Implementation of a Clinical Decision Support System for Children With Minor Blunt Head Trauma Who Are at Nonnegligible Risk for Traumatic Brain Injuries.

STUDY OBJECTIVE: To determine the effect of providing risk estimates of clinically important traumatic brain injuries and management recommendations on emergency department (ED) outcomes for children with isolated intermediate Pediatric Emergency Care Applied Research Network clinically important traumatic brain injury risk factors. METHODS: This was a secondary analysis of a nonrandomized clinical trial with concurrent controls, conducted at 5 pediatric and 8 general EDs between November 2011 and June 2014, enrolling patients younger than 18 years who had minor blunt head trauma. After a baseline period, intervention sites received electronic clinical decision support providing patient-level clinically important traumatic brain injury risk estimates and management recommendations. The following primary outcomes in patients with one intermediate Pediatric Emergency Care Applied Research Network risk factor were compared before and after clinical decision support: proportion of ED computed tomography (CT) scans, adjusted for age, time trend, and site; and prevalence of clinically important traumatic brain injuries. RESULTS: The risk of clinically important traumatic brain injuries was known for 3,859 children with isolated findings (1,711 at intervention sites before clinical decision support, 1,702 at intervention sites after clinical decision support, and 446 at control sites). In this group, pooled CT proportion decreased from 24.2% to 21.6% after clinical decision support (odds ratio 0.86; 95% confidence interval 0.73 to 1.01). Decreases in CT use were noted across intervention EDs, but not in controls. The pooled adjusted odds ratio for CT use after clinical decision support was 0.73 (95% confidence interval 0.60 to 0.88). Among the entire cohort, clinically important traumatic brain injury was diagnosed at the index ED visit for 37 of 37 (100%) patients before clinical decision support and 32 of 33 patients (97.0%) after clinical decision support. CONCLUSION: Providing specific risks of clinically important traumatic brain injury through electronic clinical decision support was associated with a modest and safe decrease in ED CT use for children at nonnegligible risk of clinically important traumatic brain injuries.

Informing the design of clinical decision support services for evaluation of children with minor blunt head trauma in the emergency department: a sociotechnical analysis.

Integration of clinical decision support services (CDSS) into electronic health records (EHRs) may be integral to widespread dissemination and use of clinical prediction rules in the emergency department (ED). However, the best way to design such services to maximize their usefulness in such a complex setting is poorly understood. We conducted a multi-site cross-sectional qualitative study whose aim was to describe the sociotechnical environment in the ED to inform the design of a CDSS intervention to implement the Pediatric Emergency Care Applied Research Network (PECARN) clinical prediction rules for children with minor blunt head trauma. Informed by a sociotechnical model consisting of eight dimensions, we conducted focus groups, individual interviews and workflow observations in 11 EDs, of which 5 were located in academic medical centers and 6 were in community hospitals. A total of 126 ED clinicians, information technology specialists, and administrators participated. We clustered data into 19 categories of sociotechnical factors through a process of thematic analysis and subsequently organized the categories into a sociotechnical matrix consisting of three high-level sociotechnical dimensions (workflow and communication, organizational factors, human factors) and three themes (interdisciplinary assessment processes, clinical practices related to prediction rules, EHR as a decision support tool). Design challenges that emerged from the analysis included the need to use structured data fields to support data capture and re-use while maintaining efficient care processes, supporting interdisciplinary communication, and facilitating family-clinician interaction for decision-making.

Innovative Use of the Electronic Health Record to Support Harm Reduction Efforts.

BACKGROUND AND OBJECTIVES: Awareness of the impact of preventable harm on patients and families has resulted in extensive efforts to make our health care systems safer. We determined that, in our hospital, patients experienced 1 of 9 types of preventable harm approximately every other day. In an effort to expedite early identification of patients at risk and provide timely intervention, we used the electronic health record's (EHR) documentation to enable decision support, data capture, and auditing and implemented reporting tools to reduce rates of harm. METHODS: Harm reduction strategies included aggregating data to generate a risk profile for hospital-acquired conditions (HACs) for all inpatients. The profile includes links to prevention bundles and available care guidelines. Additionally, lists of patients at risk for HACs autopopulate electronic audit tools contained within Research Electronic Data Capture, and data from observational audits and EHR documentation populate real-time dashboards of bundle compliance. Patient population summary reports promote the discussion of relevant HAC prevention measures during patient care and unit leadership rounds. RESULTS: The hospital has sustained a >30% reduction in harm for 9 types of HAC since 2012. In 2014, the number of HACs with >80% bundle adherence doubled coincident with the progressive rollout of these EHR-based interventions. CONCLUSIONS: Existing EHR documentation and reporting tools may be effective adjuncts to harm reduction initiatives. Additional study should include an evaluation of scalability across organizations, ongoing bundle adherence, and individual tests of change to isolate interventions with the highest impact on our results.

Use of Traumatic Brain Injury Prediction Rules With Clinical Decision Support.

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.

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

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.

