Healthcare Informatics Research最新文献

筛选
英文 中文
Nursing Records Regarding Decision-Making in Cancer Supportive Care: A Retrospective Study in Japan. 有关癌症支持性护理决策的护理记录:日本的一项回顾性研究
IF 2.3
Healthcare Informatics Research Pub Date : 2024-10-01 Epub Date: 2024-10-31 DOI: 10.4258/hir.2024.30.4.364
Yuko Kawasaki, Manab Nii, Eina Nishioka
{"title":"Nursing Records Regarding Decision-Making in Cancer Supportive Care: A Retrospective Study in Japan.","authors":"Yuko Kawasaki, Manab Nii, Eina Nishioka","doi":"10.4258/hir.2024.30.4.364","DOIUrl":"10.4258/hir.2024.30.4.364","url":null,"abstract":"<p><strong>Objectives: </strong>This study was performed to examine the content of decision-making support and patient responses, as documented in the nursing records of individuals with cancer. These patients had received outpatient treatment at hospitals that met government requirements for providing specialized cancer care.</p><p><strong>Methods: </strong>Nursing records from the electronic medical record system (in the subjective, objective, assessment, and plan [SOAP] format), along with data from interviews, were extracted for patients receiving outpatient care at the Department of Internal Medicine and Palliative Care and the Department of Breast Oncology. Data analysis involved simple tabulation and text mining, utilizing KH Coder version 3.beta.07d.</p><p><strong>Results: </strong>The study included 42 patients from palliative care internal medicine and 60 from breast oncology, with mean ages of 70.5 ± 12.2 and 55.8 ± 12.2 years, respectively. Decisions most frequently regarded palliative care unit admission (25 cases) and genetic testing (24 cases). The assessment category covered keywords including (1) \"pain,\" \"treatment,\" \"future,\" \"recuperation,\" and \"home,\" as terms related to palliative care and internal medicine, as well as (2) \"treatment,\" \"relief,\" and \"genetics\" as terms related to breast oncology. The plan category incorporated keywords such as (1) \"treatment,\" \"relaxation,\" and \"visit\" and (2) \"explanation,\" \"confirmation,\" and \"conveyance.\"</p><p><strong>Conclusions: </strong>Nurses appear crucial in evaluating patients' symptoms and treatment paths during the decision-making support process, helping them make informed choices about future treatments, care settings, and genetic testing. However, when patients cannot make a decision solely based on the information provided, clinicians must address complex psychological concepts such as disease progression and the potential genetic impact on their children. Further detailed observational studies of nurses' responses to patients' psychological reactions are warranted.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 4","pages":"364-374"},"PeriodicalIF":2.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11570663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142647146","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
Cancer-related Keywords in 2023: Insights from Text Mining of a Major Consumer Portal. 2023 年癌症相关关键词:从大型消费者门户网站的文本挖掘中获得的启示。
IF 2.3
Healthcare Informatics Research Pub Date : 2024-10-01 Epub Date: 2024-10-31 DOI: 10.4258/hir.2024.30.4.398
Wonjeong Jeong, Eunkyoung Song, Eunzi Jeong, Kyoung Hee Oh, Hye-Sun Lee, Jae Kwan Jun
{"title":"Cancer-related Keywords in 2023: Insights from Text Mining of a Major Consumer Portal.","authors":"Wonjeong Jeong, Eunkyoung Song, Eunzi Jeong, Kyoung Hee Oh, Hye-Sun Lee, Jae Kwan Jun","doi":"10.4258/hir.2024.30.4.398","DOIUrl":"10.4258/hir.2024.30.4.398","url":null,"abstract":"<p><strong>Objectives: </strong>With the growing importance of monitoring cancer patients' internet usage, there is an increasing need for technology that expands access to relevant information through text mining. This study analyzed internet articles from portal sites in 2023 to identify trends in the information available to cancer patients and to derive meaningful insights.</p><p><strong>Methods: </strong>This study analyzed 19,578 news articles published on Naver, a major Korean portal site, from January 1, 2023, to December 31, 2023. Natural language processing, text mining, network analysis, and word cloud analysis were employed. The search term \"am\" (Korean for \"cancer\") was used to identify keywords related to cancer.