Investigators at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate.
Statistical methods for predictive analysis Statistical methods for comparative effectiveness Preparation, transformation and integration of EMR/EHRĬonfounding, bias and missing data in EMR/EHR and statistical methods addressing these challenges This two-day intensive workshop will go over opportunities and potentials of EMR/EHR for health and medical studies, statistical challenges and pitfalls for analyzing EMR/EHR, and the latest developments of multiple techniques to address those challenges, followed by hands-on computer lab sessions and case studies to put concepts into practice.īy the end of the electronic medical records training, participants will be familiar with the following topics: They are important data resources for building predictive models for disease diagnosis and prognosis, thus enabling personalized medicine.ĭespite the great potential, analyzing such large, scattered and heterogeneous observational patient data is still technically challenging. EMR/EHRs provide unprecedented opportunities for cohort-wide investigations and knowledge discovery. Extensive effort has been dedicated to developing advanced clinical data processing and data management in order to integrate patient data into a computable collection of rich longitudinal patient profiles. Huge amounts of longitudinal and detailed patient information, including lab tests, medications, disease status, and treatment outcome, have been accumulated and are available electronically. Over the last decade, Electronic Health Records (EHRs) and Electronic Medical Records (EMRs) systems have been increasingly implemented at US hospitals.
Electronic medical records TRAINING OVERVIEW
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