Health Informatics Journal最新文献

Background: Digital technologies are essential for connecting to the Internet and communicating with others. Methods: This study used data from the 2022 Project Community Homelessness Assessment, Local Education and Networking Groups (CHALENG) survey, which surveyed a national sample of 1992 homeless-experienced veterans (HEV) about use of cell phones, smart phones, computers, laptops, and the Internet. Data were analyzed to compare currently and formerly homeless veterans on digital access and to identify characteristics associated with never using the Internet. Results: Over 75% of HEV reported having a cellphone and over 65% reported having a smartphone. Internet use was common among HEV as 74% of currently homeless veterans and 77% of formerly homeless veterans reported using the Internet. Among HEV who used the Internet, over 70% used the Internet at least weekly. There was no significant difference between currently and formerly homeless veterans on their Internet use in the total sample and subsamples of only black and only Hispanic veterans. Overall, HEV who were older, black non-Hispanic, and living in the Southeast were more likely to never use the Internet. Conclusions: These findings highlight the potential for technology-based interventions among HEV, and suggest a digital divide based on age, race, and geography.

Background: Skeletal malocclusion is common among populations. Its severity often increases during adolescence, yet it is frequently overlooked. The introduction of deep learning in stomatology has opened a new avenue for self-health management. Methods: In this study, networks were trained using lateral photographs of 2109 newly diagnosed patients. The performance of the models was thoroughly evaluated using various metrics, such as sensitivity, specificity, accuracy, confusion matrix analysis, the receiver operating characteristic curve, and the area under the curve value. Heat maps were generated to further interpret the models' decisions. A comparative analysis was performed to assess the proposed models against the expert judgment of orthodontic specialists. Results: The modified models reached an impressive average accuracy of 84.50% (78.73%-88.87%), with both sex and developmental stage information contributing to the AI system's enhanced performance. The heat maps effectively highlighted the distinct characteristics of skeletal class II and III malocclusion in specific regions. In contrast, the specialist achieved a mean accuracy of 71.89% (65.25%-77.64%). Conclusions: Deep learning appears to be a promising tool for assisting in the screening of skeletal malocclusion. It provides valuable insights for expanding the use of AI in self-monitoring and early detection within a family environment.

Objective: This study aimed to assess the quality of the information in WeChat and TikTok videos related to hypertension and diabetes treatment. Methods: A sample of 120 Chinese videos was collected based on specific inclusion and exclusion criteria. The quality was evaluated using DISCERN, JAMA and the latest edition of Chinese guidelines for hypertension and diabetes prevention and treatment, and two observers independently scored each video using the three assessment tools. Results: Among all 120 videos, only 10 scored above 38 points in DISCERN, with 45 videos rated as "very poor". None of the video met all JAMA criteria simultaneously, and there were gaps in accuracy and completeness compared to the two guidelines. Furthermore, there was no significant correlation between information quality and the number of likes and comments. Conclusion: The current quality of information on the treatment of hypertension and diabetes on WeChat and TikTok was unsatisfactory. Consequently, the government should strengthen oversight of information quality, and social media platforms should actively review health-related content to prevent inaccurate information dissemination. Individuals should enhance their digital and health literacy.

Background: Patient-targeted Googling (PTG) is the use of Internet search engines by care professionals to source information about their patients. Objective: To thematically analyse research evidence on PTG and explain what, why and how it can be used for the benefit of patient care. Methods: The Scale for the Assessment of Narrative Review articles was used as a reporting tool. Studies were identified via AMED, CINAHL, MEDLINE and APA PsycInfo, ProQuest, and grey literature via Google Scholar. Results: 19 studies were included, and content was thematically analysed. Themes included practitioner behaviours, attitudes and experience, the nature of online information, when PTG is not acceptable, when, why and how is PTG acceptable and the need for education and training on PTG. Discussion & conclusion: In the absence of professional guidance, it makes practical recommendations about why and in what circumstances can use patient-targeted Googling for the benefit of patient care.

Objective: This article aims to describe the implementation of a new health information technology system called Health Connect that is harmonizing cancer data in the Canadian province of Newfoundland and Labrador; explain high-level technical details of this technology; provide concrete examples of how this technology is helping to improve cancer care in the province, and to discuss its future expansion and implications. Methods: We give a technical description of the Health Connect architecture, how it integrated numerous data sources into a single, scalable health information system for cancer data and highlight its artificial intelligence and analytics capacity. Results: We illustrated two practical achievements of Health Connect. First, an analytical dashboard that was used to pinpoint variations in colon cancer screening uptake in small defined geographic regions of the province; and second, a natural language processing algorithm that provided AI-assisted decision support in interpreting appropriate follow-up action based on assessments of breast mammography reports. Conclusion: Health Connect is a cutting-edge, health systems solution for harmonizing cancer screening data for practical decision-making. The long term goal is to integrate all cancer care data holdings into Health Connect to build a comprehensive health information system for cancer care in the province.

