Health Informatics Journal最新文献_第10页

'Humans think outside the pixels' - Radiologists' perceptions of using artificial intelligence for breast cancer detection in mammography screening in a clinical setting. 人类的思维不局限于像素"--放射科医生对在临床乳房 X 射线摄影筛查中使用人工智能检测乳腺癌的看法。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241275020

Jennifer Viberg Johansson, Emma Engström

{"title":"'Humans think outside the pixels' - Radiologists' perceptions of using artificial intelligence for breast cancer detection in mammography screening in a clinical setting.","authors":"Jennifer Viberg Johansson, Emma Engström","doi":"10.1177/14604582241275020","DOIUrl":"10.1177/14604582241275020","url":null,"abstract":"Objective: This study aimed to explore radiologists' views on using an artificial intelligence (AI) tool named ScreenTrustCAD with Philips equipment) as a diagnostic decision support tool in mammography screening during a clinical trial at Capio Sankt Göran Hospital, Sweden.Methods: We conducted semi-structured interviews with seven breast imaging radiologists, evaluated using inductive thematic content analysis.Results: We identified three main thematic categories: AI in society, reflecting views on AI's contribution to the healthcare system; AI-human interactions, addressing the radiologists' self-perceptions when using the AI and its potential challenges to their profession; and AI as a tool among others. The radiologists were generally positive towards AI, and they felt comfortable handling its sometimes-ambiguous outputs and erroneous evaluations. While they did not feel that it would undermine their profession, they preferred using it as a complementary reader rather than an independent one.Conclusion: The results suggested that breast radiology could become a launch pad for AI in healthcare. We recommend that this exploratory work on subjective perceptions be complemented by quantitative assessments to generalize the findings.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241275020"},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142001405","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

Improving the academic resilience of hospital nursing interns through a hybrid multi-criteria decision analysis model. 通过混合多标准决策分析模型提高医院护理实习生的学习适应能力。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241272771

Mao Ye, Weifang Xu, Lili Feng, Siqi Liu, Jianhong Yang, Yen-Ching Chuang, Fuqin Tang

{"title":"Improving the academic resilience of hospital nursing interns through a hybrid multi-criteria decision analysis model.","authors":"Mao Ye, Weifang Xu, Lili Feng, Siqi Liu, Jianhong Yang, Yen-Ching Chuang, Fuqin Tang","doi":"10.1177/14604582241272771","DOIUrl":"10.1177/14604582241272771","url":null,"abstract":"Purpose: To identify the main variables affecting the academic adaptability of hospital nursing interns and key areas for improvement in preparing for future unpredictable epidemics. Methods: The importance of academic resilience-related variables for all nursing interns was analyzed using the random forest method, and key variables were further identified. An importance-performance analysis was used to identify the key improvement gaps regarding the academic resilience of nursing interns in the case hospital. Results: The random forest showed that five items related to cooperation, motivation, confidence, communication, and difficulty with coping were the main variables impacting the academic resilience of nursing interns. Moreover, the importance-performance analysis revealed that three items regarding options examination, communication, and confidence were the key improvement areas for participating nursing interns in the case hospital. Conclusions: For the prevention and control of future unpredictable pandemics, hospital nursing departments can strengthen the link between interns, nurses, and physicians and promote their cooperation and communication during clinical practice. At the same time, an application can be created considering the results of this study and combined with machine learning methods for more in-depth research. These will improve the academic resilience of nursing interns during the routine management of pandemics within hospitals.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241272771"},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903662","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

Epid data explorer: A visualization tool for exploring and comparing spatio-temporal epidemiological data. Epid 数据资源管理器：用于探索和比较时空流行病学数据的可视化工具。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241279720

Laetitia Viau, Jérôme Azé, Fati Chen, Pierre Pompidor, Pascal Poncelet, Vincent Raveneau, Nancy Rodriguez, Arnaud Sallaberry