</p><p><strong>Results: </strong>In 2023, an average of 1,631 cancer-related articles were published monthly, with a peak of 1,946 in September and a low of 1,371 in February. A total of 132,456 keywords were extracted, with \"cure\" (2,218 occurrences), \"lung cancer\" (1,652), and \"breast cancer\" (1,235) being the most frequent. Term frequency-inverse document frequency analysis ranked \"struggle\" (1064.172) as the most significant keyword, followed by \"lung cancer\" (839.988) and \"breast cancer\" (744.840). Network analysis revealed four distinct clusters focusing on treatment, celebrity-related issues, major cancer types, and cancer-causing factors.</p><p><strong>Conclusions: </strong>The analysis of cancer-related keywords in 2023 indicates that news articles often prioritize gossip over essential information. These findings provide foundational data for future policy directions and strategies to address misinformation. This study underscores the importance of understanding the nature of cancer-related information consumed by the public and offers insights to guide official policies and healthcare practices.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 4","pages":"398-408"},"PeriodicalIF":2.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11570664/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142647888","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
Milestones and Growth: The 30-Year Journey of Healthcare Informatics Research. 里程碑与成长:医疗保健信息学研究的 30 年历程。
IF 2.3
Healthcare Informatics Research Pub Date : 2024-10-01 Epub Date: 2024-10-31 DOI: 10.4258/hir.2024.30.4.293
Hyejung Chang
{"title":"Milestones and Growth: The 30-Year Journey of Healthcare Informatics Research.","authors":"Hyejung Chang","doi":"10.4258/hir.2024.30.4.293","DOIUrl":"10.4258/hir.2024.30.4.293","url":null,"abstract":"","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 4","pages":"293-296"},"PeriodicalIF":2.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11570660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142646351","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
Technology and Access to Healthcare with Different Scheduling Systems: A Scoping Review. 不同排班系统下的技术与医疗服务:范围审查》。
IF 2.3
Healthcare Informatics Research Pub Date : 2024-07-01 Epub Date: 2024-07-31 DOI: 10.4258/hir.2024.30.3.194
Lucas Manarte
{"title":"Technology and Access to Healthcare with Different Scheduling Systems: A Scoping Review.","authors":"Lucas Manarte","doi":"10.4258/hir.2024.30.3.194","DOIUrl":"10.4258/hir.2024.30.3.194","url":null,"abstract":"<p><strong>Objectives: </strong>Online consultation scheduling is increasingly common in health services across various countries. This paper reviews articles published in the past five years and reflects on the risks and benefits of this practice, linking it to a recent Portuguese pilot project.</p><p><strong>Methods: </strong>A search for articles from Web of Science and Scopus published since 2018 was conducted using the terms \"online scheduling,\" \"online booking,\" and \"consultations.\" This search was completed in the last week of 2023.</p><p><strong>Results: </strong>Out of 64 articles retrieved, 26 were relevant to the topic. These articles were reviewed, and their main findings, along with those from other relevant sources, were discussed.</p><p><strong>Conclusions: </strong>Several limitations of online consultations were identified, encompassing ethical, clinical, and economic aspects. While these consultations tend to be less expensive, their accessibility varies based on factors such as the users' age, whether they reside in rural or urban areas, and the technological capabilities of different countries, indicating that access disparities may continue to widen. Confidentiality concerns also arise, varying by medical specialty, along with issues related to payment. Overall, however, both users and health professionals view the advent of online consultation booking positively. In conclusion, despite the risks identified, online consultation booking has the potential to enhance user access to health services, provided that usage limitations and technological disparities are addressed. Research production has not kept pace with rapid technological advancements.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 3","pages":"194-205"},"PeriodicalIF":2.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004110","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
