2019 | Digital Health

Kocaballi AB, Laranjo L, Coiera E. Understanding and measuring user experience in conversational interfaces. Interacting with Computers. 2019; 31(2):192-207.

Although various methods have been developed to evaluate conversational interfaces, there has been a lack of methods specifically focusing on evaluating user experience. This paper reviews the understandings of user experience (UX) in conversational interfaces literature and examines the six questionnaires commonly used for evaluating conversational systems in order to assess the potential suitability of

Quiroz, J.C., Laranjo, L., Kocaballi, A.B. et al. Challenges of developing a digital scribe to reduce clinical documentation burden. npj Digit. Med. 2, 114 (2019)

Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical

Yin K, Laranjo L, Tong HL, Lau AY, Kocaballi AB, Martin P, Vagholkar S, Coiera E. Context-Aware Systems for Chronic Disease Patients: Scoping Review. J Med Internet Res 2019;21(6):e10896

Background: Context-aware systems, also known as context-sensitive systems, are computing applications designed to capture, interpret, and use contextual information and provide adaptive services according to the current context of use. Context-aware systems have the potential to support patients with chronic conditions; however, little is known about how such systems have been utilized to facilitate patient

Magrabi F, Ammenwerth E, McNair JB, De Keizer NF, Hyppönen H, Nykänen P, Rigby M, Scott PJ, Vehko T, Wong ZS-Y, Georgiou A. Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications. Yearb Med Inform. 2019; 28(01):128-34.

OBJECTIVES: This paper draws attention to: i) key considerations for evaluating artificial intelligence (AI) enabled clinical decision support; and ii) challenges and practical implications of AI design, development, selection, use, and ongoing surveillance. METHOD: A narrative review of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA)

Wang Y, Coiera E, Magrabi F. Using convolutional neural networks to identify patient safety incident reports by type and severity. Journal of the American Medical Informatics Association. 2019; 26(12):1600-8.

Objective To evaluate the feasibility of a convolutional neural network (CNN) with word embedding to identify the type and severity of patient safety incident reports. Materials and Methods A CNN with word embedding was applied to identify 10 incident types and 4 severity levels. Model training and validation used data sets (n_type = 2860, n_severity = 1160) collected from

Akbar S, Coiera, E, Magrabi F. Safety concerns with consumer-facing mobile health applications and their consequences: a scoping review. Journal of the American Medical Informatics Association. 2019.

Objective: To summarize the research literature about safety concerns with consumer-facing health apps and their consequences. Materials and methods: We searched bibliographic databases including PubMed, Web of Science, Scopus, and Cochrane libraries from January 2013 to May 2019 for articles about health apps. Descriptive information about safety concerns and consequences were extracted and classified into

Magrabi F, Habli I, Sujan M, Wong D, Thimbleby H, Baker M, Coiera E. Why is it so difficult to govern mobile apps in healthcare? BMJ Health Care Inform 2019;26:e100006. doi:10.1136/ bmjhci-2019-100006

Mobile apps have become a convenient way to provide health information and communication services directly in the hands of clinicians and consumers. Apps can be used to support consumers in a variety of health tasks to manage chronic diseases, support lifestyle changes and in self-diagnosis. For clinicians, they can improve access to patient information and

Hassanzadeh H, Nguyen A, Verspoor K. Quantifying semantic similarity of clinical evidence in the biomedical literature to facilitate related evidence synthesis. Journal of Biomedical Informatics. 2019;100:103321.

Objective: Published clinical trials and high quality peer reviewed medical publications are considered as the main sources of evidence used for synthesizing systematic reviews or practicing Evidence Based Medicine (EBM). Finding all relevant published evidence for a particular medical case is a time and labour intensive task, given the breadth of the biomedical literature. Automatic