Using a human-factors engineering approach to evaluate environmental cleaning in Veterans' Affairs acute and long-term care facilities: A qualitative analysis.

BACKGROUND: Environmental cleaning is important in the interruption of pathogen transmission. Although prevention initiatives have targeted environmental cleaning, practice variations exist and compliance is low. Evaluation of human factors influencing variations in cleaning practices can be valuable in developing interventions to standardized practices. We conducted a work-system analysis using a human-factors engineering (HFE) framework to identify barriers and facilitators to environmental cleaning practices in acute and long-term care settings within the Veterans' Affairs health system. METHODS: We conducted a qualitative study with key stakeholders at 3 VA facilities. We analyzed transcripts for thematic content and mapped themes to the HFE framework. RESULTS: Staffing consistency was felt to improve cleaning practices and teamwork. We found that many environmental management service (EMS) staff were veterans who were motivated to serve fellow veterans, especially to prevent infections. However, hiring veterans comes with regulatory hurdles that affect staffing. Sites reported some form of monitoring their cleaning process, but there was variation in method and frequency. The EMS workload was affected by whether rooms were occupied by patients or were semiprivate rooms; both were reportedly more difficult to clean. Room design and surface finishes were identified as important to cleaning efficiency. CONCLUSION: HFE work analysis identified barriers and facilitators to environmental cleaning. These findings highlight intervention entry points that may facilitate standardized work practices. There is a need to develop task-specific procedures such as cleaning occupied beds and semiprivate rooms. Future research should evaluate interventions that address these determinants of environmental cleaning.

Development of a fully automated surgical site infection detection algorithm for use in cardiac and orthopedic surgery research.

OBJECTIVE: To develop a fully automated algorithm using data from the Veterans' Affairs (VA) electrical medical record (EMR) to identify deep-incisional surgical site infections (SSIs) after cardiac surgeries and total joint arthroplasties (TJAs) to be used for research studies. DESIGN: Retrospective cohort study. SETTING: This study was conducted in 11 VA hospitals. PARTICIPANTS: Patients who underwent coronary artery bypass grafting or valve replacement between January 1, 2010, and March 31, 2018 (cardiac cohort) and patients who underwent total hip arthroplasty or total knee arthroplasty between January 1, 2007, and March 31, 2018 (TJA cohort). METHODS: Relevant clinical information and administrative code data were extracted from the EMR. The outcomes of interest were mediastinitis, endocarditis, or deep-incisional or organ-space SSI within 30 days after surgery. Multiple logistic regression analysis with a repeated regular bootstrap procedure was used to select variables and to assign points in the models. Sensitivities, specificities, positive predictive values (PPVs) and negative predictive values were calculated with comparison to outcomes collected by the Veterans' Affairs Surgical Quality Improvement Program (VASQIP). RESULTS: Overall, 49 (0.5%) of the 13,341 cardiac surgeries were classified as mediastinitis or endocarditis, and 83 (0.6%) of the 12,992 TJAs were classified as deep-incisional or organ-space SSIs. With at least 60% sensitivity, the PPVs of the SSI detection algorithms after cardiac surgeries and TJAs were 52.5% and 62.0%, respectively. CONCLUSIONS: Considering the low prevalence rate of SSIs, our algorithms were successful in identifying a majority of patients with a true SSI while simultaneously reducing false-positive cases. As a next step, validation of these algorithms in different hospital systems with EMR will be needed.

Control selection and participation in an ongoing, population-based, case-control study of birth defects: the National Birth Defects Prevention Study.

To evaluate the representativeness of controls in an ongoing, population-based, case-control study of birth defects in 10 centers across the United States, researchers compared 1997-2003 birth certificate data linked to selected controls (n = 6,681) and control participants (n = 4,395) with those from their base populations (n = 2,468,697). Researchers analyzed differences in population characteristics (e.g., percentage of births at > or =2,500 g) for each group. Compared with their base populations, control participants did not differ in distributions of maternal or paternal age, previous livebirths, maternal smoking, or diabetes, but they did differ in other maternal (i.e., race/ethnicity, education, entry into prenatal care) and infant (i.e., birth weight, gestational age, and plurality) characteristics. Differences in distributions of maternal, but not infant, characteristics were associated with participation by selected controls. Absolute differences in infant characteristics for the base population versus control participants were < or =1.3 percentage points. Differences in infant characteristics were greater at centers that selected controls from hospitals compared with centers that selected controls from electronic birth certificates. These findings suggest that control participants in the National Birth Defects Prevention Study generally are representative of their base populations. Hospital-based control selection may slightly underascertain infants affected by certain adverse birth outcomes.