.
Advanced analytics – art now meets value
Getting to the level of precision needed to more fairly compare providers requires large data sets that not only include claims and electronic health record information, but also information on patients such as whether they live alone or with other people, their education status, and their prior medical history. These social determinants of health, which are very similar in nature to the information used by credit agencies to assess credit risk when approving credit cards and mortgages, add a critical element of precision to case mix adjustment.
However, more data alone does not solve the problem. What’s also required is the ability to clean, crunch, and process that data quickly – just as rapidly as companies outside of healthcare, namely financial services companies, clean, crunch, and process data. Doing this requires moving away from electronic data warehouses, which are expensive to maintain, to cloud-based approaches where any piece of data is tagged and added to a data lake. Algorithms are then created to automatically pick, clean, and sort the data elements required for any calculation. This enables, for example, the ability to adjust the weight of any factor used to assess performance for local variations.
If standard of care in New York happens to be different than in San Francisco, we can account for this. Similarly, a different and precise weight can be assigned to the impact of high blood pressure on stroke mortality as opposed to total joint replacement mortality. While these may seem to be blindingly obvious examples, today’s case mix and risk adjustment methodologies often fail to take these realities into account.
So what’s possible when big data and the latest analytics technology is brought together? For virtually any metric that a primary care physician might consider relevant when referring a patient to another doctor or hospital – including infection rates, readmission rates, mortality rates, cost of care (impacting co-pays), and satisfaction – it is now possible to provide apples-to-apples comparisons. As a result, we have an opportunity to create our own healthcare Wins Above Replacement or aggregate metrics that in turn provide people with important additional information they can use to make decisions when selecting a doctor or hospital.
Is this new generation of healthcare value analytics perfect? By no means. There is still a crucial role to play for healthcare professionals in applying their expertise and judgment in guiding patients and families to the best choices for them. The baseball analogy highlights an important point: While human beings ultimately make the final recommendation, analytics open up a new level of understanding about individual player performance.
For clinicians and hospitals, the choice is to embrace these new insights to learn, improve, and provide superior care, or to ignore them and risk being outclassed by the Houston Astros of the healthcare world. Why take that risk? Referrals, provider selection, and reimbursement will increasingly be partly based on analytics. For patients and their families, there is finally the promise of the greater transparency we all deserve.
Dr. Jack Cochran is the retired Executive Director (CEO) of the Permanente Federation of Kaiser Permanente which supports the 8 regional medical groups. Dr. Cochran graduated from the University of Colorado Medical School and completed residencies at Stanford University and University of Wisconsin and is board certified in Head and Neck Surgery and Plastic and Reconstructive Surgery.
He entered private practice at St. Joseph Hospital in Denver in 1981 where her also served as a board member, chief of surgery and President of the Medical Staff. In 1991, he joined Kaiser Permanente to establish the Plastic Surgery department. In addition to practicing surgery, he became a member of the Colorado Permanente Medical Group’s Board of Directors, becoming Chairman and selected as President of the Medical Group from 1999 to 2007. He was then selected to lead the Permanente Federation from 2007 to 2015 during a period of major innovation in care delivery and improvement in clinical quality including the implementation of the largest civilian electronic health record. He was frequently consulted to advise in healthcare reform design and was named Top 50 U.S. Physician Leaders in 2009, 2010, and 2012 by Modern Healthcare.
Dr. Cochran lectures globally on transforming health care with a focus on physician accountability. In 2014, he co-authored The Doctor Crisis: How Physicians Can, and Must, Lead the Way to Better Health Care and has since published its sequel, Healer Leader Partner (2018), a “manual” to prepare physicians to take up the challenge of transforming healthcare. He is a Clinical Professor at The CU School of Medicine and regularly volunteers as a surgeon in East Africa.
Jean Drouin, MD, is the founder and CEO of Clarify Health Solutions, which enables health systems and payers to deliver more satisfying and efficient care through advanced analytics, machine learning, and digital care optimization solutions. Prior to Clarify, Jean was a Senior Partner at McKinsey & Company, where his roles included leading the global Healthcare IT and Digital Practice, setting up the UK and Australian Healthcare Practices, and serving as the founding Head of McKinsey Advanced Healthcare Analytics (MAHA). He also served as Head of Strategy for NHS London, which oversaw London’s $15B hospital and primary care system. In these roles, he served over 20 countries and 100 health systems and payers on engagements that generated over $1B in efficiency and quality improvements.
Jean is passionate about transforming healthcare delivery across the continuum of care. He has written and spoken extensively on value-based care, population health, new payment models and the role of big data and analytics in delivering better outcomes. He is an investor and advisor to several startups, including Roam Analytics, Kodiak Sciences, HaloNeuro, and OrthoBullets. Jean holds MD and MBA degrees from Stanford and an AB in Molecular Biology from Princeton. He is the former Vice Chair of Lester B. Pearson United World College of the Pacific.
Todd Gottula is Chief Product Officer, President, and co-Founder of Clarify Health Solutions. A visionary with over 20 years of achievement driving growth, innovation, and profitability in the high-tech sector, he brings that expertise to Clarify to deliver insights and digital solutions that empower physicians, health systems, and payers to optimize care and thrive in a value-based world. Todd fundamentally believes that through the deployment of technology and expertise, we can improve the lives of patients and those that care for them.
Prior to founding Clarify Health, Todd was Executive Vice President and Chief Technology Officer at Advent Software, where he led cutting-edge development efforts from ideation through revenue generation, serving as the critical bridge between business and engineering. Advent grew to service customers in 60 countries and over $18 trillion in assets before being acquired for $2.7B in 2015. Todd holds a BS in Chemical Engineering from the California Institute of Technology.
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