Use of a remote clinical decision support service for a multicenter trial to implement prediction rules for children with minor blunt head trauma.

OBJECTIVE: To evaluate the architecture, integration requirements, and execution characteristics of a remote clinical decision support (CDS) service used in a multicenter clinical trial. The trial tested the efficacy of implementing brain injury prediction rules for children with minor blunt head trauma. MATERIALS AND METHODS: We integrated the Epic(®) electronic health record (EHR) with the Enterprise Clinical Rules Service (ECRS), a web-based CDS service, at two emergency departments. Patterns of CDS review included either a delayed, near-real-time review, where the physician viewed CDS recommendations generated by the nursing assessment, or a real-time review, where the physician viewed recommendations generated by their own documentation. A backstopping, vendor-based CDS triggered with zero delay when no recommendation was available in the EHR from the web-service. We assessed the execution characteristics of the integrated system and the source of the generated recommendations viewed by physicians. RESULTS: The ECRS mean execution time was 0.74 ±0.72 s. Overall execution time was substantially different at the two sites, with mean total transaction times of 19.67 and 3.99 s. Of 1930 analyzed transactions from the two sites, 60% (310/521) of all physician documentation-initiated recommendations and 99% (1390/1409) of all nurse documentation-initiated recommendations originated from the remote web service. DISCUSSION: The remote CDS system was the source of recommendations in more than half of the real-time cases and virtually all the near-real-time cases. Comparisons are limited by allowable variation in user workflow and resolution of the EHR clock. CONCLUSION: With maturation and adoption of standards for CDS services, remote CDS shows promise to decrease time-to-trial for multicenter evaluations of candidate decision support interventions.

Using the electronic medical record to improve education in patients at risk for adrenal insufficiency.

Background. Adrenal insufficiency is a life-threatening event. It is recommended that patients with known adrenal insufficiency and their families receive careful and repeated education on sick-day glucocorticoid management. We hypothesized that the electronic medical record (EMR) can be used to improve patient education through automated provider notification. Methods. We established an automated electronic alert in the EMR that triggered in the outpatient endocrine clinic. The alert asked if stress dose education was reviewed at the visit. The response to this alert was evaluated between July 15, 2009 and February 19, 2010. Results. 128 unique patients had visits both prior to and following the implementation of the EMR alert. The alert was acknowledged in 58 unique patient visits. After the alert was implemented, 87/128 (68%) of the patients had documentation in their record that stress dosing was reviewed. In the visit just prior to implementation of the alert, 48/128 (38%) of the patient encounters showed written documentation of stress dose review. Conclusion. This report documents that an automated alert in the EMR can promote improved provider adherence to recommendations regarding ongoing education of patients for stress dosing of glucocorticoids. Whether this translates into better outcomes for patients remains to be seen.

Electronic health record-based decision support to improve asthma care: a cluster-randomized trial.

OBJECTIVE: Asthma continues to be 1 of the most common chronic diseases of childhood and affects approximately 6 million US children. Although National Asthma Education Prevention Program guidelines exist and are widely accepted, previous studies have demonstrated poor clinician adherence across a variety of populations. We sought to determine if clinical decision support (CDS) embedded in an electronic health record (EHR) would improve clinician adherence to national asthma guidelines in the primary care setting. METHODS: We conducted a prospective cluster-randomized trial in 12 primary care sites over a 1-year period. Practices were stratified for analysis according to whether the site was urban or suburban. Children aged 0 to 18 years with persistent asthma were identified by International Classification of Diseases, Ninth Revision codes for asthma. The 6 intervention-practice sites had CDS alerts imbedded in the EHR. Outcomes of interest were the proportion of children with at least 1 prescription for controller medication, an up-to-date asthma care plan, and the performance of office-based spirometry. RESULTS: Increases in the number of prescriptions for controller medications, over time, was 6% greater (P = .006) and 3% greater for spirometry (P = .04) in the intervention urban practices. Filing an up-to-date asthma care plan improved 14% (P = .03) and spirometry improved 6% (P = .003) in the suburban practices with the intervention. CONCLUSION: In our study, using a cluster-randomized trial design, CDS in the EHR, at the point of care, improved clinician compliance with National Asthma Education Prevention Program guidelines.

Research subject enrollment by primary care pediatricians using an electronic health record.

Clinical research relies on enrollment of appropriate subjects. Individuals who hear about research from their clinician are more likely to participate. We developed two strategies for research subject enrollment using the EHR and its underlying data: pop-up alerts for clinicians, and patient lists for on-site research assistants. Eleven studies used clinician alerts and most referred an adequate volume of potential subjects (range 17 to 1,162; median 324). Only a small portion of these potential subjects consented to participate (range 3% to 25%; median 11%). Two of the three studies that used EHR derived patient lists and on-site research assistants reached their enrollment goals. All three principal investigators were satisfied with this approach. These results demonstrate that EHR alerts and patient lists can facilitate research subject enrollment in primary care pediatrics. Future work will identify methods to improve the clinician EHR alert when on-site enrollment by research assistants is not feasible.