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BackgroundOveruse 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.ObjectivesTo 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.MethodsAs 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.ResultsDue 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.ConclusionsCC 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.

Objective: We sought to develop and evaluate an electronic health record (EHR) genetic testing tracking system to address the barriers and limitations of existing spreadsheet-based workarounds., Materials and Methods: We evaluated the spreadsheet-based system using mixed effects logistic regression to identify factors associated with delayed follow up. These factors informed the design of an EHR-integrated genetic testing tracking system. After deployment, we assessed the system in 2 ways. We analyzed EHR access logs and note data to assess patient outcomes and performed semistructured interviews with users to identify impact of the system on work., Results: We found that patient-reported race was a significant predictor of documented genetic testing follow up, indicating a possible inequity in care. We implemented a CDS system including a patient data capture form and management dashboard to facilitate important care tasks. The system significantly sped review of results and significantly increased documentation of follow-up recommendations. Interviews with key system users identified a range of sociotechnical factors (ie, tools, tasks, collaboration) that contribute to safer and more efficient care., Discussion: Our new tracking system ended decades of workarounds for identifying and communicating test results and improved clinical workflows. Interview participants related that the system decreased cognitive and time burden which allowed them to focus on direct patient interaction., Conclusion: By assembling a multidisciplinary team, we designed a novel patient tracking system that improves genetic testing follow up. Similar approaches may be effective in other clinical settings., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Research is needed to identify how clinical decision support (CDS) systems can support communication about and engagement with tobacco use treatment in pediatric settings for parents who smoke. We developed a CDS system that identifies parents who smoke, delivers motivational messages to start treatment, connects parents to treatment, and supports pediatrician-parent discussion., Objective: The objective of this study is to assess the performance of this system in clinical practice, including receipt of motivational messages and tobacco use treatment acceptance rates., Methods: The system was evaluated at one large pediatric practice through a single-arm pilot study from June to November 2021. We collected data on the performance of the CDS system for all parents. Additionally, we surveyed a sample of parents immediately after the clinical encounter who used the system and reported smoking. Measures were: (1) the parent remembered the motivational message, (2) the pediatrician reinforced the message, and (3) treatment acceptance rates. Treatments included nicotine replacement therapy, quitline referral (phone counseling), and/or SmokefreeTXT referral (text message counseling). We described survey response rates overall and with 95% confidence intervals (CIs)., Results: During the entire study period, 8,488 parents completed use of the CDS: 9.3% ( n  = 786) reported smoking and 48.2% ( n  = 379) accepted at least one treatment. A total of 102 parents who smoke who used the system were approached to survey 100 parents (98% response rate). Most parents self-identified as female (84%), aged 25 to 34 years (56%), and Black/African American (94%), and had children with Medicaid insurance (95%). Of parents surveyed, 54% accepted at least one treatment option. Most parents recalled the motivational message (79%; 95% CI: 71-87%), and 31% (95% CI: 19-44%) reported that the pediatrician reinforced the motivational message., Conclusion: A CDS system to support parental tobacco use treatment in pediatric primary care enhanced motivational messaging about smoking cessation and evidence-based treatment initiation., Competing Interests: None declared., (Thieme. All rights reserved.)

Objectives: Clinical decision support (CDS) has promise for the implementation of antimicrobial stewardship programs (ASPs) in the emergency department (ED). We sought to assess the usability of a newly developed automated CDS to improve guideline-adherent antibiotic prescribing for pediatric community-acquired pneumonia (CAP) and urinary tract infection (UTI)., Methods: We conducted comparative usability testing between an automated, prototype CDS-enhanced discharge order set and standard order set, for pediatric CAP and UTI antibiotic prescribing. After an extensive user-centered design process, the prototype CDS was integrated into the electronic health record, used passive activation, and embedded locally adapted prescribing guidelines. Participants were randomized to interact with three simulated ED scenarios of children with CAP or UTI, across both systems. Measures included task completion, decision-making and usability errors, clinical actions (order set use and correct antibiotic selection), as well as objective measures of system usability, utility, and workload using the National Aeronautics and Space Administration Task Load Index (NASA-TLX). The prototype CDS was iteratively refined to optimize usability and workflow., Results: Usability testing in 21 ED clinical providers demonstrated that, compared to the standard order sets, providers preferred the prototype CDS, with improvements in domains such as explanations of suggested antibiotic choices ( p  < 0.001) and provision of additional resources on antibiotic prescription ( p  < 0.001). Simulated use of the CDS also led to overall improved guideline-adherent prescribing, with a 31% improvement for CAP. A trend was present toward absolute workload reduction. Using the NASA-TLX, workload scores for the current system were median 26, interquartile ranges (IQR): 11 to 41 versus median 25, and IQR: 10.5 to 39.5 for the CDS system ( p  = 0.117)., Conclusion: Our CDS-enhanced discharge order set for ED antibiotic prescribing was strongly preferred by users, improved the accuracy of antibiotic prescribing, and trended toward reduced provider workload. The CDS was optimized for impact on guideline-adherent antibiotic prescribing from the ED and end-user acceptability to support future evaluative trials of ED ASPs., Competing Interests: None declared., (Thieme. All rights reserved.)

Understanding trends in vaccine refusal is critical to monitor as small declines in vaccination coverage can lead to outbreaks of vaccine-preventable diseases. Using electronic heath record (EHR) data from the Children’s Hospital of Philadelphia’s 31 outpatient primary care sites, we created a cohort of 403,448 children less than age 20 years who received at least one visit from 1 January 2013 through 31 December 2020. The sample represented 1,449,061 annualized patient and 181,131 annualized preventive vaccination visits per year. We characterized trends in vaccine refusal and acceptance using a repeated cross-sectional observational analysis of electronic health records (EHR) data using a single annual merged observation measure for patients seen multiple times for preventive healthcare within a calendar year. Refusals were identified for 212,900 annualized patient-visit year observations, which represented 14.6% of annualized patient-visit year observations and 25.1% of annualized vaccine patient-year observations. The odds of having a refusal marker were significantly increased in patients seen in suburban practices (aOR [CI]: 2.35 [2.30−2.40, p < 0.001]), in patients with increased age 11−17 years (aOR [CI]: 3.85 [3.79−3.91], p < 0.001), and those eligible for the VFC program (aOR [CI]: 1.10 [1.08−1.11]. Parental refusal (61.0%) and provider decisions (32.0%) were the most common documented in progress notes for not administering vaccines, whereas contraindications (2.5%) and supply issues (1.8%) were the least common. When offered, vaccine acceptance increased for human papillomavirus, hepatitis B, measles-mumps-rubella-containing and varicella-containing vaccines and decreased for hepatitis A and meningococcal vaccines. Repeated offering of vaccines was central to increasing acceptance, in part due to increased opportunities to address specific concerns.

