Applying the RE-AIM Framework for the Evaluation of a Clinical Decision Support Tool for Pediatric Head Trauma: A Mixed-Methods StudyFunding This study was funded by the American Recovery and Reinvestment Act-Office of the Secretary (ARRA OS): Grant #S02MC19289–01–00. PECARN is supported by the Health Resources and Services Administration (HRSA), Maternal and Child Health Bureau (MCHB), and Emergency Medical Services for Children (EMSC) Program through the following cooperative agreements: U03MC00001, U03MC00003, U03MC00006, U03MC00007, U03MC00008, U03MC22684, and U03MC22685. The authors also gratefully acknowledge funding for RMC by the National Institute of Nursing Research (NINR) of the National Institutes of Health (NIH) under Award Numbers K99NR016275 and T32NR007969. The content is solely the responsibility of the authors and does not necessarily represent the official views of the federal agencies that funded this study.
10 May 2018
20 July 2018
05 September 2018 (online)
Background The overuse of cranial computed tomography (CT) to diagnose potential traumatic brain injuries (TBIs) exposes children with minor blunt head trauma to unnecessary ionizing radiation. The Pediatric Emergency Care Applied Research Network and the Clinical Research on Emergency Services and Treatments Network implemented TBI prediction rules via electronic health record (EHR) clinical decision support (CDS) to decrease use of CTs in children with minor blunt head trauma.
Objective This article aims to facilitate implementation and dissemination of a CDS alert into emergency departments around the country.
Methods We evaluated the EHR CT CDS tool through a mixed-methods analysis of 38 audio-recorded interviews with health care stakeholders and quantitative data sources, using the Reach, Efficacy, Adoption, Implementation, and Maintenance framework.
Results Reach —The demographics of participants enrolled in the clinical trial were consistent with national estimates of TBI prevalence. Efficacy—There was a variable and modest reduction in CT rates for the 8,067 children with minor head trauma whose clinicians received CDS. Adoption —The EHR CT CDS tool was well matched with the organizational mission, values, and priorities of the implementation sites. Implementation— The most important predisposing factors for successful implementation were the presence of an approachable clinical champion at each site and belief that the tool was a relevant, reusable knowledge asset. Enabling factors included an effective integration within the clinical workflow, organizational investment in user training, and ease of use. Maintenance — Reinforcing factors for the EHR CT CDS tool included a close fit with the institutional culture, belief that it was useful for providers and families, and a good educational and informational tool. As such, the EHR CT CDS tool was maintained in clinical practice long after study completion.
Conclusion Data from this mixed-methods study complement findings from the efficacy trial and provide critical components for consideration prior to integration and subsequent dissemination of the EHR CT CDS tool.
Trial Registration NCT01453621, Registered September 27, 2011
Keywordsclinical decision support - clinical trial - blunt head trauma - traumatic brain injury - implementation - child
Protection of Human and Animal Subjects
Written or verbal informed consent was obtained at the beginning of each interview depending on the local Institutional Review Board requirements at each site.
- 1 Centers for Disease Control and Prevention. Rates of TBI-related emergency department visits, hospitalizations, and deaths — United States, 2001–2010. Secondary Rates of TBI-related emergency department visits, hospitalizations, and deaths — United States, 2001–2010; 2016 . Available at: https://www.cdc.gov/traumaticbraininjury/data/rates.html . Accessed August 13, 2018
- 2 Center for Disease Control and Prevention. Rates of TBI-related emergency department visits by age group — United States, 2001–2010. Secondary Rates of TBI-related emergency department visits by age group — United States, 2001–2010; 2016 . Available at: https://www.cdc.gov/traumaticbraininjury/data/rates_ed_byage.html . Accessed August 13, 2018
- 3 Miglioretti DL, Johnson E, Williams A. , et al. The use of computed tomography in pediatrics and the associated radiation exposure and estimated cancer risk. JAMA Pediatr 2013; 167 (08) 700-707
- 4 Marin JR, Weaver MD, Barnato AE, Yabes JG, Yealy DM, Roberts MS. Variation in emergency department head computed tomography use for pediatric head trauma. Acad Emerg Med 2014; 21 (09) 987-995
- 5 Kuppermann N, Holmes JF, Dayan PS. , et al; Pediatric Emergency Care Applied Research Network (PECARN). Identification of children at very low risk of clinically-important brain injuries after head trauma: a prospective cohort study. Lancet 2009; 374 (9696): 1160-1170
- 6 Dayan PS, Ballard DW, Tham E. , et al; Pediatric Emergency Care Applied Research Network (PECARN); Clinical Research on Emergency Services and Treatment (CREST) Network; and Partners Healthcare; Traumatic Brain Injury-Knowledge Translation Study Group. Use of traumatic brain injury prediction rules with clinical decision support. Pediatrics 2017; 139 (04) e20162709
