Information systems are increasingly playing a major role in the cost-effective delivery of healthcare services. Recently, the healthcare industry has shifted its focus toward patient-centered care, in which patients play a major role in managing their health. As a result, healthcare providers are employing patient portals - consumer-centric tools - to strengthen patients' ability to actively manage their health and healthcare. In this study, we extend the existing literature on patient portals by examining the functionalities and features of current portals from users' experiences with mobile patient portal apps, mapping the identified functionalities and features to established design principles of health behavioral change support systems, and identifying limitations of existing patient portals. Results show that current patient portal apps are limited with respect to integration with medical devices, social support, customization, persuasive messages, and peer-based technical support.

Public transit systems are crucial to mobility and access in cities throughout the world. This article addresses the importance of these transit systems in San Antonio, Texas. We show how transit systems exacerbate race and class inequality and the accessibility of city spaces with a focus on San Antonio’s buses. Using mixed methods (surveys, interviews, ethnography, and document analysis) we illustrate that poor and working-class Latinx communities experience reduced access to resource rich areas of the city when they are dependent upon the city’s public transportation. To better describe this experience we use the concepts, enclaves of exclusion and enclaves of inaccessibility. Our findings show that mobility through San Antonio for poor and working class Latinxs is limited especially for people in these communities who rely on public transit. This experience with these public transit systems often renders them as individuals who do not belong in certain neighborhoods, and ultimately reinforces the longstanding histories of race and class segregation in San Antonio.

Topic modeling is a crucial unsupervised machine learning technique for identifying themes within unstructured text. This study compares traditional topic modeling methods, like Latent Dirichlet Allocation (LDA), against advanced embedding-based models, specifically BERTopic-OpenAI. The analysis utilizes two distinct datasets: user reviews from the mental health app Replika and the 20newsgroup dataset. For the Replika dataset, both methods identified common themes, but BERTopic-OpenAI uncovered additional nuanced topics, demonstrating its enhanced semantic capabilities. Quantitative evaluation of the 20newsgroup dataset further highlighted BERTopic-OpenAI's advantage through achieving higher topic coherence and diversity than the best-performing LDA model. These results suggest that embedding-based models provide more coherent, interpretable, and diverse topics, making them valuable tools for extracting meaningful insights from extensive and variable-length text corpora. Future research should focus on refining these advanced techniques to improve their applicability and effectiveness in dynamic and varied textual environments.

The healthcare sector anticipates substantial growth, with 125,000 job openings by 2026, including 69,000 middle-skilled positions. Despite this growth, data use and integration inefficiencies cost the sector $750 billion annually. Health informatics, an interdisciplinary field blending healthcare, computer, information, and cognitive sciences, can address these challenges through enhanced healthcare management via information technology. However, there is a critical disparity between competencies taught in educational programs and those demanded by the job market. This study examines competencies from job postings on Indeed.com and accredited health informatics programs, comparing them with the Health Information Technology Competencies (HITComp) database. Utilizing advanced text-embedding models, the study found that while educational programs focus on foundational competencies, the job market demands practical applications. Forty-six specific competencies, particularly in administrative, direct patient care, informatics engineering, and research domains, are identified as gaps. Addressing these gaps is essential to prepare the workforce for the evolving healthcare industry.

Citizens in large cities utilize public transportation as an alternative to self-driving for several reasons, such as avoiding traffic congestion and parking costs and utilizing their time for other things (e.g. reading a book or responding to emails). While large cities provide public transportation as a service to their citizens, they need to consider optimizing their budget and ensuring that public transportation is available and reliable. Using our case study, the public bus transit system in the city of San Antonio, Texas, in this paper, we used predictive analytics models to evaluate the performance of public bus transportation. We used time point stops as the target variable in order to evaluate their impact on the overall performance of the system. We also evaluated methods for the detection of potential bus-time savings and reported several examples of possible savings.

Background: Kratom is a substance that alters one’s mental state and is used for pain relief, mood enhancement, and opioid withdrawal, despite potential health risks. In this study, we aim to analyze the social media discourse about kratom to provide more insights about kratom’s benefits and adverse effects. Also, we aim to demonstrate how algorithmic machine learning approaches, qualitative methods, and data visualization techniques can complement each other to discern diverse reactions to kratom’s effects, thereby complementing traditional quantitative and qualitative methods. Methods: Social media data were analyzed using the latent Dirichlet allocation (LDA) algorithm, PyLDAVis, and t-distributed stochastic neighbor embedding (t-SNE) technique to identify kratom’s benefits and adverse effects. Results: The analysis showed that kratom aids in addiction recovery and managing opiate withdrawal, alleviates anxiety, depression, and chronic pain, enhances mood, energy, and overall mental well-being, and improves quality of life. Conversely, it may induce nausea, upset stomach, and constipation, elevate heart risks, affect respiratory function, and threaten liver health. Additional reported side effects include brain damage, weight loss, seizures, dry mouth, itchiness, and impacts on sexual function. Conclusion: This combined approach underscores its effectiveness in providing a comprehensive understanding of diverse reactions to kratom, complementing traditional research methodologies used to study kratom.

