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Consumer Informatics and One Health: Shifting the Focus from the Individual to the Globe. Findings from the Yearbook 2023 Section on Education and Consumer Health Informatics. 消费者信息学与 "同一健康":将重点从个人转向全球。2023 年年鉴》教育与消费者健康信息学部分的研究成果。
Pub Date : 2023-08-01 Epub Date: 2023-12-26 DOI: 10.1055/s-0043-1768749
Pascal Staccini, Annie Y S Lau

Objective: To summarise the state of the art during the year 2022 in consumer health informatics and education, with a special emphasis on "One Health".

Methods: We conducted a systematic search of articles published in PubMed. We build queries to merge terms related to "consumer health informatics", "one health", and "digital". We retrieved 94 potential articles for review. These articles were screened according to topic relevance and 12 were selected for consideration of best paper candidates, which were then presented to a panel of international experts for full paper review and scoring. The top five papers were discussed in a consensus meeting. Three papers received the highest score from the expert panel, and these papers were selected to be representative papers on consumer informatics for exploring one health from consumer perspective in the year 2022.

Results: Bibliometrics analysis conducted on words found in abstracts of the 12 candidate papers revealed four clusters of articles, where clustering outcomes explained 96.91% of the dispersion. The first cluster composes three papers related to patient engagement in primary care practices, using digital-delivered diabetes prevention programmes, or exploring citizen involvement in co-designing environmental projects (such as air pollution exposure and health). The second cluster represents four papers related to digital health literacy and consumer behavior, such as digital vaccine literacy, and food labelling influences and whether displaying Nutri- and Eco-Score at food product level led to improved consumer choices. The third cluster consists of two papers exploring strategies to involve citizens in various science projects while analyzing the quality of citizen-collected data (e.g., mosquito bites or gastropod community dataset). The last cluster contains three papers related to the relationships between human behavior with their environment and their contribution to citizen science projects (e.g., biological water quality in the Netherlands distribution, composition, abundance of debris across sandy beaches in Australia and its regions, urbanization and reptile biodiversity across Florida).

Conclusion: Traditionally, consumer health informatics focuses on providing individuals with tools and resources to actively manage their own health. By incorporating a global health (or one health) perspective, our field is now at a crossroad, demanding us to think beyond the individual and challenging us to instill the thinking that our actions not only have consequences on the individual but also on the population and the environment. Perhaps this is also a reflective time for the consumer informatics field, to consider shifting the focus from the individual to one that is more aligned with one health, helping consumers gain awareness of how their actions impact on the individual, the population and the enviro

目的总结 2022 年消费者健康信息学和教育的最新进展,特别强调 "一体健康":我们对 PubMed 上发表的文章进行了系统检索。我们将 "消费者健康信息学"、"一体健康 "和 "数字 "相关的术语进行合并查询。我们检索到了 94 篇潜在的综述文章。根据主题相关性对这些文章进行筛选,选出 12 篇最佳论文候选文章,然后提交给国际专家小组进行全面论文审查和评分。前五篇论文在共识会议上进行了讨论。三篇论文获得了专家小组的最高分,这些论文被选为消费者信息学方面的代表性论文,用于在 2022 年从消费者角度探索一种健康:对 12 篇候选论文摘要中的词汇进行文献计量学分析,发现了四个论文群,聚类结果解释了 96.91% 的离散度。第一个群组由三篇论文组成,分别涉及初级保健实践中的患者参与、使用数字交付的糖尿病预防计划或探索公民参与共同设计环境项目(如空气污染暴露与健康)。第二组包括四篇与数字健康知识和消费者行为有关的论文,如数字疫苗知识、食品标签的影响以及在食品层面显示营养和生态分数是否会改善消费者的选择。第三组包括两篇论文,探讨让公民参与各种科学项目的策略,同时分析公民收集的数据(如蚊虫叮咬或腹足类动物群落数据集)的质量。最后一组包含三篇论文,内容涉及人类行为与环境之间的关系以及他们对公民科学项目的贡献(例如,荷兰生物水质的分布、组成,澳大利亚及其地区沙滩碎片的丰度,佛罗里达州的城市化和爬行动物生物多样性):传统上,消费者健康信息学侧重于为个人提供主动管理自身健康的工具和资源。通过纳入全球健康(或整体健康)视角,我们的领域现在正处于一个十字路口,要求我们的思维超越个人,并向我们灌输这样一种思想:我们的行为不仅会对个人产生影响,还会对人口和环境产生影响。或许这也是消费者信息学领域进行反思的时候,考虑将关注点从个人转移到更符合整体健康的角度,帮助消费者认识到他们的行为如何影响个人、群体和环境,并为他们提供工具,帮助他们集体决定他们的行为如何在这些层面上带来益处(以及危害)。
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引用次数: 0
Intersecting Pathways in Bioinformatics and Translational Informatics: A One Health Perspective on Key Contributions and Future Directions. 生物信息学和转化信息学的交叉途径:生物信息学和转化信息学的交叉途径:关于关键贡献和未来方向的单一健康视角》(One Health Perspective on Key Contributions and Future Directions)。
Pub Date : 2023-08-01 Epub Date: 2023-12-26 DOI: 10.1055/s-0043-1768745
Mary Lauren Benton, Scott McGrath