Background: Helping parents quit smoking is a public health priority. However, parents are rarely, if ever, offered tobacco use treatment through pediatric settings. Clinical decision support (CDS) systems developed for the workflows of pediatric primary care may support consistent screening, treatment, and referral., Objectives: This study aimed to develop a CDS system by using human-centered design (HCD) that identifies parents who smoke, provides motivational messages to quit smoking (informed by behavioral science), and supports delivery of evidence-based tobacco treatment., Methods: Our multidisciplinary team applied a rigorous HCD process involving analysis of the work environment, user involvement in formative design, iterative improvements, and evaluation of the system's use in context with the following three cohorts: (1) parents who smoke, (2) pediatric clinicians, and (3) clinic staff. Participants from each cohort were presented with scenario-based, high-fidelity mockups of system components and then provided input related to their role in using the CDS system., Results: We engaged 70 representative participants including 30 parents, 30 clinicians, and 10 clinic staff. A key theme of the design review sessions across all cohorts was the need to automate functions of the system. Parents emphasized a system that presented information in a simple way, highlighted benefits of quitting smoking, and allowed direct connection to treatment. Pediatric clinicians emphasized automating tobacco treatment. Clinical staff emphasized screening for parent smoking via several modalities prior to the patient's visit. Once the system was developed, most parents (80%) reported that it was easy to use, and the majority of pediatricians reported that they would use the system (97%) and were satisfied with it (97%)., Conclusion: A CDS system to support parental tobacco cessation in pediatric primary care, developed through an HCD process, proved easy to use and acceptable to parents, clinicians, and office staff. This preliminary work justifies evaluating the impact of the system on helping parents quit smoking., Competing Interests: None declared., (Thieme. All rights reserved.)

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has had a disproportionate impact on Black, Hispanic, and other individuals of color, although data on the effect of a person's language on SARS-CoV-2 infection are limited. Considering the barriers suffered by immigrants and non-English-speaking families, we tested whether children with a preferred language other than English was associated with SARS-CoV-2 infection. Children from families with a preferred language other than English had a higher predicted probability of SARS-CoV-2 test positivity (adjusted odds ratio, 3.76; 95% CI, 2.07-6.67) during the first wave of the pandemic. This discrepancy continued into the second wave (adjusted odds ratio, 1.64; 95% CI, 1.10-2.41), although the difference compared with families who prefer to speak English decreased over time. These findings suggest that children from non-English-speaking families are at increased risk of SARS-CoV-2 infection, and efforts to reverse systemic inequities causing this increased risk are needed.

Background: Appendicitis is the most common surgical condition in pediatric emergency department (ED) patients. Prompt diagnosis can reduce morbidity, including appendiceal perforation. The goal of this study was to measure racial/ethnic differences in rates of 1) appendiceal perforation, 2) delayed diagnosis of appendicitis, and 3) diagnostic imaging during prior visit(s)., Methods: This was a 3-year multicenter (seven EDs) retrospective cohort study of children diagnosed with appendicitis using the Pediatric Emergency Care Applied Research Network Registry. Delayed diagnosis was defined as having at least one prior ED visit within 7 days preceding appendicitis diagnosis. We performed multivariable logistic regression to measure associations of race/ethnicity (non-Hispanic [NH]-white, NH-Black, Hispanic, other) with 1) appendiceal perforation, 2) delayed diagnosis of appendicitis, and 3) diagnostic imaging during prior visit(s)., Results: Of 7,298 patients with appendicitis and documented race/ethnicity, 2,567 (35.2%) had appendiceal perforation. In comparison to NH-whites, NH-Black children had higher likelihood of perforation (36.5% vs. 34.9%; adjusted odds ratio [aOR] = 1.21 [95% confidence interval {CI} = 1.01 to 1.45]). A total of 206 (2.8%) had a delayed diagnosis of appendicitis. NH-Black children were more likely to have delayed diagnoses (4.7% vs. 2.0%; aOR = 1.81 [95% CI = 1.09 to 2.98]). Eighty-nine (43.2%) patients with delayed diagnosis had abdominal imaging during their prior visits. In comparison to NH-whites, NH-Black children were less likely to undergo any imaging (28.2% vs. 46.2%; aOR = 0.41 [95% CI = 0.18 to 0.96]) or definitive imaging (e.g., ultrasound/computed tomography/magnetic resonance imaging; 10.3% vs. 35.9%; aOR = 0.15 [95% CI = 0.05 to 0.50]) during prior visits., Conclusions: In this multicenter cohort, there were racial disparities in appendiceal perforation. There were also racial disparities in rates of delayed diagnosis of appendicitis and diagnostic imaging during prior ED visits. These disparities in diagnostic imaging may lead to delays in appendicitis diagnosis and, thus, may contribute to higher perforation rates demonstrated among minority children., (© 2020 by the Society for Academic Emergency Medicine.)

Objective: Develop and evaluate an interactive information visualization embedded within the electronic health record (EHR) by following human-centered design (HCD) processes and leveraging modern health information exchange standards., Materials and Methods: We applied an HCD process to develop a Fast Healthcare Interoperability Resources (FHIR) application that displays a patient's asthma history to clinicians in a pediatric emergency department. We performed a preimplementation comparative system evaluation to measure time on task, number of screens, information retrieval accuracy, cognitive load, user satisfaction, and perceived utility and usefulness. Application usage and system functionality were assessed using application logs and a postimplementation survey of end users., Results: Usability testing of the Asthma Timeline Application demonstrated a statistically significant reduction in time on task (P < .001), number of screens (P < .001), and cognitive load (P < .001) for clinicians when compared to base EHR functionality. Postimplementation evaluation demonstrated reliable functionality and high user satisfaction., Discussion: Following HCD processes to develop an application in the context of clinical operations/quality improvement is feasible. Our work also highlights the potential benefits and challenges associated with using internationally recognized data exchange standards as currently implemented., Conclusion: Compared to standard EHR functionality, our visualization increased clinician efficiency when reviewing the charts of pediatric asthma patients. Application development efforts in an operational context should leverage existing health information exchange standards, such as FHIR, and evidence-based mixed methods approaches., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: To measure the variability in discharge opioid prescription practices for children discharged from the emergency department (ED) with a long-bone fracture., Design: A retrospective cohort study of pediatric ED visits in 2015., Setting: Four pediatric EDs., Subjects: Children aged four to 18 years with a long-bone fracture discharged from the ED., Methods: A multisite registry of electronic health record data (PECARN Registry) was analyzed to determine the proportion of children receiving an opioid prescription on ED discharge. Multivariable logistic regression was performed to determine characteristics associated with receipt of an opioid prescription., Results: There were 5,916 visits with long-bone fractures; 79% involved the upper extremity, and 27% required reduction. Overall, 15% of children were prescribed an opioid at discharge, with variation between the four EDs: A = 8.2% (95% confidence interval [CI] = 6.9-9.7%), B = 12.1% (95% CI = 10.5-14.0%), C = 16.9% (95% CI = 15.2-18.8%), D = 23.8% (95% CI = 21.7-26.1%). Oxycodone was the most frequently prescribed opioid. In the regression analysis, in addition to variation by ED site of care, age 12-18 years, white non-Hispanic, private insurance status, reduced fracture, and severe pain documented during the ED visit were associated with increased opioid prescribing., Conclusions: For children with a long-bone fracture, discharge opioid prescription varied widely by ED site of care. In addition, black patients, Hispanic patients, and patients with government insurance were less likely to be prescribed opioids. This variability in opioid prescribing was not accounted for by patient- or injury-related factors that are associated with increased pain. Therefore, opioid prescribing may be modifiable, but evidence to support improved outcomes with specific treatment regimens is lacking., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Academy of Pain Medicine.All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Introduction: Primary care remains an underused venue for prevention and management of paediatric overweight and obesity. A prior trial demonstrated a significant impact of paediatrician/nurse practitioner (Ped/NP)-and registered dietitian (RD)-delivered motivational interviewing (MI) on child body mass index (BMI). The study described here will test the effectiveness of an enhanced version of this primary care-based MI counselling intervention on child BMI., Methods and Analysis: This cluster randomised effectiveness trial includes 24 Ped/NPs from 18 paediatric primary care practices that belong to the American Academy of Pediatrics (AAP) national Pediatric Research in Office Settings (PROS) practice-based research network. To date, practices have been randomised (nine to intervention and nine to usual care). Intervention Ped/NPs have been trained in MI, behavioural therapy, billing/coding for weight management and study procedures. Usual care Ped/NPs received training in billing/coding and study procedures only. Children 3- 11 years old with BMI > the 85th percentile were identified via electronic health records (EHRs). Parents from intervention practices have been recruited and enrolled. Over about 2 years, these parents are offered approximately 10 MI-based counselling sessions (about four in person sessions with their child's Ped/NP and up to six telephonic sessions with a trained RD). The primary outcome is change in child BMI (defined as per cent from median BMI for age and sex) over the study period. The primary comparison is between eligible children in intervention practices whose parents enrol in the study and all eligible children in usual care practices. Data sources will include EHRs, billing records, surveys and counselling call notes., Ethics and Dissemination: Institutional Review Board approval was obtained from the AAP. All Ped/NPs provided written informed consent, and intervention group parents provided consent and Health Insurance Portability and Accountability Act (HIPAA) authorisation. Findings will be disseminated through peer-reviewed publications, conference presentations and appropriate AAP channels., Trial Registration Number: NCT03177148; Pre-results., Competing Interests: Competing interests: None declared, (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

