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Appl Clin Inform. 2016 Jun 15;7(2):534-42. doi: 10.4338/ACI-2015-10-CR-0144. eCollection 2016.

Clinical Decision Support for a Multicenter Trial of Pediatric Head Trauma: Development, Implementation, and Lessons Learned.

Author information

1
Children's Hospital Colorado, Aurora, CO; University of Colorado, Denver, CO.
2
Children's Hospital Colorado , Aurora, CO.
3
Department Pediatrics, Section of Emergency Medicine, Nationwide Children's Hospital, Columbus, OH; Ohio State University College of Medicine, Columbus, OH.
4
Center for Biomedical Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA; Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia PA.
5
Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA; Harvard Medical School, Boston, MA.
6
Information Systems, Partners HealthCare System , Boston, MA.
7
Cincinnati Children's Hospital Medical Center , Cincinnati, Ohio.
8
Kaiser Permanente, San Rafael Medical Center ; Kaiser Permanente, Division of Research, Oakland, CA.
9
Departments of Emergency Medicine and Pediatrics, University of California Davis School of Medicine , Sacramento, CA.
10
Pediatrics, Division of Pediatric Emergency Medicine, Columbia University College of Physicians and Surgeons , New York, NY.

Abstract

INTRODUCTION:

For children who present to emergency departments (EDs) due to blunt head trauma, ED clinicians must decide who requires computed tomography (CT) scanning to evaluate for traumatic brain injury (TBI). The Pediatric Emergency Care Applied Research Network (PECARN) derived and validated two age-based prediction rules to identify children at very low risk of clinically-important traumatic brain injuries (ciTBIs) who do not typically require CT scans. In this case report, we describe the strategy used to implement the PECARN TBI prediction rules via electronic health record (EHR) clinical decision support (CDS) as the intervention in a multicenter clinical trial.

METHODS:

Thirteen EDs participated in this trial. The 10 sites receiving the CDS intervention used the Epic(®) EHR. All sites implementing EHR-based CDS built the rules by using the vendor's CDS engine. Based on a sociotechnical analysis, we designed the CDS so that recommendations could be displayed immediately after any provider entered prediction rule data. One central site developed and tested the intervention package to be exported to other sites. The intervention package included a clinical trial alert, an electronic data collection form, the CDS rules and the format for recommendations.

RESULTS:

The original PECARN head trauma prediction rules were derived from physician documentation while this pragmatic trial led each site to customize their workflows and allow multiple different providers to complete the head trauma assessments. These differences in workflows led to varying completion rates across sites as well as differences in the types of providers completing the electronic data form. Site variation in internal change management processes made it challenging to maintain the same rigor across all sites. This led to downstream effects when data reports were developed.

CONCLUSIONS:

The process of a centralized build and export of a CDS system in one commercial EHR system successfully supported a multicenter clinical trial.

KEYWORDS:

Clinical decision support; blunt head trauma; children; clinical trial; implementation; traumatic brain injury

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