Objectives: To identify and summarize the top bioinformatics and translational informatics (BTI) papers published in 2022 for the International Medical Informatics Association (IMIA) Yearbook 2023.

Methods: We conducted a comprehensive literature search to identify the top BTI papers, resulting in a set of ten candidate papers. The candidates were reviewed by the section co-editors and external reviewers to select the top three papers from 2022.

Results: From a total of 558 papers, we identified a final candidate list of ten BTI papers for peer-review. These papers apply new statistical frameworks and experimental designs to better capture individual variability in disease and incorporate data that captures differences between single cells and across environmental exposures. In addition, they highlight the importance of model generalization across diverse cohorts and scalability to large medical centers.

Conclusions: We note several important trends in the candidate top BTI papers this year, including a continued focus on developing accurate and scalable computational models to predict disease risk across diverse cohorts and new strategies to capture the molecular heterogeneity of disease.

目的为《国际医学信息学协会(IMIA)2023年年鉴》确定并总结2022年发表的生物信息学和转化信息学(BTI)顶级论文:我们进行了一次全面的文献检索,以确定顶级生物信息学和转化信息学(BTI)论文,最终确定了十篇候选论文。这些候选论文由部门联合编辑和外部审稿人进行审查,以选出2022年的前三篇论文:从总共 558 篇论文中,我们确定了十篇 BTI 论文的最终候选名单,供同行评审。这些论文应用了新的统计框架和实验设计,以更好地捕捉疾病的个体差异,并纳入了捕捉单细胞之间和不同环境暴露之间差异的数据。此外,它们还强调了模型在不同队列中的通用性以及在大型医疗中心的可扩展性的重要性:我们注意到今年候选顶级 BTI 论文的几个重要趋势,包括继续关注开发准确、可扩展的计算模型来预测不同队列的疾病风险,以及捕捉疾病分子异质性的新策略。
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引用次数: 0
Contents IMIA Yearbook of Medical Informatics 2023 目录 IMIA《2023 年医学信息学年鉴
Pub Date : 2023-08-01 DOI: 10.1055/s-0043-1768763
J. Li, K. Fultz, F. Mougin, L. F. Soualmia, Survey K. Cooper, M. Clarke, J. B. Clayton, M. L. Benton, S. P. McGrath, Survey S. Aneja, A. Avesta, H. Xu, L. O. Machado, J. L. Warner, D. Patt
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引用次数: 0
Cover IMIA Yearbook of Medical Informatics 2023 2023 年 IMIA 医学信息学年鉴》封面
Pub Date : 2023-08-01 DOI: 10.1055/s-0043-1768736
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引用次数: 0
IMIA Member Societies *as at November 2023 IMIA 会员协会 *截至 2023 年 11 月
Pub Date : 2023-08-01 DOI: 10.1055/s-0043-1768761
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引用次数: 0
Machine and Deep Learning Dominate Recent Innovations in Sensors, Signals and Imaging Informatics. 机器学习和深度学习主导了传感器、信号和成像信息学领域的最新创新。
Pub Date : 2023-08-01 Epub Date: 2023-12-26 DOI: 10.1055/s-0043-1768743
Christian Baumgartner, Leticia Rittner, Thomas M Deserno

Objectives: This review presents research papers highlighting notable developments and trends in sensors, signals, and imaging informatics (SSII) in 2022.

