Concrete Examples of How Health Intelligence Saves Lives

Health Intelligence Saves Lives

An excellent article in Health Data Management by Greg Gillespie gives some wonderful examples of the power of analytics to improve health outcomes, looking at data from some of the 2,000+ clinical trials that Cleveland Clinic is currently running.

I strongly encourage you to read the original article (also available in pdf format), but here are summaries of the three use cases highlighted: hand-washing analytics, central-line analytics, and blood-transfusion analytics.

handwashing

Data Transparency and Hand-Washing Compliance

Cleveland Clinic uses SAP BusinessObjects Dashboards (Xcelsius) to display information, change behavior, and avoid infections:

Cleveland Clinic has developed a program where staff from compliance 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 dashboard tab.

Four years ago, the system was showing a 40 percent compliance rate with hand hygiene guidelines. Now the compliance rate is staying well over 90 percent, staving off a significant number of hospital-acquired infections and other complications arising from hygiene issues.

“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,” says Steve Davis M.D. “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.”

[By coincidence, a post in the Decision Factor blog also takes up the theme of hand-washing this week, arguing that data cleansing is the single most important means of avoiding bad decisions. ]

central line

Reducing Infections While Saving Money

By carefully collecting and analyzing data, Cleveland Clinic has been able to reduce infection rates, spend less on equipment, and avoid costs of up to $30,000 per affected patient:

The Centers for Disease Control and Prevention estimate that nearly 250,000 of the bloodstream infections occur 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.

Overhauling the health system’s approach to central-line infections had a significant financial return in addition to the clinical benefits.

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. 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.

blood banner

Best-Practice Blood Transfusions

A blood transfusion dashboard helps identify physicians that haven’t kept up with the latest information in health best practice, improve the supply of blood, and reduces costs:

Andrew Proctor, administrative director of medical operations for Cleveland Clinic 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.

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.

“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 an improvement in patient outcomes.

Health Data + Analysis Saves Lives

I believe we’ve only scratched the surface of what is possible using analytics. New developments in big data mobile, cloud, analytic, and collaborative technology are combining to create new ways of improving health care.

Examples include the new SAP Collaborative E-Care Management application that connects patients, care providers and their families through medical monitoring software and mobile devices to better manage their health with individualized treatment plans:

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And the pioneering work being done in conjunction with Charité, Europe’s largest teaching hospital, to enable mobile access to health data anytime, anywhere, including the SAP HANA-based Oncolyzer cancer-research application.

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For more information, visit the Healthcare area of SAP.com

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