SmartSAT is a mobile-web application designed to enhance the efficiency and equity of San Antonio's public transit system by providing real-time bus arrival predictions, alerting for seat availability and important notifications, and collecting user data and feedback on ridership experience. The application is built using Django framework and deployed on Google Cloud Platform. Studies were conducted to analyse bus schedule adherence and rider needs to guide development and improve the commuter experience, especially for underserved communities. SmartSAT aims to leverage technology to deliver an inclusive, real-time transit service with implications for social equality, environmental impact, and overall transit service improvement.

SmartSAT is a mobile web application designed to enhance the efficiency and equitable access of San Antonio’s public transit services, providing real-time bus arrival predictions, notifying riders of seat availability, and gathering rider’s feedback. It aims to leverage technology to deliver an inclusive service with potential impacts for social equality, and enhancement of overall ridership experience. Two studies were conducted to access the impact of SmartSAT on the actual bus arrival times and rider’s communte experience. The findings of the arrival times analysis indicated that certain routes exhibited very slow average differences between their actual and schedule arrival times while a couple displayed a big average difference showing significant delayes and deviations from the schedules timetable. The rider experience study found that there is a differential in the feelings of access to the city’s public transit system held by poor, working-class, and Latinx communities in San Antonio. These findings suggest the need for regular minitoring and optimazation of the bus schedules to improve the effieiency and inclusive access to the current transportaiton system. The outcomes of the study primarily benefit San Antonio residents, especially for underserved communities, leading to an enhancement of its transit network infrastructure.

The wealth of information available through the Internet and social media is unprecedented. Within computing fields, websites such as Stack Overflow are considered important sources for users seeking solutions to their computing and programming issues. However, like other social media platforms, Stack Overflow contains a mixture of relevant and irrelevant information. In this paper, we evaluated neural network models to predict the quality of questions on Stack Overflow, as an example of Question Answering (QA) communities. Our results demonstrate the effectiveness of neural network models compared to baseline machine learning models, achieving an accuracy of 80%. Furthermore, our findings indicate that the number of layers in the neural network model can significantly impact its performance.

Background: Cognitive behavioral therapy (CBT)-based mobile apps have been shown to improve CBT-based interventions effectiveness. Despite the proliferation of these apps, user-centered guidelines pertaining to their design remain limited. The study aims to identify design features of CBT-based apps using online app reviews.Methods: We used 4- and 5-star reviews, preprocessed the reviews, and represented the reviews using word-level bigrams. Then, we leveraged latent Dirichlet allocation (LDA) and visualization techniques using python library for interactive topic model visualization to analyze the review and identify design features that contribute to the success and effectiveness of the app.Results: A total of 24,902 reviews were analyzed. LDA optimization resulted in 86 topics that were labeled by two independent researchers, with an interrater Cohen's kappa value of 0.86. The labeling and grouping process resulted in a total of six main design features for effective CBT-based mobile apps, namely, mental health management and support, credibility support, self-understanding and personality insights, therapeutic approaches and tools, beneficial rescue sessions, and personal growth and development.Conclusions: The high-level design features identified in this study could evidently serve as the backbone of successful CBT-based mobile apps for mental health.

Recent advancements in healthcare technologies, particularly wearable devices, have significantly enhanced the delivery and efficiency of healthcare. Wearable devices integration with mobile apps provides many functionalities to users including but not limited to vital sign monitoring and physical activity tracking. This chapter is a survey of current trends in wearable design with a particular focus on the impact on improved healthcare delivery. The chapter provides a foundation for the design features of wearable devices, focusing on users' experience, acceptance, adoption, and continuous use of such devices.

