Kim MO, Coiera E, Magrabi F. Problems with health information technology and their effects on care delivery and patient outcomes: a systematic review. Journal of the American Medical Informatics Association. 2017; 24(2):246-50.

Objective: To systematically review studies reporting problems with information technology (IT) in health care and their effects on care delivery and patient outcomes. Materials and methods: We searched bibliographic databases including Scopus, PubMed, and Science Citation Index Expanded from January 2004 to December 2015 for studies reporting problems with IT and their effects. A framework

Hodgson T, Magrabi F, Coiera E. Evaluating the Efficiency and Safety of Speech Recognition within a Commercial Electronic Health Record System: A Replication Study. Applied Clinical Informatics. 2018; 09(02): 326-335

Objective To conduct a replication study to validate previously identified significant risks and inefficiencies associated with the use of speech recognition (SR) for documentation within an electronic health record (EHR) system. Methods Thirty-five emergency department clinicians undertook randomly allocated clinical documentation tasks using keyboard and mouse (KBM) or SR using a commercial EHR system. The experiment design,

Wang Y, Coiera E, Runciman WB, Magrabi F. Using multiclass classification to automate the identification of patient safety incident reports by type and severity. BMC Medical Informatics and Decision Making. 2017; (Accepted 7 June 2017).

Background Approximately 10% of admissions to acute-care hospitals are associated with an adverse event. Analysis of incident reports helps to understand how and why incidents occur and can inform policy and practice for safer care. Unfortunately our capacity to monitor and respond to incident reports in a timely manner is limited by the sheer volumes

Hodgson T, Magrabi F, Coiera E. Efficiency and safety of speech recognition for documentation in the electronic health record. Journal of the American Medical Informatics Association. 2017; 24(6):1127-33.

Objective To compare the efficiency and safety of using speech recognition (SR) assisted clinical documentation within an electronic health record (EHR) system with use of keyboard and mouse (KBM). Methods Thirty-five emergency department clinicians undertook randomly allocated clinical documentation tasks using KBM or SR on a commercial EHR system. Tasks were simple or complex, and

Hodgson T, Magrabi F, Coiera E. Evaluating the usability of speech recognition to create clinical documentation using commercial electronic health record. International Journal of Medical Informatics. 2018; Published Online First: 21 February 2018.

Objective To conduct a replication study to validate previously identified significant risks and inefficiencies associated with the use of speech recognition (SR) for documentation within an electronic health record (EHR) system. Methods Thirty-five emergency department clinicians undertook randomly allocated clinical documentation tasks using keyboard and mouse (KBM) or SR using a commercial EHR system. The experiment design,

Coiera E, Magrabi F, Talmon J. Engineering technology resilience through informatics safety science [Editorial]. Journal of the American Medical Informatics Association. 2017; 24(2):244-5.

With every year that passes, our relationship to information technology becomes more complex, and our dependence deeper. Technology is our great ally, promising greater efficiency and productivity. It also promises greater safety for our patients. However, this relationship with technology can sometimes be a brittle one. We can quickly cross a safety gap from a

Lyell D, Magrabi F, Raban MZ, Pont LG, Baysari MT, Day RO, Coiera E: Automation bias in electronic prescribing. BMC Medical Informatics and Decision Making 2017, 17(1):28.

BACKGROUND: Clinical decision support (CDS) in e-prescribing can improve safety by alerting potential errors, but introduces new sources of risk. Automation bias (AB) occurs when users over-rely on CDS, reducing vigilance in information seeking and processing. Evidence of AB has been found in other clinical tasks, but has not yet been tested with e-prescribing. This

Magrabi F, Ammenwerth E, Hyppönen H, de Keizer N, Nykänen P, Rigby M, Scott P, Talmon J, Georgiou A. Improving evaluation to address the unintended consequences of health information technology. IMIA Yearbook. 2016:61-9

Abstract BACKGROUND AND OBJECTIVES: With growing use of IT by healthcare professionals and patients, the opportunity for any unintended effects of technology to disrupt care health processes and outcomes is intensified. The objectives of this position paper by the IMIA Working Group (WG) on Technology Assessment and Quality Development are to highlight how our ongoing

Rigby M, Magrabi F, Scott P, Doupi P, Hypponen H, Ammenwerth E. Steps in moving evidence-based health informatics from theory to practice. Healthcare Informatics Research. 2016; 22(4):255-60.

Abstract OBJECTIVES: To demonstrate and promote the importance of applying a scientific process to health IT design and implementation, and of basing this on research principles and techniques. METHODS: A review by international experts linked to the IMIA Working Group on Technology Assessment and Quality Development. RESULTS: Four approaches are presented, linking to the creation

Kim MO, Coiera E, Magrabi F. Problems with health information technology and their effects on care delivery and patient outcomes: a systematic review. Journal of the American Medical Informatics Association. 2016:ocw154.

Abstract Objective: To systematically review studies reporting problems with information technology (IT) in health care and their effects on care delivery and patient outcomes. Materials and methods: We searched bibliographic databases including Scopus, PubMed, and Science Citation Index Expanded from January 2004 to December 2015 for studies reporting problems with IT and their effects. A framework

Y. Wang, E. Coiera, B. Gallego, O. P. Concha, M. S. Ong, G. Tsafnat, D. Roffe, G. Jones and F. Magrabi. (2016). Measuring the effects of computer downtime on hospital pathology processes. J Biomed Inform (Vol. 59, pp. 308-15)

Abstract: Objective: To introduce and evaluate a method that uses electronic medical record (EMR) data to measure the effects of computer system downtime on clinical processes associated with pathology testing and results reporting. Materials and methods: A matched case-control design was used to examine the effects of five downtime events over 11-months, ranging from 5

F. Magrabi, S. T. Liaw, D. Arachi, W. B. Runciman, E. Coiera and M. R. Kidd. (2015). Identifying patient safety problems associated with Information Technology in general practice: an analysis of incident reports. BMJ Qual Saf.

Abstract: Objective: To identify the categories of problems with information technology (IT), which affect patient safety in general practice. Design: General practitioners (GPs) reported incidents online or by telephone between May 2012 and November 2013. Incidents were reviewed against an existing classification for problems associated with IT and the clinical process impacted. Participants and setting:

F. Magrabi, M. Baker, I. Sinha, M. S. Ong, S. Harrison, M. R. Kidd, W. B. Runciman and E. Coiera. (2015). Clinical safety of England’s national programme for IT: a retrospective analysis of all reported safety events 2005 to 2011. Int J Med Inform (Vol. 84, pp. 198-206).

Abstract: OBJECTIVE: To analyse patient safety events associated with England’s national programme for IT (NPfIT). METHODS: Retrospective analysis of all safety events managed by a dedicated IT safety team between September 2005 and November 2011 was undertaken. Events were reviewed against an existing classification for problems associated with IT. The proportion of reported events per

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