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This book teaches IT service managers how to onboard ServiceNow ITSM tools by evangelizing, educating, and coordinating their organization's service desk, developers, and stakeholders. Drawing on his own story of lessons learned in spinning up the adoption of ServiceNow throughout the Al Jazeera Media Network, application architect Gabriele Kahlout shows IT service managers how to launch automated ServiceNow ticketing tools in seamless integration with their organization's existing email and Active Directory. ...Shows IT service managers how to orchestrate their IT service desks and developers to facilitate the adoption and consumption of IT services by all users, supporting their various business needs while optimizing human-computer interaction and minimizing stress and productivity loss arising from poor human-system design. ... How to create a strategy to avoid common pitfalls that sabotage ITSM programs.-- Back cover.

Forty percent of all general-practitioner appointments are related to mental illness, although less than 35% of individuals have access to therapy and psychological care, indicating a pressing need for accessible and affordable therapy tools. The ubiquity of smartphones offers a delivery platform for such tools. Previous research suggests that gamification-turning intervention content into a game format-could increase engagement with prevention and early-stage mobile interventions. This study aimed to explore the effects of a gamified mobile mental health intervention on improvements in resilience, in comparison with active and inactive control conditions. Differences between conditions on changes in personal growth, anxiety and psychological wellbeing, as well as differences in attrition rates, were also assessed. The eQuoo app was developed and published on all leading mobile platforms. The app educates users about psychological concepts including emotional bids, generalization, and reciprocity through psychoeducation, storytelling, and gamification. In total, 358 participants completed in a 5-week, 3-armed (eQuoo, \"treatment as usual\" cognitive behavioral therapy journal app, no-intervention waitlist) randomized controlled trial. Relevant scales were administered to all participants on days 1, 17, and 35. Repeated-measures ANOVA revealed statistically significant increases in resilience in the test group compared with both control groups over 5 weeks. The app also significantly increased personal growth, positive relations with others, and anxiety. With 90% adherence, eQuoo retained 21% more participants than the control or waitlist groups. Intervention delivered via eQuoo significantly raised mental well-being and decreased self-reported anxiety while enhancing adherence in comparison with the control conditions. Mobile apps using gamification can be a valuable and effective platform for well-being and mental health interventions and may enhance motivation and reduce attrition. Future research should measure eQuoo's effect on anxiety with a more sensitive tool and examine the impact of eQuoo on a clinical population.

Bitcoin is an online currency that is used worldwide to make online payments. It has consequently become an investment vehicle in itself and is traded in a way similar to other open currencies. The ability to predict the price fluctuation of Bitcoin would therefore facilitate future investment and payment decisions. In order to predict the price fluctuation of Bitcoin, we analyse the comments posted in the Bitcoin online forum. Unlike most research on Bitcoin-related online forums, which is limited to simple sentiment analysis and does not pay sufficient attention to note-worthy user comments, our approach involved extracting keywords from Bitcoin-related user comments posted on the online forum with the aim of analytically predicting the price and extent of transaction fluctuation of the currency. The effectiveness of the proposed method is validated based on Bitcoin online forum data ranging over a period of 2.8 years from December 2013 to September 2016.

Family health history (FHH) is the most useful means of assessing risk for common chronic diseases. The odds ratio for risk of developing disease with a positive FHH is frequently greater than 2, and actions can be taken to mitigate risk by adhering to screening guidelines, genetic counselling, genetic risk testing, and other screening methods. Challenges to the routine acquisition of FHH include constraints on provider time to collect data and the difficulty in accessing risk calculators. Disease-specific and broader risk assessment software platforms have been developed, many with clinical decision support and informatics interoperability, but few access patient information directly. Software that allows integration of FHH with the electronic medical record and clinical decision support capabilities has provided solutions to many of these challenges. Patient facing, electronic medical record, and web-enabled FHH platforms have been developed, and can provide greater identification of risk compared with conventional FHH ascertainment in primary care. FHH, along with cascade screening, can be an important component of population health management approaches to overall reduction of risk.

