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Complex hospital infection data confuses consumers

Many patients misinterpret CMS information meant to help decision-making

Date:
November 23, 2015
Source:
Society for Healthcare Epidemiology of America
Summary:
Patients have difficulty deciphering complex numeric data on healthcare-associated infections used by the Centers for Medicare and Medicaid Services to help consumers choose hospitals, according to a new study.
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Patients have difficulty deciphering complex numeric data on healthcare-associated infections used by the Centers for Medicare and Medicaid Services (CMS) to help consumers choose hospitals, according to a new study published online in Infection Control & Hospital Epidemiology, the journal of the Society for Healthcare Epidemiology of America (SHEA).

"Healthcare has made great strides to engage individuals to take a more active role in their care through the public reporting of data," said lead author Max Masnick, PhD, University of Maryland School of Medicine. "However, presenting healthcare-acquired infection data is difficult. We found that the current way public HAI data are communicated is confusing for consumers who are trying to make informed decisions on their care."

The collection and publication of hospital-acquired infection (HAI) data are a key element of the Affordable Care Act and other recent healthcare reform legislation as part of an effort to improve care by having consumers make informed choices. These data are made available to the public via the CMS Hospital Compare website.

Researchers at the University of Maryland School of Medicine conducted a cross-sectional survey among 110 randomly selected patients at the University of Maryland Medical Center. The survey asked participants to compare two hypothetical hospitals' frequency of catheter-associated urinary tract infections, using data displayed in the same form currently used on CMS Hospital Compare for use by the general public.

When given only written descriptions of the HAI prevalence in two hospitals, nearly three in 10 participants (28 percent) were unable to accurately identify the better hospital. When participants were given both written descriptions and numeric data together, as presented on the CMS website, comprehension worsened, with 40 percent unable to correctly identify the better hospital. The authors note that based on these findings, members of the general public may reach incorrect conclusions when comparing hospitals using the current HAI reporting format.

"HAI data are made accessible to the public to help improve hospital quality and increase transparency among healthcare systems. We need more effective ways to communicate this information," said Masnick. Masnick says he and his co-authors are working on ways CMS could improve their website.


Story Source:

Materials provided by Society for Healthcare Epidemiology of America. Note: Content may be edited for style and length.


Journal Reference:

  1. Max Masnick, Daniel J. Morgan, John D. Sorkin, Elizabeth Kim, Jessica P. Brown, Penny Rheingans, Anthony D. Harris. Lack of Patient Understanding of Hospital-Acquired Infection Data Published on the Centers for Medicare and Medicaid Services Hospital Compare Website. Infection Control & Hospital Epidemiology, 2015; 1 DOI: 10.1017/ice.2015.260

Cite This Page:

Society for Healthcare Epidemiology of America. "Complex hospital infection data confuses consumers: Many patients misinterpret CMS information meant to help decision-making." ScienceDaily. ScienceDaily, 23 November 2015. <www.sciencedaily.com/releases/2015/11/151123203428.htm>.
Society for Healthcare Epidemiology of America. (2015, November 23). Complex hospital infection data confuses consumers: Many patients misinterpret CMS information meant to help decision-making. ScienceDaily. Retrieved May 23, 2017 from www.sciencedaily.com/releases/2015/11/151123203428.htm
Society for Healthcare Epidemiology of America. "Complex hospital infection data confuses consumers: Many patients misinterpret CMS information meant to help decision-making." ScienceDaily. www.sciencedaily.com/releases/2015/11/151123203428.htm (accessed May 23, 2017).

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