The use of artificial intelligence (AI) and big data in medicine has increased in recent years. Indeed, the use of AI in mobile health (mHealth) apps could considerably assist both individuals and health care professionals in the prevention and management of chronic diseases, in a person-centered manner. Nonetheless, there are several challenges that must be overcome to provide high-quality, usable, and effective mHealth apps. Here, we review the rationale and guidelines for the implementation of mHealth apps and the challenges regarding quality, usability, and user engagement and behavior change, with a special focus on the prevention and management of noncommunicable diseases. We suggest that a cocreation-based framework is the best method to address these challenges. Finally, we describe the current and future roles of AI in improving personalized medicine and provide recommendations for developing AI-based mHealth apps. We conclude that the implementation of AI and mHealth apps for routine clinical practice and remote health care will not be feasible until we overcome the main challenges regarding data privacy and security, quality assessment, and the reproducibility and uncertainty of AI results. Moreover, there is a lack of both standardized methods to measure the clinical outcomes of mHealth apps and techniques to encourage user engagement and behavior changes in the long term. We expect that in the near future, these obstacles will be overcome and that the ongoing European project, Watching the risk factors (WARIFA), will provide considerable advances in the implementation of AI-based mHealth apps for disease prevention and health promotion.

Interactive Journal of Medical Research

Background: A common denominator of modern hospitals is a variety of communication problems. In particular, interruptions from mobile communication devices are a cause of great concern for many physicians. Objective: To characterize how interruptions from mobile devices disturb physicians in their daily work. The gathered knowledge will be subsequently used as input for the design and development of a context-sensitive communication system for mobile communications suitable for hospitals. Methods: This study adheres to an ethnographic and interpretive field research approach. The data gathering consisted of participant observations, non-structured and mostly ad hoc interviews, and open-ended discussions with a selected group of physicians. Eleven physicians were observed for a total of 135 hours during May and June 2009. Results: The study demonstrates to what degree physicians are interrupted by mobile devices in their daily work and in which situations they are interrupted, such as surgery, examinations, and during patients/relatives high-importance level conversations. The participants in the study expected, and also indicated, that wireless phones probably led to more interruptions immediately after their introduction in a clinic, when compared to a pager, but this changed after a short while. The unpleasant feeling experienced by the caller when interrupting someone by calling them differs compared to sending a page message, which leaves it up to the receiver when to return the call. Conclusions: Mobile devices, which frequently interrupt physicians in hospitals, are a problem for both physicians and patients. The results from this study contribute to knowledge being used as input for designing and developing a prototype for a context-sensitive communication system for mobile communication suitable for hospitals. We combined these findings with results from earlier studies and also involved actual users to develop the prototype, CallMeSmart. This system intends to reduce such interruptions and at the same time minimize the number of communication devices needed per user. [J Med Internet Res 2013;15(3):e56]

Background: The time lag in detecting disease outbreaks remains a threat to global health security. The advancement of technology has made health-related data and other indicator activities easily accessible for syndromic surveillance of various datasets. At the heart of disease surveillance lies the clustering algorithm, which groups data with similar characteristics (spatial, temporal, or both) to uncover significant disease outbreak. Despite these developments, there is a lack of updated reviews of trends and modelling options in cluster detection algorithms. Objective: Our purpose was to systematically review practically implemented disease surveillance clustering algorithms relating to temporal, spatial, and spatiotemporal clustering mechanisms for their usage and performance efficacies, and to develop an efficient cluster detection mechanism framework. Methods: We conducted a systematic review exploring Google Scholar, ScienceDirect, PubMed, IEEE Xplore, ACM Digital Library, and Scopus. Between January and March 2018, we conducted the literature search for articles published to date in English in peer-reviewed journals. The main eligibility criteria were studies that (1) examined a practically implemented syndromic surveillance system with cluster detection mechanisms, including over-the-counter medication, school and work absenteeism, and disease surveillance relating to the presymptomatic stage; and (2) focused on surveillance of infectious diseases. We identified relevant articles using the title, keywords, and abstracts as a preliminary filter with the inclusion criteria, and then conducted a full-text review of the relevant articles. We then developed a framework for cluster detection mechanisms for various syndromic surveillance systems based on the review. Results: The search identified a total of 5936 articles. Removal of duplicates resulted in 5839 articles. After an initial review of the titles, we excluded 4165 articles, with 1674 remaining. Reading of abstracts and keywords eliminated 1549 further records. An in-depth assessment of the remaining 125 articles resulted in a total of 27 articles for inclusion in the review. The result indicated that various clustering and aberration detection algorithms have been empirically implemented or assessed with real data and tested. Based on the findings of the review, we subsequently developed a framework to include data processing, clustering and aberration detection, visualization, and alerts and alarms. Conclusions: The review identified various algorithms that have been practically implemented and tested. These results might foster the development of effective and efficient cluster detection mechanisms in empirical syndromic surveillance systems relating to a broad spectrum of space, time, or space-time.

We conducted interviews with two surgeons from the department of gastrointestinal surgery at the University Hospital of North Norway. The results confirmed that interruptions from mobile devices were a problem, especially in surgical theatres, outpatient wards, emergency wards and inpatient rooms. Users in hospitals, especially surgeons and physicians, need a better communication system. Our proposed system would intercept the signals from the existing communication system before they are sent out to the mobile devices. The signals would then be routed through a context-aware system, paired with context information and available rules defined by the doctor, which will decide what to do with the call/page. A single device which integrates both the pager and the phone system, and makes use of context information to control interruptions automatically yet allow the caller to decide whether to interrupt, would be highly appreciated by the users.