Method: We performed a bibliographic search in PubMed combining Medical Subject Heading (MeSH) terms and keywords to create particular queries for sensors, signals, and imaging informatics. Only papers published in journals containing greater than three articles in the search query were considered. Using a three-point Likert scale (1 = not include, 2 = perhaps include, 3 = include), we reviewed the titles and abstracts of all database results. Only articles that scored three times Likert scale 3, or two times Likert scale 3, and one time Likert scale 2 were considered for full paper review. On this pre-selection, only papers with a total of at least eight points of the three section co-editors were considered for external review. Based on the external reviewers, we selected the top two papers representing significant research in SSII.

Results: Among the 469 returned papers published in 2022 in the various areas of SSII, 90, 31, and 348 papers for sensors, signals, and imaging informatics, and then, the full review process selected the two best papers. From the 469 papers, the section co-editors identified 29 candidate papers with at least 8 Likert points in total, of which 9 were nominated as the best contributions after a full paper assessment. Five external reviewers evaluated the nominated papers, and the two highest-scoring papers were selected based on the overall scores of all external reviewers. A consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board finally approved the nominated papers. Machine and deep learning-based techniques continue to be the dominant theme in this field.

Conclusions: Sensors, signals, and imaging informatics is a dynamic field of intensive research with increasing practical applications to support medical decision-making on a personalized basis.

目的:本综述介绍 2022 年传感器、信号和成像信息学(SSII)领域的重要发展和趋势:本综述介绍了 2022 年传感器、信号和成像信息学(SSII)领域的重要发展和趋势:我们在 PubMed 上进行了文献检索,结合医学主题词表(MeSH)和关键词创建了传感器、信号和成像信息学的特定查询。只有在搜索查询中包含三篇以上文章的期刊上发表的论文才会被考虑。我们采用三点 Likert 评分法(1 = 不包含,2 = 可能包含,3 = 包含)对所有数据库结果的标题和摘要进行了审查。只有获得三次李克特量表 3 分或两次李克特量表 3 分和一次李克特量表 2 分的文章才会被考虑进行全文审阅。在此预选的基础上,只有三个部分的共同编辑总分至少达到 8 分的论文才会被考虑进行外部评审。根据外审人员的意见,我们选出了代表 SSII 重要研究的前两篇论文:在2022年发表的SSII各领域的469篇退稿论文中,传感器、信号和成像信息学的论文分别为90篇、31篇和348篇,然后,通过完整的评审过程选出了两篇最佳论文。从这 469 篇论文中,该领域的联合编辑确定了 29 篇总分至少达到 8 分 Likert 分的候选论文,其中 9 篇经过完整的论文评估后被提名为最佳贡献。五位外部评审员对提名论文进行了评估,并根据所有外部评审员的综合评分选出了两篇得分最高的论文。国际医学信息学协会(IMIA)年鉴编辑委员会最终一致通过了提名论文。基于机器和深度学习的技术仍然是这一领域的主导主题:传感器、信号和成像信息学是一个充满活力的深入研究领域,在支持个性化医疗决策方面的实际应用越来越多。
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引用次数: 0
Security and Privacy in Machine Learning for Health Systems: Strategies and Challenges. 医疗系统机器学习中的安全与隐私:战略与挑战。
Pub Date : 2023-08-01 Epub Date: 2023-12-26 DOI: 10.1055/s-0043-1768731
Erikson J de Aguiar, Caetano Traina, Agma J M Traina

Objectives: Machine learning (ML) is a powerful asset to support physicians in decision-making procedures, providing timely answers. However, ML for health systems can suffer from security attacks and privacy violations. This paper investigates studies of security and privacy in ML for health.

Methods: We examine attacks, defenses, and privacy-preserving strategies, discussing their challenges. We conducted the following research protocol: starting a manual search, defining the search string, removing duplicated papers, filtering papers by title and abstract, then their full texts, and analyzing their contributions, including strategies and challenges. Finally, we collected and discussed 40 papers on attacks, defense, and privacy.

Results: Our findings identified the most employed strategies for each domain. We found trends in attacks, including universal adversarial perturbation (UAPs), generative adversarial network (GAN)-based attacks, and DeepFakes to generate malicious examples. Trends in defense are adversarial training, GAN-based strategies, and out-of-distribution (OOD) to identify and mitigate adversarial examples (AE). We found privacy-preserving strategies such as federated learning (FL), differential privacy, and combinations of strategies to enhance the FL. Challenges in privacy comprehend the development of attacks that bypass fine-tuning, defenses to calibrate models to improve their robustness, and privacy methods to enhance the FL strategy.

