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Background Injury is the leading cause of death in people under 45 years of age in the United States; however, how care decisions occur in critical injury is poorly understood. This exploratory study sought to generate hypotheses about how care decisions are made among interdisciplinary providers caring for patients who have been critically injured. Methods This was a qualitative study conducted at two intensive care units in a level 1 trauma center in an urban, teaching, safety-net hospital. Semistructured interviews consisted of case scenarios with competing clinical priorities presented to 25 interdisciplinary providers, elucidating how decisions are approached. Responses were recorded, transcribed, and coded. Thematic analysis was conducted to discover central themes. Category formulation and sorting was done for data reduction and thematic structuring of the data. The range and central tendency of these themes are reported. Results The central theme for how care decisions are made among interdisciplinary providers was through the distribution of shared responsibility. The distribution of shared responsibility depended on interdisciplinary communication to navigate the two subthemes of time and roles. Time had to be navigated carefully, because it was both an opportunity for data acquisition and consensus building but also a pressure to decisively progress care. Roles were distinct but interchangeable and consisted of experts, actualizers, and questioners. Conclusion Care decisions are made in the context of shared responsibility among interdisciplinary providers. Interdisciplinary communication is a means of establishing roles and navigating time to distribute shared responsibility among interdisciplinary providers.

Introduction/objectives Pneumocystis jirovecii pneumonia (PJP) is a rare but potentially fatal opportunistic infection; however, consensus varies around which conditions or medications confer a level of risk sufficient to justify antibiotic prophylaxis for PJP. We used electronic health record (EHR) data to assess the current patterns of PJP prophylaxis, PJP outcomes, and prophylaxis-related adverse events among patients with rheumatic diseases who were receiving high-risk immunosuppressant drugs. Methods Data derive from the EHR of a large health system. We included new immunosuppressant users with diagnoses of vasculitis, myositis, or systemic lupus erythematosus. We calculated the proportion of patients who received PJP prophylaxis for each diagnosis and drug combination. We also calculated the number of PJP infections and the number of antibiotic adverse drug events (ADEs) per patient-year of exposure. Results We followed 316 patients for 23.2 + /- 14.2 months. Overall, 124 (39%) of patients received prophylactic antibiotics for PJP. At least 25% of patients with the highest risk conditions (e.g. vasculitis) or highest risk immunosuppressants (e.g. cyclophosphamide) did not receive PJP prophylaxis. We found no cases of PJP infection over 640 patient-years of follow up, including among those not receiving prophylaxis, and an overall incidence rate of ADEs of 2.2% per patient-year. Conclusions PJP prophylaxis for patients with rheumatic conditions is inconsistent, with one quarter of patients who have high risk conditions or high risk immunosuppressants not receiving prophylaxis. However, given extremely low rates of PJP infection, but detectable ADEs to prophylactic antibiotics, our findings suggest that evidence to guide more personalized risk assessments are needed to inform PJP prophylaxis.

Introduction The Affordable Care Act’s Medicaid expansion in 2014 improved access to care. However, not all states adopted the expansion, and debates on reform or repeal continue. In addition, few studies have analyzed differential effects by demographic characteristics. Methods Using nationally representative data from the 2011-2016 Behavioral Risk Factor Surveillance System, we applied a difference-in-differences approach to compare changes in health care access, preventive care utilization, health status, and chronic illness diagnoses from pre- to post-expansion years stratified by race, age, income, and urban or rural residence. Results Medicaid expansion was associated with reductions in the uninsured rate (-5.4 percentage points, p

The Affordable Care Act’s (ACA) expansion of Medicaid in 2014 has improved access to care, but data on its impact on health and mortality is limited. In this study, we use U.S. mortality data from 2011 to 2017 to analyze trends in mortality among adults ages 25-64 before and after Medicaid expansion. We analyze both all-cause mortality and mortality from causes considered amenable to health care and compare states that did and did not expand Medicaid. We found that while all-cause mortality rose steadily for non-elderly adults between 2013 and 2017, health care amenable mortality fell 68% more (-3.70% vs. -2.20%) in states that expanded Medicaid compared to those that did not. Although trends varied widely by state, Medicaid expansion states comprised 13 of the 15 states with the largest reductions in health care amenable mortality. The data suggest that Medicaid expansion may have prevented premature deaths during a period of rising overall mortality.

Objectives To evaluate ROP screening rates in a population-based cohort; and to identify characteristics of patients that were missed. Study design We used the California Perinatal Quality Care Collaborative data from 2005-2007 for a cross-sectional study. Using eligibility criteria, screening rates were calculated for each hospital. Multivariable regression was used to assess associations between patient clinical and sociodemographic factors and the odds of missing screening. Results Overall rates of missed ROP screening decreased from 18.6% in 2005 to 12.8% in 2007. Higher gestational age (OR = 1.25 for increase of 1 week, 95% CI, 1.21-1.29), higher birth weight (OR = 1.13; 95% CI, 1.10-1.15), and singleton birth (OR = 1.2; 95% CI, 1.07-1.34) were associated with higher probability of missing screening. Level II neonatal intensive care units and neonatal intensive care units with lower volume were more likely to miss screenings. Conclusion Although ROP screening rates improved over time, larger and older infants are at risk for not receiving screening. Furthermore, large variations in screening rates exist among hospitals in California. Identification of gaps in quality of care creates an opportunity to improve ROP screening rates and prevent impaired vision in this vulnerable population.

