Risk-Adjusting Mortality in the Nationwide Veterans Affairs Healthcare System.

BACKGROUND: The US Veterans Affairs (VA) healthcare system began reporting risk-adjusted mortality for intensive care (ICU) admissions in 2005. However, while the VA's mortality model has been updated and adapted for risk-adjustment of all inpatient hospitalizations, recent model performance has not been published. We sought to assess the current performance of VA's 4 standardized mortality models: acute care 30-day mortality (acute care SMR-30); ICU 30-day mortality (ICU SMR-30); acute care in-hospital mortality (acute care SMR); and ICU in-hospital mortality (ICU SMR). METHODS: Retrospective cohort study with split derivation and validation samples. Standardized mortality models were fit using derivation data, with coefficients applied to the validation sample. Nationwide VA hospitalizations that met model inclusion criteria during fiscal years 2017-2018(derivation) and 2019 (validation) were included. Model performance was evaluated using c-statistics to assess discrimination and comparison of observed versus predicted deaths to assess calibration. RESULTS: Among 1,143,351 hospitalizations eligible for the acute care SMR-30 during 2017-2019, in-hospital mortality was 1.8%, and 30-day mortality was 4.3%. C-statistics for the SMR models in validation data were 0.870 (acute care SMR-30); 0.864 (ICU SMR-30); 0.914 (acute care SMR); and 0.887 (ICU SMR). There were 16,036 deaths (4.29% mortality) in the SMR-30 validation cohort versus 17,458 predicted deaths (4.67%), reflecting 0.38% over-prediction. Across deciles of predicted risk, the absolute difference in observed versus predicted percent mortality was a mean of 0.38%, with a maximum error of 1.81% seen in the highest-risk decile. CONCLUSIONS AND RELEVANCE: The VA's SMR models, which incorporate patient physiology on presentation, are highly predictive and demonstrate good calibration both overall and across risk deciles. The current SMR models perform similarly to the initial ICU SMR model, indicating appropriate adaption and re-calibration.

Implementing Oral Care as a Nursing Intervention to Reduce Hospital-Acquired Pneumonia Across the United States Department of Veterans Affairs Healthcare System.

Hospital-acquired pneumonia is a preventable complication. The primary source of pneumonia among hospitalized and long-term care residents is aspiration of bacteria present in the oral biofilm. Reducing the bacterial burden in the mouth through consistent oral care is associated with a reduction in the incidence of hospital-acquired pneumonia. Following a significant reduction in pneumonia among non-ventilated patients in the research pilots, the Veterans Health Administration deployed the evidence-based, nurse-led oral care intervention called Hospital Acquired Pneumonia Prevention by Engaging Nurses as quality improvement nationwide. In this article, nursing informatics experts on the team describe the design and implementation of process and outcome measures of Hospital-Acquired Pneumonia Prevention by Engaging Nurses and outline lessons learned. The team used standardized terms and observations embedded within the EHR documentation templates to measure the oral care intervention in acute care areas. They also developed a tracking system for hospital-acquired pneumonia cases among non-ventilated patients. In addition to improving patient safety and care quality, Hospital-Acquired Pneumonia Prevention by Engaging Nurses links evidence-based practice with nursing informatics principles to generate numerous opportunities to measure the value of nursing at the point of care. This initiative was reported using SQUIRE 2.0: Standards for QUality Improvement Reporting Excellence.

Achieving balance between implementing effective infection prevention and control practices and maintaining a home-like setting in U.S. Department of Veterans Affairs nursing homes.

Nursing homes present a unique challenge for implementing infection prevention and control practices while striving to maintain a home-like environment. Medical devices such as urinary catheters and central venous catheters have become a part of nursing home care but can predispose residents to associated infections. Because evidence-based prevention bundles were implemented, catheter-associated urinary tract infections (CAUTIs) and central line-associated bloodstream infections (CLABSIs) were monitored in all U.S. Department of Veterans Affairs (VA) nursing homes, and outcomes were evaluated. Bundle components for CLABSIs focused on insertion technique, site selection, and routine assessment of central line necessity, while the CAUTI bundle focused on insertion technique, appropriate indication, and routine assessment of urinary catheter necessity. From October 2010 through September 2016, VA nursing homes reported nationwide reductions of CAUTIs (51.2%; P < .0001) and CLABSIs (25.0%; P = .0009).

Does nurse job satisfaction influence adherence to the central line insertion checklist and central line-associated bloodstream infections in the Veterans Health Administration?

