Developing EMR-based algorithms to Identify hospital adverse events for health system performance evaluation and improvement: Study protocol.

Journal: PloS one
PMID: 36197944

Abstract

BACKGROUND: Measurement of care quality and safety mainly relies on abstracted administrative data. However, it is well studied that administrative data-based adverse event (AE) detection methods are suboptimal due to lack of clinical information. Electronic medical records (EMR) have been widely implemented and contain detailed and comprehensive information regarding all aspects of patient care, offering a valuable complement to administrative data. Harnessing the rich clinical data in EMRs offers a unique opportunity to improve detection, identify possible risk factors of AE and enhance surveillance. However, the methodological tools for detection of AEs within EMR need to be developed and validated. The objectives of this study are to develop EMR-based AE algorithms from hospital EMR data and assess AE algorithm's validity in Canadian EMR data.

Authors

  • Guosong Wu
    Centre for Health Informatics, Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • Cathy Eastwood
    Centre for Health Informatics, Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • Yong Zeng
    a College of Pharmacy , Chengdu University of Traditional Chinese Medicine , Chengdu , P.R. China.
  • Hude Quan
    Department of Community Health Sciences, University of Calgary, Calgary, Canada.
  • Quan Long
    Department of Biochemistry and Molecular Biology, Department of Medical Genetics, Department of Mathematics and Statistics, University of Calgary, Calgary, Alberta, Canada.
  • Zilong Zhang
    School of Computer Science and Technology, Hainan University, Haikou 570228, China.
  • William A Ghali
    Office of Vice President of Research & O'Brien Institute of Public Health, University of Calgary, Calgary, Alberta, Canada.
  • Jeffrey Bakal
    Centre for Health Informatics, Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • Bastien Boussat
    Clinical Epidemiology and Quality of Care Unit, University Grenoble Alpes, Faculty of Medicine, Grenoble University Hospital, France.
  • Ward Flemons
    Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • Alan Forster
    Department of Clinical Epidemiology, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
  • Danielle A Southern
    Centre for Health Informatics, Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • Søren Knudsen
    Digital Design Department, IT University of Copenhagen, Copenhagen, Denmark.
  • Brittany Popowich
    Centre for Health Informatics, Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • Yuan Xu
    Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, College of Life Sciences, Northwest University, Xi'an, China.

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