This article describes the process of extracting electronic health record (EHR) data into a format that supports analyses related to the timeliness of antibiotic administration. The de-identified data that accompanies this article were collected from a cohort of infants who were evaluated for possible sepsis in the Neonatal Intensive Care Unit (NICU) at the Children's Hospital of Philadelphia (CHOP). The interpretation of findings from these data are reported in a separate manuscript [1]. For purposes of illustration for interested readers, scripts written in the R programming language related to the creation and use of the dataset have also been provided. Interested researchers are encouraged to contact the research team to discuss opportunities for collaboration., (© 2019 The Author(s).)

Background: Septic arthritis is a serious infection, but the results of blood and joint fluid cultures are often negative in children. We describe here the clinical features and management of culture-negative septic arthritis in children at our hospital and their outcomes., Methods: We performed a retrospective review of a cohort of children with septic arthritis who were hospitalized at Children's Hospital of Philadelphia between January 2002 and December 2014. Culture-negative septic arthritis was defined as a joint white blood cell count of >50000/μL with associated symptoms, a clinical diagnosis of septic arthritis, and a negative culture result. Children with pretreatment, an intensive case unit admission, Lyme arthritis, immunodeficiency, or surgical hardware were excluded. Treatment failure included a change in antibiotics, surgery, and/or reevaluation because of a lack of improvement/worsening., Results: We identified 157 children with septic arthritis. The patients with concurrent osteomyelitis (n = 28) had higher inflammatory marker levels at presentation, had a longer duration of symptoms (median, 4.5 vs 3 days, respectively; P < .001), and more often had bacteremia (46.4% vs 6.2%, respectively; P < .001). Among children with septic arthritis without associated osteomyelitis, 69% (89 of 129) had negative culture results. These children had lower C-reactive protein levels (median, 4.0 vs 7.3 mg/dL, respectively; P = .001) and erythrocyte sedimentation rates (median, 39 vs 51 mm/hour, respectively; P = .01) at admission and less often had foot/ankle involvement (P = .02). Among the children with culture-negative septic arthritis, the inpatient treatment failure rate was 9.1%, and treatment failure was more common in boys than in girls (17.1% vs 3.8%, respectively; P = .03). We found no association between treatment failure and empiric antibiotics or patient age. No outpatient treatment failures occurred during the 6-month follow-up period, although 17% of the children discharged with a peripherally inserted central catheter line experienced complications, including 3 with bacteremia., Conclusions: The majority of septic arthritis infections at our institution were culture negative. Among patients with culture-negative infection, empiric antibiotics failed for 9% and necessitated a change in therapy. More sensitive diagnostic testing should be implemented to elucidate the causes of culture-negative septic arthritis in children., (© The Author(s) 2018. Published by Oxford University Press on behalf of The Journal of the Pediatric Infectious Diseases Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: With the widespread adoption of vendor-supplied electronic health record (EHR) systems, clinical decision support (CDS) customization efforts beyond those anticipated by the vendor may require the use of technologies external to the EHR such as web services. Pursuing such customizations, however, is not without risk. Validating the expected behavior of a customized CDS system in the high-volume, complex environment of the live EHR is a challenging problem., Objective: This article identifies technology failures that impacted clinical care related to web service-based advanced custom CDS systems embedded in the complex sociotechnical context of a production EHR., Methods: In an academic health system's primary care network, we performed an inventory of incidents between January 1, 2008 and December 31, 2016 related to a customized CDS system and performed a targeted review of changes in the CDS source code. Additional feedback on the root cause of individual incidents was obtained through interviews with members of the CDS project teams., Results: We identified five CDS malfunctions that impaired clinical workflow. The mechanisms for these failures are mapped to four characteristics of well-behaved applications: (1) system integrity; (2) data integrity; (3) reliability; and (4) scalability. Over the 9-year period, two malfunctions of the customized CDS significantly impaired clinical workflow for a total of 5 hours. Lesser impacts-loss of individual features with straightforward workarounds-arose from three malfunctions, which affected users on 53 days., Discussion: Advanced customization of EHRs for the purpose of CDS can present significant risks to clinical workflow., Conclusion: This case study highlights that advanced customization of CDS within a commercial EHR may support care for complex patient populations, but ongoing monitoring and support is required to ensure its safe use., Competing Interests: Drs. Fiks and Grundmeier are coinventors of the “Care Assistant” software that was evaluated in this study. Mr. Miller is a coinventor of “Project SHARE” which is a Care Assistant module to support the care of children with attention deficit disorder. They hold no patent on the software and have earned no money from this invention. No licensing agreement exists. Dr. Fiks has also received an Independent Research Grant from Pfizer from which he personally drew no support. The other authors have indicated they have no potential conflicts of interest to disclose., (Georg Thieme Verlag KG Stuttgart · New York.)