{"title":"Epid data explorer: A visualization tool for exploring and comparing spatio-temporal epidemiological data.","authors":"Laetitia Viau, Jérôme Azé, Fati Chen, Pierre Pompidor, Pascal Poncelet, Vincent Raveneau, Nancy Rodriguez, Arnaud Sallaberry","doi":"10.1177/14604582241279720","DOIUrl":"10.1177/14604582241279720","url":null,"abstract":"The analysis of large sets of spatio-temporal data is a fundamental challenge in epidemiological research. As the quantity and the complexity of such kind of data increases, automatic analysis approaches, such as statistics, data mining, machine learning, etc., can be used to extract useful information. While these approaches have proven effective, they require a priori knowledge of the information being sought, and some interesting insights into the data may be missed. To bridge this gap, information visualization offers a set of techniques for not only presenting known information, but also exploring data without having a hypothesis formulated beforehand. In this paper, we introduce Epid Data Explorer (EDE), a visualization tool that enables exploration of spatio-temporal epidemiological data. EDE allows easy comparisons of indicators and trends across different geographical areas and times. It facilitates this exploration through ready-to-use pre-loaded datasets as well as user-chosen datasets. The tool also provides a secure architecture for easily importing new datasets while ensuring confidentiality. In two use cases using data associated with the COVID-19 epidemic, we demonstrate the substantial impact of implemented lockdown measures on mobility and how EDE allows assessing correlations between the spread of COVID-19 and weather conditions.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241279720"},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142121215","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

AI-based epidemic and pandemic early warning systems: A systematic scoping review. 基于人工智能的流行病和大流行病预警系统：系统性范围审查。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241275844

Christo El Morr, Deniz Ozdemir, Yasmeen Asdaah, Antoine Saab, Yahya El-Lahib, Elie Salem Sokhn

{"title":"AI-based epidemic and pandemic early warning systems: A systematic scoping review.","authors":"Christo El Morr, Deniz Ozdemir, Yasmeen Asdaah, Antoine Saab, Yahya El-Lahib, Elie Salem Sokhn","doi":"10.1177/14604582241275844","DOIUrl":"10.1177/14604582241275844","url":null,"abstract":"Background: Timely detection of disease outbreaks is critical in public health. Artificial Intelligence (AI) can identify patterns in data that signal the onset of epidemics and pandemics. This scoping review examines the effectiveness of AI in epidemic and pandemic early warning systems (EWS). Objective: To assess the capability of AI-based systems in predicting epidemics and pandemics and to identify challenges and strategies for improvement. Methods: A systematic scoping review was conducted. The review included studies from the last 5 years, focusing on AI and machine learning applications in EWS. After screening 1087 articles, 33 were selected for thematic analysis. Results: The review found that AI-based EWS have been effectively implemented in various contexts, using a range of algorithms. Key challenges identified include data quality, model explainability, bias, data volume, velocity, variety, availability, and granularity. Strategies for mitigating AI bias and improving system adaptability were also discussed. Conclusion: AI has shown promise in enhancing the speed and accuracy of epidemic detection. However, challenges related to data quality, bias, and model transparency need to be addressed to improve the reliability and generalizability of AI-based EWS. Continuous monitoring and improvement, as well as incorporating social and environmental data, are essential for future development.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241275844"},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037828","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

Machine learning model for osteoporosis diagnosis based on bone turnover markers. 基于骨转换标志物的骨质疏松症诊断机器学习模型。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241270778

Seung Min Baik, Hi Jeong Kwon, Yeongsic Kim, Jehoon Lee, Young Hoon Park, Dong Jin Park

{"title":"Machine learning model for osteoporosis diagnosis based on bone turnover markers.","authors":"Seung Min Baik, Hi Jeong Kwon, Yeongsic Kim, Jehoon Lee, Young Hoon Park, Dong Jin Park","doi":"10.1177/14604582241270778","DOIUrl":"10.1177/14604582241270778","url":null,"abstract":"To assess the diagnostic utility of bone turnover markers (BTMs) and demographic variables for identifying individuals with osteoporosis. A cross-sectional study involving 280 participants was conducted. Serum BTM values were obtained from 88 patients with osteoporosis and 192 controls without osteoporosis. Six machine learning models, including extreme gradient boosting (XGBoost), light gradient boosting machine (LGBM), CatBoost, random forest, support vector machine, and k-nearest neighbors, were employed to evaluate osteoporosis diagnosis. The performance measures included the area under the receiver operating characteristic curve (AUROC), F1-score, and accuracy. After AUROC optimization, LGBM exhibited the highest AUROC of 0.706. Post F1-score optimization, LGBM's F1-score was improved from 0.50 to 0.65. Combining the top three optimized models (LGBM, XGBoost, and CatBoost) resulted in an AUROC of 0.706, an F1-score of 0.65, and an accuracy of 0.73. BTMs, along with age and sex, were found to contribute significantly to osteoporosis diagnosis. This study demonstrates the potential of machine learning models utilizing BTMs and demographic variables for diagnosing preexisting osteoporosis. The findings highlight the clinical relevance of accessible clinical data in osteoporosis assessment, providing a promising tool for early diagnosis and management.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241270778"},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903663","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