ChatGPT Predicts In-Hospital All-Cause Mortality for Sepsis: In-Context Learning with the Korean Sepsis Alliance Database. ChatGPT 预测脓毒症院内全因死亡率:利用韩国脓毒症联盟数据库进行情景学习。
IF 2.3
Healthcare Informatics Research Pub Date : 2024-07-01 Epub Date: 2024-07-31 DOI: 10.4258/hir.2024.30.3.266
Namkee Oh, Won Chul Cha, Jun Hyuk Seo, Seong-Gyu Choi, Jong Man Kim, Chi Ryang Chung, Gee Young Suh, Su Yeon Lee, Dong Kyu Oh, Mi Hyeon Park, Chae-Man Lim, Ryoung-Eun Ko
{"title":"ChatGPT Predicts In-Hospital All-Cause Mortality for Sepsis: In-Context Learning with the Korean Sepsis Alliance Database.","authors":"Namkee Oh, Won Chul Cha, Jun Hyuk Seo, Seong-Gyu Choi, Jong Man Kim, Chi Ryang Chung, Gee Young Suh, Su Yeon Lee, Dong Kyu Oh, Mi Hyeon Park, Chae-Man Lim, Ryoung-Eun Ko","doi":"10.4258/hir.2024.30.3.266","DOIUrl":"10.4258/hir.2024.30.3.266","url":null,"abstract":"<p><strong>Objectives: </strong>Sepsis is a leading global cause of mortality, and predicting its outcomes is vital for improving patient care. This study explored the capabilities of ChatGPT, a state-of-the-art natural language processing model, in predicting in-hospital mortality for sepsis patients.</p><p><strong>Methods: </strong>This study utilized data from the Korean Sepsis Alliance (KSA) database, collected between 2019 and 2021, focusing on adult intensive care unit (ICU) patients and aiming to determine whether ChatGPT could predict all-cause mortality after ICU admission at 7 and 30 days. Structured prompts enabled ChatGPT to engage in in-context learning, with the number of patient examples varying from zero to six. The predictive capabilities of ChatGPT-3.5-turbo and ChatGPT-4 were then compared against a gradient boosting model (GBM) using various performance metrics.</p><p><strong>Results: </strong>From the KSA database, 4,786 patients formed the 7-day mortality prediction dataset, of whom 718 died, and 4,025 patients formed the 30-day dataset, with 1,368 deaths. Age and clinical markers (e.g., Sequential Organ Failure Assessment score and lactic acid levels) showed significant differences between survivors and non-survivors in both datasets. For 7-day mortality predictions, the area under the receiver operating characteristic curve (AUROC) was 0.70-0.83 for GPT-4, 0.51-0.70 for GPT-3.5, and 0.79 for GBM. The AUROC for 30-day mortality was 0.51-0.59 for GPT-4, 0.47-0.57 for GPT-3.5, and 0.76 for GBM. Zero-shot predictions using GPT-4 for mortality from ICU admission to day 30 showed AUROCs from the mid-0.60s to 0.75 for GPT-4 and mainly from 0.47 to 0.63 for GPT-3.5.</p><p><strong>Conclusions: </strong>GPT-4 demonstrated potential in predicting short-term in-hospital mortality, although its performance varied across different evaluation metrics.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 3","pages":"266-276"},"PeriodicalIF":2.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333818/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004143","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
Data Market-related Issues in the Medical Field: Accelerating Digital Healthcare. 医疗领域与数据市场相关的问题:加速数字医疗。
IF 2.3
Healthcare Informatics Research Pub Date : 2024-07-01 Epub Date: 2024-07-31 DOI: 10.4258/hir.2024.30.3.290
Myung-Gwan Kim, Hyeong Won Yu, Hyun Wook Han
{"title":"Data Market-related Issues in the Medical Field: Accelerating Digital Healthcare.","authors":"Myung-Gwan Kim, Hyeong Won Yu, Hyun Wook Han","doi":"10.4258/hir.2024.30.3.290","DOIUrl":"10.4258/hir.2024.30.3.290","url":null,"abstract":"","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 3","pages":"290-292"},"PeriodicalIF":2.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004144","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
Integration of Artificial Intelligence in Pediatric Education: Perspectives from Pediatric Medical Educators and Residents. 将人工智能融入儿科教育:儿科医学教育工作者和住院医师的观点。
IF 2.3
Healthcare Informatics Research Pub Date : 2024-07-01 Epub Date: 2024-07-31 DOI: 10.4258/hir.2024.30.3.244
Antonius Hocky Pudjiadi, Fatima Safira Alatas, Muhammad Faizi, Rusdi, Eko Sulistijono, Yetty Movieta Nency, Madarina Julia, Aidah Juliaty Alimuddin Baso, Edi Hartoyo, Susi Susanah, Rocky Wilar, Hari Wahyu Nugroho, Indrayady, Bugis Mardina Lubis, Syafruddin Haris, Ida Bagus Gede Suparyatha, Daniar Amarassaphira, Ervin Monica, Lukito Ongko
