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.

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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.

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