1 Centre for Health Informatics, Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
2 Concordia Institute for Information Systems Engineering, Gina Cody School of Engineering and Computer Science, Concordia University, Montreal, Quebec, Canada.
3 Department of Biochemistry and Molecular Biology, Department of Medical Genetics, Department of Mathematics and Statistics, University of Calgary, Calgary, Alberta, Canada.
4 Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.
5 Hotchkiss Brain Institute, Calgary, Alberta, Canada.
6 Office of Vice President of Research & O'Brien Institute of Public Health, University of Calgary, Calgary, Alberta, Canada.
7 Provincial Research Data Services, Data and Analytics, Alberta Health Services, Calgary, Alberta, Canada.
8 Alberta Health Services, Calgary, Alberta, Canada.
9 Clinical Epidemiology and Quality of Care Unit, University Grenoble Alpes, Faculty of Medicine, Grenoble University Hospital, France.
10 Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
11 Department of Clinical Epidemiology, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.
12 Digital Design Department, IT University of Copenhagen, Copenhagen, Denmark.
13 Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
14 Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.

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.

Methods: Patient EMR structured and text data from acute care hospitals in Calgary, Alberta, Canada will be linked with discharge abstract data (DAD) between 2010 and 2020 (n~1.5 million). AE algorithms development. First, a comprehensive list of AEs will be generated through a systematic literature review and expert recommendations. Second, these AEs will be mapped to EMR free texts using Natural Language Processing (NLP) technologies. Finally, an expert panel will assess the clinical relevance of the developed NLP algorithms. AE algorithms validation: We will test the newly developed AE algorithms on 10,000 randomly selected EMRs between 2010 to 2020 from Calgary, Alberta. Trained reviewers will review the selected 10,000 EMR charts to identify AEs that had occurred during hospitalization. Performance indicators (e.g., sensitivity, specificity, positive predictive value, negative predictive value, F1 score, etc.) of the developed AE algorithms will be assessed using chart review data as the reference standard.

Discussion: The results of this project can be widely implemented in EMR based healthcare system to accurately and timely detect in-hospital AEs.