E. Coiera, Y. Wang, F. Magrabi, O. P. Concha, B. Gallego and W. Runciman. (2014). Predicting the cumulative risk of death during hospitalization by modeling weekend, weekday and diurnal mortality risks. BMC Health Serv Res (Vol. 14, pp. 226).

Abstract: BACKGROUND: Current prognostic models factor in patient and disease specific variables but do not consider cumulative risks of hospitalization over time. We developed risk models of the likelihood of death associated with cumulative exposure to hospitalization, based on time-varying risks of hospitalization over any given day, as well as day of the week. Model

M. S. Ong, F. Magrabi and E. Coiera. (2013). Syndromic surveillance for health information system failures: a feasibility study. J Am Med Inform Assoc (Vol. 20, pp. 506-12).

Abstract: OBJECTIVE: To explore the applicability of a syndromic surveillance method to the early detection of health information technology (HIT) system failures. METHODS: A syndromic surveillance system was developed to monitor a laboratory information system at a tertiary hospital. Four indices were monitored: (1) total laboratory records being created; (2) total records with missing results;

F. Magrabi, J. Aarts, C. Nohr, M. Baker, S. Harrison, S. Pelayo, J. Talmon, D. F. Sittig and E. Coiera. (2013). A comparative review of patient safety initiatives for national health information technology. Int J Med Inform (Vol. 82, pp. e139-48).

Abstract: OBJECTIVE: To collect and critically review patient safety initiatives for health information technology (HIT). METHOD: Publicly promulgated set of advisories, recommendations, guidelines, or standards potentially addressing safe system design, build, implementation or use were identified by searching the websites of regional and national agencies and programmes in a non-exhaustive set of exemplar countries including

M. S. Ong, F. Magrabi and E. Coiera. (2012). Automated identification of extreme-risk events in clinical incident reports. J Am Med Inform Assoc (Vol. 19, pp. e110-8).

Abstract: OBJECTIVES: To explore the feasibility of using statistical text classification to automatically detect extreme-risk events in clinical incident reports. METHODS: Statistical text classifiers based on Naive Bayes and Support Vector Machine (SVM) algorithms were trained and tested on clinical incident reports to automatically detect extreme-risk events, defined by incidents that satisfy the criteria of

A. M. D. Jensen, M. M. Jensen, A. S. Korsager, M. S. Ong, F. Magrabi and E. Coiera. (2012). Using virtual worlds to train healthcare workers – a case study using Second Life to improve the safety of inpatient transfers. eJHI-The Electronic Journal of Health Informatics (Vol. 7, pp. e7).

Abstract: Virtual worlds such as Second Life may offer a new environment to deliver simulation-based safety training to clinicians. The objective of this study was to design and implement a simulation of inpatient transfers in the virtual world of Second Life, and to undertake a preliminary evaluation of its usability as an educational tool. A