Using process model to define the legislative framework of electronic prescription in the Czech Republic. 利用流程模型确定捷克共和国电子处方的立法框架。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241270902

Jiří Berger, Jan Bruthans, Adam Vojtěch, Jiří Kofránek

{"title":"Using process model to define the legislative framework of electronic prescription in the Czech Republic.","authors":"Jiří Berger, Jan Bruthans, Adam Vojtěch, Jiří Kofránek","doi":"10.1177/14604582241270902","DOIUrl":"10.1177/14604582241270902","url":null,"abstract":"Defining legislation for electronic prescription systems (EPS) is inherently challenging due to conflicting interests and requirements. The study aimed to develop a comprehensive EPS within the Czech healthcare framework, integrating legislative, process, and technical aspects to ensure security, user acceptability, and compliance with health regulations. A process modeling tool based on hierarchical state machines was employed to create a detailed process architecture for the EPS. Key participants, scenarios, and state transitions were identified and incorporated into a process model using the Craft.CASE based on the BORM methodology. The final process architecture model facilitated interdisciplinary communication and consensus-building among stakeholders, including healthcare professionals, IT specialists, and legislators. The model served as a foundation for the legislative framework and was included in the explanatory memorandum for the draft amendment to the Pharmaceuticals Act. The use of hierarchical state machines and process modeling tools in developing healthcare legislation effectively reduced misunderstandings and ensured precise implementation. This method can be applied to other complex legislative and system design projects, enhancing stakeholder communication and project success.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241270902"},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903665","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

Comparison of machine learning models to predict complications of bariatric surgery: A systematic review. 预测减肥手术并发症的机器学习模型比较：系统综述。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241285794

Raoof Nopour

{"title":"Comparison of machine learning models to predict complications of bariatric surgery: A systematic review.","authors":"Raoof Nopour","doi":"10.1177/14604582241285794","DOIUrl":"https://doi.org/10.1177/14604582241285794","url":null,"abstract":"Background and aim: Due to changes in lifestyle, bariatric surgery is expanding worldwide. However, this surgery has numerous complications, and early identification of these complications could be essential in assisting patients to have a higher-quality surgery. Machine learning has a significant role in prediction tasks. So far, no systematic review has been carried out on leveraging ML techniques for predicting complications of bariatric surgery. Therefore, this study aims to perform a systematic review for better prediction insight. Materials and methods: This review was conducted in 2023 based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We searched scientific databases using the inclusion and exclusion criteria to obtain articles. The data extraction form was used to gather data. To analyze the data, we leveraged the narrative synthesis of the quantitative data. Results: Ensemble algorithms outperformed others in large databases, especially at the national registries. Artificial Neural Networks (ANN) performed better than others based on one-single-center database. Also, Deep Belief Networks (DBN) and ANN obtained favorable performance for complications such as diabetes, dyslipidemia, hypertension, thrombosis, leakage, and depression. Conclusion: This review gave us insight into using ensemble and non-ensemble algorithms based on the types of datasets and complications.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241285794"},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142301399","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 use of the e-SUS Território mobile application in the work of community health workers in Brazil. e-SUS Território 移动应用程序在巴西社区卫生工作者工作中的应用。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241286436

I C Celuppi, Rlc Prado, Etb Mohr, F Mioto, Jmd Oliveira, M Felisberto, J F Hammes, R S Wazlawick, E M Dalmarco

