Trends in gestational age at delivery for intrahepatic cholestasis of pregnancy and adoption of society guidelines.

BACKGROUND: Intrahepatic cholestasis of pregnancy is associated with a significant risk of stillbirth, which contributes to variation in clinical management. Recent Society for Maternal-Fetal Medicine guidance recommends delivery at 36 weeks of gestation for patients with serum bile acid levels of >100 µmol/L, consideration for delivery between 36 and 39 weeks of gestation stratified by bile acid level, and against preterm delivery for those with clinical features of cholestasis without bile acid elevation. OBJECTIVE: This study aimed to investigate institutional practices before the publication of the new delivery timing recommendations to establish the maternal and neonatal effects of late preterm, early-term, and term deliveries in the setting of cholestasis. STUDY DESIGN: This study examined maternal and neonatal outcomes of 441 patients affected by cholestasis delivering 484 neonates in a 4-hospital system over a 30-month period. Logistic and linear regression analyses were performed to assess neonatal outcomes concerning peak serum bile acid levels at various gestational ages controlling for maternal comorbidities, multiple pregnancies, and neonatal birthweight. RESULTS: With the clinical flexibility afforded by the new guidelines, pregnancy prolongation to term may have been achieved in 91 patients (21%), and 286 patients (74%) with bile acid elevation could have delivered at a later gestational age. Preterm deliveries of patients with bile acid levels of >10 µmol/L were associated with higher rates of neonatal intensive care unit admission and adverse neonatal outcomes than early-term deliveries. CONCLUSION: Study data suggested an opportunity for education and practice change to reflect current Society for Maternal-Fetal Medicine guidelines in efforts to reduce potential neonatal morbidities associated with late preterm deliveries among pregnancies affected by cholestasis.

Implementation approaches and barriers for rule-based and machine learning-based sepsis risk prediction tools: a qualitative study.

Objective: Many options are currently available for sepsis surveillance clinical decision support (CDS) from electronic medical record (EMR) vendors, third party, and homegrown models drawing on rule-based (RB) and machine learning (ML) algorithms. This study explores sepsis CDS implementation from the perspective of implementation leads by describing the motivations, tool choices, and implementation experiences of a diverse group of implementers. Materials and Methods: Semi-structured interviews were conducted with and a questionnaire was administered to 21 hospital leaders overseeing CDS implementation at 15 US medical centers. Participants were recruited via convenience sampling. Responses were coded by 2 coders with consensus approach and inductively analyzed for themes. Results: Use of sepsis CDS is motivated in part by quality metrics for sepsis patients. Choice of tool is driven by ease of integration, customization capability, and perceived predictive potential. Implementation processes for these CDS tools are complex, time-consuming, interdisciplinary undertakings resulting in heterogeneous choice of tools and workflow integration. To improve clinician acceptance, implementers addressed both optimization of the alerts as well as clinician understanding and buy in. More distrust and confusion was reported for ML models, as compared to RB models. Respondents described a variety of approaches to overcome implementation barriers; these approaches related to alert firing, content, integration, and buy-in. Discussion: While there are shared socio-technical challenges of implementing CDS for both RB and ML models, attention to user education, support, expectation management, and dissemination of effective practices may improve feasibility and effectiveness of ML models in quality improvement efforts. Conclusion: Further implementation science research is needed to determine real world efficacy of these tools. Clinician acceptance is a significant barrier to sepsis CDS implementation. Successful implementation of less clinically intuitive ML models may require additional attention to user confusion and distrust.

Health system-based housing navigation for patients experiencing homelessness: A new care coordination framework.

Patients experiencing homelessness are among the most disadvantaged in our society, suffering from poor health outcomes and exhibiting disproportionately high hospital utilization and spending. However, to date, hospitals have only scantily devoted time or resources to the housing coordination aspect of homelessness. Implementing better systems to coordinate housing for patients experiencing homelessness may improve health outcomes and reduce health care utilization for this population. This objective is now more important than ever as the economic impact of COVID-19 is expected to exacerbate the homelessness crisis. Ensuring that patients are properly connected to temporary or permanent housing is valuable to patient health, health care system metrics such as excess spending and utilization, and provider performance under Accountable Care Organizations or other risk-bearing payment models. Here, we propose a health systems-based housing coordination framework that may improve care delivery for patients experiencing homelessness. This framework relies on the coordination between dedicated hospital-based housing navigators who can identity patients experiencing homelessness and outpatient housing navigators equipped to coordinate short- and long-term housing specifically for patients experiencing homelessness who frequently interact with the health care system.

Applying crisis standards of care to critically ill patients during the COVID-19 pandemic: Does race/ethnicity affect triage scoring?

OBJECTIVE: Given the variability in crisis standards of care (CSC) guidelines during the COVID-19 pandemic, we investigated the racial and ethnic differences in prioritization between 3 different CSC triage policies (New York, Massachusetts, USA), as well as a first come, first served (FCFS) approach, using a single patient population. METHODS: We performed a retrospective cohort study of patients with intensive care unit (ICU) needs at a tertiary hospital on its peak COVID-19 ICU census day. We used medical record data to calculate a CSC score under 3 criteria: New York, Massachusetts with full comorbidity list (Massachusetts1), and MA with a modified comorbidity list (Massachusetts2). The CSC scores, as well as FCFS, determined which patients were eligible to receive critical care under 2 scarcity scenarios: 50 versus 100 ICU bed capacity. We assessed the association between race/ethnicity and eligibility for critical care with logistic regression. RESULTS: Of 211 patients, 139 (66%) were male, 95 (45%) were Hispanic, 23 (11%) were non-Hispanic Black, and 69 (33%) were non-Hispanic White. Hispanic patients had the fewest comorbidities. Assuming a 50 ICU bed capacity, Hispanic patients had significantly higher odds of receiving critical care services across all CSC guidelines, except FCFS. However, assuming a 100 ICU bed capacity, Hispanic patients had greater odds of receiving critical care services under only the Massachusetts2 guidelines (odds ratio, 2.05; 95% CI, 1.09 to 3.85). CONCLUSION: Varying CSC guidelines differentially affect racial and ethnic minority groups with regard to risk stratification. The equity implications of CSC guidelines require thorough investigation before CSC guidelines are implemented.