Health Informatics Journal最新文献_第4页

Desirable design: What aesthetics are important to young people when designing a mental health app? 理想的设计：在设计心理健康应用程序时，哪些美感对年轻人来说很重要？

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-10-01 DOI: 10.1177/14604582241295948

Sandra Garrido, Barbara Doran, Eliza Oliver, Katherine Boydell

引用次数: 0

Evaluation of the reliability and readability of answers given by chatbots to frequently asked questions about endophthalmitis: A cross-sectional study on chatbots.

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-10-01 DOI: 10.1177/14604582241304679

Suleyman Demir

{"title":"Evaluation of the reliability and readability of answers given by chatbots to frequently asked questions about endophthalmitis: A cross-sectional study on chatbots.","authors":"Suleyman Demir","doi":"10.1177/14604582241304679","DOIUrl":"https://doi.org/10.1177/14604582241304679","url":null,"abstract":"Objective: This study aimed to investigate the accuracy, reliability, and readability of A-Eye Consult, ChatGPT-4.0, Google Gemini and Copilot AI large language models (LLMs) in responding to patient questions about endophthalmitis. Methods: The LLMs' responses to 25 questions about endophthalmitis, frequently asked by patients, were evaluated by two ophthalmologists using a five-point Likert scale, with scores ranging from 1-5. The DISCERN scale assessed the reliability of the LLMs' responses, whereas the Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL) indices assessed readability and text complexity, respectively. Results: A-Eye Consult and ChatGPT-4.0 outperformed Google Gemini and Copilot in providing comprehensive and precise responses. The Likert score significantly differed across all four LLMs (p < .001), with A-Eye Consult scoring significantly higher than Google Gemini and Copilot (p < .001). Conclusions: A-Eye Consult and ChatGPT-4.0 responses, while more complex than those of other LLMs, provided more reliable and accurate information.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241304679"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142755789","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}

引用次数: 0

Governance intricacies in implementing regional shared care records: A qualitative study in the national health service, England. 实施区域共享护理记录过程中的治理难题：英国国家医疗服务定性研究。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-10-01 DOI: 10.1177/14604582241290709

Andrey Elizondo

{"title":"Governance intricacies in implementing regional shared care records: A qualitative study in the national health service, England.","authors":"Andrey Elizondo","doi":"10.1177/14604582241290709","DOIUrl":"https://doi.org/10.1177/14604582241290709","url":null,"abstract":"Objectives: This study explores the governance intricacies in establishing a large-scale information infrastructure for integrated care within the National Health Service of England. By focusing on the initial 5 years of a regional interoperability program, we examine the challenges and dynamics of creating a unified system across organizational boundaries.Methods: A longitudinal multi-methods approach was utilized, collecting data through interviews with health and social care personnel involved in project governance, meeting observations, and document analysis. This provided an in-depth understanding of the processes and challenges of developing the regional interoperability program.Results: This study reveals a dynamic negotiation process and evolving governance structures that must continuously adapt to integrate diverse systems. Key findings include challenges in reconciling multiplicity and heterogeneity, the importance of social learning and stakeholder adaptation, and the role of ongoing negotiation and collaborative group construction in implementing interorganizational infrastructures.Conclusion: This study underscores the necessity of continuous adaptation and negotiation among stakeholders in establishing effective governance of integrated care infrastructures. Social learning and active participation play pivotal roles in overcoming the challenges associated with system integration. Strategic insights from this research can guide stakeholders in developing sociotechnical solutions for digital interorganizational infrastructures in integrated care.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241290709"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481345","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}

引用次数: 0

The direct effect of institutional factors on healthcare information systems (HIS) organisational interoperability in Malaysian public hospitals. 制度因素对马来西亚公立医院医疗信息系统（HIS）组织互操作性的直接影响。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-10-01 DOI: 10.1177/14604582241294218