Background Computer Aided Diagnostics (CAD) can support medical practitioners to make critical decisions about their patients’ disease conditions. Practitioners require access to the chain of reasoning behind CAD to build trust in the CAD advice and to supplement their own expertise. Yet, CAD systems might be based on black box machine learning models and high dimensional data sources such as electronic health records, magnetic resonance imaging scans, cardiotocograms, etc. These foundations make interpretation and explanation of the CAD advice very challenging. This challenge is recognised throughout the machine learning research community. eXplainable Artificial Intelligence (XAI) is emerging as one of the most important research areas of recent years because it addresses the interpretability and trust concerns of critical decision makers, including those in clinical and medical practice. Methods In this work, we focus on AdaBoost, a black box model that has been widely adopted in the CAD literature. We address the challenge – to explain AdaBoost classification – with a novel algorithm that extracts simple, logical rules from AdaBoost models. Our algorithm, Adaptive-Weighted High Importance Path Snippets (Ada-WHIPS), makes use of AdaBoost’s adaptive classifier weights. Using a novel formulation, Ada-WHIPS uniquely redistributes the weights among individual decision nodes of the internal decision trees of the AdaBoost model. Then, a simple heuristic search of the weighted nodes finds a single rule that dominated the model’s decision. We compare the explanations generated by our novel approach with the state of the art in an experimental study. We evaluate the derived explanations with simple statistical tests of well-known quality measures, precision and coverage, and a novel measure stability that is better suited to the XAI setting. Results Experiments on 9 CAD-related data sets showed that Ada-WHIPS explanations consistently generalise better (mean coverage 15%-68%) than the state of the art while remaining competitive for specificity (mean precision 80%-99%). A very small trade-off in specificity is shown to guard against over-fitting which is a known problem in the state of the art methods. Conclusions The experimental results demonstrate the benefits of using our novel algorithm for explaining CAD AdaBoost classifiers widely found in the literature. Our tightly coupled, AdaBoost-specific approach outperforms model-agnostic explanation methods and should be considered by practitioners looking for an XAI solution for this class of models.

Adherence to medications is an important indicator of the quality of medication management and impacts on health outcomes and cost-effectiveness of healthcare delivery. Electronic healthcare data (EHD) are increasingly used to estimate adherence in research and clinical practice, yet standardization and transparency of data processing are still a concern. Comprehensive and flexible open-source algorithms can facilitate the development of high-quality, consistent, and reproducible evidence in this field. Some EHD-based clinical decision support systems (CDSS) include visualization of medication histories, but this is rarely integrated in adherence analyses and not easily accessible for data exploration or implementation in new clinical settings. We introduce AdhereR, a package for the widely used open-source statistical environment R, designed to support researchers in computing EHD-based adherence estimates and in visualizing individual medication histories and adherence patterns. AdhereR implements a set of functions that are consistent with current adherence guidelines, definitions and operationalizations. We illustrate the use of AdhereR with an example dataset of 2-year records of 100 patients and describe the various analysis choices possible and how they can be adapted to different health conditions and types of medications. The package is freely available for use and its implementation facilitates the integration of medication history visualizations in open-source CDSS platforms.

This contribution investigates higher education students’ perceptions about mobile learning (m-learning) applications, as well as the effects of social influences and of appropriate facilitating conditions, on their intentions to continue using them. A structured survey questionnaire integrated valid measures from the Technology Acceptance Model (TAM) and from the Unified Theory of Acceptance and Use of Technology (UTAUT) to better explain their acceptance and use of m-learning software. The findings reported that facilitating conditions including the provision of resources, ongoing training opportunities and technical support, were affecting the respondents’ engagement with m-learning programs. The respondents indicated that they were not influenced by others to use mobile technologies for educational purposes. The results also suggest that they were well acquainted (and habituated) with the use of mobile devices and their applications. Evidently, they helped them improve their learning journeys.

Perioperative adverse events (PAEs) pose a substantial global health burden, contributing to elevated morbidity, mortality, and health care expenditures. The adoption of clinical decision support systems (CDSS), particularly mobile-based solutions, offers a promising avenue to address these challenges. However, successful implementation hinges on understanding anesthesia providers' knowledge, attitudes, and willingness to embrace such technologies. This study aimed to evaluate the knowledge, attitudes, and willingness of Chinese anesthesia professionals to adopt a mobile CDSS for PAE management, and to identify key factors influencing its implementation. A nationwide cross-sectional survey was conducted among anesthesia providers in China from September 5 to December 31, 2023. Participants included anesthesiologists and nurse anesthetists, who play pivotal roles in perioperative care. A 51-item questionnaire, structured around the Knowledge-Attitude-Practice (KAP) framework, was distributed via WeChat through professional anesthesia associations. The questionnaire covered four domains: (1) demographic characteristics, (2) knowledge assessment, (3) attitude evaluation, and (4) practice willingness. Multivariable regression analyses identified predictors of KAP outcomes, with sensitivity analyses focusing on nurse anesthetists. The study included 2440 anesthesia professionals (2226 anesthesiologists and 214 nurse anesthetists). Overall, 87.3% (2130/2440) expressed willingness to adopt the CDSS, with 87.5% (1947/2226) of anesthesiologists and 85.5% (183/214) of nurse anesthetists showing readiness. However, only 39.2% (956/2440) were satisfied with existing incident management systems. Key findings indicated that higher knowledge scores were associated with female gender (coefficient=0.19, P=.003), advanced education, and lack of previous informatics experience (coefficient=0.29, P<.001). Nurse anesthetists scored lower than anesthesiologists (coefficient=-0.76, P<.001). Negative attitudes were more prevalent among older practitioners (coefficient=-0.13, P<.001), females (coefficient=-0.66, P<.001), nurse anesthetists (coefficient=-1.12, P=.003), and those without prior PAE exposure (coefficient=-0.97, P<.001). Higher willingness was observed among practitioners in Southwest China (coefficient=0.10, P=.048), those with positive attitudes (coefficient=0.06, P<.001), and those dissatisfied (coefficient=0.32, P<.001) or neutral (coefficient=0.11, P=.02) towards existing systems. Infrequent departmental incident discussions would reduce practice willingness (coefficient=-0.08, P=.01). This national study highlights a strong readiness among Chinese anesthesia professionals to adopt mobile CDSS for PAE management. However, critical barriers, including role-specific knowledge disparities and ineffective organizational communication, must be addressed to ensure successful implementation. Collaborative efforts among local authorities, health care facilities, anesthesia departments, and technology developers are essential to design and implement tailored strategies. Key recommendations include interdisciplinary training programs to enhance nurse anesthetists' competencies, institution-level incentives to promote incident reporting, and user-centered CDSS designs that prioritize seamless integration into clinical workflows. These measures are vital for improving perioperative incident reporting systems and ultimately advancing the safety and outcomes of surgical patients.

