Health Informatics Journal最新文献

{"title":"Barriers to mobile personal health assistant in patients living with diabetes.","authors":"Mei-Chen Kuo, Chiou-Fang Liou, Jyh-Horng Lin, Ching-Feng Huang, Li-Chueh Weng","doi":"10.1177/14604582241291522","DOIUrl":"https://doi.org/10.1177/14604582241291522","url":null,"abstract":"<p><strong>Objectives: </strong>Continued use of a digital health assistant that helps patients living with diabetes to self-manage and deal with complex problems in order to enhance their health status is a healthcare priority. The objective was to explore the barriers related to the use of a mobile personal health assistant for patients with type 2 diabetes.</p><p><strong>Methods: </strong>Eighty-one participants were offered a personal health assistant through a smartphone application. They completed a questionnaire after initial training (T₀) and after 1 month's experience (T₁).</p><p><strong>Results and conclusion: </strong>Most had a positive behavioral intention before using it, but the opposite was found after 1 month. There were positive correlations between behavioral intention and the eight related factors. The strongest correlations were with satisfaction and perceived usefulness at T₀ and T₁, respectively. The factors' mean values decreased after 1 month. The best predictors of behavioral intention were satisfaction and performance expectancy at T₀ and T₁, respectively, which predicted the status of 88.4% and 82.7% of the sample. Our findings will help health experts to build better tools that satisfy patients and meet their expectations.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241291522"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A web-based platform for studying the impact of artificial intelligence in video capsule endoscopy.","authors":"Georgios Apostolidis, Antigoni Kakouri, Ioannis Dimaridis, Eleni Vasileiou, Ioannis Gerasimou, Vasileios Charisis, Stelios Hadjidimitriou, Nikolaos Lazaridis, Georgios Germanidis, Leontios Hadjileontiadis","doi":"10.1177/14604582241296072","DOIUrl":"https://doi.org/10.1177/14604582241296072","url":null,"abstract":"<p><p><b>Objective:</b> Integrating artificial intelligence (AI) solutions into clinical practice, particularly in the field of video capsule endoscopy (VCE), necessitates the execution of rigorous clinical studies. <b>Methods:</b> This work introduces a novel software platform tailored to facilitate the conduct of multi-reader multi-case clinical studies in VCE. The platform, developed as a web application, prioritizes remote accessibility to accommodate multi-center studies. Notably, considerable attention was devoted to user interface and user experience design elements to ensure a seamless and engaging interface. To evaluate the usability of the platform, a pilot study is conducted. <b>Results:</b> The results indicate a high level of usability and acceptance among users, providing valuable insights into the expectations and preferences of gastroenterologists navigating AI-driven VCE solutions. <b>Conclusion:</b> This research lays a foundation for future advancements in AI integration within clinical VCE practice.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241296072"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The development of an augmented reality application for exercise prescription within paediatric oncology: App design and protocol of a pilot study.","authors":"Kim Straun, Hayley Marriott, Alba Solera-Sanchez, Stan Windsor, Marie A Neu, Elias Dreismickenbecker, Joerg Faber, Peter Wright","doi":"10.1177/14604582241288784","DOIUrl":"https://doi.org/10.1177/14604582241288784","url":null,"abstract":"<p><p><b>Background:</b> Children and young people with cancer face barriers when engaging with exercise, such as treatment-related side effects, psychosocial burdens and lack of individualised provisions. Digital health tools, such as smartphone applications, have emerged as a promising driver to support healthcare provisions in exercise prescription among patients. It is vital to explore how such technologies can be developed more effectively in order to strengthen the evidence supporting their use and for more appropriate implementation within healthcare. This study aims to explore user experiences, preferences and suggested improvements from healthy children and young people aged 9-21 years. <b>Methods:</b> An augmented reality (AR) application was specifically developed for children and young people aged 9-21 years undergoing cancer treatment and a protocol for a pilot study was designed. The target sample of this pilot study is 90 healthy children and young people aged 9-21 years. Practical 30-min workshops will be conducted encouraging participants to engage with the smartphone app. Focus groups will explore participant experiences, preferences, and suggested improvements. Data will be analysed deductively with apriori themes derived from the semi-structured interviews. <b>Discussion:</b> Obtaining user experiences, preferences and suggested improvements is especially important for the development of novel apps, such as those prescribing exercise and using algorithms and augmented reality software. Results from this study will directly influence the development of an augmented reality application, which will also be applied within a long-term trial in paediatric oncology.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241288784"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating data-driven and knowledge-driven approaches to analyze clinical notes with structured data for sarcopenia detection.","authors":"Xiao Luo, Haoran Ding, Stuart J Warden, Ranjani N Moorthi, Erik A Imel","doi":"10.1177/14604582241300025","DOIUrl":"10.1177/14604582241300025","url":null,"abstract":"<p><p><b>Background:</b> Patients with sarcopenia often go undetected in busy clinical practices since the muscle measurements are not easily incorporated into routine clinical practice. The current research fills the gap by utilizing unstructured clinical notes combined with structured data from electronic health records (EHR), to increase sarcopenia detection. <b>Methods:</b> We developed and evaluated four approaches to first extract clinical note features, then integrate with structured data for sarcopenia detection models. Case studies were used to demonstrate the interpretation of the results and show the important association between predictors and outcomes. <b>Results:</b> Out of 1304 participants, 1055 were controls, 249 met at least one criterion for Sarcopenia. The best performing model which incorporated both data-driven and knowledge-driven approaches to integrate clinical note features demonstrated a higher mean area under the curve (AUC = 73.93%, (95% CI, 73.83-74.02)) compared to the baseline model (AUC 71.59%, (95%CI, 71.56-71.61)). The case study shows that the important clinical note predictors may contribute to detection of sarcopenia such as \"cane\", \"walker\", \"unsteady\", etc. <b>Conclusions:</b> Incorporating clinical note features in sarcopenia detection models can identify a greater number of patients at risk for sarcopenia, potentially leading to targeted muscle testing assessments and corresponding treatments to address sarcopenia.