Online social networks (OSNs) are inundated with an enormous daily influx of news shared by users worldwide. Information can originate from any OSN user and quickly spread, making the task of fact-checking news both time-consuming and resource-intensive. To address this challenge, researchers are exploring machine learning techniques to automate fake news detection. This paper specifically focuses on detecting the stance of content producers—whether they support or oppose the subject of the content. Our study aims to develop and evaluate advanced text-mining models that leverage pre-trained language models enhanced with meta features derived from headlines and article bodies. We sought to determine whether incorporating the cosine distance feature could improve model prediction accuracy. After analyzing and assessing several previous competition entries, we identified three key tasks for achieving high accuracy: (1) a multi-stage approach that integrates classical and neural network classifiers, (2) the extraction of additional text-based meta features from headline and article body columns, and (3) the utilization of recent pre-trained embeddings and transformer models.

This paper used web scraping and data mining to analyze 831 health informatics job advertisements on indeed.com. Results showed that 87% of jobs explicitly required a college degree in a related field, 41% of jobs preferred a graduate degree, while 29% preferred or required professional certification. The analysis showed that preferred skills were analytics problem solving, communication skills, oral communication, interpersonal skills, project management, statistics, and critical thinking. The analysis also showed that college degrees, certifications, and the above-mentioned skill set are in high demand for working in the field of health informatics, especially in states with large populations and strong economies. Our results inform curriculum development of health informatics programs in higher education, which helps map knowledge units across the curricula to bridge the skills gap and meet employers’ expectations. At the same time, the results help job seekers familiarize themselves with what employers seek in a successful candidate.

Model optimization in deep learning (DL) and neural networks is concerned about how and why the model can be successfully trained towards one or more objective functions. The evolutionary learning or training process continuously considers the dynamic parameters of the model. Many researchers propose a deep learning-based solution by randomly selecting a single classifier model architecture. Such approaches generally overlook the hidden and complex nature of the model’s internal working, producing biased results. Larger and deeper NN models bring many complexities and logistic challenges while building and deploying them. To obtain high-quality performance results, an optimal model generally depends on the appropriate architectural settings, such as the number of hidden layers and the number of neurons at each layer. A challenging and time-consuming task is to select and test various combinations of these settings manually. This paper presents an extensive empirical analysis of various deep learning algorithms trained recursively using permutated settings to establish benchmarks and find an optimal model. The paper analyzed the Stack Overflow dataset to predict the quality of posted questions. The extensive empirical analysis revealed that some famous deep learning algorithms such as CNN are the least effective algorithm in solving this problem compared to multilayer perceptron (MLP), which provides efficient computing and the best results in terms of prediction accuracy. The analysis also shows that manipulating the number of neurons alone at each layer in a network does not influence model optimization. This paper’s findings will help to recognize the fact that future models should be built by considering a vast range of model architectural settings for an optimal solution.

Since the start of the coronavirus 2019 (COVID-19) outbreak, governments across the world have mobilized to inform citizens on the virus spread details, nation-level processes, and best health measures and practices to be taken. A large percentage of the media posted through the COVID-19 crisis has been graphical, which raised the question of whether Arabic-speaking blind and deaf persons were able to independently access reliable information. This article presents the results of two studies. The first study involves a content analysis of official social media posts about COVID-19 during critical phases of the outbreak via heuristic evaluation of WCAG2.1 on an iOS smartphone and an iPad. The second study explores the experiences of native Arabic-speaking blind and deaf persons on social media during the pandemic and curfew or lockdown periods in the State of Kuwait using a semi-structured interview (11 people who are blind/low vision and 7 people who are deaf). Overall, our findings highlight the accessibility gaps in the current government social media information content and its dissemination practices and barriers in providing information and services. Also, it gives insights into how people who are blind and people who are deaf are able to manage their lifestyle within and beyond the COVID-19 pandemic.

Lung cancer is a common type of cancer that causes death if not detected early enough. Doctors use computed tomography (CT) images to diagnose lung cancer. The accuracy of the diagnosis relies highly on the doctor's expertise. Recently, clinical decision support systems based on deep learning valuable recommendations to doctors in their diagnoses. In this paper, we present several deep learning models to detect non-small cell lung cancer in CT images and differentiate its main subtypes namely adenocarcinoma, large cell carcinoma, and squamous cell carcinoma. We adopted standard convolutional neural networks (CNN), visual geometry group-16 (VGG16), and VGG19. Besides, we introduce a variant of the CNN that is augmented with convolutional block attention modules (CBAM). CBAM aims to extract informative features by combining cross-channel and spatial information. We also propose variants of VGG16 and VGG19 that utilize a support vector machine (SVM) at the classification layer instead of SoftMax. We validated all models in this study through extensive experiments on a CT lung cancer dataset. Experimental results show that supplementing CNN with CBAM leads to consistent improvements over vanilla CNN. Results also show that the VGG variants that use the SVM classifier outperform the original VGGs by a significant margin.