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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

Coorey GM, Neubeck L, Usherwood T, Peiris D, Parker S, Lau AY, Chow C, Panaretto K, Harris M, Zwar N. Implementation of a consumer-focused eHealth intervention for people with moderate-to-high cardiovascular disease risk: protocol for a mixed-methods process evaluation. BMJ Open. 2017; 7(1):e014353.

Introduction Technology-mediated strategies have potential to engage patients in modifying unhealthy behaviour and improving medication adherence to reduce morbidity and mortality from cardiovascular disease (CVD). Furthermore, electronic tools offer a medium by which consumers can more actively navigate personal healthcare information. Understanding how, why and among whom such strategies have an effect can help determine the

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

Wang Y, Coiera E, Gallego B, Perez-Concha O, Ong M-S, Tsafnat G, Roffe D, Jones G, Magrabi F. Measuring the effects of computer downtime on hospital pathology processes. Journal of biomedical informatics. 2016; 59:308-15.

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