Stephanie Kennebeck

The association of emergency department crowding and time to antibiotics in febrile neonates.

OBJECTIVES The objective was to assess the relationship between emergency department (ED) crowding and timeliness of antibiotic administration to neonates presenting with fever in a pediatric ED. METHODS This was a retrospective cohort study of febrile neonates (aged 0-30 days) evaluated for serious bacterial infections (SBIs) in a pediatric ED from January 2006 to January 2008. General linear models were used to evaluate the association of five measures of ED crowding with timeliness of antibiotic administration, controlling for patient characteristics. A secondary analysis was conducted to determine which part of the ED visit for this population was most affected by crowding. RESULTS A total of 190 patients met inclusion criteria. Mean time to first antibiotic was 181.7 minutes (range = 18-397 minutes). At the time of case presentation, the number of patients waiting in the waiting area, total number of hours spent in the ED by current ED patients, number of ED patients awaiting admission, and hourly boarding time were all positively associated with longer times to antibiotic. The time from patient arrival to room placement exhibited the strongest association with measures of crowding. CONCLUSIONS Emergency department crowding is associated with delays in antibiotic administration to the febrile neonate despite rapid recognition of this patient population as a high-risk group. Each component of ED crowding, in terms of input, throughput, and output factors, was associated with delays. Further work is required to develop processes that foster a more rapid treatment protocol for these high-risk patients, regardless of ED crowding pressures.

A Comprehensive View of Parental Satisfaction With Pediatric Emergency Department Visits

STUDY OBJECTIVE We develop a comprehensive view of aspects of care associated with parental satisfaction with pediatric emergency department (ED) visits, using both quantitative and qualitative data. METHODS This was a retrospective observational study using data from an institution-wide system to measure patient satisfaction. For this study, 2,442 parents who brought their child to the ED were interviewed with telephone survey methods. The survey included closed-ended (quantitative) and open-ended (qualitative data) questions, in addition to a cognitive interview-style question. RESULTS Overall parental satisfaction was best predicted by how well physicians and nurses work together, followed by wait time and pain management. Issues concerning timeliness of care, perceived quality of medical care, and communication were raised repeatedly by parents in response to open-ended questions. A cognitive interview-style question showed that physicians and nurses sharing information with each other, parents receiving consistent and detailed explanations of their childs diagnosis and treatments, and not having to answer the same question repeatedly informed parent perceptions of physicians and nurses working well together. Staff showing courtesy and respect through compassion and caring words and behaviors and paying attention to nonmedical needs are other potential satisfiers with emergency care. CONCLUSION Using qualitative data to augment and clarify quantitative data from patient experience of care surveys is essential to obtaining a complete picture of aspects of emergency care important to parents and can help inform quality improvement work aimed at improving satisfaction with care.

EMR-linked GWAS study: investigation of variation landscape of loci for body mass index in children

Common variations at the loci harboring the fat mass and obesity gene (FTO), MC4R, and TMEM18 are consistently reported as being associated with obesity and body mass index (BMI) especially in adult population. In order to confirm this effect in pediatric population five European ancestry cohorts from pediatric eMERGE-II network (CCHMC-BCH) were evaluated. Method: Data on 5049 samples of European ancestry were obtained from the Electronic Medical Records (EMRs) of two large academic centers in five different genotyped cohorts. For all available samples, gender, age, height, and weight were collected and BMI was calculated. To account for age and sex differences in BMI, BMI z-scores were generated using 2000 Centers of Disease Control and Prevention (CDC) growth charts. A Genome-wide association study (GWAS) was performed with BMI z-score. After removing missing data and outliers based on principal components (PC) analyses, 2860 samples were used for the GWAS study. The association between each single nucleotide polymorphism (SNP) and BMI was tested using linear regression adjusting for age, gender, and PC by cohort. The effects of SNPs were modeled assuming additive, recessive, and dominant effects of the minor allele. Meta-analysis was conducted using a weighted z-score approach. Results: The mean age of subjects was 9.8 years (range 2–19). The proportion of male subjects was 56%. In these cohorts, 14% of samples had a BMI ≥95 and 28 ≥ 85%. Meta analyses produced a signal at 16q12 genomic region with the best result of p = 1.43 × 10-7 [p(rec) = 7.34 × 10-8) for the SNP rs8050136 at the first intron of FTO gene (z = 5.26) and with no heterogeneity between cohorts (p = 0.77). Under a recessive model, another published SNP at this locus, rs1421085, generates the best result [z = 5.782, p(rec) = 8.21 × 10-9]. Imputation in this region using dense 1000-Genome and Hapmap CEU samples revealed 71 SNPs with p < 10-6, all at the first intron of FTO locus. When hetero-geneity was permitted between cohorts, signals were also obtained in other previously identified loci, including MC4R (rs12964056, p = 6.87 × 10-7, z = -4.98), cholecystokinin CCK (rs8192472, p = 1.33 × 10-6, z = -4.85), Interleukin 15 (rs2099884, p = 1.27 × 10-5, z = 4.34), low density lipoprotein receptor-related protein 1B [LRP1B (rs7583748, p = 0.00013, z = -3.81)] and near transmembrane protein 18 (TMEM18) (rs7561317, p = 0.001, z = -3.17). We also detected a novel locus at chromosome 3 at COL6A5 [best SNP = rs1542829, minor allele frequency (MAF) of 5% p = 4.35 × 10-9, z = 5.89]. Conclusion: An EMR linked cohort study demonstrates that the BMI-Z measurements can be successfully extracted and linked to genomic data with meaningful confirmatory results. We verified the high prevalence of childhood rate of overweight and obesity in our cohort (28%). In addition, our data indicate that genetic variants in the first intron of FTO, a known adult genetic risk factor for BMI, are also robustly associated with BMI in pediatric population.

Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department

Objectives (1) To develop an automated eligibility screening (ES) approach for clinical trials in an urban tertiary care pediatric emergency department (ED); (2) to assess the effectiveness of natural language processing (NLP), information extraction (IE), and machine learning (ML) techniques on real-world clinical data and trials. Data and methods We collected eligibility criteria for 13 randomly selected, disease-specific clinical trials actively enrolling patients between January 1, 2010 and August 31, 2012. In parallel, we retrospectively selected data fields including demographics, laboratory data, and clinical notes from the electronic health record (EHR) to represent profiles of all 202795 patients visiting the ED during the same period. Leveraging NLP, IE, and ML technologies, the automated ES algorithms identified patients whose profiles matched the trial criteria to reduce the pool of candidates for staff screening. The performance was validated on both a physician-generated gold standard of trial–patient matches and a reference standard of historical trial–patient enrollment decisions, where workload, mean average precision (MAP), and recall were assessed. Results Compared with the case without automation, the workload with automated ES was reduced by 92% on the gold standard set, with a MAP of 62.9%. The automated ES achieved a 450% increase in trial screening efficiency. The findings on the gold standard set were confirmed by large-scale evaluation on the reference set of trial–patient matches. Discussion and conclusion By exploiting the text of trial criteria and the content of EHRs, we demonstrated that NLP-, IE-, and ML-based automated ES could successfully identify patients for clinical trials.

Sustained reductions in time to antibiotic delivery in febrile immunocompromised children: results of a quality improvement collaborative

Background Timely delivery of antibiotics to febrile immunocompromised (F&I) paediatric patients in the emergency department (ED) and outpatient clinic reduces morbidity and mortality. Objective The aim of this quality improvement initiative was to increase the percentage of F&I patients who received antibiotics within goal in the clinic and ED from 25% to 90%. Methods Using the Model of Improvement, we performed Plan-Do-Study-Act cycles to design, test and implement high-reliability interventions to decrease time to antibiotics. Pre-arrival interventions were tested and implemented, followed by post-arrival interventions in the ED. Many processes were spread successfully to the outpatient clinic. The Chronic Care Model was used, in addition to active family engagement, to inform and improve processes. Results The study period was from January 2010 to January 2015. Pre-arrival planning improved our F&I time to antibiotics in the ED from 137 to 88 min. This was sustained until October 2012, when further interventions including a pre-arrival huddle decreased the median time to <50 min. Implementation of the various processes to the clinic delivery system increased the mean percentage of patients receiving antibiotics within 60 min to >90%. In September 2014, we implemented a rapid response team to improve reliable venous access in the ED, which increased our mean percentage of patients receiving timely antibiotics to its highest rate (95%). Conclusions This stepwise approach with pre-arrival planning using the Chronic Care Model, followed by standardisation of processes, created a sustainable improvement of timely antibiotic delivery in F&I patients.

Preparing for International Classification of Diseases, 10th Revision, Clinical Modification implementation: strategies for maintaining an efficient workflow.

Abstract The International Classification of Diseases, 10th Revision, is required to be used by the Centers for Medicare and Medicaid Services health care billing data starting in October 2015 in the United States. The International Classification of Diseases, 10th Revision, is an update to the International Classification of Diseases, Ninth Revision, and contains approximately 70,000 codes compared with 14,000 codes. We aimed to discuss how our institution is updating the coding system in a manner that alleviates the possible burden placed on providers including more coding information required and longer load times. We performed a simulation test including testing the diagnosis calculator, timing, and how well the new and old codes mapped. We conducted a gap analysis to ensure that coding could begin in October of 2015 with minimal service interruptions. We will describe strategies and procedures to transition between systems while maintaining efficiency and helping to improve classification.