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241300025"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142752133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Language disparities in pandemic information: Autocomplete analysis of COVID-19 searches in New York.","authors":"Vivek K Singh, Pamela Valera, Ishaan Singh, Ritesh Sawant, Yisel Breton","doi":"10.1177/14604582241307836","DOIUrl":"https://doi.org/10.1177/14604582241307836","url":null,"abstract":"<p><p><b>Objective:</b> To audit and compare search autocomplete results in Spanish and English during the early COVID-19 pandemic in the New York metropolitan area. The pandemic led to significant online search activity about the disease, its spread, and remedies. As gatekeepers, search engines like Google can influence public opinion. Autocomplete predictions help users complete searches faster but may also shape their views. Understanding these differences is crucial to identify biases and ensure equitable information dissemination. <b>Methods:</b> The study tracked autocomplete results daily for five COVID-19 related search terms in English and Spanish over 100+ days in 2020, yielding a total of 9164 autocomplete predictions. <b>Results:</b> Queries in Spanish yielded fewer autocomplete options and often included more negative content than English autocompletes. The topical coverage differed, with Spanish autocompletes including themes related to religion and spirituality that were absent in the English search autocompletes. <b>Conclusion:</b> The contrast in search autocomplete results could lead to divergent impressions about the pandemic and remedial actions among different sections of society. Continuous auditing of autocompletes by public health stakeholders and search engine organizations is recommended to reduce potential bias and misinformation.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241307836"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142814933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Big data analytics in the healthcare sector: Opportunities and challenges in developing countries. A literature review.","authors":"David Muhunzi, Lucy Kitambala, Harold L Mashauri","doi":"10.1177/14604582241294217","DOIUrl":"https://doi.org/10.1177/14604582241294217","url":null,"abstract":"<p><p><b>Background:</b> Despite the ongoing efforts to digitalize the healthcare sector in developing countries, the full adoption of big data analytics in healthcare settings is yet to be attained Exploring opportunities and challenges encountered is essential for designing and implementing effective interventional strategies. <b>Objective:</b> Exploring opportunities and challenges towards integrating big data analytics technologies in the healthcare industry in developing countries. <b>Methodology:</b> This was a narrative review study design. A literature search on different databases was conducted including PubMed, ScienceDirect, MEDLINE, Scopus, and Google Scholar. Articles with predetermined keywords and written in English were included. <b>Results:</b> Big data analytics finds its application in population health management and clinical decision-support systems even in developing countries. The major challenges towards the integration of big data analytics in the healthcare sector in developing countries include fragmentation of healthcare data and lack of interoperability, data security, privacy and confidentiality concerns, limited resources and inadequate regulatory and policy frameworks for governing big data analytics technologies and limited reliable power and internet infrastructures. <b>Conclusion:</b> Digitalization of healthcare delivery in developing countries faces several significant challenges. However, the integration of big data analytics can potentially open new avenues for enhancing healthcare delivery with cost-effective benefits.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241294217"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Architecture designing of digital twin in a healthcare unit.","authors":"Piya Noeikham, Dollaya Buakum, Nikorn Sirivongpaisal","doi":"10.1177/14604582241296792","DOIUrl":"https://doi.org/10.1177/14604582241296792","url":null,"abstract":"<p><p><b>Objectives:</b> This study proposes a novel architecture for designing digital twins in healthcare units. <b>Methods:</b> A systematic research methodology was employed to develop architecture design patterns. In particular, a systematic literature review was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to answer specific research questions and provide guidelines for designing the architecture. Subsequently, a case study was designed and analyzed at a chemotherapy treatment center for outpatients. <b>Results:</b> System architecture knowledge was distilled from this real-world case study, supplemented by existing software and systems design patterns. A novel five-layer architecture for digital twins in healthcare units was proposed with a focus on the security and privacy of patients' information. <b>Conclusion:</b> The proposed digital twin architecture for healthcare units offers a comprehensive solution that provides modularity, scalability, security, and interoperability. The architecture provides a robust framework for effectively and efficiently managing healthcare environments.