Design and Implementation of a Real-time Monitoring Platform for Optimal Sepsis Care in an Emergency Department: Observational Cohort Study

The role of artificial intelligence in sepsis in the Emergency Department: a narrative review

Background and Objective

Early recognition and treatment of sepsis in the emergency department (ED) is important. Traditional predictive analytics and clinical decision rules lack accuracy in identifying patients with sepsis. Artificial intelligence (AI) is increasingly prevalent in healthcare and offers application potential in the care of patients with sepsis. This review examines the evidence of AI in diagnosing, managing and prognosticating sepsis in the ED.

Methods

We performed literature search in PubMed, Embase, Google Scholar and Scopus databases for studies published between 1 January 2010 and 30 June 2024 that evaluated the use of AI in adult patients with sepsis in ED, using the following search terms: ("artificial intelligence" OR "machine learning" OR "neural networks, computer" OR "deep learning" OR "natural language processing"), AND ("sepsis" OR "septic shock", AND "emergency services" OR "emergency department"). Independent searches were conducted in duplicate with discrepancies adjudicated by a third member.

Key Content and Findings

Incorporating multiple variables such as vital signs, free text input, laboratory tests and electrocardiogram was possible with AI compared to traditional models leading to improvement in diagnostic performance. Machine learning (ML) models outperformed traditional scoring tools in both diagnosis and prognosis of sepsis. ML models were able to analyze trends over time and showed utility in predicting mortality, severe sepsis and septic shock. Additionally, real-time ML-assisted alert systems are effective in improving time-to-antibiotic administration and ML algorithms can differentiate sepsis patients into distinct phenotypes to tailor management (especially fluid therapy and critical care interventions), potentially improving outcomes. Existing AI tools for sepsis currently lack generalizability and user acceptance. This is risk of automation bias with loss of clinicians' skills if over-reliance develops.

Conclusions

Overall, AI holds great promise in revolutionizing management of patients with sepsis in the ED as a clinical support tool. However, its application is currently still constrained by inherent limitations. Balanced integration of AI technology with clinician input is essential to harness its full potential and ensure optimal patient outcomes.

Effect of Automated Real-Time Feedback on Early-Sepsis Care: A Pragmatic Clinical Trial

OBJECTIVES

To determine if a real-time monitoring system with automated clinician alerts improves 3-hour sepsis bundle adherence.

DESIGN

Prospective, pragmatic clinical trial. Allocation alternated every 7 days.

SETTING

Quaternary hospital from December 1, 2020 to November 30, 2021.

PATIENTS

Adult emergency department or inpatients meeting objective sepsis criteria triggered an electronic medical record (EMR)-embedded best practice advisory. Enrollment occurred when clinicians acknowledged the advisory indicating they felt sepsis was likely.

INTERVENTION

Real-time automated EMR monitoring identified suspected sepsis patients with incomplete bundle measures within 1-hour of completion deadlines and generated reminder pages. Clinicians responsible for intervention group patients received reminder pages; no pages were sent for controls. The primary analysis cohort was the subset of enrolled patients at risk of bundle nonadherent care that had reminder pages generated.

MEASUREMENTS AND MAIN RESULTS

The primary outcome was orders for all 3-hour bundle elements within guideline time limits. Secondary outcomes included guideline-adherent delivery of all 3-hour bundle elements, 28-day mortality, antibiotic discontinuation within 48-hours, and pathogen recovery from any culture within 7 days of time-zero. Among 3,269 enrolled patients, 1,377 had reminder pages generated and were included in the primary analysis. There were 670 (48.7%) at-risk patients randomized to paging alerts and 707 (51.3%) to control. Bundle-adherent orders were placed for 198 intervention patients (29.6%) versus 149 (21.1%) controls (difference: 8.5%; 95% CI, 3.9-13.1%; p = 0.0003). Bundle-adherent care was delivered for 152 (22.7%) intervention versus 121 (17.1%) control patients (difference: 5.6%; 95% CI, 1.4-9.8%; p = 0.0095). Mortality was similar between groups (8.4% vs 8.3%), as were early antibiotic discontinuation (35.1% vs 33.4%) and pan-culture negativity (69.0% vs 68.2%).

CONCLUSIONS

Real-time monitoring and paging alerts significantly increased orders for and delivery of guideline-adherent care for suspected sepsis patients at risk of 3-hour bundle nonadherence. The trial was underpowered to determine whether adherence affected mortality. Despite enrolling patients with clinically suspected sepsis, early antibiotic discontinuation and pan-culture negativity were common, highlighting challenges in identifying appropriate patients for sepsis bundle application.

Meaningful time-related aspects of alerts in Clinical Decision Support Systems. A unified framework

Alerts are a common functionality of clinical decision support systems (CDSSs). Although they have proven to be useful in clinical practice, the alert burden can lead to alert fatigue and significantly reduce their usability and acceptance. Based on a literature review, we propose a unified framework consisting of a set of meaningful timestamps that allows the use of state-of-the-art measures for alert burden, such as alert dwell time, alert think time, and response time. In addition, it can be used to investigate other measures that could be relevant as regards dealing with this problem. Furthermore, we provide a case study concerning three different types of alerts to which the framework was successfully applied. We consider that our framework can easily be adapted to other CDSSs and that it could be useful for dealing with alert burden measurement thus contributing to its appropriate management.