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Background: The electronic health record (EHR) was designed as a clinical and administrative tool to improve clinical patient care. Electronic healthcare systems have been successfully adopted across the world through use of government mandates and incentives., Methods: Using electronic health record, health information system, electronic medical record, health information systems, research, outcomes, pediatric, surgery, and urology as initial search terms, the literature focusing on clinical documentation data capture and the EHR as a potential resource for research related to clinical outcomes, quality improvement, and comparative effectiveness was reviewed. Relevant articles were supplemented by secondary review of article references as well as seminal articles in the field as identified by the senior author., Findings: US federal funding agencies, including the Agency for Healthcare Research and Quality, the Patient-Centered Outcomes Research Institute, the National Institutes of Health, and the Food and Drug Administration have recognized the EHR's role supporting research. The main approached to using EHR data include enhanced lists, direct data extraction, structured data entry, and unstructured data entry. The EHR's potential to facilitate research, overcoming cost and time burdens associated with traditional data collection, has not resulted in widespread use of EHR-based research tools., Conclusion: There are strengths and weaknesses for all existing methodologies of using EHR data to support research. Collaboration is needed to identify the method that best suits the institution for incorporation of research-oriented data collection into routine pediatric urologic clinical practice., (Copyright © 2018 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.)

Objective: PEDSnet is a clinical data research network (CDRN) that aggregates electronic health record data from multiple children's hospitals to enable large-scale research. Assessing data quality to ensure suitability for conducting research is a key requirement in PEDSnet. This study presents a range of data quality issues identified over a period of 18 months and interprets them to evaluate the research capacity of PEDSnet., Materials and Methods: Results were generated by a semiautomated data quality assessment workflow. Two investigators reviewed programmatic data quality issues and conducted discussions with the data partners' extract-transform-load analysts to determine the cause for each issue., Results: The results include a longitudinal summary of 2182 data quality issues identified across 9 data submission cycles. The metadata from the most recent cycle includes annotations for 850 issues: most frequent types, including missing data (>300) and outliers (>100); most complex domains, including medications (>160) and lab measurements (>140); and primary causes, including source data characteristics (83%) and extract-transform-load errors (9%)., Discussion: The longitudinal findings demonstrate the network's evolution from identifying difficulties with aligning the data to a common data model to learning norms in clinical pediatrics and determining research capability., Conclusion: While data quality is recognized as a critical aspect in establishing and utilizing a CDRN, the findings from data quality assessments are largely unpublished. This paper presents a real-world account of studying and interpreting data quality findings in a pediatric CDRN, and the lessons learned could be used by other CDRNs., (© 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)

Objectives: To reflect on the notable events and significant developments in Clinical Research Informatics (CRI) in the year of 2015 and discuss near-term trends impacting CRI., Methods: We selected key publications that highlight not only important recent advances in CRI but also notable events likely to have significant impact on CRI activities over the next few years or longer, and consulted the discussions in relevant scientific communities and an online living textbook for modern clinical trials. We also related the new concepts with old problems to improve the continuity of CRI research., Results: The highlights in CRI in 2015 include the growing adoption of electronic health records (EHR), the rapid development of regional, national, and global clinical data research networks for using EHR data to integrate scalable clinical research with clinical care and generate robust medical evidence. Data quality, integration, and fusion, data access by researchers, study transparency, results reproducibility, and infrastructure sustainability are persistent challenges., Conclusion: The advances in Big Data Analytics and Internet technologies together with the engagement of citizens in sciences are shaping the global clinical research enterprise, which is getting more open and increasingly stakeholder-centered, where stakeholders include patients, clinicians, researchers, and sponsors.

Introduction: Existing record linkage methods do not handle missing linking field values in an efficient and effective manner. The objective of this study is to investigate three novel methods for improving the accuracy and efficiency of record linkage when record linkage fields have missing values., Methods: By extending the Fellegi-Sunter scoring implementations available in the open-source Fine-grained Record Linkage (FRIL) software system we developed three novel methods to solve the missing data problem in record linkage, which we refer to as: Weight Redistribution, Distance Imputation, and Linkage Expansion. Weight Redistribution removes fields with missing data from the set of quasi-identifiers and redistributes the weight from the missing attribute based on relative proportions across the remaining available linkage fields. Distance Imputation imputes the distance between the missing data fields rather than imputing the missing data value. Linkage Expansion adds previously considered non-linkage fields to the linkage field set to compensate for the missing information in a linkage field. We tested the linkage methods using simulated data sets with varying field value corruption rates., Results: The methods developed had sensitivity ranging from .895 to .992 and positive predictive values (PPV) ranging from .865 to 1 in data sets with low corruption rates. Increased corruption rates lead to decreased sensitivity for all methods., Conclusions: These new record linkage algorithms show promise in terms of accuracy and efficiency and may be valuable for combining large data sets at the patient level to support biomedical and clinical research., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Purpose: The electronic health record is becoming central to routine medical practice and has the potential to facilitate large scale clinical research. We evaluated the completeness and accuracy of data collection using designated research fields integrated into a semistructured clinical note. We hypothesized that prospective research data collection as part of routine clinical charting is feasible, with a high rate of utilization (greater than 80%) and accuracy (kappa greater than 0.80)., Materials and Methods: Infants with congenital hydronephrosis were followed prospectively at a single institution. Existing functionality in the electronic health record was used for data collection by creation of 28 different data elements captured from a hydronephrosis note or phrase template. Completeness (percent utilization) was calculated and accuracy was assessed by comparing the structured data to manual chart review. Comparisons were conducted using the chi-square test, with 2-tailed p values <0.05 considered statistically significant., Results: A total of 80 patients were eligible for manual chart review. Data were recorded through template use in 64 patients for an overall completeness of 80.0%. Of 28 elements 17 (60%) demonstrated "almost perfect" agreement (kappa greater than 0.80), and all variables reached at least "moderate" agreement (greater than 0.40)., Conclusions: Integrating research fields into routine clinical practice is feasible by using semistructured clinical templates within an electronic health record. High completion and accuracy rates were captured from a variety of fields within a hydronephrosis template., (Copyright © 2014 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.)

Prospective clinical trials are a key step in translating bench findings into bedside therapies. Electronic medical records (EMRs) are often cited as a significant new tool for advancing clinical trial capabilities into standard clinical practice. However, combining clinical research and clinical care activities into one unified electronic information system requires integrating a substantial body of regulatory requirements and institutional policies. Differing interpretations of external regulations and internal policies need to be reconciled so that the EMR configuration simultaneously conforms to all requirements. The authors describe how they used a detailed clinical vignette to help focus discussions about their institution's current research policies and how regulations and policies might be implemented in a commercial EMR. The vignette highlighted a number of inconsistencies in the institution's policies and in individual interpretations of regulatory intent. Attempts to implement potential policies in the EMR system also revealed a number of limitations and inconsistencies in the commercial system. The authors describe a set of compromises that will be implemented at The Children's Hospital until missing functionality is made available from the commercial vendor. Each institution that implements an EMR will need to resolve similar policy and configuration issues at its own facility. The authors highlight these configuration challenges by presenting a list of questions that must be answered unambiguously before implementing translational research capabilities into an operational EMR.