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Design and Implementation of a Pediatric ICU Acuity Scoring Tool as Clinical Decision Support
Eric Shelov1, Naveen Muthu1, Heather Wolfe2
1Department of General Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States.
Insights
This study successfully implemented a clinical decision support (CDS) tool in the electronic health record (EHR) to identify high-risk pediatric intensive care unit (PICU) patients. The tool accurately identified patients needing closer monitoring for clinical deterioration.
Area of Science:
- Pediatric critical care medicine
- Clinical informatics
- Health systems engineering
Background:
- Pediatric in-hospital cardiac arrest often follows detectable clinical deterioration.
- Early identification of high-risk patients is crucial for timely intervention.
Purpose of the Study:
- To adapt and implement a paper-based checklist into an electronic health record (EHR)-integrated clinical decision support (CDS) tool.
- To evaluate the accuracy and impact of the CDS tool in a pediatric intensive care unit (PICU) setting.
Main Methods:
- A validated paper-based tool was adapted by clinicians and informaticians.
- The tool was integrated into the EHR using a vendor-based rule engine.
- Evaluation included data quality verification via SQL queries and preparedness surveys.
Main Results:
- The CDS tool was deployed, evaluating ~340 patients monthly.
- Data verification showed 99.3% concordance of positive triggers.
- Staff preparedness improved significantly post-implementation.
Conclusions:
- A real-time CDS tool was successfully adapted and implemented to identify at-risk PICU patients.
- Further prospective, multicenter studies are needed to confirm the tool's impact on clinical outcomes.
Background And Objective:
Pediatric 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 Methods:
The 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.
Results:
The 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%.
Conclusion:
We 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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