- 7 Tham E, Swietlik M, Deakyne S. , et al; Pediatric Emergency Care Applied Research Network (PECARN). Clinical decision support for a multicenter trial of pediatric head trauma. Appl Clin Inform 2016; 7 (02) 534-542
- 8 Goldberg HS, Paterno MD, Grundmeier RW. , et al. Use of a remote clinical decision support service for a multicenter trial to implement prediction rules for children with minor blunt head trauma. Int J Med Inform 2016; 87: 101-110
- 9 Deakyne SJ, Bajaj L, Hoffman J. , et al; Pediatric Emergency Care Applied Research Network (PECARN). Development, evaluation and implementation of chief complaint groupings to activate data collection: a multi-center study of clinical decision support for children with head trauma. Appl Clin Inform 2015; 6 (03) 521-535
- 10 Sheehan B, Nigrovic LE, Dayan PS. , et al; Pediatric Emergency Care Applied Research Network (PECARN). Informing the design of clinical decision support services for evaluation of children with minor blunt head trauma in the emergency department: a sociotechnical analysis. J Biomed Inform 2013; 46 (05) 905-913
- 11 Glasgow RE, Vogt TM, Boles SM. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health 1999; 89 (09) 1322-1327
- 12 Glasgow RE, Klesges LM, Dzewaltowski DA, Estabrooks PA, Vogt TM. Evaluating the impact of health promotion programs: using the RE-AIM framework to form summary measures for decision making involving complex issues. Health Educ Res 2006; 21 (05) 688-694
- 13 Bakken S, Ruland CM. Translating clinical informatics interventions into routine clinical care: how can the RE-AIM framework help?. J Am Med Inform Assoc 2009; 16 (06) 889-897
- 14 Green LW, Kreuter MW. Health Promotion Planning: An Educational and Ecological Approach. 4th ed. Boston, MA: McGraw-Hill; 2005
- 15 Holmes-Rovner M, Valade D, Orlowski C, Draus C, Nabozny-Valerio B, Keiser S. Implementing shared decision-making in routine practice: barriers and opportunities. Health Expect 2000; 3 (03) 182-191
- 16 Aronsky D, Chan KJ, Haug PJ. Evaluation of a computerized diagnostic decision support system for patients with pneumonia: study design considerations. J Am Med Inform Assoc 2001; 8 (05) 473-485
- 17 Faul MXL, Wald MM, Coronado VG. . Traumatic Brain Injury in the United States: Emergency Department Visits, Hospitalizations and Deaths 2002–2006. Atlanta, GA: Centers for Disease Control and Prevention, National Center for Injury Prevention and Control; 2010
- 18 EMR 2016: The Market for Electronic Medical Records by Kalorama Information. Available at: https://www.kaloramainformation.com/EMR-Electronic-Medical-10009693/ . Accessed August 27, 2018
- 19 Monegain B. . Kalorama: Cerner, McKesson earn EHR marketshare lead, Epic and Allscripts follow. Secondary Kalorama: Cerner, McKesson earn EHR marketshare lead, Epic and Allscripts follow; 2016 . Available at: http://www.healthcareitnews.com/news/kalorama-cerner-mckesson-earn-ehr-marketshare-lead-epic-and-allscripts-follow . Accessed August 13, 2018
- 20 Epic software. Epic fact sheet. In: Communications ET, ed.; 2017
- 21 Sittig DF, Wright A, Osheroff JA. , et al. Grand challenges in clinical decision support. J Biomed Inform 2008; 41 (02) 387-392
- 22 Miller A, Moon B, Anders S, Walden R, Brown S, Montella D. Integrating computerized clinical decision support systems into clinical work: a meta-synthesis of qualitative research. Int J Med Inform 2015; 84 (12) 1009-1018
- 23 Hess EP, Wyatt KD, Kharbanda AB. , et al. Effectiveness of the head CT choice decision aid in parents of children with minor head trauma: study protocol for a multicenter randomized trial. Trials 2014; 15: 253
- 24 Bressan S, Romanato S, Mion T, Zanconato S, Da Dalt L. Implementation of adapted PECARN decision rule for children with minor head injury in the pediatric emergency department. Acad Emerg Med 2012; 19 (07) 801-807
- 25 Ballard DW, Vemula R, Chettipally UK. , et al; KP CREST Network Investigators. Optimizing clinical decision support in the electronic health record. clinical characteristics associated with the use of a decision tool for disposition of ED patients with pulmonary embolism. Appl Clin Inform 2016; 7 (03) 883-898
- 26 Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005; 330 (7494): 765
- 27 Finlayson M, Cattaneo D, Cameron M. , et al. Applying the RE-AIM framework to inform the development of a multiple sclerosis falls-prevention intervention. Int J MS Care 2014; 16 (04) 192-197
- 28 Meeker D, Linder JA, Fox CR. , et al. Effect of behavioral interventions on inappropriate antibiotic prescribing among primary care practices: a randomized clinical trial. JAMA 2016; 315 (06) 562-570
- 29 Ivers N, Jamtvedt G, Flottorp S. , et al. Audit and feedback: effects on professional practice and healthcare outcomes. Cochrane Database Syst Rev 2012; (06) CD000259
- 30 Le Grand Rogers R, Narvaez Y, Venkatesh AK. , et al. Improving emergency physician performance using audit and feedback: a systematic review. Am J Emerg Med 2015; 33 (10) 1505-1514