Background: Rapid antibiotic administration is known to improve sepsis outcomes, however early diagnosis remains challenging due to complex presentation. Our objective was to develop a model using readily available electronic health record (EHR) data capable of recognizing infant sepsis at least 4 hours prior to clinical recognition., Methods and Findings: We performed a retrospective case control study of infants hospitalized ≥48 hours in the Neonatal Intensive Care Unit (NICU) at the Children's Hospital of Philadelphia between September 2014 and November 2017 who received at least one sepsis evaluation before 12 months of age. We considered two evaluation outcomes as cases: culture positive-positive blood culture for a known pathogen (110 evaluations); and clinically positive-negative cultures but antibiotics administered for ≥120 hours (265 evaluations). Case data was taken from the 44-hour window ending 4 hours prior to evaluation. We randomly sampled 1,100 44-hour windows of control data from all times ≥10 days removed from any evaluation. Model inputs consisted of up to 36 features derived from routine EHR data. Using 10-fold nested cross-validation, 8 machine learning models were trained to classify inputs as sepsis positive or negative. When tasked with discriminating culture positive cases from controls, 6 models achieved a mean area under the receiver operating characteristic (AUC) between 0.80-0.82 with no significant differences between them. Including both culture and clinically positive cases, the same 6 models achieved an AUC between 0.85-0.87, again with no significant differences., Conclusions: Machine learning models can identify infants with sepsis in the NICU hours prior to clinical recognition. Learning curves indicate model improvement may be achieved with additional training examples. Additional input features may also improve performance. Further research is warranted to assess potential performance improvements and clinical efficacy in a prospective trial., Competing Interests: The authors have declared that no competing interests exist.

Background: Surveillance for surgical site infections (SSIs) after ambulatory surgery in children requires a detailed manual chart review to assess criteria defined by the National Health and Safety Network (NHSN). Electronic health records (EHRs) impose an inefficient search process where infection preventionists must manually review every postsurgical encounter (< 30 days). Using text mining and business intelligence software, we developed an information foraging application, the SSI Workbench, to visually present which postsurgical encounters included SSI-related terms and synonyms, antibiotic, and culture orders., Objective: This article compares the Workbench and EHR on four dimensions: (1) effectiveness, (2) efficiency, (3) workload, and (4) usability., Methods: Comparative usability test of Workbench and EHR. Objective test metrics are time per case, encounters reviewed per case, time per encounter, and retrieval of information meeting NHSN definitions. Subjective measures are cognitive load using the National Aeronautics and Space Administration (NASA) Task Load Index (NASA TLX), and a questionnaire on system usability and utility., Results: Eight infection preventionists participated in the test. There was no difference in effectiveness as subjects retrieved information from all cases, using both systems, to meet the NHSN criteria. There was no difference in efficiency in time per case between the Workbench and EHR (8.58 vs. 7.39 minutes, p  = 0.36). However, with the Workbench subjects opened fewer encounters per case (3.0 vs. 7.5, p  = 0.002), spent more time per encounter (2.23 vs. 0.92 minutes, p  = 0.002), rated the Workbench lower in cognitive load (NASA TLX, 24 vs. 33, p  = 0.02), and significantly higher in measures of usability., Conclusion: Compared with the EHR, the Workbench was more usable, short, and reduced cognitive load. In overall efficiency, the Workbench did not save time, but demonstrated a shift from between-encounter foraging to within-encounter foraging and was rated as significantly more efficient. Our results suggest that infection surveillance can be better supported by systems applying information foraging theory., Competing Interests: None., (Georg Thieme Verlag KG Stuttgart · New York.)

Background: Appropriate management of attention-deficit/hyperactivity disorder (ADHD) involves parents, clinicians, and teachers. Fragmentation of interventions between different settings can lead to suboptimal care and outcomes. Electronic systems can bridge gaps across settings. Our institution developed an email-based software to collect ADHD information from parents and teachers, which delivered data directly to the clinician within the electronic health record (EHR)., Objective: We sought to adapt our institution's existing EHR-linked system for ADHD symptom monitoring to support communication between parents and teachers and then to assess child characteristics associated with sharing of ADHD information., Methods: We updated our software to support automated sharing of ADHD information between parents and teachers. Sharing was optional for parents but obligatory for teachers. We conducted a retrospective cohort study involving 590 patients at 31 primary care sites to evaluate a system for sharing of ADHD-specific health information between parents and teachers. We used multivariable logistic regression to estimate associations between child characteristics and parental sharing. We further investigated the association between child characteristics and viewing of survey results delivered through the electronic communication system., Results: Most parents (64%) elected to share survey results with teachers at the first opportunity and the vast majority (80%) elected to share all possible information. Parents who elected to share usually continue sharing at subsequent opportunities (89%). Younger child age and performance impairments were associated with increased likelihood of sharing. However, parents viewed only 16% of teacher submitted surveys and teachers only viewed 30% of surveys shared by parents., Conclusion: This study demonstrates that electronic systems to capture ADHD information from parents and teachers can be adapted to support communication between them, and that parents are amenable to sharing ADHD information with teachers. However, strategies are needed to encourage viewing of shared information., Competing Interests: Dr. Michel, Dr. Grundmeier, and Mr. Miller are the primary authors of the ADHD Care Assistant, which was used to implement portions of the intervention evaluated in this article. No patent or licensing agreement exists for this technology and the invention has generated no revenue. The remaining authors have no conflicts of interest relevant to this article to disclose.Drs. Grundmeier and Fiks are coinventors of the Care Assistant framework, which was used to implement portions of the intervention evaluated in this article. No patent or licensing agreement exists for this technology and the invention has generated no revenue. The remaining authors have no financial relationships relevant to this article to disclose. Though he personally took no salary support, Dr. Fiks's research team received grant support from Pfizer for an independent research project to improve ADHD care., (Georg Thieme Verlag KG Stuttgart · New York.)

Purpose: Secondary findings from genomic sequencing are becoming more common. We compared how health-care providers with and without specialized genetics training anticipated responding to different types of secondary findings., Methods: Providers with genomic sequencing experience reviewed five secondary-findings reports and reported attitudes and potential clinical follow-up. Analyses compared genetic specialists and physicians without specialized genetics training, and examined how responses varied by secondary finding., Results: Genetic specialists scored higher than other providers on four-point scales assessing understandings of reports (3.89 vs. 3.42, p = 0.0002), and lower on scales assessing reporting obligations (2.60 vs. 3.51, p < 0.0001) and burdens of responding (1.73 vs. 2.70, p < 0.0001). Nearly all attitudes differed between findings, although genetic specialists were more likely to assert that laboratories had no obligations when findings had less-established actionability (p < 0.0001 in interaction tests). The importance of reviewing personal and family histories, documenting findings, learning more about the variant, and recommending familial discussions also varied according to finding (all p < 0.0001)., Conclusion: Genetic specialists felt better prepared to respond to secondary findings than providers without specialized genetics training, but perceived fewer obligations for laboratories to report them, and the two groups anticipated similar clinical responses. Findings may inform development of targeted education and support.

