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To determine whether patients using the Centers for Medicare and Medicaid Services (CMS) Hospital Compare website (http://medicare.gov/hospitalcompare) can use nationally reported healthcare-associated infection (HAI) data to differentiate hospitals.
Secondary analysis of publicly available HAI data for calendar year 2013.
We assessed the availability of HAI data for geographically proximate hospitals (ie, hospitals within the same referral region) and then analyzed these data to determine whether they are useful to differentiate hospitals. We assessed data for the 6 HAIs reported by hospitals to the Centers for Disease Control and Prevention (CDC).
Data were analyzed for 4,561 hospitals representing 88% of registered community and federal government hospitals in the United States. Healthcare-associated infection data are only useful for comparing hospitals if they are available for multiple hospitals within a geographic region. We found that data availability differed by HAI. Clostridium difficile infections (CDI) data were most available, with 82% of geographic regions (ie, hospital referral regions) having >50% of hospitals reporting them. In contrast, 4% of geographic regions had >50% of member hospitals reporting surgical site infections (SSI) for hysterectomies, which had the lowest availability. The ability of HAI data to differentiate hospitals differed by HAI: 72% of hospital referral regions had at least 1 pair of hospitals with statistically different risk-adjusted CDI rates (SIRs), compared to 9% for SSI (hysterectomy).
HAI data generally are reported by enough hospitals to meet minimal criteria for useful comparisons in many geographic locations, though this varies by type of HAI. CDI and catheter-associated urinary tract infection (CAUTI) are more likely to differentiate hospitals than the other publicly reported HAIs.
Whether healthcare-associated infection data should be presented using indirect (current CMS/CDC methodology) or direct standardization remains controversial. We applied both methods to central-line–associated bloodstream infection data from 45 acute-care hospitals in Maryland from 2012 to 2014. We found that the 2 methods generate different hospital rankings with payment implications.
Hospital-acquired infection (HAI) data are reported to the public on the Centers for Medicare and Medicaid Services (CMS) Hospital Compare website. We previously found that public understanding of these data is poor. Our objective was to develop an improved method for presenting HAI data that could be used on the CMS website.
Randomized controlled trial comparing understanding of data presented using the current CMS presentation strategy versus a new strategy.
A 760-bed tertiary referral hospital.
A total of 61 patients were randomly selected within 24 hours of admission.
Participants were shown HAI data as presented on the CMS Hospital Compare website (control arm) or data formatted using a new method (experimental arm).
No statistically significant demographic differences were identified between study arms. Although 47% percent of participants said a website for comparing hospitals would have been helpful, only 10% had ever used such a website. Participants viewing data using the new presentation strategy compared hospitals correctly 56% of the time, compared with 32% in the control arm (P=.0002).
Understanding of HAI data increased significantly with the new data presentation method compared to the method currently used on the CMS Hospital Compare website. Many participants expressed interest in a website for comparing hospitals. Improved methods for presenting CMS HAI data, such as the one assessed here, should be adopted to increase public understanding.
Public reporting of hospital quality data is a key element of US healthcare reform. Data for hospital-acquired infections (HAIs) are especially complex.
To assess interpretability of HAI data as presented on the Centers for Medicare and Medicaid Services Hospital Compare website among patients who might benefit from access to these data.
We randomly selected inpatients at a large tertiary referral hospital from June to September 2014. Participants performed 4 distinct tasks comparing hypothetical HAI data for 2 hospitals, and the accuracy of their comparisons was assessed. Data were presented using the same tabular formats used by Centers for Medicare and Medicaid Services. Demographic characteristics and healthcare experience data were also collected.
Participants (N=110) correctly identified the better of 2 hospitals when given written descriptions of the HAI measure in 72% of the responses (95% CI, 66%–79%). Adding the underlying numerical data (number of infections, patient-time, and standardized infection ratio) to the written descriptions reduced correct responses to 60% (55%–66%). When the written HAI measure description was not informative (identical for both hospitals), 50% answered correctly (42%–58%). When no written HAI measure description was provided and hospitals differed by denominator for infection rate, 38% answered correctly (31%–45%).
Current public HAI data presentation methods may be inadequate. When presented with numeric HAI data, study participants incorrectly compared hospitals on the basis of HAI data in more than 40% of the responses. Research is needed to identify better ways to convey these data to the public.
Infect. Control Hosp. Epidemiol. 2016;37(2):182–187
We assessed frequency and predictors of seasonal influenza vaccination acceptance among inpatients at a large tertiary referral hospital, as well as reasons for vaccination refusal. Over 5 seasons, >60% of patients unvaccinated on admission refused influenza vaccination while hospitalized; “believes not at risk” was the reason most commonly given.
Centers for Disease Control and Prevention (CDC) risk adjustment methods for central-line–associated bloodstream infections (CLABSI) only adjust for type of intensive care unit (ICU). This cohort study explored risk factors for CLABSI using 2 comorbidity classification schemes, the Charlson Comorbidity Index (CCI) and the Chronic Disease Score (CDS). Our study supports the need for additional research into risk factors for CLABSI, including electronically available comorbid conditions.
We surveyed hospital epidemiologists and infection preventionists on their usage of and satisfaction with infection prevention–specific software supplementing their institution’s electronic medical record. Respondents with supplemental software were more satisfied with their software’s infection prevention and antimicrobial stewardship capabilities than those without. Infection preventionists were more satisfied than hospital epidemiologists.
We examined contamination of healthcare worker (HCW) gown and gloves after caring for patients with Klebsiella pneumoniae carbapenemase (KPC)–producing and non-KPC-producing Klebsiella as a proxy for horizontal transmission. The rate of contamination with Klebsiella species is similar to that of contamination with methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococcus, with 31 (14%) of 220 of HCW-patient interactions resulting in contamination of gloves and gowns.
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