Notable Projects

notable projects

Heart Failure Identification

Heart Failure Identification

Heart Failure (HF) is a chronic condition with a large impact on both patients and health care systems. In Canada (2004-2013), the average cost of direct services for a patient is $10,000. There have been more than 42,000 hospitalizations annually in that time frame, with an average stay of six days. Decreasing the cost and improving the quality of care for HF patients is a top priority.

When identifying how common HF is, surveillance is often done using the International Classification of Diseases (ICD) codes in administrative databases. ICD algorithms for identifying the prevalence of HF tend to have poor sensitivity. An alternative is to perform manual chart review, though this process is tedious, time consuming, and requires medical experts, making it impracticable for large studies.

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Alberta Wastewater

Alberta Wastewater

Wastewater samples are collected, processed and reported by teams at the University of Alberta and the University of Calgary using different protocols. This means that direct comparisons between sites using different protocols are not possible. Comparing sites is also problematic because different communities have different proportions of residential and industrial water use contributing to their municipal wastewater. The most important signals to compare are within a given community by following trends over time. 

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Hypertension Identification

Hypertension Identification

The prevalence of hypertension is often underestimated. When a patient is discharged from a hospital, their conditions are coded using the International Classification of Diseases (ICD). Due to time constraints, and the fact that hypertension may not be the cause of the hospital visit, coding of hypertension is often omitted. This can lead to issues for surveillance and policy planning.

After a patient has been discharged, the most reliable way to determine whether they have hypertension is to have a trained clinician manually go through the documents in the patient chart. However, this is expensive and time-consuming. Electronic medical records (EMRs) provide all of the information necessary to identify hypertension. It is difficult to obtain the necessary information though, as most documents are in free-text form.

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Publications

  • Indicators of missing Electronic Medical Record (EMR) discharge summaries: A retrospective study on data from a large Canadian cohort (2021)

    • Natalie Wiebe, Yuan Xu, Abdel Aziz Shaheen, Catherine Eastwood, Bastien Boussat and Hude Quan

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  • Unlocking the Potential of Electronic Health Records for Health Research (2020)
    Seungwon Lee, Yuan Xu, Adam G. D’Souza, Elliot A. Martin, Chelsea Doktorchik, Zilong Zhang and Hude Quan

    • Seungwon Lee, Yuan Xu, Adam G. D’Souza, Elliot A. Martin, Chelsea Doktorchik, Zilong Zhang and Hude Quan

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  • Abstract 246: Validating the Accuracy of Routine Electrocardiogram Arrhythmia Diagnosis From an Electronic Repository (2019)

    • Hongwei Liu, Reid Collins, Robert J Miller, Danielle A Southern, Bryan J Har, Matthew T James, Stephen B Wilton

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  • Developing a case definition for type 1 diabetes mellitus in a primary care electronic medical record database: an exploratory study (2019)

    • B.Cord Lethebe, Tyler Williamson, Stephanie Garies, Kerry McBrien, Charles Leduc, Sonia Butalia, Boglarka Soos, Marta Shaw, and Neil Drummond

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  • Identification of validated case definitions for medical conditions used in primary care electronic medical record databases (2018)

    • Kerry A. McBrien, Sepideh Souri, Nicola E. Symonds, Azin Rouhi, Brendan C. Lethebe, Tyler S. Williamson, Stephanie Garies, Richard Birtwhistle, Hude Quan, Gabriel E. Fabreau and Paul E. Ronksley

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  • Predicting death by suicide following an .... with administrative health care system data and machine learning (2020)

    • Michael Sanderson, Andrew G.M. Bulloch, JianLi Wang, Kimberly G. Williams, Tyler Williamson and Scott B. Pattern

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  • Predicting death by suicide using administrative health care system data: Can recurrent neural network, one-dimensional convolutional neural ...(2020)

    • Michael Sanderson, Andrew G.M. Bulloch, JianLi Wang, Tyler Williamson and Scott B. Pattern

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  • Artificial intelligence (AI) and cancer prevention: ... control programming needs to be explored ... COMPASS (2019)

  • Data changes everything: Challenges and opportunities in data visualization design handoff

    • Jagoda Walny, Christian Frisson, Mieka West, Doris Kosminsky, Søren Knudsen, Sheelagh Carpendale and Wesley Willett

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  • Comparing ICD-data Across Countries: A Case for Visualization

    • Lucia Otero Varela, Søren Knudsen, Sheelagh Carpendale, Catherine Eastwood and Hude Quan.