According to the UNWTO, within 4 to 5 years, the proportion of tourism e-commerce in e-commerce will reach 20%-25%. The purpose of this paper is to improve the inadequacy of tourism e-commerce in customer experience, to conduct customer e-commerce satisfaction surveys, and to draw customers' dissatisfaction with tourism e-commerce. The experimental results show that the overall customer satisfaction is 2.6128. According to the division of the scale vector, the overall satisfaction of the travel e-commerce customers is generally level. The first-level fuzzy comprehensive evaluation is 0.0967, 0.1696, 0.3366, 0.2469, 0.502. According to the principle of maximum membership degree, the evaluation grades of R3 and R5 in the first-level fuzzy comprehensive evaluation are “unsatisfactory,” that is, the tourism-supporting services and contract-performance services become the main factors affecting customer satisfaction. In order to improve customer satisfaction, the tourism e-commerce platform should strengthen the management of tourism-supporting services and contract-fulfillment services.

An increase in physical activity for secondary prevention of cardiovascular disease and cardiac rehabilitation has multiple therapeutic benefits, including decreased mortality. Internet- and mobile-based interventions for physical activity have shown promising results in helping users increase or maintain their level of physical activity in general and specifically in secondary prevention of cardiovascular diseases and cardiac rehabilitation. One component related to the efficacy of these interventions is tailoring of the content to the individual. Our trial assessed the effect of a longitudinally tailored Internet- and mobile-based intervention for physical activity as an extension of a face-to-face cardiac rehabilitation stay. We hypothesized that users of the tailored intervention would maintain their physical activity level better than users of the nontailored version. The study population included adult participants of a cardiac rehabilitation program in Norway with home Internet access and a mobile phone. The participants were randomized in monthly clusters to a tailored or nontailored (control) intervention group. All participants had access to a website with information regarding cardiac rehabilitation, an online discussion forum, and an online activity calendar. Those using the tailored intervention received tailored content based on models of health behavior via the website and mobile fully automated text messages. The main outcome was self-reported level of physical activity, which was obtained using an online international physical activity questionnaire at baseline, at discharge, and at 1 month and 3 months after discharge from the cardiac rehabilitation program. Included in the study were 69 participants. One month after discharge, the tailored intervention group (n=10) had a higher median level of overall physical activity (median 2737.5, IQR 4200.2) than the control group (n=14, median 1650.0, IQR 2443.5), but the difference was not significant (Kolmogorov-Smirnov Z=0.823, P=.38, r=.17). At 3 months after discharge, the tailored intervention group (n=7) had a significantly higher median level of overall physical activity (median 5613.0, IQR 2828.0) than the control group (n=12, median 1356.0, IQR 2937.0; Kolmogorov-Smirnov Z=1.397, P=.02, r=.33). The median adherence was 45.0 (95% CI 0.0-169.8) days for the tailored group and 111.0 (95% CI 45.1-176.9) days for the control group; however, the difference was not significant (P=.39). There were no statistically significant differences between the 2 groups in stage of change, self-efficacy, social support, perceived tailoring, anxiety, or depression. Because of the small sample size and the high attrition rate at the follow-up visits, we cannot make conclusions regarding the efficacy of our approach, but the results indicate that the tailored version of the intervention may have contributed to the long-term higher physical activity maintained after cardiac rehabilitation by participants receiving the tailored intervention compared with those receiving the nontailored intervention. ClinicalTrials.gov: NCT01223170; http://clinicaltrials.gov/show/NCT01223170 (Archived by WebCite at http://www.webcitation.org/6Nch4ldcL).