Lessons in big data: From New York to Pasadena
Dr. Nirav R. Shah serves as Kaiser Permanente Southern California’s senior vice president and chief operating officer for Clinical Operations. He previously served as the New York State Health Commissioner, where he oversaw the implementation of a statewide healthcare technology initiative, the creation of a successful health insurance exchange, and the redesign of the Medicaid program. He is a board-certified Internist, an elected member of the Institute of Medicine of the National Academy of Sciences, and formerly served on the faculty of NYU School of Medicine.
1. During your time as NY State Health Commissioner, New York set up a great model for data sharing—what was the biggest win?
One of our most notable achievements was creating a statewide network for health information exchange. What does that mean? It means that if you got into a car accident in Buffalo, the doctor could pull up your electronic medical records from Brooklyn. This network for nearly 20 million New Yorkers is funded by the state—which means it’s each and every citizen’s data—not the insurance company’s data or the hospital’s data. As a New Yorker, you will have full access to your records through a patient portal (or can download the data using the blue button standard) and you can decide who has access to your data or even decide to opt out.
2. Some tech entrepreneurs are overwhelmed by the standards and regulations associated with the healthcare industry. Do you have any advice for entrepreneurs interested in partnering with government agencies?
When you work with government, it’s for the long play. Add 6-12 months to the average timeline, because of competing priorities of overworked civil servants. It’s hard to work with government but nevertheless can pay off because of the huge potential impact, and because federal and state agencies now are beginning to understand the many benefits of public-private partnerships.
What can entrepreneurs do? One of the areas that is extremely fruitful is around data transparency. Everyone talks about moving towards value-based medicine, and one important part of value is how much things cost. In December of 2012, NY State released all of the data for hospital costs and charges for over 1,400 procedures, across every hospital in the state of New York. So you could use the data to see that a hip replacement surgery cost $15,000 at one hospital and $103,000 across the street! That kind of data was never available to the public. This becomes even more important with the consumerization of health care, and a big deal when someone has a high-deductible health plan, where $6,000 may be coming from out-of-pocket before insurance even kicks in.
Entrepreneurs can mine and use that data to create tools for customers. For example, where should I go to get an elective knee replacement surgery done? I now can go to the healthdata.ny.gov website and look at not only the cost of this procedure at any hospital in New York, but much more. I may not want to go to where my doctor just says I should go based on his knowledge of one hospital. I can be an informed consumer and make those decisions on my own, using data on quality, safety, cost, and much more to make an informed choice. It’s a big deal, and creating simple, user-friendly tools is a market need and something entrepreneurs should consider.
3. What is an example of a Kaiser Permanente best practices that can be shared with other facilities?
Kaiser Permanente has two distinct advantages to most others—an integrated delivery system because the insurance side and clinical care delivery side are both under one roof, and high quality care provided by Permanente physicians. I’ll give you an example of what’s possible at Kaiser Permanente but nearly impossible to do elsewhere.
The average hospital stay for a hip replacement surgery is over 3 days, defined by overnight stays. Every extra day in any hospital involves some risk—of getting hospital-acquired infections, falling on your way to the bathroom, not to mention the hospital food. At several of our medical centers, we can complete a hip replacement surgery in ways to send a patient home the very same day—a zero day hospital stay. We do that by first, knowing who the patient is very well—all their medical history, prescriptions, etc. in an electronic health record with up-to-date information which every care provider has access to. Second, we send walkers or canes to the patient’s home before the surgery even occurs – so you’re not searching for one at the last minute and delaying discharge by teaching the patient how to use it thereafter. Third, we schedule the patient as the first operating room case in the morning. The operation starts at 7:00 AM—not a later case which might get delayed, which means you can closely watch the patient for 12 hours in the hospital before you send him home in the evening. The next day, like clockwork, the physical therapist will visit the patient at home at 9:00 AM (and again in the afternoon), and a nurse visits the patient at home at 10:00 AM. At noon, a doctor reviews the electronic medical record notes of both these morning visits and contacts other providers and the patient as needed, and a specialist will see the patient in her office a few days later.
Those are the basic building blocks—a system built around a patient to optimize his experience, keep him away from possible infections, allowing him to recover with family at home, with built-in safety nets to ensure he recovers optimally. But you can only do this if you can guarantee that every part of the system works together flawlessly, reliably, with agility and resilience to meet the individual needs of each patient every time, what I like to refer to as “mass customization.” For most healthcare systems, the hard part is the having the necessary data (i.e. the patient medical record, scheduling operating room times, physical therapy work schedules, prescription changes, nurse home visit times, specialist bookings, etc.) in a perfectly choreographed dance. In the near future, big data—and even small data used in such a way—will make this the standard that every American should expect.