The Pediatric Emergency Care Applied Research Network: a history of multicenter collaboration in the United States.

In this article, we review the history and progress of a large multicenter research network pertaining to emergency medical services for children. We describe the history, organization, infrastructure, and research agenda of the Pediatric Emergency Care Applied Research Network (PECARN), and highlight some of the important accomplishments since its inception. We also describe the network's strategy to grow its research portfolio, train new investigators, and study how to translate new evidence into practice. This strategy ensures not only the sustainability of the network in the future, but the growth of research in emergency medical services for children in general

cMRI-BED: A novel informatics framework for cardiac MRI biomarker extraction and discovery applied to pediatric cardiomyopathy classification

Background\ud Pediatric cardiomyopathies are a rare, yet heterogeneous group of pathologies of the myocardium that are routinely examined clinically using Cardiovascular Magnetic Resonance Imaging (cMRI). This gold standard powerful non-invasive tool yields high resolution temporal images that characterize myocardial tissue. The complexities associated with the annotation of images and extraction of markers, necessitate the development of efficient workflows to acquire, manage and transform this data into actionable knowledge for patient care to reduce mortality and morbidity.\ud \ud Methods\ud We develop and test a novel informatics framework called cMRI-BED for biomarker extraction and discovery from such complex pediatric cMRI data that includes the use of a suite of tools for image processing, marker extraction and predictive modeling. We applied our workflow to obtain and analyze a dataset of 83 de-identified cases and controls containing cMRI-derived biomarkers for classifying positive versus negative findings of cardiomyopathy in children. Bayesian rule learning (BRL) methods were applied to derive understandable models in the form of propositional rules with posterior probabilities pertaining to their validity. Popular machine learning methods in the WEKA data mining toolkit were applied using default parameters to assess cross-validation performance of this dataset using accuracy and percentage area under ROC curve (AUC) measures.\ud \ud Results\ud The best 10-fold cross validation predictive performance obtained on this cMRI-derived biomarker dataset was 80.72% accuracy and 79.6% AUC by a BRL decision tree model, which is promising from this type of rare data. Moreover, we were able to verify that mycocardial delayed enhancement (MDE) status, which is known to be an important qualitative factor in the classification of cardiomyopathies, is picked up by our rule models as an important variable for prediction.\ud \ud Conclusions\ud Preliminary results show the feasibility of our framework for processing such data while also yielding actionable predictive classification rules that can augment knowledge conveyed in cardiac radiology outcome reports. Interactions between MDE status and other cMRI parameters that are depicted in our rules warrant further investigation and validation. Predictive rules learned from cMRI data to classify positive and negative findings of cardiomyopathy can enhance scientific understanding of the underlying interactions among imaging-derived parameters

Comparing predictions made by a prediction model, clinical score, and physicians Pediatric asthma exacerbations in the emergency department

Background: Asthma exacerbations are one of the most common medical reasons for children to be brought to the hospital emergency department (ED). Various prediction models have been proposed to support diagnosis of exacerbations and evaluation of their severity. Objectives: First, to evaluate prediction models constructed from data using machine learning techniques and to select the best performing model. Second, to compare predictions from the selected model with predictions from the Pediatric Respiratory Assessment Measure (PRAM) score, and predictions made by ED physicians. Design: A two-phase study conducted in the ED of an academic pediatric hospital. In phase 1 data collected prospectively using paper forms was used to construct and evaluate five prediction models, and the best performing model was selected. In phase 2, data collected prospectively using a mobile system was used to compare the predictions of the selected prediction model with those from PRAM and ED physicians. Measurements: Area under the receiver operating characteristic curve and accuracy in phase 1; accuracy, sensitivity, specificity, positive and negative predictive values in phase 2. Results: In phase 1 prediction models were derived from a data set of 240 patients and evaluated using 10-fold cross validation. A naive Bayes (NB) model demonstrated the best performance and it was selected for phase 2. Evaluation in phase 2 was conducted on data from 82 patients. Predictions made by the NB model were less accurate than the PRAM score and physicians (accuracy of 70.7%, 73.2% and 78.0% respectively), however, according to McNemar’s test it is not possible to conclude that the differences between predictions are statistically significant. Conclusion: Both the PRAM score and the NB model were less accurate than physicians. The NB model can handle incomplete patient data and as such may complement the PRAM score. However, it requires further research to improve its accuracy

A tree-based decision model to support prediction of the severity of asthma exacerbations in children

This paper describes the development of a tree-based decision model to predict the severity of pediatric asthma exacerbations in the emergency department (ED) at 2 h following triage. The model was constructed from retrospective patient data abstracted from the ED charts. The original data was preprocessed to eliminate questionable patient records and to normalize values of age-dependent clinical attributes. The model uses attributes routinely collected in the ED and provides predictions even for incomplete observations. Its performance was verified on independent validating data (split-sample validation) where it demonstrated AUC (area under ROC curve) of 0.83, sensitivity of 84%, specificity of 71% and the Brier score of 0.18. The model is intended to supplement an asthma clinical practice guideline, however, it can be also used as a stand-alone decision tool

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technology-based KM approaches

Reducing Respiratory Virus Testing In Hospitalized Children With Machine Learning And Text Mining

Despite pressure from the federal government for US hospitals to adopt electronic medical records systems (EMR), the benefits of adopting such systems have not been fully realized. One proposed advantage of EMRs involves secondary use, in which personal health information is used for purposes other than direct health care delivery, particularly quality improvement. We sought to determine whether information recorded in the EMR could improve diagnostic pathways used to diagnose respiratory viruses in children, the most common etiology of diagnoses in the pediatric population. These tests potentially represent a source of unnecessary testing. We performed a retrospective observational study analyzing pediatric inpatients receiving respiratory virus testing at Yale-New Haven Children\u27s Hospital between March 2010 to March 2012. Billing data (age, gender, season), laboratory data (sample adequacy, results), and clinical documents were gathered. We used MetaMap, a program distributed by the National Library of Medicine, to identify phrases denoting symptoms and diseases in the admission notes of patients. Identified concepts were added as additional variables to be modeled. Weka, another freely available software that allows for easy incorporation of machine learning algorithms, was used to derive models based on the C4.5 decision tree algorithm that aim to predict whether or not patients should be tested. Orders for pediatric patients accounted for 26.3% of all respiratory virus test orders placed during this time. Negative test results accounted for 69.5% of all tests ordered during the study period. The lengths of stay for all viral diagnoses were not statistically different. Models based on age, gender and season alone, were predictive for influenza (AUC 0.743, SE = 0.126), parainfluenza (AUC 0.686, SE = 0.078), RSV (AUC 0.658, SE = 0.048), and hMPV (AUC 0.713, SE = 0.143). Using MetaMap terms alone, only the model for RSV showed discriminatory ability (AUC 0.661, SE = 0.048). When basic variables were used in conjunction with MetaMap concepts, only the model for RSV showed improved performance (AUC 0.722, SE = 0.051) in comparison to both the basic and MetaMap models. Respiratory virus tests for general admission pediatric inpatients are ordered year-round and are mostly negative. Using models based on decision tree learning, our results showed that test volume could be reduced by about 20-50% for certain tests, as measured by model specificity. Furthermore, clinical concepts obtained via text mining in conjunction with basic variables improved prediction of RSV test results. The tradeoff between the false negative rates required to achieve any substantive specificity may be mitigated by our finding that hospital stays were nearly identical, regardless of the diagnostic outcome. These results support the use of EMR data for the auditing of and improvement of laboratory utilization. In addition, the improvement of predictive modeling for RSV with a simple implementation of text mining support the idea that clinical notes can be used for secondary use