ArticleApplied clinical informatics2018
Design and Implementation of a Pediatric ICU Acuity Scoring Tool as Clinical Decision Support.
Article in Applied clinical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Machine Learning-Based Prediction of Critical Deterioration in the PICU.Critical care explorations · 2026Article
- Promise and Caution: Mapping Opportunities for AI Decision Support in Emergency Medical Services.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Review
- Evaluating Equity in Usage and Effectiveness of the CONCERN Early Warning System.Applied clinical informatics · 2025Article
- Artificial intelligence and electronic health records: a narrative review of current applications and challenges in pediatric surgery.World journal of pediatric surgery · 2025Review
- Usability Testing of Situation Awareness Clinical Decision Support in the Intensive Care Unit.Applied clinical informatics · 2024Article
- Information displays for automated surveillance algorithms of in-hospital patient deterioration: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Use of design thinking and human factors approach to improve situation awareness in the pediatric intensive care unit.Journal of hospital medicine · 2023Article
- Integration of Risk Scores and Integration Capability in Electronic Patient Records.Applied clinical informatics · 2022Article
- The Effects of Care Team Roles on Situation Awareness in the Pediatric Intensive Care Unit: A Prospective Cross-Sectional Study.Journal of hospital medicine · 2020Observational
- Performance of a Clinical Decision Support Tool to Identify PICU Patients at High Risk for Clinical Deterioration.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2020Article
- Data Model Requirements for a Digital Cognitive Aid for Anesthesia to Support Intraoperative Crisis Management.Applied clinical informatics · 2020Article
- Data Challenges With Real-Time Safety Event Detection And Clinical Decision Support.Journal of medical Internet research · 2019Article
- Diffusing an Innovation: Clinician Perceptions of Continuous Predictive Analytics Monitoring in Intensive Care.Applied clinical informatics · 2019Article
- Sustainability of a PICU Situation Awareness Intervention: A Qualitative Study.Pediatric quality & safetyArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
objectivePediatric in-hospital cardiac arrest most commonly occurs in the pediatric intensive care unit (PICU) and is frequently preceded by early warning signs of clinical deterioration. In this study, we describe the implementation and evaluation of criteria to identify high-risk patients from a paper-based checklist into a clinical decision support (CDS) tool in the electronic health record (EHR). MATERIALS AND
methodsThe validated paper-based tool was first adapted by PICU clinicians and clinical informaticians and then integrated into clinical workflow following best practices for CDS design. A vendor-based rule engine was utilized. Littenberg's assessment framework helped guide the overall evaluation. Preliminary testing took place in EHR development environments with more rigorous evaluation, testing, and feedback completed in the live production environment. To verify data quality of the CDS rule engine, a retrospective Structured Query Language (SQL) data query was also created. As a process metric, preparedness was measured in pre- and postimplementation surveys.
resultsThe system was deployed, evaluating approximately 340 unique patients monthly across 4 clinical teams. The verification against retrospective SQL of 15-minute intervals over a 30-day period revealed no missing triggered intervals and demonstrated 99.3% concordance of positive triggers. Preparedness showed improvements across multiple domains to our a priori goal of 90%.
conclusionWe describe the successful adaptation and implementation of a real-time CDS tool to identify PICU patients at risk of deterioration. Prospective multicenter evaluation of the tool's effectiveness on clinical outcomes is necessary before broader implementation can be recommended.
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