Aim The broad objective of this study was to examine multiple dimensions of depression in a large, diverse population of adults with diabetes. Specific aims were to measure the association of depression with: (1) patient characteristics; (2) outcomes; and (3) diabetes-related quality of care. Methods Cross-sectional analyses were performed using survey and chart data from the Translating Research Into Action for Diabetes (TRIAD) study, including 8790 adults with diabetes, enrolled in 10 managed care health plans in 7 states. Depression was measured using the Patient Health Questionnaire (PHQ-8). Patient characteristics, outcomes and quality of care were measured using validated survey items and chart data. Results Nearly 18% of patients had major depression, with prevalence 2–3 times higher among patients with low socioeconomic status. Pain and limited mobility were strongly associated with depression, controlling for other patient characteristics. Depression was associated with slightly worse glycemic control, but not other intermediate clinical outcomes. Depressed patients received slightly fewer recommended diabetes-related processes of care. Conclusions In a large, diverse cohort of patients with diabetes, depression was most prevalent among patients with low socioeconomic status and those with pain, and was associated with slightly worse glycemic control and quality of care.

The aim of this study was to determine the percentage of hospitals with adequate sample size to meaningfully compare performance by using the Agency for Healthcare Research and Quality (AHRQ) pediatric quality indicators (PDIs), which measure pediatric inpatient adverse events such as decubitus ulcer rate and infections due to medical care, have been nationally endorsed, and are currently publicly reported in at least 2 states.We performed a cross-sectional analysis of California hospital discharges from 2005-2007 for patients aged18 years. For 9 hospital-level PDIs, after excluding discharges with PDIs indicated as present on admission, we determined for each PDI the volume of eligible pediatric patients for each measure at each hospital, the statewide mean rate, and the percentage of hospitals with adequate volume to identify an adverse event rate twice the statewide mean.Unadjusted California-wide event rates for PDIs during the study period (N = 2 333 556 discharges) were 0.2 to 38 per 1000 discharges. Event rates for specific measures were, for example, 0.2 per 1000 (iatrogenic pneumothorax in non-neonates), 19 per 1000 (postoperative sepsis), and 38 per 1000 (pediatric heart surgery mortality), requiring patient volumes of 49 869, 419, and 201 to detect an event rate twice the statewide average; 0%, 6.6%, and 25%, respectively, of California hospitals had this pediatric volume.Using these AHRQ-developed, nationally endorsed measures of the quality of inpatient pediatric care, one would not be able to identify many hospitals with performance 2 times worse than the statewide average due to extremely low event rates and inadequate pediatric hospital volume.

Background To develop and compare ICU length-of-stay (LOS) risk-adjustment models using three commonly used mortality or LOS prediction models. Methods Between 2001 and 2004, we performed a retrospective, observational study of 11,295 ICU patients from 35 hospitals in the California Intensive Care Outcomes Project. We compared the accuracy of the following three LOS models: a recalibrated acute physiology and chronic health evaluation (APACHE) IV-LOS model; and models developed using risk factors in the mortality probability model III at zero hours (MPM 0 ) and the simplified acute physiology score (SAPS) II mortality prediction model. We evaluated models by calculating the following: (1) grouped coefficients of determination; (2) differences between observed and predicted LOS across subgroups; and (3) intraclass correlations of observed/expected LOS ratios between models. Results The grouped coefficients of determination were APACHE IV with coefficients recalibrated to the LOS values of the study cohort (APACHE IVrecal) [ R 2 = 0.422], mortality probability model III at zero hours (MPM 0 III) [ R 2 = 0.279], and simplified acute physiology score (SAPS II) [ R 2 = 0.008]. For each decile of predicted ICU LOS, the mean predicted LOS vs the observed LOS was significantly different (p ≤ 0.05) for three, two, and six deciles using APACHE IVrecal, MPM 0 III, and SAPS II, respectively. Plots of the predicted vs the observed LOS ratios of the hospitals revealed a threefold variation in LOS among hospitals with high model correlations. Conclusions APACHE IV and MPM 0 III were more accurate than SAPS II for the prediction of ICU LOS. APACHE IV is the most accurate and best calibrated model. Although it is less accurate, MPM 0 III may be a reasonable option if the data collection burden or the treatment effect bias is a consideration.