Objective: The objective was to prospectively derive and validate a prediction rule for detecting cases warranting investigation for surgical site infections (SSI) after ambulatory surgery., Methods: We analysed electronic health record (EHR) data for children who underwent ambulatory surgery at one of 4 ambulatory surgical facilities. Using regularized logistic regression and random forests, we derived SSI prediction rules using 30 months of data (derivation set) and evaluated performance with data from the subsequent 10 months (validation set). Models were developed both with and without data extracted from free text. We also evaluated the presence of an antibiotic prescription within 60 days after surgery as an independent indicator of SSI evidence. Our goal was to exceed 80% sensitivity and 10% positive predictive value (PPV)., Results: We identified 234 surgeries with evidence of SSI among the 7910 surgeries available for analysis. We derived and validated an optimal prediction rule that included free text data using a random forest model (sensitivity = 0.9, PPV = 0.28). Presence of an antibiotic prescription had poor sensitivity (0.65) when applied to the derivation data but performed better when applied to the validation data (sensitivity = 0.84, PPV = 0.28)., Conclusions: EHR data can facilitate SSI surveillance with adequate sensitivity and PPV.

Background: Electronic health record (EHR)-based registries allow for robust data to be derived directly from the patient clinical record and can provide important information about processes of care delivery and patient health outcomes., Methods: A data dictionary, and subsequent data model, were developed describing EHR data sources to include all processes of care within the emergency department (ED). ED visit data were deidentified and XML files were created and submitted to a central data coordinating center for inclusion in the registry. Automated data quality control occurred prior to submission through an application created for this project. Data quality reports were created for manual data quality review., Results: The Pediatric Emergency Care Applied Research Network (PECARN) Registry, representing four hospital systems and seven EDs, demonstrates that ED data from disparate health systems and EHR vendors can be harmonized for use in a single registry with a common data model. The current PECARN Registry represents data from 2,019,461 pediatric ED visits, 894,503 distinct patients, more than 12.5 million narrative reports, and 12,469,754 laboratory tests and continues to accrue data monthly., Conclusion: The Registry is a robust harmonized clinical registry that includes data from diverse patients, sites, and EHR vendors derived via data extraction, deidentification, and secure submission to a central data coordinating center. The data provided may be used for benchmarking, clinical quality improvement, and comparative effectiveness research., Competing Interests: None., (Schattauer.)

Objective: Large electronic health record (EHR) datasets are increasingly used to facilitate research on growth, but measurement and recording errors can lead to biased results. We developed and tested an automated method for identifying implausible values in pediatric EHR growth data., Materials and Methods: Using deidentified data from 46 primary care sites, we developed an algorithm to identify weight and height values that should be excluded from analysis, including implausible values and values that were recorded repeatedly without remeasurement. The foundation of the algorithm is a comparison of each measurement, expressed as a standard deviation score, with a weighted moving average of a child's other measurements. We evaluated the performance of the algorithm by (1) comparing its results with the judgment of physician reviewers for a stratified random selection of 400 measurements and (2) evaluating its accuracy in a dataset with simulated errors., Results: Of 2 000 595 growth measurements from 280 610 patients 1 to 21 years old, 3.8% of weight and 4.5% of height values were identified as implausible or excluded for other reasons. The proportion excluded varied widely by primary care site. The automated method had a sensitivity of 97% (95% confidence interval [CI], 94-99%) and a specificity of 90% (95% CI, 85-94%) for identifying implausible values compared to physician judgment, and identified 95% (weight) and 98% (height) of simulated errors., Discussion and Conclusion: This automated, flexible, and validated method for preparing large datasets will facilitate the use of pediatric EHR growth datasets for research., (© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com)

Clinical genome and exome sequencing can diagnose pediatric patients with complex conditions that often require follow-up care with multiple specialties. The American Academy of Pediatrics emphasizes the role of the medical home and the primary care pediatrician in coordinating care for patients who need multidisciplinary support. In addition, the electronic health record (EHR) with embedded clinical decision support is recognized as an important component in providing care in this setting. We interviewed 6 clinicians to assess their experience caring for patients with complex and rare genetic findings and hear their opinions about how the EHR currently supports this role. Using these results, we designed a candidate EHR clinical decision support application mock-up and conducted formative exploratory user testing with 26 pediatric primary care providers to capture opinions on its utility in practice with respect to a specific clinical scenario. Our results indicate agreement that the functionality represented by the mock-up would effectively assist with care and warrants further development., (© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Objective: Delayed antimicrobial therapy in sepsis is associated with increased hospital mortality, but the impact of antimicrobial timing on long-term outcomes is unknown. We tested the hypothesis that hourly delays to antimicrobial therapy are associated with 1-year mortality in pediatric severe sepsis., Design: Retrospective observational study., Setting: Quaternary academic pediatric intensive care unit (PICU) from February 1, 2012 to June 30, 2013., Patients: One hundred sixty patients aged ≤21 years treated for severe sepsis., Interventions: None., Measurements and Main Results: We tested the association of hourly delays from sepsis recognition to antimicrobial administration with 1-year mortality using multivariable Cox and logistic regression. Overall 1-year mortality was 24% (39 patients), of whom 46% died after index PICU discharge. Median time from sepsis recognition to antimicrobial therapy was 137 min (IQR 65-287). After adjusting for severity of illness and comorbid conditions, hourly delays up to 3 h were not associated with 1-year mortality. However, increased 1-year mortality was evident in patients who received antimicrobials ≤1 h (aOR 3.8, 95% CI 1.2, 11.7) or >3 h (aOR 3.5, 95% CI 1.3, 9.8) compared with patients who received antimicrobials within 1 to 3 h from sepsis recognition. For the subset of patients who survived index PICU admission, antimicrobial therapy ≤1 h was also associated with increased 1-year mortality (aOR 5.5, 95% CI 1.1, 27.4), while antimicrobial therapy >3 h was not associated with 1-year mortality (aOR 2.2, 95% CI 0.5, 11.0)., Conclusions: Hourly delays to antimicrobial therapy, up to 3 h, were not associated with 1-year mortality in pediatric severe sepsis in this study. The finding that antimicrobial therapy ≤1 h from sepsis recognition was associated with increased 1-year mortality should be regarded as hypothesis-generating for future studies.

Background: Important information to support healthcare quality improvement is often recorded in free text documents such as radiology reports. Natural language processing (NLP) methods may help extract this information, but these methods have rarely been applied outside the research laboratories where they were developed., Objective: To implement and validate NLP tools to identify long bone fractures for pediatric emergency medicine quality improvement., Methods: Using freely available statistical software packages, we implemented NLP methods to identify long bone fractures from radiology reports. A sample of 1,000 radiology reports was used to construct three candidate classification models. A test set of 500 reports was used to validate the model performance. Blinded manual review of radiology reports by two independent physicians provided the reference standard. Each radiology report was segmented and word stem and bigram features were constructed. Common English "stop words" and rare features were excluded. We used 10-fold cross-validation to select optimal configuration parameters for each model. Accuracy, recall, precision and the F1 score were calculated. The final model was compared to the use of diagnosis codes for the identification of patients with long bone fractures., Results: There were 329 unique word stems and 344 bigrams in the training documents. A support vector machine classifier with Gaussian kernel performed best on the test set with accuracy=0.958, recall=0.969, precision=0.940, and F1 score=0.954. Optimal parameters for this model were cost=4 and gamma=0.005. The three classification models that we tested all performed better than diagnosis codes in terms of accuracy, precision, and F1 score (diagnosis code accuracy=0.932, recall=0.960, precision=0.896, and F1 score=0.927)., Conclusions: NLP methods using a corpus of 1,000 training documents accurately identified acute long bone fractures from radiology reports. Strategic use of straightforward NLP methods, implemented with freely available software, offers quality improvement teams new opportunities to extract information from narrative documents.

