In a panel discussion led by Timothy Fry, JD, associate at McGuireWoods, at the Becker’s Hospital Review inaugural CIO/HIT + Revenue Cycle Summit in Chicago, the following panelists weighed in on the future of big data:
- Bruce Darrow, MD, PhD, professor of medicine and cardiology at Mount Sinai Hospital, CMIO of Mount Sinai Health System in New York City.
- Jonathan Isaacs, executive vice president and general manager of the surgery division at SourceMedical, based in Birmingham, Ala.
Note: Responses have been edited for length and style.
On what “big data” really means
Dr. Bruce Darrow: Big data is an outgrowth of information, in which you can look across multiple patients, hospitals, systems and sources of information in a way that computing power just didn’t allow before, in a way that you wouldn’t be able to understand just by looking at it with your eyes, Dr. Darrow said. “Ten years ago, if I wanted to answer these kinds of questions, I would have needed an array of large towers of gleaming things with lights on them to make it happen. That is no longer the case.”
Mr. Jonathon Isaacs: “I personally have distaste for the term big data. It’s buzzwordy and nebulous.” However, he said, the term describes the volume, the velocity and the variety of a large pool of complex data, and it typically comes into play in healthcare when attempting to solve an analytical problem.
On hypothesis-led vs. nonhypothesis-led queries
Dr. Darrow: “Big data has evolved in two directions — hypothesis driven and nonhypothesis-driven. Before big data, you had to think of a question before you asked it.” According to Dr. Darrow, hypothesis-driven queries are just what you might expect — data scientists or healthcare professionals pinpoint something to track within the data set, such as the use of a specific medication. Nonhypothesis-driven queries are born out of connections found within the data that you may not have thought to even ask, he said.
Mr. Isaacs: Big data is an inundation of information, according to Mr. Isaacs, and it requires an objective. Using big data is an investment of resources, money and energy, all of which are finite for someone, he said. “Get a charter for the exercise you are going to analyze and that can spur those hypotheses. The key is to have a sense of what you are trying to accomplish and not just have the gaps present themselves.”
On incorporating data analytics into the healthcare workforce
Dr. Darrow: “It’s not really a core competency of your average physician to be able to do this, unless it’s been incorporated into medical school,” he said. Data scientists are not clinicians and clinicians are not data scientists, according to Dr. Darrow.
Mr. Isaacs: “There are not enough data scientists. They are extremely expensive and extremely rare,” he said. According to Mr. Isaacs, some universities are working to better understand where healthcare technology is going so they can print out people with the skill set needed to implement these technologies. However, it’s no quick fix. “There are enough vendors, strategies and tools to help now,” he said.
On collecting the right data
Dr. Darrow: Hospitals and health systems are consistently hampered by not having the right data unless a collection process is put in place, according to Dr. Darrow. “Not every clinician comes in and says, ‘I’m going to feed the data machine today.'”
Mr. Isaacs: “There is certainly a process around data collection and also around action after that,” he said. However, the design of the products currently used in healthcare ultimately does not meet what physicians or clinicians want. “We are putting the burden on [physicians] to record the data consistently. Why do we do that? We don’t do that elsewhere,” Mr. Isaacs said. “Why don’t we let physicians and clinicians practice and record the way they were taught and put the burden back on the software company?”
On assuaging the skeptics
Dr. Darrow: “I have more data skeptics than I have data believers in my organization. The best way to deal with them is to be as transparent as possible about where data comes from and what its limitations and strengths are.”
Mr. Isaacs: “Will you be wrong with an initial analysis? Absolutely,” he said. “There is always some sort of risk to analyzing what is going to happen, but if you develop a consistent process to wash, rinse and repeat, you are armed with the tools to address the skeptics in your organization.”
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