Saravanan Rajagopal, Vimala Balakrishnan, Yin Kia Chiam

{"title":"The direct effect of institutional factors on healthcare information systems (HIS) organisational interoperability in Malaysian public hospitals.","authors":"Saravanan Rajagopal, Vimala Balakrishnan, Yin Kia Chiam","doi":"10.1177/14604582241294218","DOIUrl":"https://doi.org/10.1177/14604582241294218","url":null,"abstract":"Background: Organisational interoperability (OIoP) of the Healthcare Information System (HIS) is crucial for the success of HIS, however little is known about the impact of institutional factors. Objective: This cross-sectional study aimed to investigate the direct effect of institutional factors on OIoP for HIS in public sector hospitals in Malaysia. Methods: A conceptual OIoP framework was developed using the Personal Health Systems Interoperability and Refined eHealth European Interoperability frameworks. A self-administered questionnaire survey was used to solicit data from 300 healthcare professionals. Data were assessed through an Exploratory Factor Analysis followed by a Confirmatory Factor Analysis. Results: Structured equation modelling revealed Security and Privacy Compliance, and Stakeholder Engagement and Awareness to significantly and positively affect OIoP (R2 = 0.380). Conclusion: Healthcare organisations should prioritise clear and effective policies and regulations and enough budget and resources for the suggested framework.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241294218"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481347","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}

引用次数: 0

Analysis of health recommendations using longitudinal quality of life data: QoL@TbA - A transformer-based approach. 利用纵向生活质量数据分析健康建议：QoL@TbA - 基于转换器的方法。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-10-01 DOI: 10.1177/14604582241291789

Clauirton Siebra, Mascha Kurpicz-Briki, Katarzyna Wac

{"title":"Analysis of health recommendations using longitudinal quality of life data: QoL@TbA - A transformer-based approach.","authors":"Clauirton Siebra, Mascha Kurpicz-Briki, Katarzyna Wac","doi":"10.1177/14604582241291789","DOIUrl":"https://doi.org/10.1177/14604582241291789","url":null,"abstract":"Objective: Health recommendation systems suggest behavioral modifications to improve quality of life. However, current approaches do not facilitate the generation or examination of such recommendations considering the multifeature longitudinal evolution of behaviors. This paper proposes the use of a deep learning transformer-based model that allows the analysis of recommendations for behavior changes. Methods: We adapted a prediction approach, namely Behavior Sequence Transformer (BST), which analyzes temporal human routines and patterns, generating inductive outcomes. The evaluation relied on a case study that employed the behavioral history and profile of the English Longitudinal Study of Ageing (ELSA) participants (n = 2682), predicting their psychological mood (normal, pre-depressed, depressed) according to input recommendations for behavioral changes. Root mean squared error (RMSE) and learning curves were used to track the recommendation accuracy evolution and possible overfitting problems. Results: Experiments demonstrated lower RMSE values for the multifeature model (0.28/0.03) when compared to its single-feature versions (marital status, 0.59/0.001), (high pressure, 0.357/0.04), (diabetes, 0.36/0.01), (sleep quality, 0.57/0.02), (level of physical activity, 0.57/0.01). Conclusions: The results demonstrate the architecture's capability to analyze multifeatured longitudinal data, supporting the generation of suggestions for concurrent modifications across multiple input features. Moreover, these suggestions align with findings in specialized literature.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241291789"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395430","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}

引用次数: 0

Analysis of the content of YouTube videos discussing acne, psoriasis, and anti-aging skincare in Arabic. 分析 YouTube 上用阿拉伯语讨论痤疮、牛皮癣和抗衰老护肤品的视频内容。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-10-01 DOI: 10.1177/14604582241310427

Karim Rizk, Maria Farhat, Boutros Soutou

引用次数: 0

Reducing bias in healthcare artificial intelligence: A white paper. 减少医疗人工智能中的偏见：白皮书。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-10-01 DOI: 10.1177/14604582241291410

Carolyn Sun, Shannon L Harris

引用次数: 0

Six years of Project ECHO: Implementation and evaluation of a hospice-led multiprofessional education programme. ECHO 项目六年：临终关怀主导的多专业教育计划的实施与评估。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-10-01 DOI: 10.1177/14604582241290719

Phil Joddrell, Jane Manson, Sam Kyeremateng, Gerlinde Pilkington, Steven Ariss, Kinga Lowrie, Laura McTague, Paul M Taylor