Conclusions: In conclusion, it is critical to explore security and privacy in ML for health, because it has grown risks and open vulnerabilities. Our study presents strategies and challenges to guide research to investigate issues about security and privacy in ML applied to health systems.

目的:机器学习(ML)是一种强大的资产,可在决策过程中为医生提供支持,并及时提供答案。然而,用于医疗系统的 ML 可能会受到安全攻击和隐私侵犯。本文将对用于医疗保健的 ML 的安全性和隐私性进行研究:我们研究了攻击、防御和隐私保护策略,并讨论了它们所面临的挑战。我们采用了以下研究方案:开始手动搜索,定义搜索字符串,删除重复的论文,根据标题和摘要筛选论文,然后是论文全文,分析论文的贡献,包括策略和挑战。最后,我们收集并讨论了 40 篇关于攻击、防御和隐私的论文:我们的研究结果确定了每个领域最常用的策略。我们发现了攻击方面的趋势,包括通用对抗扰动(UAP)、基于生成式对抗网络(GAN)的攻击以及生成恶意示例的 DeepFakes。防御方面的趋势包括对抗性训练、基于 GAN 的策略以及用于识别和减轻对抗性示例 (AE) 的分布外 (OOD)。我们发现了一些保护隐私的策略,如联合学习(FL)、差分隐私以及增强联合学习的策略组合。隐私保护面临的挑战包括:开发绕过微调的攻击、校准模型以提高其鲁棒性的防御措施,以及增强FL策略的隐私保护方法:总之,探索用于健康的 ML 的安全性和隐私性至关重要,因为它存在越来越多的风险和开放性漏洞。我们的研究提出了指导研究的策略和挑战,以调查应用于卫生系统的人工智能的安全和隐私问题。
{"title":"Security and Privacy in Machine Learning for Health Systems: Strategies and Challenges.","authors":"Erikson J de Aguiar, Caetano Traina, Agma J M Traina","doi":"10.1055/s-0043-1768731","DOIUrl":"10.1055/s-0043-1768731","url":null,"abstract":"<p><strong>Objectives: </strong>Machine learning (ML) is a powerful asset to support physicians in decision-making procedures, providing timely answers. However, ML for health systems can suffer from security attacks and privacy violations. This paper investigates studies of security and privacy in ML for health.</p><p><strong>Methods: </strong>We examine attacks, defenses, and privacy-preserving strategies, discussing their challenges. We conducted the following research protocol: starting a manual search, defining the search string, removing duplicated papers, filtering papers by title and abstract, then their full texts, and analyzing their contributions, including strategies and challenges. Finally, we collected and discussed 40 papers on attacks, defense, and privacy.</p><p><strong>Results: </strong>Our findings identified the most employed strategies for each domain. We found trends in attacks, including universal adversarial perturbation (UAPs), generative adversarial network (GAN)-based attacks, and DeepFakes to generate malicious examples. Trends in defense are adversarial training, GAN-based strategies, and out-of-distribution (OOD) to identify and mitigate adversarial examples (AE). We found privacy-preserving strategies such as federated learning (FL), differential privacy, and combinations of strategies to enhance the FL. Challenges in privacy comprehend the development of attacks that bypass fine-tuning, defenses to calibrate models to improve their robustness, and privacy methods to enhance the FL strategy.</p><p><strong>Conclusions: </strong>In conclusion, it is critical to explore security and privacy in ML for health, because it has grown risks and open vulnerabilities. Our study presents strategies and challenges to guide research to investigate issues about security and privacy in ML applied to health systems.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"32 1","pages":"269-281"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10751106/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139040674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Telehealth as a Component of One Health: a Position Paper. 作为 "一体健康 "组成部分的远程医疗:立场文件。
Pub Date : 2023-08-01 Epub Date: 2023-12-26 DOI: 10.1055/s-0043-1768728
Arindam Basu, Vije Kumar Rajput, Marcia Ito, Prasad Ranatunga, Craig Kuziemsky, Gumindu Kulatunga, Inga Hunter, Najeeb Al-Shorbaji, Shashi Gogia, Sriram Iyengar

Introduction: One Health (OH) refers to the integration of human, animal, and ecosystem health within one framework in the context of zoonoses, antimicrobial resistance and stewardship, and food security. Telehealth refers to distance delivery of healthcare. A systems approach is central to both One Health and telehealth, and telehealth can be a core component of One Health. Here we explain how telehealth might be integrated into One Health.

