Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis.

Early recognition and treatment of sepsis are linked to improved patient outcomes. Machine learning-based early warning systems may reduce the time to recognition, but few systems have undergone clinical evaluation. In this prospective, multi-site cohort study, we examined the association between patient outcomes and provider interaction with a deployed sepsis alert system called the Targeted Real-time Early Warning System (TREWS). During the study, 590,736 patients were monitored by TREWS across five hospitals. We focused our analysis on 6,877 patients with sepsis who were identified by the alert before initiation of antibiotic therapy. Adjusting for patient presentation and severity, patients in this group whose alert was confirmed by a provider within 3 h of the alert had a reduced in-hospital mortality rate (3.3%, confidence interval (CI) 1.7, 5.1%, adjusted absolute reduction, and 18.7%, CI 9.4, 27.0%, adjusted relative reduction), organ failure and length of stay compared with patients whose alert was not confirmed by a provider within 3 h. Improvements in mortality rate (4.5%, CI 0.8, 8.3%, adjusted absolute reduction) and organ failure were larger among those patients who were additionally flagged as high risk. Our findings indicate that early warning systems have the potential to identify sepsis patients early and improve patient outcomes and that sepsis patients who would benefit the most from early treatment can be identified and prioritized at the time of the alert.

Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing.

Machine learning-based clinical decision support tools for sepsis create opportunities to identify at-risk patients and initiate treatments at early time points, which is critical for improving sepsis outcomes. In view of the increasing use of such systems, better understanding of how they are adopted and used by healthcare providers is needed. Here, we analyzed provider interactions with a sepsis early detection tool (Targeted Real-time Early Warning System), which was deployed at five hospitals over a 2-year period. Among 9,805 retrospectively identified sepsis cases, the early detection tool achieved high sensitivity (82% of sepsis cases were identified) and a high rate of adoption: 89% of all alerts by the system were evaluated by a physician or advanced practice provider and 38% of evaluated alerts were confirmed by a provider. Adjusting for patient presentation and severity, patients with sepsis whose alert was confirmed by a provider within 3 h had a 1.85-h (95% CI 1.66-2.00) reduction in median time to first antibiotic order compared to patients with sepsis whose alert was either dismissed, confirmed more than 3 h after the alert or never addressed in the system. Finally, we found that emergency department providers and providers who had previous interactions with an alert were more likely to interact with alerts, as well as to confirm alerts on retrospectively identified patients with sepsis. Beyond efforts to improve the performance of early warning systems, efforts to improve adoption are essential to their clinical impact and should focus on understanding providers' knowledge of, experience with and attitudes toward such systems.

A Tool to Guide Clinical Nurse Specialist Orientation, Professional Development, and Evaluation.

PURPOSE/OBJECTIVES: Currently, orientation for clinical nurse specialists (CNSs) in the Baltimore region is based on past practices: facility-specific or position-specific. A Chesapeake Bay affiliate work group identified a need to develop a theory-driven, competency-based program and tool to guide orientation and ongoing professional development reflecting the scope of CNS practice. DESCRIPTION OF THE PROJECT/PROGRAM: The tool incorporates Benner's concepts of novice-to-expert competence levels, guides progressive development of the CNS, and has relevant assessment metrics that highlight contributions to the patient, nurse, and system. OUTCOME: The group developed a comprehensive orientation tool grounded in the spheres of influence and advanced practice competencies and specific, measurable behavioral statements related to competencies from the 2018 National Association of Clinical Nurse Specialists' draft. This program is adaptable to guide the practice of a CNS in any facility, validate competence, and relate to those with varied experience in the role. CONCLUSION: In a method similar to the process for developing nationally recognized educational standards used to develop the competencies, the program was revised based on an iterative, stepwise process. It was distributed to the membership for evaluation and feedback, which was incorporated into the final version.