Health system CIOs can access a wealth of data on the use of precious resources, from clinicians to MRI machines. Here are specific steps to start using that data better.
For healthcare CIOs, trying to make operational improvements through better data analysis should be paramount. Optimizing use of expensive resources, improving access for patients, and cutting wait times and red tape are high on the agenda of any hospital executive committee.
There is no shortage of examples of ways to make operational improvements. For example, some health systems have streamlined scheduling to shorten the time it takes for a patient to get in to see a specialist, or for diagnostic procedures such as an MRI or CT scan. Some have collected data on patient no-shows, which are often as high as 20%, to develop ways to overbook and stem the loss of revenues and wasted resources that no-shows can cause.
Others have reorganized the design of their health system to move services such as outpatient surgery into ambulatory surgery centers that can operate efficiently and effectively at lower cost and with increased patient safety.
Many of these projects would not have been easy even five or ten years ago. Now, data collection and analysis techniques have advanced to the point where providers can rapidly implement operational improvement projects, measuring things such as how long it takes from booking an appointment to getting in to see a provider, or the time and steps it takes to get an MRI. However, many health systems are surprisingly still behind the curve in this area of using data to improve operational performance.
A lack of data analysis expertise is one reason for this lag, but it doesn't have to be an insurmountable obstacle, even for smaller institutions. What shocks me most after seeing so many successful operational improvements is how many health systems in the United States still do not have the expertise they need. They are missing a major competitive edge. Data is available inside these organizations. But unless they use a scientific approach to turn that data into information and subsequent action, it can mean lower revenue, or even worse, lower quality of care.
So what are the steps that healthcare institutions can take to begin to harness the power of advanced analytics and operations research professionals? Here are a few progressive steps designed to make the move to operational excellence.
Build a center excellence: For large health systems, it is important to build an internal center of excellence charged with measuring performance, identifying best practices, and implementing operational improvement projects. Developing a talent pipeline is a critical activity that takes time and effort, but which holds the opportunity for major improvements to the efficiency and effectiveness of care.
For examples, one can look to some bellwether centers of excellence for this: Mayo Clinic, Duke University, University of North Carolina, and University of Michigan (yes, my very own institution). I've collaborated with all of these institutions.
Partner with universities: For smaller health systems that can't build their own centers of excellence can partner with local, reputable academic organizations to secure student teams and get faculty involved. Student projects are lower cost, yet still quite effective because the student base is at the cusp of moving into the workforce. Student teams get an equal benefit by gaining cutting edge experience, which positions them well for their leap into the workforce. Often the institutions that reach out for assistance from universities end up identifying good students to bring onto their staffs when they graduate.
Partner with nonprofits: Another option is to turn to nonprofit trade organizations, such as INFORMS (the Institute for Operations Research and Management Science), a group I am heavily involved with. Knowledge sharing and peer collaboration is a valuable force in identifying best practices and opening doors to talent. Such groups can also be a source of educational offerings to develop the necessary expertise internally within an organization.
CIOs are increasingly tasked with increasing or at least maintaining revenues associated with high value resources, since that is a crucial component of running any institution. Developing expert professionals in analytics and operations research who are capable of taking on these challenges will require time, effort, and an initial investment. But as other industries have shown, this can have a substantial long term pay off in the form of higher revenues and lower costs, while at the same time improving the quality of care that patients receive.
And there's a new wave of operational understanding coming, where we will predict what may happen months into the future. Imagine getting a glimpse into what impacts proposed system changes will have on your operations: foreseeing the revenue and cost impacts of buying a new MRI, or of hiring additional nurses. This knowledge will help healthcare systems make more informed decisions about which actions to take.
It’s time for health systems to step up and put the same care into the health of their internal operations, as they do for their patients. There are underutilized ways that all health institutions can gain access to analytics brains and brawn, starting with a few ideas suggested in this piece. As an industry, we can bring our analytical minds together. Let's improve the ways that we deliver healthcare by taking advantage of new types of data, making new scientific discoveries in the realm of operational excellence, and leveraging the talent that turns that translates data into better care.
Download Healthcare IT In The Obamacare Era, the InformationWeek Healthcare digital issue on changes driven by regulation. Modern technology created the opportunity to restructure the healthcare industry around accountable care organizations, but ACOs also put new demands on IT.
Brian Denton is associate professor of industrial and operations engineering at the University of Michigan. His primary research interests are in optimization under uncertainty with applications to medical decision making related to the detection, treatment, and prevention of ... View Full Bio
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