Suboptimal Clinical Documentation in Young Children with Severe Obesity at Tertiary Care Centers

Background and Objectives. The prevalence of severe obesity in children has doubled in the past decade. The objective of this study is to identify the clinical documentation of obesity in young children with a BMI ≥ 99th percentile at two large tertiary care pediatric hospitals. Methods. We used a standardized algorithm utilizing data from electronic health records to identify children with severe early onset obesity (BMI ≥ 99th percentile at age <6 years). We extracted descriptive terms and ICD-9 codes to evaluate documentation of obesity at Boston Childrens Hospital and Cincinnati Childrens Hospital and Medical Center between 2007 and 2014. Results. A total of 9887 visit records of 2588 children with severe early onset obesity were identified. Based on predefined criteria for documentation of obesity, 21.5% of children (13.5% of visits) had positive documentation, which varied by institution. Documentation in children first seen under 2 years of age was lower than in older children (15% versus 26%). Documentation was significantly higher in girls (29% versus 17%, p < 0.001), African American children (27% versus 19% in whites, p < 0.001), and the obesity focused specialty clinics (70% versus 15% in primary care and 9% in other subspecialty clinics, p < 0.001). Conclusions. There is significant opportunity for improvement in documentation of obesity in young children, even years after the 2007 AAP guidelines for management of obesity.

Developing an Algorithm to Detect Early Childhood Obesity in Two Tertiary Pediatric Medical Centers

OBJECTIVE The objective of this study is to develop an algorithm to accurately identify children with severe early onset childhood obesity (ages 1-5.99 years) using structured and unstructured data from the electronic health record (EHR). INTRODUCTION Childhood obesity increases risk factors for cardiovascular morbidity and vascular disease. Accurate definition of a high precision phenotype through a standardize tool is critical to the success of large-scale genomic studies and validating rare monogenic variants causing severe early onset obesity. DATA AND METHODS Rule based and machine learning based algorithms were developed using structured and unstructured data from two EHR databases from Boston Childrens Hospital (BCH) and Cincinnati Childrens Hospital and Medical Center (CCHMC). Exclusion criteria including medications or comorbid diagnoses were defined. Machine learning algorithms were developed using cross-site training and testing in addition to experimenting with natural language processing features. RESULTS Precision was emphasized for a high fidelity cohort. The rule-based algorithm performed the best overall, 0.895 (CCHMC) and 0.770 (BCH). The best feature set for machine learning employed Unified Medical Language System (UMLS) concept unique identifiers (CUIs), ICD-9 codes, and RxNorm codes. CONCLUSIONS Detecting severe early childhood obesity is essential for the intervention potential in children at the highest long-term risk of developing comorbidities related to obesity and excluding patients with underlying pathological and non-syndromic causes of obesity assists in developing a high-precision cohort for genetic study. Further such phenotyping efforts inform future practical application in health care environments utilizing clinical decision support.

Modifications and integration of the electronic tracking board in a pediatric emergency department.

Electronic health records (EHRs) are used for data storage; provider, laboratory, and patient communication; clinical decision support; procedure and medication orders; and decision support alerts. Clinical decision support is part of any EHR and is designed to help providers make better decisions. The emergency department (ED) poses a unique environment to the use of EHRs and clinical decision support. Used effectively, computerized tracking boards can help improve flow, communication, and the dissemination of pertinent visit information between providers and other departments in a busy ED. We discuss the unique modifications and decisions made in the implementation of an EHR and computerized tracking board in a pediatric ED. We discuss the changing views based on provider roles, customization to the user interface including the layout and colors, decision support, tracking board best practices collected from other institutions and colleagues, and a case study of using reminders on the electronic tracking board to drive pain reassessments.

Factors influencing emergency department care of young children at-risk for clinically important traumatic brain injury

Objectives Care decisions for young children presenting to the emergency department (ED) with head injury are often challenging (e.g. whether to obtain neuroimaging). We sought to identify factors associated with acute management of children at‐risk for clinically important traumatic brain injury (ciTBI) and describe symptom management. Methods Observational evaluation of children, ages 0–4 years, presenting to a pediatric ED following minor head injury. Children with ≥1 risk element per the Pediatric Emergency Care Academic Research Networks decision rule were deemed “at‐risk” for ciTBI. Clinician surveys regarding their initial clinical management were used to identify three care groups. Nonparametric tests analyzed group differences and logistic regression investigated associations of putative high‐risk factors with neuroimaging. Results Of 104 children enrolled: (i) 30 underwent neuroimaging, (ii) 59 were observed, and (iii) 15 were discharged following the clinicians initial patient exam. Children with a non‐frontal scalp hematoma were more likely to receive immediate neuroimaging and children not acting like themselves per caregiver report were more likely to be initially observed, relative to the other care groups (p ≤ 0.01). Among high‐risk factors, altered mental status (OR 5.12, 95% CI 1.8–21.1), presence of ≥3 risk elements of the decision rule (OR 3.5, 95% CI 1.2–10.6), unclear skull fracture on exam (OR 31.3, 95% CI 5.4–593.8), and age < 3 months (OR 5.3, 95% CI 1.5–21.9) were associated with neuroimaging. No child had ciTBI. TBI symptoms (e.g. vomiting) were infrequently treated. Conclusions ED management varied for young children with similar risk stratification. Investigation of how age in concert with specific risk factors influences medical decision making would advance evidenced‐based care.