Showing Them What They Do, Not What They Think They Do

Cleveland Clinic uses data analytics to show clinicians the true measure of their performance and drive improvements at the intersection of financial and clinical operations.

Absent evidence to the contrary, everyone thinks they’re doing the best job they possibly can. Data analytics at Cleveland Clinic has proved itself to be that “contrary,” to the benefit of its clinical practice and patients.

Steve Davis, M.D., starts simple. “If you ask a clinician if they always wash their hands before they see a patient, they’re going to say ‘Of course I do.’ But that’s not always the case,” says the chair of the department of pediatric critical care medicine and the head of ICU operations for the health system.  

Cleveland Clinic has developed a program where staff from the compliance department anonymously watch workers in different departments and record whether they do in fact follow hand hygiene guidelines. Their findings are uploaded into Cleveland Clinic’s enterprise analytics system and are accessible via a tab in an analytics dashboard.

Four years ago, the system was showing a 40 percent compliance rate with hand hygiene guidelines. Now that compliance rate is staying well over 90 percent, staving off a significant number of hospital-acquired infections and other complications arising from hygiene issues. And that’s the tip of the iceberg on how clinical analytics coupled with a focus on evidence-based practice has enabled Cleveland Clinic to raise the bar on quality while looking to reduce costs on a virtually real-time basis.

Speaking to the Competitive Streak

“That’s the critical value of data transparency — you can show people what they’re really doing as opposed to what they think they’re doing, and we can show it on a department, unit-by-unit or individual practitioner level,” Davis says. “I’ve found that when you put that kind of information in front of physicians, their competitive streak really comes out. No one likes to get a ‘C’ on their report card, and if you don’t have data everyone assumes they’re getting an ‘A.’ When they find out they’re not, then they get moving.”

That’s perhaps most apparent in the health system’s approach to reducing central-line infections. The Centers for Disease Control and Prevention estimate that nearly 250,000 of the bloodstream infections occur per annually from procedural issues associated with inserting and maintaining central lines -- tubes inserted near the heart or a large blood vessel that are used to give fluids, antibiotics, medical treatments such as chemotherapy, and liquid food if a patient is unable to eat or digest food normally.

“Ten years ago people didn’t think central line infections were preventable — they considered them the cost of doing business,” Davis says. “Part of the problem was the data — here at Cleveland Clinic when we got a report about a central-line infection, we got that information on a monthly or quarterly basis, which means no one could remember what happened and we couldn’t really determine if it was preventable or not.”

But that was then. Andrew Proctor, administrative director of medical operations for Cleveland Clinic, led the development of an analytics infrastructure that combs the clinical data for central-line infection indications and alerts clinical leaders in real-time when infections are detected. That capability enables quick root cause analysis of what happened while the case is still fresh in the minds of the care team. By checking whether the proper line maintenance procedures were followed  —how the dressing was maintained, how often the line was being used, etc. — Cleveland Clinic has injected accountability and precision into the processes. 

And the data continues to drive improvement, says Davis, who hasn’t had a central-line infection in his department in more than six months. For example, not every department was sold on the idea that full-sterile techniques were necessary for central-line administration, or that biopatches (which slowly release chlorhexidine, an antiseptic anti-bacterial medication, into the patient’s bloodstream) would make a difference. But the data consistently showed units that were utilizing both were far better at preventing the infections, Davis says, and that competitive streak kicked in. Full-sterile techniques and biopatches are now standard procedure at the health system.

Standing at the Intersection

Central-line infections are an example of where business and clinical intelligence truly intersect, according to Proctor, the medical operations leader. Overhauling the health system’s approach to central-line infections had a significant financial return.  Before clinical and business analytics were applied, each individual unit was responsible for ordering their own lines, which meant that more than 30 different lines (and more than 90 different PICC lines, another type of tube) were being used across Cleveland Clinic, which was not only financially inefficient but also clinically dangerous. 

By streamlining the purchasing to one vendor, the equipment and maintenance costs dropped significantly, Proctor says. And standardizing the clinical processes resulted in major cost avoidance — it’s estimated by the Health Research & Educational trust that central-line infections add upwards of $30,000 in treatment costs per afflicted patient. All well and good, Proctor says, but the real bottom line is the elimination of the pain and health risks for the already seriously ill patients who require lines. 

Another area that financial and clinical intelligence intersect is data-driven management of Cleveland Clinic’s blood supply. Proctor has developed a blood utilization dashboard that enables department heads and others to drill down to a physician level how much blood is being used for transfusions. That data — which is captured in the health system’s electronic health records system from Epic Systems, or passed to Epic via a homegrown OR record -- is cross-referenced to clinical readings about patient hemoglobin values, which traditionally are used as the trigger for transfusions designed to increase the number of red blood cells. 

Standard industry practice used call for ordering transfusions if a patient’s hemoglobin count was below 10 after surgery or due to critical illness, But about a decade ago, says Davis, medical research showed convincingly that blood transfusions given at those hemoglobin values, and even significantly lower, in nearly all cases did more harm than good, providing few benefits and increasing the risks of nosocomial infections.

Behavior Lags Behind Evidence

“Blood transfusions is another area where physician behavior has changed slower than the evidence,  and our data is helping drive that behavioral change by enabling us to determine where blood utilization still goes against best practices, and addressing the issue on a unit or individual physician basis,” Davis says. 

The result has been a significant reduction in blood utilization, which equates to a significant reductions in costs associated with maintaining the blood supply and improvement in patient outcomes. Cleveland Clinic has published landmark studies on the benefits of the evidence-based practices in action in the New England Journal of Medicine.

“If you can show someone how they are doing relative to others, and how that affects their outcomes, you’ve used the power of transparency,” Davis says.

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