{"title":"The use of the e-SUS Território mobile application in the work of community health workers in Brazil.","authors":"I C Celuppi, Rlc Prado, Etb Mohr, F Mioto, Jmd Oliveira, M Felisberto, J F Hammes, R S Wazlawick, E M Dalmarco","doi":"10.1177/14604582241286436","DOIUrl":"https://doi.org/10.1177/14604582241286436","url":null,"abstract":"Objective: Community health workers work directly in the communities and are the intermediaries between the population's needs and the primary health care teams. Their work focuses on health education and preventing diseases and disorders, accompanying citizens, families, and households in a particular neighborhood. This study sought to analyze the use of the e-SUS Território application in the work of community health workers in Brazil. Methods: Usability data extracted from Google Analytics from 2019 to 2022 were analyzed, including productivity indicators, number and location of users, and engagement. An overview of the application's main features was also provided. Results: The application is an important work tool used by these professionals, who stopped using printed sheets to record their activities and began recording them in a digital, unified, asynchronous way anywhere in Brazil, regardless of internet connectivity. The application had 425,000 active users in 2022, reaching 141,000 monthly active users in June of the same year, representing 54.8% of all community health workers in Brazil. Conclusion: This study demonstrates the wide and exponential adherence of the e-SUS Território application over the years and the increase in the productivity of professionals who use it, facilitating and encouraging the recording of health information.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241286436"},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142301400","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

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241260643

Pauleen Mannevaara, Kaija Saranto, Ulla-Mari Kinnunen, Ursula Hübner

{"title":"Recommended target audience, course content and learning arrangements for teaching health informatics competencies: A scoping review.","authors":"Pauleen Mannevaara, Kaija Saranto, Ulla-Mari Kinnunen, Ursula Hübner","doi":"10.1177/14604582241260643","DOIUrl":"10.1177/14604582241260643","url":null,"abstract":"Background: As healthcare depends on health information technology, there is a growing need for Health Informatics competencies in daily practice. This review aimed to explore how the teaching of education in HI has been arranged. 28 publications, published in English between 2016 and 2020 and obtained from selected bibliographic databases, were reviewed. The data was analyzed using deductive content analysis with the following pre-formulated topics: target audience, course content and learning arrangements. The results highlight three key competencies: documentation and communication, management, and understanding of health information technology. It underlines a blended teaching method to improve the competencies of healthcare professionals, graduates, undergraduates, and suggests adding active interactions, multi-professional interactions, and hands-on skills. This study highlights the importance of adapting to changes in healthcare, improving HI competencies in healthcare, and fostering positive digital experiences. It underlined the need for practical training, in theory and hands-on sessions, including key competencies in documentation and communication, management and health information systems.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241260643"},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762788","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 digital divide between primary and secondary care: An analysis using SARS-CoV-2 hospital admissions. 初级和二级医疗之间的数字鸿沟：利用 SARS-CoV-2 住院病例进行分析。

IF 2.2 3区医学

Health Informatics Journal Pub Date : 2024-07-01 DOI: 10.1177/14604582241249929

Amit Sagi, Vipin Asopa, Benjamin Mitchell, Mahalingam Shiyamasundaran, Caleb Koch, Fanuelle Getachew, Irrum Afzal, David Sochart, Richard Field

{"title":"The digital divide between primary and secondary care: An analysis using SARS-CoV-2 hospital admissions.","authors":"Amit Sagi, Vipin Asopa, Benjamin Mitchell, Mahalingam Shiyamasundaran, Caleb Koch, Fanuelle Getachew, Irrum Afzal, David Sochart, Richard Field","doi":"10.1177/14604582241249929","DOIUrl":"10.1177/14604582241249929","url":null,"abstract":"Using data from two ED. departments of 773 patients admitted with SARS-CoV-2, ICD-10 codes derived from the General Practitioner - Summary Care Record (GP-SCR) and Emergency Department (ED.) records were analysed for code discrepancies and whether this related to increased mortality. The average number of ICD-10 codes in both GP-SCR and ED. records was higher for patients who died than patients who survived (all p < .0001). Pre-existing GP digital data provides a better prediction of mortality than data collected manually during admission clerking in the ED. Up to 78.47% of GP-SCR codes were missed in the ED. records and up to 45.49% of the ED. record codes were not in the GP-SCR. A subset of missed ICD-10 codes were identified as being able to predict outcome; a trend towards increasing death rate as the proportion of missed codes increases. Initiatives to make the GP-SCR available to the wider healthcare community should improve patient care and reduce bias during development of machine learning based algorithms.","PeriodicalId":55069,"journal":{"name":"Health Informatics Journal","volume":"30 3","pages":"14604582241249929"},"PeriodicalIF":2.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762789","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