引用次数: 0

Smart variant filtering - A blueprint solution for massively parallel sequencing-based variant analysis. 智能变异过滤--基于大规模并行测序的变异分析蓝图解决方案。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-10-01 DOI: 10.1177/14604582241290725

Orlinda Brahimllari, Sandra Eloranta, Patrik Georgii-Hemming, Zahra Haider, Sabine Koch, Aleksandra Krstic, Frantzeska Papadopoulou Skarp, Richard Rosenquist, Karin E Smedby, Fulya Taylan, Birna Thorvaldsdottir, Valtteri Wirta, Tove Wästerlid, Magnus Boman

{"title":"Smart variant filtering - A blueprint solution for massively parallel sequencing-based variant analysis.","authors":"Orlinda Brahimllari, Sandra Eloranta, Patrik Georgii-Hemming, Zahra Haider, Sabine Koch, Aleksandra Krstic, Frantzeska Papadopoulou Skarp, Richard Rosenquist, Karin E Smedby, Fulya Taylan, Birna Thorvaldsdottir, Valtteri Wirta, Tove Wästerlid, Magnus Boman","doi":"10.1177/14604582241290725","DOIUrl":"https://doi.org/10.1177/14604582241290725","url":null,"abstract":"Massively parallel sequencing helps create new knowledge on genes, variants and their association with disease phenotype. This important technological advancement simultaneously makes clinical decision making, using genomic information for cancer patients, more complex. Currently, identifying actionable pathogenic variants with diagnostic, prognostic, or predictive impact requires substantial manual effort. Objective: The purpose is to design a solution for clinical diagnostics of lymphoma, specifically for systematic variant filtering and interpretation. Methods: A scoping review and demonstrations from specialists serve as a basis for a blueprint of a solution for massively parallel sequencing-based genetic diagnostics. Results: The solution uses machine learning methods to facilitate decision making in the diagnostic process. A validation round of interviews with specialists consolidated the blueprint and anchored it across all relevant expert disciplines. The scoping review identified four components of variant filtering solutions: algorithms and Artificial Intelligence (AI) applications, software, bioinformatics pipelines and variant filtering strategies. The blueprint describes the input, the AI model and the interface for dynamic browsing. Conclusion: An AI-augmented system is designed for predicting pathogenic variants. While such a system can be used to classify identified variants, diagnosticians should still evaluate the classification's accuracy, make corrections when necessary, and ultimately decide which variants are truly pathogenic.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241290725"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407224","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}

引用次数: 0

A scoping review of the drivers and barriers influencing healthcare professionals' behavioral intentions to comply with electronic health record data privacy policy. 对影响医疗保健专业人员遵守电子健康记录数据隐私政策的行为意向的驱动因素和障碍进行范围审查。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-10-01 DOI: 10.1177/14604582241296398

Nabil D Alhassani, Richard Windle, Stathis Th Konstantinidis

{"title":"A scoping review of the drivers and barriers influencing healthcare professionals' behavioral intentions to comply with electronic health record data privacy policy.","authors":"Nabil D Alhassani, Richard Windle, Stathis Th Konstantinidis","doi":"10.1177/14604582241296398","DOIUrl":"10.1177/14604582241296398","url":null,"abstract":"Objective: Electronic Health Records (EHRs) are now an integral part of health systems in middle and high-income countries despite recognized deficits in the digital competencies of Healthcare Professionals (HCPs). Therefore, we undertook a scoping review of factors influencing compliance with EHR data privacy policies. Methods: Seven databases revealed 27 relevant studies, covering a range of countries, professional groups, and research methods. The diverse nature of these factors meant that 18 separate theoretical frameworks representing technology-acceptance to behavioral psychology were used to interpret these. Results: The predominant factors influencing compliance with EHR data privacy policies included confidence and competence to comply, perceived ease of use, facilitatory environmental factors, perceived usefulness, fear that non-compliance would be detected and/or punished and the expectations of others. Conclusion: Human factors such as attitudes, social pressure, confidence, and perceived usefulness are as important as technical factors and must be addressed to improve compliance.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 4","pages":"14604582241296398"},"PeriodicalIF":2.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481343","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}

引用次数: 0