Objective: This study aimed to evaluate the current situation of Chinese mobile apps for hypertension management and explore patients' real requirements for app use, providing a theoretical basis for the future improvement of hypertension apps.

Methods: We reviewed hypertension management apps from mobile app platforms, and summarized their functional characteristics. In addition, we conducted an online survey among 1000 hypertensive patients, collected valid responses, and analyzed the feedback data.

Results: Forty hypertension management apps were analyzed, with 72.5% offering no more than six functions, indicating limited coverage of advanced and comprehensive functionalities. Among the 934 valid survey responses, patients emphasized four main functions in apps for hypertension management: long-term dynamic blood pressure monitoring, scientific lifestyle management, strict medication management and systematic health knowledge delivering.

Conclusion: The existing hypertension management apps mainly serve as "Digital Health" tools with unclear clinical efficacy. The future development of these apps lies in how they transition to "Digital Therapeutics" solutions to better meet patients' needs and provide clear clinical advantages.

Keeping track of data semantics and data changes in the databases is essential to support retrospective studies and the reproducibility of longitudinal clinical analysis by preventing false conclusions from being drawn from outdated data. A knowledge model combined with a temporal model plays an essential role in organizing the data and improving query expressiveness across time and multiple institutions. This paper presents a modelling framework for temporal relational databases using an ontology to derive a shareable and interoperable data model. The framework is based on: OntoRela an ontology-driven database modelling approach and Unified Historicization Framework a temporal database modelling approach. The method was applied to hospital organizational structures to show the impact of tracking organizational changes on data quality assessment, healthcare activities and data access rights. The paper demonstrated the usefulness of an ontology to provide a formal, interoperable, and reusable definition of entities and their relationships, as well as the adequacy of the temporal database to store, trace, and query data over time.

Background: Although exercise benefits female cancer survivors, clinical decision-making regarding timing, frequency, duration, and intensity is lacking. Optimizing exercise interventions in this population is necessary. This study aimed to describe existing digital home-based exercises and to assess their effectiveness at improving physical health in female cancer survivors upon completion of therapy.

Design: We conducted a systematic review using articles from Web of Science, Embase and Medline (Ovid). We included intervention studies examining the effects of digital home-based exercise programs on post-treatment recovery in female cancer survivors. Rob2 and ROBIN I were used to assess quality of studies. Quality-of-life, fatigue score, and physical performance were assessed using meta-analysis.

Results: This study involved 1578 female cancer survivors in 21 interventions. Following guidelines and supervised exercise with coaches led to better outcomes than interventions without guidelines, programs without coaches, or lower intensity exercise. Exercise led to significant improvement in some physical performance outcomes. Significant improvements were seen in physical performance outcomes, including the 6-min walk test, metabolic equivalent task, and number of steps per day.

Conclusion: Providing cancer survivors with standard guidelines for home-based, coach-supervised, vigorous exercise on digital platforms could improve their physical function, health, and quality-of-life.

Objectives: This systematic review and meta-analysis aimed to investigate the effect of fall prevention interventions using information and communication technology (ICT). Methods: A comprehensive search across four databases was performed. The inclusion criteria were fall prevention interventions including telehealth, computerized balance training, exergaming, mobile application education, virtual reality exercise, and cognitive-behavioral training for community-dwelling adults aged ≥60 years. Results: Thirty-four studies were selected. Telehealth, smart home systems, and exergames reduced the risk of falls (RR = 0.63, 95% CI [0.54, 0.75]). Telehealth and exergame improved balance (MD = 3.30, 95% CI [1.91, 4.68]; MD = 4.40, 95% CI [3.09, 5.71]). Telehealth improved physical function (SMD = 0.69, 95% CI [0.23, 1.16]). Overall, ICT fall interventions improved fall efficacy but not cognitive function. For quality of life (QOL), mixed results were found depending on the assessment tools. Conclusion: Future investigations on telehealth, smart home systems, or exergames are needed to motivate older adults to exercise and prevent falls.