Rural hospitals in the United States have demonstrated lower adherence to evidence based guidelines than their urban counterparts in national public reporting initiatives. We compared the quality of rural hospitals participating in a public reporting initiative to that of their urban counterparts using Hospital Compare, a new national database containing process measures.Cross-sectional analyses of hospitals participating in Hospital Compare in 2005, evaluating percent adherence to guidelines for 10 processes of care for acute myocardial infarction (AMI), heart failure (HF), and community-acquired pneumonia (CAP) using multivariable linear regression analyses.Participating rural hospitals demonstrated lower adherence to evidence based guidelines in MI and HF quality measures (p0.05) and higher adherence to prescribing antibiotics in a timely manner in CAP (p0.05). Differences increased with bed size (F test for linear trend, p0.05). After adjustment, the trends demonstrating lower adherence persisted in 6 AMI and HF measures and higher adherence in 1 CAP measure in spite of a disproportionate number of drop-outs among lower performing urban hospitals.Participating rural hospitals had lower performance than their urban counterparts. As the rural/urban quality gap varies by condition, bed size, and participation, we recommend comparing performance across a wide variety of condition-specific measures to enable targeted quality improvement.

Background Federal and state agencies are considering ICU performance assessment and public reporting; however, an accurate method for measuring performance must be selected. In this study, we determine whether a substantial variation in ICU mortality performance still exists in modern ICUs, and compare the predictive accuracy, reliability, and data burden of existing ICU risk-adjustment models. Methods A retrospective chart review of 11,300 ICU patients from 35 California hospitals from 2001 to 2004 was performed. We calculated standardized mortality ratios (SMRs) for each hospital using the mortality probability model III (MPM0 III), the simplified acute physiology score (SAPS) II, and the acute physiology and chronic health evaluation (APACHE) IV risk-adjustment models. We compared discrimination, calibration, data reliability, and abstraction time for the models. Results Regardless of the model used, there was a large variation in SMRs among the ICUs studied. The discrimination and calibration were adequate for all risk-adjustment models. APACHE IV had the best discrimination (area under the receiver operating characteristic curve [AUC], 0.892) compared to MPM0 III (AUC, 0.809), and SAPS II (AUC, 0.873; p Conclusions We found substantial variation in the ICU risk-adjusted mortality rates that persisted regardless of the risk-adjustment model. With unlimited resources, the APACHE IV model offers the best predictive accuracy. If constrained by cost and manual data collection, the MPM0 III model offers a viable alternative without a substantial loss in accuracy.

The cost of treating disease depends on patient characteristics, but standard tools for analyzing the clinical predictors of cost have deficiencies. To explore whether survival analysis techniques might overcome some of these deficiencies in the analysis of cost data, we compared ordinary least square (OLS) linear regression (with and without transformation of the data) and binary logistic regression with two survival models: the Cox proportional hazards model and a parametric model assuming a Weibull distribution. Each model was applied to data from 155 patients undergoing coronary artery bypass grafting. We examined the effects of age, sex, ejection fraction, unstable angina, and number of diseased vessels on univariable and multivariable predictions of costs. The significant univariable predictors of cost were consistent in all models: ejection fraction was significant in all five models, and age and number of diseased vessels were each significant in all but the OLS model, while sex and angina type were significant in none of the models. The significant multivariable predictors of cost, however, differed according to model: ejection fraction was a significant multivariable predictor of cost in all five models, age was significant in three models, and number of diseased vessels was significant in one model. All five models were also used to predict the costs for an average patient undergoing surgery. The Cox model provided the most accurate predictions of mean cost, median cost, and the proportion of patients with high cost. This study shows: (1) lower ejection fraction and older age are independent clinical predictors of increased cost of CABG, and (2) the Cox proportional hazards model shows considerable promise for the analysis of the impact of clinical factors upon cost.

In the mid-1990s, the Department of Veterans Affairs (VA) health care system initiated a systemwide reengineering to, among other things, improve its quality of care. We sought to determine the subsequent change in the quality of health care and to compare the quality with that of the Medicare fee-for-service program.Using data from an ongoing performance-evaluation program in the VA, we evaluated the quality of preventive, acute, and chronic care. We assessed the change in quality-of-care indicators from 1994 (before reengineering) through 2000 and compared the quality of care with that afforded by the Medicare fee-for-service system, using the same indicators of quality.In fiscal year 2000, throughout the VA system, the percentage of patients receiving appropriate care was 90 percent or greater for 9 of 17 quality-of-care indicators and exceeded 70 percent for 13 of 17 indicators. There were statistically significant improvements in quality from 1994-1995 through 2000 for all nine indicators that were collected in all years. As compared with the Medicare fee-for-service program, the VA performed significantly better on all 11 similar quality indicators for the period from 1997 through 1999. In 2000, the VA outperformed Medicare on 12 of 13 indicators.The quality of care in the VA health care system substantially improved after the implementation of a systemwide reengineering and, during the period from 1997 through 2000, was significantly better than that in the Medicare fee-for-service program. These data suggest that the quality-improvement initiatives adopted by the VA in the mid-1990s were effective.