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241296792"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Public mobile chronic obstructive pulmonary disease applications for self-management: Patients and healthcare professionals' perspectives.","authors":"Shirley Quach, Adam Benoit, Tara L Packham, Roger Goldstein, Dina Brooks","doi":"10.1177/14604582241292206","DOIUrl":"https://doi.org/10.1177/14604582241292206","url":null,"abstract":"<p><p>Poorly controlled chronic obstructive pulmonary disease (COPD) can negatively impact quality of life but mobile applications (apps) are popular digital tools that may mitigate these support needs. However, it is unclear if public mobile COPD apps are acceptable to healthcare professionals and patients, people living with COPD. <b>Objectives:</b> The primary objective is to determine people with COPD and healthcare professionals' perspectives on the appropriateness of public mobile COPD apps for supporting individuals' needs. The secondary objectives were to identify the ideal features and styles of mobile COPD apps for COPD self-management; and to identify the facilitators, barriers and needs for future COPD app research and development. <b>Methods:</b> Public mobile COPD apps were rated by questionnaires administered before and after focus group meetings. Ratings were reported as medians with interquartile ranges and median scores were categorized into three levels of appropriateness: 1-3 for inappropriate; 4-6 for uncertain; and 7-9 for appropriate. <b>Results:</b> A total of 6 people with COPD (mean age 68.2 ± 4.8years) and 22 healthcare professionals (mean age 45 ± 8.3years) completed this study. People with COPD identified one and healthcare professionals identified three public mobile COPD apps to be appropriate. They had different preferences for features and engagement styles but similar preferences for facilitators and barriers to use. Stakeholders mutually rated one public mobile COPD app as appropriate for self-management and emphasized the need for apps to be supplementary and customizable, rather than replacements for clinical management.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241292206"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do we need a high level of detail in health information animations? An experimental study investigating the association between level of detail and information recall.","authors":"Pernille Blom Pedersen, Mette T Høybye, Line Borreskov Dahl, Cecilie Rud Budtz","doi":"10.1177/14604582241274525","DOIUrl":"https://doi.org/10.1177/14604582241274525","url":null,"abstract":"<p><p><b>Objective:</b> This study examined the potential of simple animations with a low level of detail and their impact on patient's ability to recall information. Also, we examined how the patients' digital health literacy influenced the association.<b>Methods:</b> Over 900 Danish adults were continuously included in this experimental study, and they were allocated to either an animation with a low or high level of detail. Participants answered questionnaires about demographics, digital health literacy, and the ability to recall information. The association between level of detail and information recall was examined by OR (95% CI).<b>Results:</b> The results showed no association between the level of detail and information recall.<b>Conclusion:</b> This novel study supports the potential of simple animations, and future research could advantageously investigate animations with more significant differences in level of detail. The results should be cautiously interpreted, as selection and information problems may have caused bias.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241274525"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal representation learning for medical analytics - a systematic literature review.","authors":"Emil Riis Hansen, Tomer Sagi, Katja Hose","doi":"10.1177/14604582241290474","DOIUrl":"https://doi.org/10.1177/14604582241290474","url":null,"abstract":"<p><p><b>Objectives:</b> Machine learning-based analytics over uni-modal medical data has shown considerable promise and is now routinely deployed in diagnostic procedures. However, patient data consists of diverse types of data. By exploiting such data, multimodal approaches promise to revolutionize our ability to provide personalized care. Attempts to combine two modalities in a single diagnostic task have utilized the evolving field of multimodal representation learning (MRL), which learns a shared latent space between related modality samples. This new space can be used to improve the performance of machine-learning-based analytics. So far, however, our understanding of how modalities have been applied in MRL-based medical applications and which modalities are best suited for specific medical tasks is still unclear, as previous reviews have not addressed the medical analytics domain and its unique challenges and opportunities. Instead, this work aims to review the landscape of MRL for medical tasks to highlight opportunities for advancing medical applications. <b>Methods:</b> This paper presents a framework for positioning MRL techniques and medical modalities. More than 1000 papers related to medical analytics were reviewed, positioned, and classified using the proposed framework in the most extensive review to date. The paper further provides an online tool for researchers and developers of medical analytics to dive into the rapidly changing landscape of MRL for medical applications. <b>Results:</b> The main finding is that work in the domain has been sparse: only a few medical informatics tasks have been the target of much MRL-based work, with the overwhelming majority of tasks being diagnostic rather than prognostic. Similarly, numerous potentially compatible information modality combinations are unexplored or under-explored for most medical tasks. <b>Conclusions:</b> There is much to gain from using MRL in many unexplored combinations of medical tasks and modalities. This work can guide researchers working on a specific medical application to identify under-explored modality combinations and identify novel and emerging MRL techniques that can be adapted to the task at hand.</p>","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241290474"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142607507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}