Automated identification of incident reports

Automated identification of incident reports

Centre for health informatics

Research stream

Patient Safety Informatics

A diagram showing automated incident reports

Project members

Associate Professor Farah Magrabi - Associate Professor

Dr Ying Wang - Research Fellow

Former Project members

Dr Mei-Sing Ong - Honorary Research Fellow

Dr Mi Ok Kim - PostDoctoral Research Fellow

Project contact

Associate Professor Farah Magrabi
T: +612 9850 2429

Project main description

Ten percent of admissions to Australian acute-care hospitals are associated with harm to patients or adverse events. The reporting of critical incidents by health professionals is now well established and the rate of reporting continues to increase worldwide. Current methods, which rely on retrospective manual review of incident reports, do not permit timely detection of safety problems and can no longer keep up with this growing volume of data. In New South Wales alone, more than 137,000 patient-safety incidents were reported in 2011.

We are evaluating text classification methods to capture incident reports automatically by type and risk rating. The goal is to track ten types of patient-safety problems nationally working in collaboration with St Vincent’s Hospital, Sydney and the NSW Clinical Excellence Commission. Working with the Australian Commission on Safety and Quality in Health Care we have shown that text classifiers based on well-evaluated machine-learning techniques such as Naïve Bayes and Support Vector Machines can be effective in automatically identifying incidents in two priority areas – clinical handover and patient identification. More recently we have shown the feasibility of using machine learning to identify IT incidents.


  1. Chai KE, Anthony S, Coiera E, Magrabi F. Using statistical text classification to identify health information technology incidents. J Am Med Inform Assoc. 2013;20(5):980-5.
  2. Ong MS, Magrabi F, Coiera E. Automated identification of extreme-risk events in clinical incident reports. J Am Med Inform Assoc. 2012;19(1e):e110-8.
  3. Ong MS, Magrabi F, Coiera E. Automated categorisation of clinical incident reports using statistical text classification. Qual Saf Health Care. 2010;19(6):e55.

Project sponsors

NHMRC Project APP1022964

Collaborative partners

Related projects

Project status


Centres related to this project

Centre for Health Informatics

Content owner: Australian Institute of Health Innovation Last updated: 22 Jun 2020 1:05pm

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