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

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

SUBMITTER: Tham E

PROVIDER: S-EPMC4941858 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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

Tham Eric E Swietlik Marguerite M Deakyne Sara S Hoffman Jeffrey M JM Grundmeier Robert W RW Paterno Marilyn D MD Rocha Beatriz H BH Schaeffer Molly H MH Pabbathi Deepika D Alessandrini Evaline E Ballard Dustin D Goldberg Howard S HS Kuppermann Nathan N Dayan Peter S PS

Applied clinical informatics 20160615 2

<h4>Introduction</h4>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 des ...[more]

PMID: 27437059

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Project description:Overuse of cranial computed tomography scans in children with blunt head trauma unnecessarily exposes them to radiation. The Pediatric Emergency Care Applied Research Network (PECARN) blunt head trauma prediction rules identify children who do not require a computed tomography scan. Electronic health record (EHR) based clinical decision support (CDS) may effectively implement these rules but must only be provided for appropriate patients in order to minimize excessive alerts.To develop, implement and evaluate site-specific groupings of chief complaints (CC) that accurately identify children with head trauma, in order to activate data collection in an EHR.As part of a 13 site clinical trial comparing cranial computed tomography use before and after implementation of CDS, four PECARN sites centrally developed and locally implemented CC groupings to trigger a clinical trial alert (CTA) to facilitate the completion of an emergency department head trauma data collection template. We tested and chose CC groupings to attain high sensitivity while maintaining at least moderate specificity.Due to variability in CCs available, identical groupings across sites were not possible. We noted substantial variability in the sensitivity and specificity of seemingly similar CC groupings between sites. The implemented CC groupings had sensitivities greater than 90% with specificities between 75-89%. During the trial, formal testing and provider feedback led to tailoring of the CC groupings at some sites.CC groupings can be successfully developed and implemented across multiple sites to accurately identify patients who should have a CTA triggered to facilitate EHR data collection. However, CC groupings will necessarily vary in order to attain high sensitivity and moderate-to-high specificity. In future trials, the balance between sensitivity and specificity should be considered based on the nature of the clinical condition, including prevalence and morbidity, in addition to the goals of the intervention being considered.

Project description:BackgroundMany large-scale cardiovascular clinical trials are plagued with escalating costs and low enrollment. Implementing a computable phenotype, which is a set of executable algorithms, to identify a group of clinical characteristics derivable from electronic health records or administrative claims records, is essential to successful recruitment in large-scale pragmatic clinical trials. This methods paper provides an overview of the development and implementation of a computable phenotype in ADAPTABLE (Aspirin Dosing: a Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness)-a pragmatic, randomized, open-label clinical trial testing the optimal dose of aspirin for secondary prevention of atherosclerotic cardiovascular disease events.Methods and resultsA multidisciplinary team developed and tested the computable phenotype to identify adults ≥18 years of age with a history of atherosclerotic cardiovascular disease without safety concerns around using aspirin and meeting trial eligibility criteria. Using the computable phenotype, investigators identified over 650 000 potentially eligible patients from the 40 participating sites from Patient-Centered Outcomes Research Network-a network of Clinical Data Research Networks, Patient-Powered Research Networks, and Health Plan Research Networks. Leveraging diverse recruitment methods, sites enrolled 15 076 participants from April 2016 to June 2019. During the process of developing and implementing the ADAPTABLE computable phenotype, several key lessons were learned. The accuracy and utility of a computable phenotype are dependent on the quality of the source data, which can be variable even with a common data model. Local validation and modification were required based on site factors, such as recruitment strategies, data quality, and local coding patterns. Sustained collaboration among a diverse team of researchers is needed during computable phenotype development and implementation.ConclusionsThe ADAPTABLE computable phenotype served as an efficient method to recruit patients in a multisite pragmatic clinical trial. This process of development and implementation will be informative for future large-scale, pragmatic clinical trials. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02697916.

Project description:BackgroundRapid-cycle feedback loops provide timely information and actionable feedback to healthcare organizations to accelerate implementation of interventions. We aimed to (1) describe a mixed-method approach for generating and delivering rapid-cycle feedback and (2) explore key lessons learned while implementing an enhanced recovery protocol (ERP) across 18 pediatric surgery centers.MethodsAll centers are members of the Pediatric Surgery Research Collaborative (PedSRC, www.pedsrc.org ), participating in the ENhanced Recovery In CHildren Undergoing Surgery (ENRICH-US) trial. To assess implementation efforts, we conducted a mixed-method sequential explanatory study, administering surveys and follow-up interviews with each center's implementation team 6 and 12 months following implementation. Along with detailed notetaking and iterative discussion within our team, we used these data to generate and deliver a center-specific implementation report card to each center. Report cards used a traffic light approach to quickly visualize implementation status (green = excellent; yellow = needs improvement; red = needs significant improvement) and summarized strengths and opportunities at each timepoint.ResultsWe identified several benefits, challenges, and practical considerations for assessing implementation and using rapid-cycle feedback among pediatric surgery centers. Regarding potential benefits, this approach enabled us to quickly understand variation in implementation and corresponding needs across centers. It allowed us to efficiently provide actionable feedback to centers about implementation. Engaging consistently with center-specific implementation teams also helped facilitate partnerships between centers and the research team. Regarding potential challenges, research teams must still allocate substantial resources to provide feedback rapidly. Additionally, discussions and consensus are needed across team members about the content of center-specific feedback. Practical considerations include carefully balancing timeliness and comprehensiveness when delivering rapid-cycle feedback. In pediatric surgery, moreover, it is essential to actively engage all key stakeholders (including physicians, nurses, patients, caregivers, etc.) and adopt an iterative, reflexive approach in providing feedback.ConclusionFrom a methodological perspective, we identified three key lessons: (1) using a rapid, mixed method evaluation approach is feasible in pediatric surgery and (2) can be beneficial, particularly in quickly understanding variation in implementation across centers; however, (3) there is a need to address several methodological challenges and considerations, particularly in balancing the timeliness and comprehensiveness of feedback.Trial registrationNIH National Library of Medicine Clinical Trials.Clinicaltrialsgov Identifier: NCT04060303. Registered August 7, 2019, https://clinicaltrials.gov/ct2/show/NCT04060303.

Project description: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.

Dataset Information

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

Publications

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

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