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  • PADE: Supporting Collaborative Visual Analysis of Patient Administrative Systems Data with a Large Touch Display System

  • Information Visualization and Proxemics: Design Opportunities and Empirical Findings

    • Mikkel Rønne Jakobsen, Yonas Sahlemariam Haile, Søren Knudsen and Kasper Hornbæk.

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  • Dynamic Visual Analytics for Elicitation Meetings with ELICA

    • Zahra Shakeri Hossein Abad, Munib Rahman, Abdullah Cheema, Vincenzo Gervasi, Didar Zowghi, and Ken Barker.

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  • Visualizing Density of Healthcare Workers Across Time and Countries

    • Andie Green-Harrison, Søren Knudsen and Sheelagh Carpendale.

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  • Evaluation of interventions to improve inpatient hospital documentation within electronic health records: a systematic review

    • Natalie Wiebe, Lucia O. Varela, Daniel J. Niven, Paul E. Ronksley, Nicolas Iragorri and Hude Quan

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  • Achieving quality primary care data: a description of the Canadian Primary Care Sentinel Surveillance Network data capture, extraction ... in Alberta

    • Stephanie Garies, Michael Cummings, Brian Forst, Kerry McBrien, Boglarka Soos, Matt Taylor, Neil Drummond, Donna Manca, Kimberley Duerksen, Hude Quan and Tyler Williamson

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  • Evaluation of interventions to improve electronic health record documentation within the inpatient setting: a protocol for a systematic review

    • Lucia O. Varela, Natalie Wiebe, Daniel J. Niven, Paul E. Ronksley, Nicolas Iragorri, Helen Lee Robertson and Hude Quan

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  • Trajectories of perceived social support in acute coronary syndrome

    • Meng Wang, Colleen M. Norris, Michelle M. Graham, Maria Santana, Zhiying Liang, Oluwagbohunmi Awosoga, Danielle A. Southern, Matthew T. James, Stephen B. Wilton, Hude Quan, Mingshan Lu, William Ghali, Merril Knudtson and Tolulope T. Sajobi

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  • Prospective validation and refinement of the APPROACH cardiovascular surgical intensive care unit readmission score

    • Sanam Verma, Danielle A Southern, Ismail R. Raslan, Colleen M. Norris, Michelle M. Graham, Sean M. Bagshaw, Jayan Nagendran, Todd Maclure, Louise Y. Sun, Wu Dat Chin and Sean van Diepen

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  • A disease-specific comorbidity index for predicting mortality in patients admitted to hospital with a cardiac condition

    • Lorenzo Azzalini, Malorie Chabot-Blanchet, Danielle A. Southern, Anna Nozza, Stephen B. Wilton, Michelle M. Graham, Guillaume Marquis Gravel, Jean-Pierre Bluteau, Jean-Lucien Rouleau, Marie-Claude Guertin and E. Marc Jolicoeur

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  • Challenges Associated with Cross-Jurisdictional Analyses using Administrative Health Data and Primary Care Electronic Medical Records in Canada

    • Alan Kats, Jennifer Enns, Sabrina T. Wong, Tyler Williamson, Alexander Singer, Kimberlyn McGrail, Jeffery A. Bakal, Carole Taylor and Sandra Peterson

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  • Development and validation of case-finding algorithms for recurrence of breast cancer using routinely collected administrative data

    • Yuan Xu, Shiying Kong, Winson Y. Cheung, Antoine Bouchard-Fortier, Joseph C. Dort, Hude Quan, Elizabeth M. Buie, Geoff McKinnon and May Lynn Quan

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  • Oncocytic subtypes of adrenal cortical carcinoma: Aggressive in appearance yet more indolent in behavior?