Methods: We have considered antimicrobial resistance (AMR) as a use case where both One Health and telehealth can be used for coordination among the farming sector, the veterinary services, and human health providers to mitigate the risk of AMR. We conducted a narrative review of the literature to develop a position on the inter-relationships between telehealth and One Health. We have summarised how telehealth can be incorporated within One Health.

Results: Clinicians have used telehealth to address antimicrobial resistance, zoonoses, food borne infection, improvement of food security and antimicrobial stewardship. We identified little existing evidence in support of the usage of telehealth within a One Health paradigm, although in isolation, both are useful for the same purpose, i.e., mitigation of the significant public health risks posed by zoonoses, food borne infections, and antimicrobial resistance.

Conclusions: It is possible to integrate telehealth within a One Health framework to develop effective inter-sectoral communication essential for the mitigation and addressing of zoonoses, food security, food borne infection containment and antimicrobial stewardship. More research is needed to substantiate and investigate this model of healthcare.

导言:统一健康(OH)是指在人畜共患病、抗菌药耐药性和管理以及食品安全的背景下,将人类、动物和生态系统的健康整合在一个框架内。远程保健指的是远程提供医疗保健服务。系统方法是 "一个健康 "和远程保健的核心,远程保健可以成为 "一个健康 "的核心组成部分。在此,我们将解释如何将远程保健纳入 "一体健康":我们将抗菌药耐药性(AMR)作为一个使用案例,在这个案例中,"一体健康 "和远程保健可用于农业部门、兽医服务和人类健康提供者之间的协调,以降低抗菌药耐药性的风险。我们对文献进行了叙述性回顾,以确立远程保健与 "一体健康 "之间相互关系的立场。我们总结了如何将远程保健纳入 "一体健康":结果:临床医生利用远程保健来解决抗菌药耐药性、人畜共患病、食源性感染、改善食品安全和抗菌药管理等问题。我们发现支持在 "一个健康 "范例中使用远程保健的现有证据很少,尽管单独来看,两者对同一目的都有用,即减轻人畜共患病、食源性感染和抗菌药耐药性带来的重大公共卫生风险:在 "一个健康 "框架内整合远程保健是可能的,以发展有效的部门间沟通,这对缓解和解决人畜共患病、食品安全、食源性感染控制和抗菌药物管理问题至关重要。需要开展更多的研究来证实和调查这种医疗保健模式。
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引用次数: 0
Transforming Clinical Information Systems: Empowering Healthcare through Telemedicine, Data Science, and Artificial Intelligence Applications. 改造临床信息系统:通过远程医疗、数据科学和人工智能应用为医疗保健赋能。
Pub Date : 2023-08-01 Epub Date: 2023-12-26 DOI: 10.1055/s-0043-1768756
Werner O Hackl, Sabrina B Neururer, Bernhard Pfeifer

Objective: In this synopsis, the editors of the Clinical Information Systems (CIS) section of the IMIA Yearbook of Medical Informatics overview recent research and propose a selection of best papers published in 2022 in the CIS field.

Methods: The editors follow a systematic approach to gather relevant articles and select the best papers for the section. This year, they updated the query to incorporate the topic of telemedicine and removed search terms related to geographic information systems. The revised query resulted in a larger number of identified papers, necessitating the appointment of a third section editor to handle the increased workload. The editors narrowed the initial pool of articles to 15 candidate papers through a multi-stage selection process. At least seven independent reviews were collected for each candidate paper, and a selection meeting with the IMIA Yearbook editorial board led to the final selection of the best papers for the CIS section.

Results: The query was carried out in mid-January 2023 and retrieved a deduplicated result set of 5,206 articles from 1,500 journals. This year, 15 papers were nominated as candidates, and four were finally selected as the best papers in the CIS section.Including telemedicine in the query resulted in a substantial increase in the number of papers found. The analysis highlights the growing convergence between clinical information systems and telemedicine, with mobile health (mHealth) technologies and data science applications gaining prominence. The selected candidate papers emphasize the practical impact of research efforts, focusing on patient-centric outcomes and benefits, including intelligent mobile health monitoring systems and AI-assisted decision-making in healthcare.

Conclusions: Looking ahead, the field of CIS is expected to continue evolving, driven by advances in telemedicine, mHealth technologies, data science, and AI integration, leading to more efficient, patient-oriented, and intelligent healthcare systems and overall improvement of global healthcare outcomes.

