Dashboards: From Data to Discovery

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Paul J. Chang, MD, FSIIM, says, “Because of the external expectations that we will all do more in radiology with less time and fewer resources, we are now entering a maturation phase that I call image management. The emphasis, now, is on understanding what we do to help the value proposition. The key is now measurable improvement in efficiency, productivity, or whatever key performance indicators create value for your institution.” Chang, an abdominal radiologist, is professor and vice chair of radiology informatics and medical director of pathology informatics at the University of Chicago School of Medicine and is medical director of enterprise imaging and of service-oriented architecture at the University of Chicago Hospitals in Illinois. He presented “Technical Aspects: Developing and Deploying a Dashboard” as part of the multisession course “Quality Improvement: Quality and Productivity Dashboards” on November 29, 2011, at the annual meeting of the RSNA in Chicago. The PACS, RIS, and electronic medical record (EMR) were originally designed to let radiology departments do their work, not assess their work, and that is still their primary function. For this reason, Chang says, there will usually need to be a separate business-intelligence entity (Figure 1), deployed on a service-oriented architecture and constructed to bring together the information that a dashboard will then show. The adoption of standards for information systems can only be seen as a huge improvement over the proprietary protocols that came before them (and made communication between systems profoundly difficult, absurdly expensive, or simply impossible). There are still communication problems among information system today, however—along with far better ways of overcoming them. Watching the Battle The hospital information system (HIS) typically uses the HL7 communication standard, for instance, while the PACS is likely to support at least one form of the DICOM standard. Once the EMR, billing/financial systems, any relevant stand-alone pathology and laboratory systems, and the RIS are added—as they must be, to obtain a comprehensive picture of the department’s operations—it is easy to see why manual data aggregation became the norm in many organizations, if they tried to bring together their data at all. Even today, guessing (whether educated or less so) based on the output of the RIS alone is not an uncommon management method in radiology. Of course, manual data-aggregation methods produced information of relatively low utility (at high cost), and never in real time. Business-intelligence systems replace manual aggregation by pulling relevant data from all available sources in the organization. “The critical, absolutely essential tool for us to have to navigate through this environment is business intelligence/analytics,” Chang says. “Health care is about 10 years behind every other industry in IT, but it’s closer to 15 years behind in business intelligence.” Chang adds, “It is useful to distinguish dashboards from scorecards,” despite the fact that the two are the forms of business intelligence/analytics most likely to be seen in health-care settings. Although both scorecards and dashboards can be built using similar data from the organization’s information systems, Chang explains, “The dashboard is a performance-monitoring tool similar to a pilot’s heads-up display: It’s a tactical, real-time, operational tool typically achieved using graphics, charts, and gauges.” The dashboard, he adds, provides tactical situational awareness. It tells users whether they are winning the battle; scorecards (or report cards), in contrast, are strategic, rather than tactical. They tell users whether they are winning the war. The business-intelligence layer is necessary because it is dangerous, Chang explains, to consume naked data. Without integration and analysis, data sourced from a single information system might not be relevant because it is not comparable to data acquired, handled, defined, changed, or retrieved (according to different criteria) from another system. The solutions to this problem of comparability and compatibility are data standardization and normalization. Winning the War The data normalization performed using business-intelligence systems makes large tables into smaller entities in which an added, changed, or deleted field is less likely to affect other areas adversely and is more likely to maintain its actual meaning as it propagates