    • Jane Mills, Moosa Khalil, Janice Pasieka, Shiying (Kristine) Kong, Yuan Xu, & Adrian Harvey.

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  • Identifying Atrial Fibrillation From 30,000 Feet: Strengths and Weaknesses of Health Administrative Data

    • Stephen B. Wilton, Hongwei Lui and Danielle A. Southern

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  • Empowering knowledge generation through international data network: the IMeCCHI-DATANETWORK

    • Lucia O. Varela, Marie-Annick Le Pogam, Amy Metcalfe, Pia Kjaer Kristensen, Hongsoo Kim, Raffaele Perego, Rosa Gini and Emanuele Carlini

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  • Developing case‐finding algorithms for second events of oropharyngeal cancer using administrative data: A population‐based validation study

    • Yuan Xu, Shiying Kong, Winson Y. Cheung, May Lynn Quan, Steven C. Nakoneshny and Joseph C. Dort

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  • Association of Levels of Specialized Care With Risk of Premature Mortality in Patients With Epilepsy.

    • Lowerison M, Josephson CB, Jette N, Sajobi TT, Patten S, Williamson T, Deardon R, Barkema HW, and Wiebe S

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  • IFHIMA Fosters Planning for ICD-11 Adoption with Global Case Studies

    • Includes a case study titled "Clinical Coders as Researchers in a Field Trial Testing ICD-11 for Mortality and Morbidity Statistics Coding: A Case Study from Canada" written by Cathy Eastwood (page 23).

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  • A World Health Organization field trial assessing a proposed ICD-11 framework for classifying patient safety events

    • AJ Forster, Burnand B, Droesler S, Gurevich Y, Harrison J, Januel JM, Romano PS, Southern DA, Sundararajan V, Quan H, Vanderloo SE, Pincus HA, Ghali WA.

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  • International variation in the definition of 'main condition' in ICD-coded health data.

    • Quan H, Moskal L, Forster AJ, Brien S, Walker R, Romano PS, Sundararajan V, Burnand B, Henriksson G, Steinum O, Droesler S, Pincus HA, Ghali WA.

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  • Opportunities and challenges for quality and safety applications in ICD-11: an international survey of users of coded health data.

    • Southern DA, Hall M, White DE, Romano PS, Sundararajan V, Droesler SE, Pincus HA, Ghali WA.

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  • Deriving ICD-10 Codes for Patient Safety Indicators for Large-scale Surveillance Using Administrative Hospital Data.

    • DA Southern, Burnand B, Droesler S, Flemons W, Forster A, Gurevich Y, Harrison J, Quan H, Pincus HA, Romano PS, Sundararajan V, Kostanjsek N, Ghali WA

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  • ICD-11 for quality and safety: overview of the WHO Quality and Safety Topic Advisory Group.

    • Ghali WA, Pincus HA, Southern DA, Brien SE, Romano PS, Burnand B, Drösler SE, Sundararajan V, Moskal L, Forster AJ, Gurevich Y, Quan H, Colin C, Munier WB, Harrison J, Spaeth-Rublee B, Kostanjsek N, Ustün TB.

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  • Capturing diagnosis-timing in ICD-coded hospital data: recommendations from the WHO ICD-11 topic advisory group on quality and safety.

    • Sundararajan V, Romano PS, Quan H, Burnand B, Drösler SE, Brien S, Pincus HA, Ghali WA

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  • Enhanced capture of healthcare-related harms and injuries in the 11th revision of the International Classification of Diseases (ICD-11).

    • Southern DA, Pincus HA, Romano PS, Burnand B, Harrison J, Forster AJ, Moskal L, Quan H, Droesler SE, Sundararajan V, Colin C, Gurevich Y, Brien SE, Kostanjsek N, Üstün B, Ghali WA; World Health Organization ICD-11 Revision Topic Advisory Group on Quality & Safety.

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  • Retrospective Database Analysis to Estimate Adherence Rates of HIV Patients in Canada

    • To request supplementary results files, please email chi@ucalgary.ca. 

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