Clinical Research Informatics and Electronic Health Record Data
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TLDR
Considerations for the use of EHR data provide a starting point for practical applications and a CRI research agenda, which will be facilitated by CRI's key role in the infrastructure of a learning healthcare system.Abstract:
Objectives: The goal of this survey is to discuss the impact of the growing availability of electronic health record (EHR) data on the evolving field of Clinical Research Informatics (CRI), which is the union of biomedical research and informatics. Results: Major challenges for the use of EHR-derived data for research include the lack of standard methods for ensuring that data quality, completeness, and provenance are sufficient to assess the appropriateness of its use for research. Areas that need continued emphasis include methods for integrating data from heterogeneous sources, guidelines (including explicit phenotype definitions) for using these data in both pragmatic clinical trials and observational investigations, strong data governance to better understand and control quality of enterprise data, and promotion of national standards for representing and using clinical data. Conclusions: The use of EHR data has become a priority in CRI. Awareness of underlying clinical data collection processes will be essential in order to leverage these data for clinical research and patient care, and will require multi-disciplinary teams representing clinical research, informatics, and healthcare operations. Considerations for the use of EHR data provide a starting point for practical applications and a CRI research agenda, which will be facilitated by CRI's key role in the infrastructure of a learning healthcare system.read more
Citations
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The Learning Healthcare System Workshop Summary
TL;DR: The learning healthcare system workshop summary will help you to enjoy a good book with a cup of tea in the afternoon, instead they are facing with some harmful bugs inside their desktop computer.
Journal ArticleDOI
Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance.
TL;DR: Multiple EHR components provide a more consistent and higher performance than a single one for the selected phenotypes, and are suggested for future phenotyping design in order to obtain an ideal result.
Journal ArticleDOI
Electronic medical record phenotyping using the anchor and learn framework
TL;DR: A phenotype library that uses both structured and unstructured data from the EMR to represent patients for real-time clinical decision support is developed and the resulting phenotypes are interpretable and fast to build, and perform comparably to statistically learned phenotypes developed with 5000 manually labeled patients.
Journal ArticleDOI
Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models.
TL;DR: A review of the evolution of electronic phenotyping, from the early rule-based methods to the cutting edge of supervised and unsupervised machine learning models, with a focus on both methodology and implementation.
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The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities.
Lauren J. Beesley,Maxwell Salvatore,Lars G. Fritsche,Anita Pandit,Arvind Rao,Chad M. Brummett,Cristen J. Willer,Lynda D. Lisabeth,Bhramar Mukherjee +8 more
TL;DR: The current landscape of availableBiobanks is characterized and specific biobanks are described, including their place of origin, size, and data types, to discuss statistical issues related to biobank research such as study design, sampling strategy, phenotype identification, and missing data.
References
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Jan P. Vandenbroucke,Erik von Elm,Douglas G. Altman,Peter C Gøtzsche,Cynthia D. Mulrow,Stuart J. Pocock,Charles Poole,James J. Schlesselman,Matthias Egger,Matthias Egger +9 more
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