Radiology’s Next Move: Bigger Data

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In the 1990s, it was easy to be a success. You had to work hard not to be a success. That’s not true any more,” according to Michael P. Recht, MD, Louis Marx professor of radiology at New York University School of Medicine and chair of the radiology department at NYU Langone Medical Center (New York, New York). Heightened competition, declining reimbursements, and pressure from payors to cut unnecessary imaging have forced the radiology department to watch every penny. Recht says, “We are looking at denials by site and by insurance company, asking whether there is something incorrect that we are doing. We can’t leave any money on the table.” To heighten its chances of financial success, NYU Langone Medical Center is doing what so many other radiology providers of size are doing: investing in business analytics to streamline operations and unveil opportunities perhaps otherwise missed. Analytics—a computerized way of searching for patterns—is assuming a higher profile in radiology business. The move to big data in radiology will require another leap. Characterized by the quantity and variety of data processed, as well as the speed of analysis (see article, page 12), the big-data movement seeks correlations, rather than causes, and patterns, rather than sums. It is as fundamental a management/technological revolution as the move from film to PACS was—and equally impossible to ignore. Concepts such as data governance and pattern recognition are joining key performance indicators and balanced scorecards in the radiology management lexicon. Access to data has already proven its worth. When Hurricane Sandy knocked out a CT scanner at NYU Langone Medical Center (a four-hospital complex anchored by the 705-bed Tisch Hospital), real-time workflow measurements were used to reassign patients to the three remaining scanners more or less seamlessly: 95% of cases were completed in 15 minutes and 99% were done in 30 minutes. “These data have given our people autonomy and mastery,” Recht says. Analytics programs are being run from billing systems, RIS, PACS, and voice-recognition systems, and multiple vendors offer turnkey solutions for both business and clinical analytics. Some patterns and processes are similar in all business-analytics rollouts, but beneath the surface, it’s a mixed bag. With fragmentation being endemic to health care, there is a demand for solutions that stitch everything together—a necessary precursor to a big-data solution. Usable Data Thought leader James H. Thrall, MD, FACR, is an analytics pioneer. Formerly chair of the ACR® board of chancellors, Thrall stepped down as chair of the radiology department at Massachusetts General Hospital (MGH) in Boston (a job he had held for a quarter century). Thrall, who is active on the MGH medical staff and holds a professorship at Harvard Medical School, now spends more time on research, including data analytics. “There is a construct,” Thrall begins, “that goes: data to information to knowledge to wisdom. Data are individual facts, or a collection of individual facts. Information is organization of those data, at some level. Knowledge is a higher-level organization of information. Wisdom is the ability to assimilate knowledge and also to draw inferences based on assimilated knowledge and experience.” Thrall says that business analytics for radiology has reached the information stage—and sometimes, the knowledge stage—but never the wisdom stage. “The big shortcoming I see in information systems—RIS and PACS—is that they provide no analytic capability,” Thrall says. “For example, we practice in nine different locations and we have CT scanners in seven different locations. I’d like to know how many of what kind of CT scans we did in each place. It sounds simple, but the RIS can’t give us that.” Thrall says that to get the data, MGH has to export the numbers from the RIS daily and feed them into a separate server that has the software to provide the information needed to run the radiology department. “How do you do hand-cleansing protocols or timeout protocols? There are no commercial systems available that encompass all these elements so vital to managing a department,” Thrall adds. MGH calls its dashboard The Same Page, and it gave Thrall the ability to see how many exams of what type were done at each location. “I could then trend that for a month, a year, or two years,” Thrall says. “That is very basic, tangible analytic feedback you use as the department leader. It