{"title":"Integration of Artificial Intelligence in Pediatric Education: Perspectives from Pediatric Medical Educators and Residents.","authors":"Antonius Hocky Pudjiadi, Fatima Safira Alatas, Muhammad Faizi, Rusdi, Eko Sulistijono, Yetty Movieta Nency, Madarina Julia, Aidah Juliaty Alimuddin Baso, Edi Hartoyo, Susi Susanah, Rocky Wilar, Hari Wahyu Nugroho, Indrayady, Bugis Mardina Lubis, Syafruddin Haris, Ida Bagus Gede Suparyatha, Daniar Amarassaphira, Ervin Monica, Lukito Ongko","doi":"10.4258/hir.2024.30.3.244","DOIUrl":"10.4258/hir.2024.30.3.244","url":null,"abstract":"<p><strong>Objectives: </strong>The use of technology has rapidly increased in the past century. Artificial intelligence (AI) and information technology (IT) are now applied in healthcare and medical education. The purpose of this study was to assess the readiness of Indonesian teaching staff and pediatric residents for AI integration into the curriculum.</p><p><strong>Methods: </strong>An anonymous online survey was distributed among teaching staff and pediatric residents from 15 national universities. The questionnaire consisted of two sections: demographic information and questions regarding the use of IT and AI in child health education. Responses were collected using a 5-point Likert scale: strongly disagree, disagree, neutral, agree, and highly agree.</p><p><strong>Results: </strong>A total of 728 pediatric residents and 196 teaching staff from 15 national universities participated in the survey. Over half of the respondents were familiar with the terms IT and AI. The majority agreed that IT and AI have simplified the process of learning theories and skills. All participants were in favor of sharing data to facilitate the development of AI and expressed readiness to incorporate IT and AI into their teaching tools.</p><p><strong>Conclusions: </strong>The findings of our study indicate that pediatric residents and teaching staff are ready to implement AI in medical education.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 3","pages":"244-252"},"PeriodicalIF":2.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333820/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004148","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
Review of the 2024 Spring Conference of the Korean Society of Medical Informatics - Omnibus Omnia. 韩国医学信息学会 2024 年春季会议回顾 - Omnibus Omnia。
IF 2.3
Healthcare Informatics Research Pub Date : 2024-07-01 Epub Date: 2024-07-31 DOI: 10.4258/hir.2024.30.3.169
Jisan Lee, Suehyun Lee, Seo Yeon Baik, Taehoon Ko, Kwangmo Yang, Younghee Lee
{"title":"Review of the 2024 Spring Conference of the Korean Society of Medical Informatics - Omnibus Omnia.","authors":"Jisan Lee, Suehyun Lee, Seo Yeon Baik, Taehoon Ko, Kwangmo Yang, Younghee Lee","doi":"10.4258/hir.2024.30.3.169","DOIUrl":"10.4258/hir.2024.30.3.169","url":null,"abstract":"","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 3","pages":"169-171"},"PeriodicalIF":2.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333812/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004150","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
Genetic Algorithm-based Convolutional Neural Network Feature Engineering for Optimizing Coronary Heart Disease Prediction Performance. 基于遗传算法的卷积神经网络特征工程优化冠心病预测性能
IF 2.3
Healthcare Informatics Research Pub Date : 2024-07-01 Epub Date: 2024-07-31 DOI: 10.4258/hir.2024.30.3.234
Erwin Yudi Hidayat, Yani Parti Astuti, Ika Novita Dewi, Abu Salam, Moch Arief Soeleman, Zainal Arifin Hasibuan, Ahmed Sabeeh Yousif
{"title":"Genetic Algorithm-based Convolutional Neural Network Feature Engineering for Optimizing Coronary Heart Disease Prediction Performance.","authors":"Erwin Yudi Hidayat, Yani Parti Astuti, Ika Novita Dewi, Abu Salam, Moch Arief Soeleman, Zainal Arifin Hasibuan, Ahmed Sabeeh Yousif","doi":"10.4258/hir.2024.30.3.234","DOIUrl":"10.4258/hir.2024.30.3.234","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to optimize early coronary heart disease (CHD) prediction using a genetic algorithm (GA)-based convolutional neural network (CNN) feature engineering approach. We sought to overcome the limitations of traditional hyperparameter optimization techniques by leveraging a GA for superior predictive performance in CHD detection.