Background: The rates of childhood allergic conditions are changing, prompting the need for continued surveillance. Examination of healthcare provider-based diagnosis data is an important and lacking methodology needed to complement existing studies that rely on participant reporting., Methods: Utilizing our care network of 1,050,061 urban and sub-urban children, we defined two retrospective cohorts: (1) a closed birth cohort of 29,662 children and (2) a cross-sectional cohort of 333,200 children. These cohorts were utilized to determine the epidemiologic characteristics of the conditions studied. Logistic regression was utilized to determine the extent to which food allergy was associated with respiratory allergy., Results: In our birth cohort, the peak age at diagnosis of eczema, asthma, rhinitis, and food allergy was between 0 and 5 months (7.3 %), 12 and 17 months (8.7 %), 24 and 29 months (2.5 %), and 12 and 17 months (1.9 %), respectively. In our cross-sectional cohort, eczema and rhinitis prevalence rates were 6.7 % and 19.9 %, respectively. Asthma prevalence was 21.8 %, a rate higher than previously reported. Food allergy prevalence was 6.7 %, with the most common allergenic foods being peanut (2.6 %), milk (2.2 %), egg (1.8 %), shellfish (1.5 %), and soy (0.7 %). Food allergy was associated with development of asthma (OR 2.16, 95 % CI 1.94-2.40), and rhinitis (OR 2.72, 95 % CI 2.45-3.03)., Conclusions: Compared with previous reports, we measure lower rates of eczema and higher rates of asthma. The distribution of the major allergenic foods diverged from prior figures, and food allergy was associated with the development of respiratory allergy. The utilization of provider-based diagnosis data contributes an important and lacking methodology that complements existing studies.

Objective: To describe the use of digital images captured by parents or guardians and sent to clinicians for assessment of wounds after pediatric ambulatory surgery., Methods: Subjects with digital images of post-operative wounds were identified as part of an on-going cohort study of infections after ambulatory surgery within a large pediatric healthcare system. We performed a structured review of the electronic health record (EHR) to determine how digital images were documented in the EHR and used in clinical care., Results: We identified 166 patients whose parent or guardian reported sending a digital image of the wound to the clinician after surgery. A corresponding digital image was located in the EHR in only 121 of these encounters. A change in clinical management was documented in 20% of these encounters, including referral for in-person evaluation of the wound and antibiotic prescription., Conclusion: Clinical teams have developed ad hoc workflows to use digital images to evaluate post-operative pediatric surgical patients. Because the use of digital images to support follow-up care after ambulatory surgery is likely to increase, it is important that high-quality images are captured and documented appropriately in the EHR to ensure privacy, security, and a high-level of care.

Background: Patient portals may improve communication between families of children with asthma and their primary care providers and improve outcomes. However, the feasibility of using portals to collect patient-reported outcomes from families and the barriers and facilitators of portal implementation across diverse pediatric primary care settings have not been established., Objective: We evaluated the feasibility of using a patient portal for pediatric asthma in primary care, its impact on management, and barriers and facilitators of implementation success., Methods: We conducted a mixed-methods implementation study in 20 practices (11 states). Using the portal, parents of children with asthma aged 6-12 years completed monthly surveys to communicate treatment concerns, treatment goals, symptom control, medication use, and side effects. We used logistic regression to evaluate the association of portal use with child characteristics and changes to asthma management. Ten clinician focus groups and 22 semistructured parent interviews explored barriers and facilitators of use in the context of an evidence-based implementation framework., Results: We invited 9133 families to enroll and 237 (2.59%) used the portal (range by practice, 0.6%-13.6%). Children of parents or guardians who used the portal were significantly more likely than nonusers to be aged 6-9 years (vs 10-12, P=.02), have mild or moderate/severe persistent asthma (P=.009 and P=.04), have a prescription of a controller medication (P<.001), and have private insurance (P=.002). Portal users with uncontrolled asthma had significantly more medication changes and primary care asthma visits after using the portal relative to the year earlier (increases of 14% and 16%, respectively). Qualitative results revealed the importance of practice organization (coordinated workflows) as well as family (asthma severity) and innovation (facilitated communication and ease of use) characteristics for implementation success., Conclusions: Although use was associated with higher treatment engagement, our results suggest that achieving widespread portal adoption is unlikely in the short term. Implementation efforts should include workflow redesign and prioritize enrollment of symptomatic children., Clinicaltrial: Clinicaltrials.gov NCT01966068; https://clinicaltrials.gov/ct2/show/NCT01966068 (Archived by WebCite at http://www.webcitation.org/6i9iSQkm3).

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.

Background: Radiology reports are a rich resource for biomedical research. Prior to utilization, trained experts must manually review reports to identify discrete outcomes. The Audiological and Genetic Database (AudGenDB) is a public, de-identified research database that contains over 16,000 radiology reports. Because the reports are unlabeled, it is difficult to select those with specific abnormalities. We implemented a classification pipeline using a human-in-the-loop machine learning approach and open source libraries to label the reports with one or more of four abnormality region labels: inner, middle, outer, and mastoid, indicating the presence of an abnormality in the specified ear region., Methods: Trained abstractors labeled radiology reports taken from AudGenDB to form a gold standard. These were split into training (80 %) and test (20 %) sets. We applied open source libraries to normalize and convert every report to an n-gram feature vector. We trained logistic regression, support vector machine (linear and Gaussian), decision tree, random forest, and naïve Bayes models for each ear region. The models were evaluated on the hold-out test set., Results: Our gold-standard data set contained 726 reports. The best classifiers were linear support vector machine for inner and outer ear, logistic regression for middle ear, and decision tree for mastoid. Classifier test set accuracy was 90 %, 90 %, 93 %, and 82 % for the inner, middle, outer and mastoid regions, respectively. The logistic regression method was very consistent, achieving accuracy scores within 2.75 % of the best classifier across regions and a receiver operator characteristic area under the curve of 0.92 or greater across all regions., Conclusions: Our results indicate that the applied methods achieve accuracy scores sufficient to support our objective of extracting discrete features from radiology reports to enhance cohort identification in AudGenDB. The models described here are available in several free, open source libraries that make them more accessible and simplify their utilization as demonstrated in this work. We additionally implemented the models as a web service that accepts radiology report text in an HTTP request and provides the predicted region labels. This service has been used to label the reports in AudGenDB and is freely available.

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., (Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.)

