Key Areas of Research

EMR Phenotyping

CHI aims to be the leading experts in electronic medical record (EMR) analytics and data science methodologies, for the development of efficient and accurate handling of digital health data for precision medicine. Our cutting-edge data science methods can identify comorbidities in EMR data for purposes such as risk adjustment and cohort identification.

Danielle Southern and Tyler Williamson

Cathy Eastwood, Adam D'Souza, Elliot Martin, Frank Lee, Todd Zhang, Oscar Chen, Alex Krusina, Shawn Xu, Natalie Wiebe, Alexis Guigue, Tannistha Nandi, Guosong Wu, and Hude Quan.

Watch our episode from Population Data BC's webinar series!

Unlocking the Potential of Electronic Health Records for Health Research 

Adverse Events

Like our efforts in EMR phenotyping, we aim to be the leading experts in adverse event identification in EMR data. The ability to sort through massive amounts of hospital data (including free text) to identify unintended harms can help our health system better understand areas of improvement.

Shawn Xu and Catherine Eastwood

Danielle Southern, Todd Zhang, Elvira Nurmambetova, Guosong Wu, and Frank Lee

Heart Failure

One of our main goals is to work with a team of diverse experts to tackle real-world problems. In Canada, nearly one quarter of patients with heart failure are readmitted to hospitals within a 30-day period. However, readmissions are often preventable. One of our cornerstone projects is to use linked data to predict readmission for heart failure patients and adapt care pathways according to risk.

Cathy Eastwood 

Camilia Thieba, Natalie Wiebe, Alexis Guigue, Elliot Martin, Frank Lee, Melanie Rosario, Shawn Xu, and  Hude Quan.

Database Enhancement

We are proud of our extensive expertise in the handling of various health databases. We are building on this passion by creating our own databases that can advance research quality, and provide immense opportunities to answer imperative questions critical for optimal health system functioning.

Cathy Eastwood

Adam D'Souza, Tannistha Nandi, Todd Zhang, Jeff Bakal, Rachel Eastwood, Mike Alcaz, and Guosong Wu.

Computer Assisted Qualitative Analysis

Our team consists of experts who are specialized in qualitative data analysis as well as natural language processing, a form of machine learning that automates the analysis of free text. As researchers are faced with large amounts of free text data in various forms (e.g. patient interviews), we are currently devising a unique method to automate the qualitative analytic process. 

Shaminder Singh and Lin Yang

Chelsea Doktorchik, Tannistha Nandi, Lin Yang, Todd Zhang, and Hude Quan.

ICD-11

The CHI is a designated World Health Organization Family of International Classification (WHO-FIC) Collaborating Centre. As part of a larger field trial, we have conducted important work in developing training materials for ICD-11, improving its content, and providing knowledge and tools for Canadian decision-makers to facilitate a seamless transition from ICD-10-CA to ICD-11.

COVID-19

In light of the COVID-19 pandemic, the City of Calgary and CSM called upon CHI’s experts to create a surveillance dashboard. Our team sprung into action and created a COVID-19 “Tracker” that displays data from Alberta, Canada, and international countries. This Tracker was intended to keep the public up to date using comprehensive data, as well as inform policies made by the city and the province.

Tyler Williamson

Alex Krusina, Oscar Chen, Cathy Eastwood, Danielle Southern, Lucia Otero Varela, Chelsea Doktochik, Frank Lee, Alexis Guigue, and Tannistha Nandi.

Shaminder Singh and Hude Quan

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