Use of Traumatic Brain Injury Prediction Rules With Clinical Decision Support

Author:

Dayan Peter S.1,Ballard Dustin W.23,Tham Eric4,Hoffman Jeff M.5,Swietlik Marguerite6,Deakyne Sara J.6,Alessandrini Evaline A.7,Tzimenatos Leah89,Bajaj Lalit4,Vinson David R.310,Mark Dustin G.11,Offerman Steve R.12,Chettipally Uli K.13,Paterno Marilyn D.14,Schaeffer Molly H.15,Wang Jun16,Casper T. Charles16,Goldberg Howard S.1415,Grundmeier Robert W.17,Kuppermann Nathan89, , ,

Affiliation:

1. Division of Emergency Medicine, Department of Pediatrics, Columbia University College of Physicians and Surgeons, New York, New York;

2. Kaiser Permanente, San Rafael Medical Center, San Rafael, California;

3. Division of Research, Kaiser Permanente, Oakland, California;

4. Section of Emergency Medicine, Department of Pediatrics, University of Colorado, Aurora, Colorado;

5. Nationwide Children’s Hospital, Columbus, Ohio;

6. Department of Research Informatics, Children’s Hospital Colorado, Aurora, Colorado;

7. Division of Emergency Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio;

8. Departments of Emergency Medicine and

9. Pediatrics, University of California Davis School of Medicine, Sacramento, California;

10. Kaiser Permanente, Roseville Medical Center, Roseville, California;

11. Kaiser Permanente, Oakland Medical Center, Oakland, California;

12. Kaiser Permanente, South Sacramento Medical Center, Sacramento, California,;

13. Kaiser Permanente, South San Francisco Medical Center, San Francisco, California;

14. Division of General Internal Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts;

15. Information Systems, Partners HealthCare System, Boston, Massachusetts;

16. Department of Pediatrics, University of Utah, Salt Lake City, Utah; and

17. Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia and Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania

Abstract

OBJECTIVES: We determined whether implementing the Pediatric Emergency Care Applied Research Network (PECARN) traumatic brain injury (TBI) prediction rules and providing risks of clinically important TBIs (ciTBIs) with computerized clinical decision support (CDS) reduces computed tomography (CT) use for children with minor head trauma. METHODS: Nonrandomized trial with concurrent controls at 5 pediatric emergency departments (PEDs) and 8 general EDs (GEDs) between November 2011 and June 2014. Patients were <18 years old with minor blunt head trauma. Intervention sites received CDS with CT recommendations and risks of ciTBI, both for patients at very low risk of ciTBI (no Pediatric Emergency Care Applied Research Network rule factors) and those not at very low risk. The primary outcome was the rate of CT, analyzed by site, controlling for time trend. RESULTS: We analyzed 16 635 intervention and 2394 control patients. Adjusted for time trends, CT rates decreased significantly (P < .05) but modestly (2.3%–3.7%) at 2 of 4 intervention PEDs for children at very low risk. The other 2 PEDs had small (0.8%–1.5%) nonsignificant decreases. CT rates did not decrease consistently at the intervention GEDs, with low baseline CT rates (2.1%–4.0%) in those at very low risk. The control PED had little change in CT use in similar children (from 1.6% to 2.9%); the control GED showed a decrease in the CT rate (from 7.1% to 2.6%). For all children with minor head trauma, intervention sites had small decreases in CT rates (1.7%–6.2%). CONCLUSIONS: The implementation of TBI prediction rules and provision of risks of ciTBIs by using CDS was associated with modest, safe, but variable decreases in CT use. However, some secular trends were also noted.

Publisher

American Academy of Pediatrics (AAP)

Subject

Pediatrics, Perinatology, and Child Health

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