This study evaluates the efficiency of federal hospitals, specifically those hospitals administered by the US Department of Veterans Affairs and the US Department of Defense. Hospital executives, health care policymakers, taxpayers, and federal hospital beneficiaries benefit from studies that improve hospital efficiency. This study uses data envelopment analysis to evaluate a panel of 165 federal hospitals in 2007 and 157 of the same hospitals again in 2011. Results indicate that overall efficiency in federal hospitals improved from 81% in 2007 to 86% in 2011. The number of federal hospitals operating on the efficiency frontier decreased slightly from 25 in 2007 to 21 in 2011. The higher efficiency score clearly documents that federal hospitals are becoming more efficient in the management of resources. From a policy perspective, this study highlights the economic importance of encouraging increased efficiency throughout the health care industry. This research examines benchmarking strategies to improve the efficiency of hospital services to federal beneficiaries. Through the use of strategies such as integrated information systems, consolidation of services, transaction-cost economics, and focusing on preventative health care, these organizations have been able to provide quality service while maintaining fiscal responsibility. In addition, the research documented the characteristics of those federal hospitals that were found to be on the Efficiency Frontier. These hospitals serve as benchmarks for less efficient federal hospitals as they develop strategies for improvement.
BACKGROUND: A leading cause of preventable maternal death is related to delayed response to clinical warning signs. Electronic surveillance systems may improve detection of maternal morbidity with automated notifications. This retrospective observational study evaluates the ability of an automated surveillance system and the Maternal Early Warning Criteria (MEWC) to detect severely morbid postpartum hemorrhage (sPPH) after delivery. METHODS: The electronic health records of adult obstetric patients of any gestational age delivering between April 1, 2017 and December 1, 2018 were queried to identify scheduled or unscheduled vaginal or cesarean deliveries. Deliveries complicated by sPPH were identified and defined by operative management of postpartum hemorrhage, transfusion of ≥4 units of packed red blood cells (pRBCs), ≥2 units of pRBCs and ≥2 units of fresh-frozen plasma, transfusion with >1 dose of furosemide, or transfer to the intensive care unit. The test characteristics of automated pages and the MEWC for identification of sPPH 24 hours after delivery were determined and compared using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and their 95% confidence intervals (CIs). McNemar test was used to compare these estimates for both early warning systems. RESULTS: The average age at admission was 30.7 years (standard deviation [SD] = 5.1 years), mean gestational age 38 weeks 4 days, and cesarean delivery accounted for 30.0% of deliveries. Of 7853 deliveries, 120 (1.5%) were complicated by sPPH. The sensitivity of automated pages for sPPH within 24 hours of delivery was 60.8% (95% CI, 52.1–69.6), specificity 82.5% (95% CI, 81.7–83.4), PPV 5.1% (95% CI, 4.0–6.3), and NPV 99.3% (95% CI, 99.1–99.5). The test characteristics of the MEWC for sPPH were sensitivity 75.0% (95% CI, 67.3–82.7), specificity 66.3% (95% CI, 65.2–67.3), PPV 3.3% (95% CI, 2.7–4.0), and NPV 99.4% (95% CI, 99.2–99.6). There were 10 sPPH cases identified by automated pages, but not by the MEWC. Six of these cases were identified by a page for anemia, and 4 cases were the result of vital signs detected by the bedside monitor, but not recorded in the patient’s medical record by the bedside nurse. Therefore, the combined sensitivity of the 2 systems was 83.3% (95% CI, 75.4–89.5). CONCLUSIONS: The automated system identified 10 of 120 deliveries complicated by sPPH not identified by the MEWC. Using an automated alerting system in combination with a labor and delivery unit’s existing nursing-driven early warning system may improve detection of sPPH.
Objectives: The level of support among Michigan adults for the use of residual newborn screening dried blood spots (DBS) was investigated. Methods: We analyzed data from 4 questions on the 2008 Michigan Behavioral Risk Factor Surveillance System (n = 3,108). The questions asked about general support for the use of DBS for research and for research investigating childhood diseases, adult diseases and diseases related to environmental exposures. Results: The majority of adults (72.3%) favored the use of DBS for research intended to benefit the health of residents. With more question specificity, a higher proportion of adults (84.2%–86.8%) were found to favor the use of DBS for research, and a lower proportion had no opinion. The odds of favoring use were higher among those who were younger, female, white, healthy, or with at least a high school degree. Conclusions: This is the first population survey of adult attitudes regarding use of DBS for different types of health research, with results showing considerable public support. The findings are being used in community outreach efforts and highlight the need to investigate opposition in vulnerable populations.
Objective: A recent study leveraging machine learning methods found that postpartum hemorrhage (PPH) can be predicted accurately at the time of labor admission in the U.S. Consortium for Safe Labor (CSL) dataset, with a C-statistic as high as 0.93. These CSL models were developed in older data (2002-2008) and used an estimated blood loss (EBL) of ≥1000 mL to define PPH. We sought to externally validate these models using a more recent cohort of births where blood loss was measured using quantitative blood loss (QBL) methods. Study Design: Using data from 5,261 deliveries between February 1, 2019 to May 11, 2020 at a single tertiary hospital, we mapped our electronic health record (EHR) data to the 55 predictors described in previously published CSL models. PPH was defined as QBL ≥1000 mL within 24 hours after delivery. Model discrimination and calibration of the four CSL models were measured using our cohort. In a secondary analysis, we fit new models in our study cohort using the same predictors and algorithms as the original CSL models. Results: The original study cohort had a substantially lower rate of PPH, 4.8% (7,279/228,438) vs. 25% (1,321/5,261), possibly due to differences in measurement. The CSL models had lower discrimination in our study cohort, with a C-statistic as high as 0.57 (logistic regression). Models refit in our study cohort achieved better discrimination, with a C-statistic as high as 0.64 (random forrest). Calibration improved in the refit models as compared to the original models. Conclusion: The CSL models’ accuracy was lower in a contemporary EHR where PPH is assessed using QBL. As institutions continue to adopt QBL methods, further data are needed to understand the differences between EBL and QBL to enable accurate prediction of PPH.
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