Indiana Health System Takes On Infection Control With Predictive Analytics

Posted on February 22, 2017 I Written By

Anne Zieger is veteran healthcare editor and analyst with 25 years of industry experience. Zieger formerly served as editor-in-chief of FierceHealthcare.com and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also contributed content to hundreds of healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

At Indiana University Health, a 15-hospital non-profit health system, they’ve taken aim at reducing the rate of central-line associated bloodstream infections – better known to infection control specialists as CLABSIs.

According to the CDC, CLABSIs are preventable, but at present still result in thousands of deaths each year and add billions of dollars in costs to U.S. healthcare system spending. According to CDC data, patient mortality rates related to CLABSI range from 12% to 25%, and the infections cost $3,700 to $36,000 per episode.

Hospitals have been grappling with this problem for a long time, but now technology may offer preventive options. To cut its rate of CLABSIs, IU Health has decided to use predictive analytics in addition to traditional prevention strategies, according to an article in the AHA’s Hospitals & Health Systems magazine.

Reducing the level of hospital-acquired infections suffered by your patients always makes sense, but IU Health arguably has additional incentives to do it. The decision to attack CLABSIs comes as IU Health takes on a strategic initiative likely to demand a close watch on such metrics. At the beginning of January, Indiana University Health kicked off its participation in the CMS Next Generational Accountable Care Organization Model, putting its ACO in the national spotlight as a potential model for improving fee-for-service Medicare.

According to H&HN, IU Health has launched its predictive analytics pilot for CLABSI prevention at its University Hospital location, which includes a 600-bed Level I trauma center and 300-bed tertiary care center which also serves as one of the 10 largest transplant centers in the U.S.

Executives there told the magazine that the predictive analytics effort was an outgrowth of its long-term EMR development effort, which has pushed them to streamline data flow across platforms and locations over the past several years.

The hospital’s existing tech prior to the predictive analytics effort did include an e-surveillance program for hospital-acquired infections, but even using the full powers of the EMR and e-surveillance solution together, the hospitals could only monitor for CLABSI which had already been diagnosed.

This retrospective approach succeeded in cutting IU Health’s CLABSI rate from 1.7 CLABSIs over central-line days in 2015 to 1.2 last year. But IU Health hopes to improve the hospital’s results even further by getting ahead of the game.

Last year, the system implemented a data visualization platform designed to give providers a quick-and-easy look at data in real time. The platform lets managers keep track of many important variables easily, including whether hospital units have skipped any line maintenance activities or failed to follow-through on CLABSI bundles. It’s also saving time for nurse managers, who used to have to track data manually, and letting them check on patient trend line data at a glance.

The H&HN article doesn’t say whether the hospital has managed to cut its CLABSI rate any further, but it’s hard to imagine how predictive analytics could deliver zero results. Let’s wish IU Health further luck in cutting CLABSI rates down further.