Background and Objectives: Palivizumab can reduce hospitalizations due to respiratory syncytial virus (RSV), but many eligible infants fail to receive the full 5-dose series. The efficacy of clinical decision support (CDS) in fostering palivizumab receipt has not been studied. We sought a comprehensive solution for identifying eligible patients and addressing barriers to palivizumab administration., Methods: We developed workflow and CDS tools targeting patient identification and palivizumab administration. We randomized 10 practices to receive palivizumab-focused CDS and 10 to receive comprehensive CDS for premature infants in a 3-year longitudinal cluster-randomized trial with 2 baseline and 1 intervention RSV seasons., Results: There were 356 children eligible to receive palivizumab, with 194 in the palivizumab-focused group and 162 in the comprehensive CDS group. The proportion of doses administered to children in the palivizumab-focused intervention group increased from 68.4% and 65.5% in the two baseline seasons to 84.7% in the intervention season. In the comprehensive intervention group, proportions of doses administered declined during the baseline seasons (from 71.9% to 62.4%) with partial recovery to 67.9% during the intervention season. The palivizumab-focused group improved by 19.2 percentage points in the intervention season compared to the prior baseline season (p < 0.001), while the comprehensive intervention group only improved 5.5 percentage points (p = 0.288). The difference in change between study groups was significant (p = 0.05)., Conclusions: Workflow and CDS tools integrated in an EHR may increase the administration of palivizumab. The support focused on palivizumab, rather than comprehensive intervention, was more effective at improving palivizumab administration.

Background: Outpatient respiratory tract infections are the most common reason for antibiotic prescribing to children. Although prior studies suggest that antibiotic overuse occurs, patient-specific data or data exploring the variability and determinants of variability across practices and practitioners is lacking., Methods: This study was conducted from a retrospective cohort of encounters to 25 diverse pediatric practices with 222 clinicians, from January 1 to December 31, 2009. Diagnoses, medications, comorbid conditions, antibiotic allergy, and demographic data were obtained from a shared electronic health record and validated by manual review. Practice-specific antibiotic prescription and acute respiratory tract infection diagnosis rates were calculated to assess across-practice differences after adjusting for patient demographics and clustering of encounters within clinicians., Results: A total of 102 102 (28%) of 399 793 acute visits by 208 015 patients resulted in antibiotic prescriptions. After adjusting for patient age, sex, race, and insurance type, and excluding encounters by patients with chronic conditions, antibiotic prescribing by practice ranged from 18% to 36% of acute visits, and the proportion of antibiotic prescriptions that were broad-spectrum ranged from 15% to 58% across practices, despite additional exclusion of patients with antibiotic allergies or prior antibiotic use. Diagnosis of (Dx) and broad-spectrum antibiotic prescribing (Broad) for acute otitis media (Dx: 8%-20%; Broad: 18%-60%), sinusitis (Dx: 0.5%-9%; Broad: 12%-78%), Streptococcal pharyngitis (Dx: 1.8%-6.4%; Broad: 2%-30%), and pneumonia (Dx: 0.4%-2%; Broad: 1%-70%) also varied by practice (P < 0.001 for all comparisons)., Conclusions: Antibiotic prescribing for common pediatric infections varied substantially across practices. This variability could not be explained by patient-specific factors. These data suggest the need for and provide high-impact targets for outpatient antimicrobial stewardship interventions., (© The Author 2014. Published by Oxford University Press on behalf of the Pediatric Infectious Diseases Society. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Objective: Clinicians' ability to use and interpret genetic information depends upon how those data are displayed in electronic health records (EHRs). There is a critical need to develop systems to effectively display genetic information in EHRs and augment clinical decision support (CDS)., Materials and Methods: The National Institutes of Health (NIH)-sponsored Clinical Sequencing Exploratory Research and Electronic Medical Records & Genomics EHR Working Groups conducted a multiphase, iterative process involving working group discussions and 2 surveys in order to determine how genetic and genomic information are currently displayed in EHRs, envision optimal uses for different types of genetic or genomic information, and prioritize areas for EHR improvement., Results: There is substantial heterogeneity in how genetic information enters and is documented in EHR systems. Most institutions indicated that genetic information was displayed in multiple locations in their EHRs. Among surveyed institutions, genetic information enters the EHR through multiple laboratory sources and through clinician notes. For laboratory-based data, the source laboratory was the main determinant of the location of genetic information in the EHR. The highest priority recommendation was to address the need to implement CDS mechanisms and content for decision support for medically actionable genetic information., Conclusion: Heterogeneity of genetic information flow and importance of source laboratory, rather than clinical content, as a determinant of information representation are major barriers to using genetic information optimally in patient care. Greater effort to develop interoperable systems to receive and consistently display genetic and/or genomic information and alert clinicians to genomic-dependent improvements to clinical care is recommended., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2015. This work is written by US Government employees and is in the public domain in the US.)

Objectives: The objective was to compare the effectiveness of physician judgment and an electronic algorithmic alert to identify pediatric patients with severe sepsis/septic shock in a pediatric emergency department (ED)., Methods: This was an observational cohort study of patients older than 56 days with fever or hypothermia. All patients were evaluated for potential sepsis in real time by the ED clinical team. An electronic algorithmic alert was retrospectively applied to identify patients with potential sepsis independent of physician judgment. The primary outcome was the proportion of patients correctly identified with severe sepsis/septic shock defined by consensus criteria. Test characteristics were determined and receiver operating characteristic (ROC) curves were compared., Results: Of 19,524 eligible patient visits, 88 patients developed consensus-confirmed severe sepsis or septic shock. Physician judgment identified 159 and the algorithmic alert identified 3,301 patients with potential sepsis. Physician judgment had sensitivity of 72.7% (95% confidence interval [CI] = 72.1% to 73.4%) and specificity of 99.5% (95% CI = 99.4% to 99.6%); the algorithmic alert had sensitivity of 92.1% (95% CI = 91.7% to 92.4%) and specificity of 83.4% (95% CI = 82.9% to 83.9%) for severe sepsis/septic shock. There was no significant difference in the area under the ROC curve for physician judgment (0.86, 95% CI = 0.81 to 0.91) or the algorithm (0.88, 95% CI = 0.85 to 0.91; p = 0.54). A combination method using either positive physician judgment or an algorithmic alert improved sensitivity to 96.6% and specificity to 83.3%. A sequential approach, in which positive identification by the algorithmic alert was then confirmed by physician judgment, achieved 68.2% sensitivity and 99.6% specificity. Positive and negative predictive values for physician judgment versus algorithmic alert were 40.3% versus 2.5% and 99.88% versus 99.96%, respectively., Conclusions: The electronic algorithmic alert was more sensitive but less specific than physician judgment for recognition of pediatric severe sepsis and septic shock. These findings can help to guide institutions in selecting pediatric sepsis recognition methods based on institutional needs and priorities., (© 2015 by the Society for Academic Emergency Medicine.)

