Observational studyCritical care medicine2019
Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.
Observational study in Critical care medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 8 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
75 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Nurses' Experiences Using AI in Clinical Practice: Systematic Review.JMIR nursing · 2026Pooled it
- Impact of manual sepsis screening in hospitalized adult patients: A systematic review.Journal of hospital medicine · 2026Pooled it
- Predictors of rural hospital closures in the United States: a systematic review and call for AI-driven early warning systems.BMC health services research · 2025Pooled it
- Implementation of Machine Learning Applications in Health Care Organizations: Systematic Review of Empirical Studies.Journal of medical Internet research · 2024Pooled it
- Health Care Professionals' Experience of Using AI: Systematic Review With Narrative Synthesis.Journal of medical Internet research · 2024Pooled it
- Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Applications of Artificial Intelligence in Nursing Care: A Systematic Review.Journal of nursing management · 2023Pooled it
- Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review.Journal of medical Internet research · 2021Pooled it
- AI-Assisted Cardiovascular Risk Assessment by General Practitioners in Resource-Constrained Indonesian Settings Using a Conceptual Prototype: Randomized Controlled Study.Journal of medical Internet research · 2025Trial
- Clinical Decision Support for Septic Shock in the Emergency Department: A Cluster Randomized Trial.Pediatrics · 2025Trial
- Systems Bioengineering of Septic Shock Metabolism: Citrulline, β-Hydroxybutyrate and Plasma Biomarker-Based Phenotyping.Biomolecules · 2026Review
- Improving sepsis best practice utility and clinical acceptance using an LLM-enhanced prediction system.npj health systems · 2026Article
- How Can Clinicians Decide if AI-Enabled Risk Prediction Models Are Fit for Purpose for Real World Use?Journal of evaluation in clinical practice · 2026Article
- Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2026Article
- Patient Benefits in the Context of Sepsis-Related AI-Based Clinical Decision Support Systems: Scoping Review.Journal of medical Internet research · 2026Article
- Implementation of Passive Deterioration Index Alerts in an Intermediate Care Unit: A Failed Early Warning System Strategy.Applied clinical informatics · 2025Article
- Opportunities and Challenges Associated with the Pilot Implementation of Clinical Decision Support Systems in a Rural Hospital: A Qualitative Study.Applied clinical informatics · 2025Article
- Refining a Machine Learning Model for Predicting Infant Sepsis: A Multidisciplinary Team Supported by Human-Centered Design Methods.Applied clinical informatics · 2025Article
- Improving Prognostic Accuracy of MASCC Score with Lactate and CRP Measurements in Febrile Neutropenic Patients.Diagnostics (Basel, Switzerland) · 2025Article
- Qualitative Study of the Context of Health Information Technology in Sepsis Care Transitions: Facilitators, Barriers, and Strategies for Improvement.Journal of the American Medical Directors Association · 2025Article
15 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
objectiveTo assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning System 2.0).
designProspective observational study.
settingTertiary teaching hospital in Philadelphia, PA. PATIENTS: Non-ICU admissions November-December 2016.
interventionsDuring a 6-week study period conducted 5 months after Early Warning System 2.0 alert implementation, nurses and providers were surveyed twice about their perceptions of the alert's helpfulness and impact on care, first within 6 hours of the alert, and again 48 hours after the alert. MEASUREMENTS AND MAIN
resultsFor the 362 alerts triggered, 180 nurses (50% response rate) and 107 providers (30% response rate) completed the first survey. Of these, 43 nurses (24% response rate) and 44 providers (41% response rate) completed the second survey. Few (24% nurses, 13% providers) identified new clinical findings after responding to the alert. Perceptions of the presence of sepsis at the time of alert were discrepant between nurses (13%) and providers (40%). The majority of clinicians reported no change in perception of the patient's risk for sepsis (55% nurses, 62% providers). A third of nurses (30%) but few providers (9%) reported the alert changed management. Almost half of nurses (42%) but less than a fifth of providers (16%) found the alert helpful at 6 hours.
conclusionsIn general, clinical perceptions of Early Warning System 2.0 were poor. Nurses and providers differed in their perceptions of sepsis and alert benefits. These findings highlight the challenges of achieving acceptance of predictive and machine learning-based sepsis alerts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.