目的:在本概要中,IMIA《医学信息学年鉴》临床信息系统(CIS)部分的编辑们概述了最近的研究,并提出了2022年发表的CIS领域最佳论文的建议:方法:编辑们采用系统的方法收集相关文章,并为该版块遴选最佳论文。今年,他们更新了查询条件,纳入了远程医疗这一主题,并删除了与地理信息系统相关的搜索词。修改后的查询导致确定的论文数量增加,因此有必要任命第三位栏目编辑来处理增加的工作量。编辑们通过多阶段筛选过程,将最初的文章库缩小到 15 篇候选论文。对每篇候选论文都收集了至少七份独立评论,并与《IMIA年鉴》编辑委员会召开了一次评选会议,最终为独联体版块选出了最佳论文:查询于2023年1月中旬进行,检索到来自1500种期刊的5206篇文章的重复结果集。今年有 15 篇论文被提名为候选论文,最终有 4 篇被选为 CIS 部分的最佳论文。分析结果表明,随着移动医疗(mHealth)技术和数据科学应用的日益突出,临床信息系统和远程医疗之间的融合也在不断加强。入选的候选论文强调了研究工作的实际影响,重点关注以患者为中心的成果和效益,包括智能移动健康监测系统和医疗保健中的人工智能辅助决策:展望未来,在远程医疗、移动医疗技术、数据科学和人工智能整合的推动下,CIS 领域有望继续发展,从而建立更高效、以患者为导向的智能医疗系统,全面改善全球医疗成果。
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引用次数: 0
Health Equity in Clinical Research Informatics. 临床研究信息学中的健康公平。
Pub Date : 2023-08-01 Epub Date: 2023-07-06 DOI: 10.1055/s-0043-1768720
Sigurd Maurud, Silje H Henni, Anne Moen

Objectives: Through a scoping review, we examine in this survey what ways health equity has been promoted in clinical research informatics with patient implications and especially published in the year of 2021 (and some in 2022).

Method: A scoping review was conducted guided by using methods described in the Joanna Briggs Institute Manual. The review process consisted of five stages: 1) development of aim and research question, 2) literature search, 3) literature screening and selection, 4) data extraction, and 5) accumulate and report results.

Results: From the 478 identified papers in 2021 on the topic of clinical research informatics with focus on health equity as a patient implication, 8 papers met our inclusion criteria. All included papers focused on artificial intelligence (AI) technology. The papers addressed health equity in clinical research informatics either through the exposure of inequity in AI-based solutions or using AI as a tool for promoting health equity in the delivery of healthcare services. While algorithmic bias poses a risk to health equity within AI-based solutions, AI has also uncovered inequity in traditional treatment and demonstrated effective complements and alternatives that promotes health equity.

Conclusions: Clinical research informatics with implications for patients still face challenges of ethical nature and clinical value. However, used prudently-for the right purpose in the right context-clinical research informatics could bring powerful tools in advancing health equity in patient care.

目的:通过范围综述,我们在本调查中研究了对患者有影响的临床研究信息学促进健康公平的方式,尤其是在 2021 年(以及一些 2022 年)发表的文章:通过范围综述,我们在本调查中研究了对患者有影响的临床研究信息学促进健康公平的方式,尤其是在 2021 年(部分在 2022 年)发表的文章:在《乔安娜-布里格斯研究所手册》所述方法的指导下进行了范围界定审查。审查过程包括五个阶段1)制定目标和研究问题;2)文献检索;3)文献筛选;4)数据提取;5)积累和报告结果:从 2021 年发现的 478 篇以临床研究信息学为主题的论文中,有 8 篇符合我们的纳入标准。所有被纳入的论文都侧重于人工智能(AI)技术。这些论文通过揭露基于人工智能的解决方案中存在的不公平现象,或将人工智能作为在提供医疗保健服务过程中促进健康公平的工具,探讨了临床研究信息学中的健康公平问题。虽然在基于人工智能的解决方案中,算法偏差对健康公平构成风险,但人工智能也揭示了传统治疗中的不公平现象,并展示了促进健康公平的有效补充和替代方案:结论:对患者有影响的临床研究信息学仍然面临着伦理性质和临床价值方面的挑战。然而,只要审慎使用--在正确的背景下用于正确的目的--临床研究信息学就能为促进患者护理中的健康公平提供强有力的工具。
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引用次数: 0
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Yearbook of medical informatics
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