Unlocking the Potential of Electronic Health Records for Health Research

12:00noon to 1:00pm MT (11:00am to 12:00noon PST)| All sessions will be delivered live and online via the Gotowebinar system.

This webinar is part of the Power of Population Data Science Series

Electronic medical records (EMRs) were originally designed to facilitate clinical decision making by systematizing the collection of patient health information and generation of documentation in real-time.  The widespread adoption of EMRs in health systems has led to unprecedented availability of big health data, which has enabled new research activities targeting precision medicine and precision public health. The Centre for Health Informatics (CHI) at the University of Calgary was established to pursue EMR-based research activities. In this webinar we will introduce the Centre for Health Informatics (CHI) and discuss our experiences with using EMR for precision medicine research.

We will discuss our manuscript describing a city-wide inpatient EMR system used in Calgary hospitals called AllScripts Sunrise Clinical Manager (SCM), published in IJPDS earlier this year. This work outlines the SCM EMR data components, methodological (machine learning and natural language processing), and analytical considerations and approaches.

Within the past year, CHI has demonstrated its EMR-related research capacities by completing some key projects. We will discuss some of these projects, focusing not only on the techniques and results themselves but also the technical requirements and logistical challenges associated with this work.

View original IJPDS article at: https://ijpds.org/article/view/1123

View recorded presentation below:

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Adam D'Souza Adam D'Souza is a Data Scientist at the Centre for Health Informatics at the University of Calgary, and a Senior Analyst at Alberta Health Services. He has a background in quantum computing, obtaining a PhD in Physics and Astronomy from the University of Calgary in 2014, and as an entrepreneur, having co-founded and run a private data analytics firm in Calgary. Presently, he works on developing algorithms to identify diseases and conditions and extract clinical concepts from free-text Electronic Medical Record data, using machine learning and natural language processing techniques.


Frank LeeFrank Lee is the Ph.D candidate in the Department of Community Health Sciences, co-supervised by Dr. Hude Quan and Dr. Joon Lee. He is also a Senior Analyst at Alberta Health Services and for Alberta SPOR Support Unit’s Methods Support and Development Platform. He completed the Bachelor of Medical Sciences at the University of Western Ontario and received the Master of Public Health from the University of California, Berkeley. He has previously worked at the University of Western Ontario, the University of California San Francisco Medical Center, and has completed research projects for provincial and federal agencies. Frank’s research is focused on developing electronic medical record-based case phenotypes and utilizing them for risk adjustment analysis.

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