Objective: To assess the utility of imputing race/ethnicity using U.S. Census race/ethnicity, residential address, and surname information compared to standard missing data methods in a pediatric cohort., Data Sources/study Setting: Electronic health record data from 30 pediatric practices with known race/ethnicity., Study Design: In a simulation experiment, we constructed dichotomous and continuous outcomes with pre-specified associations with known race/ethnicity. Bias was introduced by nonrandomly setting race/ethnicity to missing. We compared typical methods for handling missing race/ethnicity (multiple imputation alone with clinical factors, complete case analysis, indicator variables) to multiple imputation incorporating surname and address information., Principal Findings: Imputation using U.S. Census information reduced bias for both continuous and dichotomous outcomes., Conclusions: The new method reduces bias when race/ethnicity is partially, nonrandomly missing., (© Health Research and Educational Trust.)

Objective: Substantial investment in electronic health records (EHRs) has provided an unprecedented opportunity to use clinical decision support (CDS) to increase guideline adherence. To inform efforts to maximize adoption, we characterized the adoption of an otitis media (OM) CDS system, the impact of performance feedback on adoption, and the effects of adoption on guideline adherence., Study Setting: A total of 41,391 OM visits with 108 clinicians at 16 pediatric practices between February 2009 and August 2010., Study Design: Prospective cohort study of EHR-based CDS adoption during OM visits, comparing clinicians receiving performance feedback to none. CDS was available to all physicians; use was voluntary., Data Collection: Extraction from a common EHR., Principal Findings: Clinicians and practices used the CDS system for a mean of 21 percent (range: 0-85 percent) and 17 percent (0-51 percent) of eligible OM visits, respectively. Clinicians who received performance feedback reports summarizing CDS use and guideline adherence had a relative increase in CDS use of 9.0 percentage points compared to others (p = .001). CDS adoption was associated with increased OM guideline adherence. Effects were greatest among clinicians with the lowest adherence prior to the study., Conclusions: Performance feedback increased CDS adoption, but additional strategies are needed to integrate CDS into primary care workflows., (© Health Research and Educational Trust.)

Objective: To compare practice patterns regarding the diagnosis and management of streptococcal pharyngitis across pediatric primary care practices., Design: Retrospective cohort study., Setting: All encounters to 25 pediatric primary care practices sharing an electronic health record., Methods: Streptococcal pharyngitis was defined by an International Classification of Diseases, Ninth Revision code for acute pharyngitis, positive laboratory test, antibiotic prescription, and absence of an alternative bacterial infection. Logistic regression models standardizing for patient-level characteristics were used to compare diagnosis, testing, and broad-spectrum antibiotic treatment for children with pharyngitis across practices. Fixed-effects models and likelihood ratio tests were conducted to analyze within-practice variation., Results: Of 399,793 acute encounters in 1 calendar year, there were 52,658 diagnoses of acute pharyngitis, including 12,445 diagnoses of streptococcal pharyngitis. After excluding encounters by patients with chronic conditions and standardizing for age, sex, insurance type, and race, there was significant variability across and within practices in the diagnosis and testing for streptococcal pharyngitis. Excluding patients with antibiotic allergies or prior antibiotic use, off-guideline antibiotic prescribing for confirmed group A streptococcal pharyngitis ranged from 1% to 33% across practices (P < .001). At the clinician level, 13 of 25 sites demonstrated significant within-practice variability in off-guideline antibiotic prescribing (P ≤ .05). Only 18 of the 222 clinicians in the network accounted for half of all off-guideline antibiotic prescribing., Conclusions: Significant variability in the diagnosis and treatment of pharyngitis exists across and within pediatric practices, which cannot be explained by relevant clinical or demographic factors. Our data support clinician-targeted interventions to improve adherence to prescribing guidelines for this common condition.

Purpose: Genome-scale clinical sequencing is being adopted more broadly in medical practice. The National Institutes of Health developed the Clinical Sequencing Exploratory Research (CSER) program to guide implementation and dissemination of best practices for the integration of sequencing into clinical care. This study describes and compares the state of the art of incorporating whole-exome and whole-genome sequencing results into the electronic health record, including approaches to decision support across the six current CSER sites., Methods: The CSER Medical Record Working Group collaboratively developed and completed an in-depth survey to assess the communication of genome-scale data into the electronic health record. We summarized commonalities and divergent approaches., Results: Despite common sequencing platform (Illumina) adoptions, there is a great diversity of approaches to annotation tools and workflow, as well as to report generation. At all sites, reports are human-readable structured documents available as passive decision support in the electronic health record. Active decision support is in early implementation at two sites., Conclusion: The parallel efforts across CSER sites in the creation of systems for report generation and integration of reports into the electronic health record, as well as the lack of standardized approaches to interfacing with variant databases to create active clinical decision support, create opportunities for cross-site and vendor collaborations.

We developed an electronic medical record (EMR)-based HPV vaccine decision support intervention targeting clinicians, (immunization alerts, education, and feedback) and families (phone reminders and referral to an educational website). Through telephone surveys completed by 162 parents of adolescent girls, we assessed the acceptability of the family-focused intervention and its effect on information-seeking behavior, communication, and HPV vaccine decision-making. The intervention was acceptable to parents and 46% remembered receiving the reminder call. Parents reported that the call prompted them to seek out information regarding the HPV vaccine, discuss the vaccine with friends and family, and reach a decision. Parents whose adolescent girls attended practices receiving the clinician-focused intervention were more likely to report that their clinician discussed the HPV vaccine at preventive visits. The results of this study demonstrate the acceptability and potential impact on clinical care of a comprehensive decision support system directed at both clinicians and families.

Objective: Two recent genome-wide association (GWA) studies have revealed novel loci for type 1 diabetes, a common multifactorial disease with a strong genetic component. To fully utilize the GWA data that we had obtained by genotyping 563 type 1 diabetes probands and 1,146 control subjects, as well as 483 case subject-parent trios, using the Illumina HumanHap550 BeadChip, we designed a full stage 2 study to capture other possible association signals., Research Design and Methods: From our existing datasets, we selected 982 markers with P < 0.05 in both GWA cohorts. Genotyping these in an independent set of 636 nuclear families with 974 affected offspring revealed 75 markers that also had P < 0.05 in this third cohort. Among these, six single nucleotide polymorphisms in five novel loci also had P < 0.05 in the Wellcome Trust Case-Control Consortium dataset and were further tested in 1,303 type 1 diabetes probands from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) plus 1,673 control subjects., Results: Two markers (rs9976767 and rs3757247) remained significant after adjusting for the number of tests in this last cohort; they reside in UBASH3A (OR 1.16; combined P = 2.33 x 10(-8)) and BACH2 (1.13; combined P = 1.25 x 10(-6))., Conclusions: Evaluation of a large number of statistical GWA candidates in several independent cohorts has revealed additional loci that are associated with type 1 diabetes. The two genes at these respective loci, UBASH3A and BACH2, are both biologically relevant to autoimmunity.

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.

Missed billing opportunities can be a negative outcome of the implementation of non-interfaced clinical and billing information systems. We describe a decision support system that prompts physicians with suggested billing options at the point of ordering nebulized medications. A baseline rate of 70% missed billing opportunities decreased to 38% with education alone and to 7% with an alert system. Decision support can best support billing opportunities when automated and appearing at the point of care.