</p><p><strong>Methods: </strong>Utilizing a GA for hyperparameter optimization, we navigated a complex combinatorial space to identify optimal configurations for a CNN model. We also employed information gain for feature selection optimization, transforming the CHD datasets into an image-like input for the CNN architecture. The efficacy of this method was benchmarked against traditional optimization strategies.</p><p><strong>Results: </strong>The advanced GA-based CNN model outperformed traditional methods, achieving a substantial increase in accuracy. The optimized model delivered a promising accuracy range, with a peak of 85% in hyperparameter optimization and 100% accuracy when integrated with machine learning algorithms, namely naïve Bayes, support vector machine, decision tree, logistic regression, and random forest, for both binary and multiclass CHD prediction tasks.</p><p><strong>Conclusions: </strong>The integration of a GA into CNN feature engineering is a powerful technique for improving the accuracy of CHD predictions. This approach results in a high degree of predictive reliability and can significantly contribute to the field of AI-driven healthcare, with the possibility of clinical deployment for early CHD detection. Future work will focus on expanding the approach to encompass a wider set of CHD data and potential integration with wearable technology for continuous health monitoring.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 3","pages":"234-243"},"PeriodicalIF":2.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333810/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004147","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
Status and Trends of the Digital Healthcare Industry. 数字医疗行业的现状与趋势。
IF 2.3
Healthcare Informatics Research Pub Date : 2024-07-01 Epub Date: 2024-07-31 DOI: 10.4258/hir.2024.30.3.172
Na Kyung Lee, Jong Seung Kim
{"title":"Status and Trends of the Digital Healthcare Industry.","authors":"Na Kyung Lee, Jong Seung Kim","doi":"10.4258/hir.2024.30.3.172","DOIUrl":"10.4258/hir.2024.30.3.172","url":null,"abstract":"<p><strong>Objectives: </strong>This review presents a comprehensive overview of the rapidly evolving digital healthcare industry, aiming to provide a broad understanding of the recent landscape and directions for the future of digital healthcare.</p><p><strong>Methods: </strong>This review examines the key trends in sectors of the digital healthcare industry, which can be divided into four main categories: digital hardware, software solutions, platforms, and enablers. We discuss electroceuticals, wearables, standalone medical software, non-medical health management services, telehealth, decentralized clinical trials, and infrastructural systems such as health data systems. The review covers both global and domestic perspectives, addressing definitions, significance, revenue trends, major companies, regulations, and socioenvironmental factors.</p><p><strong>Results: </strong>Diverse growth patterns are evident across digital healthcare sectors. The applications of electroceuticals are expanding. Wearables are becoming more ubiquitous, facilitating continuous health monitoring and data collection. Artificial intelligence in standalone medical software is demonstrating clinical efficacy, with regulatory frameworks adapting to support commercialization. Non-medical health management services are expanding their scope to address chronic conditions under professional guidance. Telemedicine and decentralized clinical trials are gaining traction, driven by the need for flexible healthcare solutions post-pandemic. Efforts to build robust digital infrastructure with health data are underway, supported by data banks and data aggregation platforms.</p><p><strong>Conclusions: </strong>Advancements in digital healthcare create a dynamic, transformative landscape, integrating, complementing, and offering alternatives to traditional paradigms. This evolution is driven by continuous innovation, increased stakeholder participation, regulatory adaptations promoting commercialization, and supportive initiatives. Ongoing discussions about optimal digital technology integration and effective healthcare strategy implementation are essential for progress.</p>","PeriodicalId":12947,"journal":{"name":"Healthcare Informatics Research","volume":"30 3","pages":"172-183"},"PeriodicalIF":2.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142004109","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
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信