Nurses satisfied with their jobs report less job stress, more effective nurse-physician collaboration, and higher patient satisfaction scores. It is unknown if job satisfaction influences adherence to best practices or patient outcomes. This secondary data analysis investigated the relationship between job satisfaction, adherence to the central line insertion checklist, and central line-associated bloodstream infections (CLABSIs). Results showed a decreased risk of CLABSI with higher job satisfaction, on average. No relationship was observed with checklist adherence.

Relationship Between Psychological Safety and Reporting Nonadherence to a Safety Checklist.

Patient safety checklists are ubiquitous in health care. Nurses bear significant responsibility for ensuring checklist adherence. To report nonadherence to a checklist and stop an unsafe procedure, a workplace climate of psychological safety is needed. Thus, an analysis of organizational data was conducted to examine the relationship between psychological safety and reports of nonadherence to the central line bundle checklist. Results showed varied perceptions of psychological safety but no relationship with nonadherence. Considerations for this finding and assessing psychological safety are provided.

Reduction of central line infections in Veterans Administration intensive care units: an observational cohort using a central infrastructure to support learning and improvement.

BACKGROUND: Elimination of hospital-acquired infections is an important patient safety goal. SETTING: All 174 medical, cardiac, surgical and mixed Veterans Administration (VA) intensive care units (ICUs). INTERVENTION: A centralised infrastructure (Inpatient Evaluation Center (IPEC)) supported the practice bundle implementation (handwashing, maximal barriers, chlorhexidinegluconate site disinfection, avoidance of femoral catheterisation and timely removal) to reduce central line-associated bloodstream infections (CLABSI). Support included recruiting leadership, benchmarked feedback, learning tools and selective mentoring. DATA COLLECTION: Sites recorded the number of CLABSI, line days and audit results of bundle compliance on a secure website. ANALYSIS: CLABSI rates between years were compared with incidence rate ratios (IRRs) from a Poisson regression and with National Healthcare Safety Network referent rates (standardised infection ratio (SIR)). Pearson's correlation coefficient compared bundle adherence with CLABSI rates. Semi-structured interviews with teams struggling to reduce CLABSI identified common themes. RESULTS: From 2006 to 2009, CLABSI rates fell (3.8-1.8/1000 line days; p<0.01); as did IRR (2007; 0.83 (95% CI 0.73 to 0.94), 2008; 0.65 (95% CI 0.56 to 0.76), 2009; 0.47 (95% CI 0.40 to 0.55)). Bundle adherence and CLABSI rates showed strong correlation (r = 0.81). VA CLABSI SIR, January to June 2009, was 0.76 (95% CI 0.69 to 0.90), and for all FY2009 0.88 (95% CI 0.80 to 0.97). Struggling sites lacked a functional team, forcing functions and feedback systems. CONCLUSION: Capitalising on a large healthcare system, VA IPEC used strategies applicable to non-federal healthcare systems and communities. Such tactics included measurement through information technology, leadership, learning tools and mentoring.

Infrastructure for quality transformation: measurement and reporting in veterans administration intensive care units.

BACKGROUND Veterans Health Administration (VA) intensive care units (ICUs) develop an infrastructure for quality improvement using information technology and recruiting leadership. METHODS Setting Participation by the 183 ICUs in the quality improvement program is required. Infrastructure includes measurement (electronic data extraction, analysis), quarterly web-based reporting and implementation support of evidence-based practices. Leaders prioritise measures based on quality improvement objectives. The electronic extraction is validated manually against the medical record, selecting hospitals whose data elements and measures fall at the extremes (10th, 90th percentile). results are depicted in graphic, narrative and tabular reports benchmarked by type and complexity of ICU. RESULTS The VA admits 103 689±1156 ICU patients/year. Variation in electronic business practices, data location and normal range of some laboratory tests affects data quality. A data management website captures data elements important to ICU performance and not available electronically. A dashboard manages the data overload (quarterly reports ranged 106-299 pages). More than 85% of ICU directors and nurse managers review their reports. Leadership interest is sustained by including ICU targets in executive performance contracts, identification of local improvement opportunities with analytic software, and focused reviews. CONCLUSION Lessons relevant to non-VA institutions include the: (1) need for ongoing data validation, (2) essential involvement of leadership at multiple levels, (3) supplementation of electronic data when key elements are absent, (4) utility of a good but not perfect electronic indicator to move practice while improving data elements and (5) value of a dashboard.