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Applying AI to Care Management and Claims Processing 将人工智能应用于护理管理和索赔处理
North Carolina Medical Journal Pub Date : 2024-07-11 DOI: 10.18043/001c.120569
Sean Harrison, Melissa Leigh, Daniel Hallenbeck
{"title":"Applying AI to Care Management and Claims Processing","authors":"Sean Harrison, Melissa Leigh, Daniel Hallenbeck","doi":"10.18043/001c.120569","DOIUrl":"https://doi.org/10.18043/001c.120569","url":null,"abstract":"This article explores the current and potential applications of AI in care management and health care administration through the experience of Acentra Health. By discussing various AI use cases, this paper highlights how AI can augment the capabilities of healthcare professionals and streamline operations. Ethical considerations, legal compliance, and the future implications of AI in the health care sector are also examined.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141656836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
AI and Social Determinants of Health in Health Care: A Personal Perspective 医疗保健中的人工智能和健康的社会决定因素：个人视角
North Carolina Medical Journal Pub Date : 2024-07-11 DOI: 10.18043/001c.120568
Ashok Krishnamurthy, J. Zègre-Hemsey, Rebecca R. Kitzmiller, Brandy Farlow
{"title":"AI and Social Determinants of Health in Health Care: A Personal Perspective","authors":"Ashok Krishnamurthy, J. Zègre-Hemsey, Rebecca R. Kitzmiller, Brandy Farlow","doi":"10.18043/001c.120568","DOIUrl":"https://doi.org/10.18043/001c.120568","url":null,"abstract":"As a biomedical data scientist, when I think of the future of artificial intelligence in health care, the potential fills me with both excitement and caution. A promising area of innovation, AI can be used to assess the impact of social determinants of health on health outcomes, though more standardization is needed.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141658102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Toward an “Equitable” Assimilation of Artificial Intelligence and Machine Learning into Our Health Care System 让人工智能和机器学习 "公平 "地融入我们的医疗保健系统
North Carolina Medical Journal Pub Date : 2024-07-11 DOI: 10.18043/001c.120565
Ritu Agarwal, G. Gao
{"title":"Toward an “Equitable” Assimilation of Artificial Intelligence and Machine Learning into Our Health Care System","authors":"Ritu Agarwal, G. Gao","doi":"10.18043/001c.120565","DOIUrl":"https://doi.org/10.18043/001c.120565","url":null,"abstract":"Enthusiasm about the promise of artificial intelligence and machine learning in health care must be accompanied by oversight and remediation of any potential adverse effects on health equity goals that these technologies may create. We describe five equity imperatives for the use of AI/ML in health care that require attention from health care professionals, developers, and policymakers.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A Compass for North Carolina Health Care Workers Navigating the Adoption of Artificial Intelligence 北卡罗来纳州医护人员采用人工智能的指南针
North Carolina Medical Journal Pub Date : 2024-07-11 DOI: 10.18043/001c.120571
Yvonne Mosley, Miriam Tardif-Douglin, LaPonda Edmondson
{"title":"A Compass for North Carolina Health Care Workers Navigating the Adoption of Artificial Intelligence","authors":"Yvonne Mosley, Miriam Tardif-Douglin, LaPonda Edmondson","doi":"10.18043/001c.120571","DOIUrl":"https://doi.org/10.18043/001c.120571","url":null,"abstract":"This article underscores the economic benefits of AI, the importance of collaborative innovation, and the need for workforce development to prepare health care professionals for an AI-enhanced future. We include guidance for strategic and ethical AI adoption while advocating for a unified approach to leveraging technology to improve patient outcomes.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141655859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
A Health Care Clinical Data Platform for Rapid Deployment of Artificial Intelligence and Machine Learning Algorithms for Cancer Care and Oncology Clinical Trials 用于快速部署癌症护理和肿瘤临床试验人工智能和机器学习算法的医疗保健临床数据平台
North Carolina Medical Journal Pub Date : 2024-07-11 DOI: 10.18043/001c.120572
Soma Sengupta, Rohan Rao, Zachary Kaufman, Timothy J Stuhlmiller, Kenny K. Wong, Santosh Kesari, Mark A Shapiro, Glenn A. Kramer
{"title":"A Health Care Clinical Data Platform for Rapid Deployment of Artificial Intelligence and Machine Learning Algorithms for Cancer Care and Oncology Clinical Trials","authors":"Soma Sengupta, Rohan Rao, Zachary Kaufman, Timothy J Stuhlmiller, Kenny K. Wong, Santosh Kesari, Mark A Shapiro, Glenn A. Kramer","doi":"10.18043/001c.120572","DOIUrl":"https://doi.org/10.18043/001c.120572","url":null,"abstract":"The xCures platform aggregates, organizes, structures, and normalizes clinical EMR data across care sites, utilizing advanced technologies for near real-time access. The platform generates data in a format to support clinical care, accelerate research, and promote artificial intelligence/ machine learning algorithm development, highlighted by a clinical decision support algorithm for precision oncology.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141657239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Artificial Intelligence in Health Care: Opportunities, Challenges, and the Road Ahead 医疗保健领域的人工智能：机遇、挑战和未来之路
North Carolina Medical Journal Pub Date : 2024-07-11 DOI: 10.18043/001c.120561
Sean Sylvia, Junier Oliva
{"title":"Artificial Intelligence in Health Care: Opportunities, Challenges, and the Road Ahead","authors":"Sean Sylvia, Junier Oliva","doi":"10.18043/001c.120561","DOIUrl":"https://doi.org/10.18043/001c.120561","url":null,"abstract":"A comprehensive, collective approach to navigating the challenges of bias, privacy, and ethical considerations presented by the use of artificial intelligence in health care will require robust frameworks, continuous learning, and a commitment to equity. The insights and discussions presented in this issue are a testament to the ongoing efforts in North Carolina and beyond to find a balance between innovation with responsibility, ensuring that AI can deliver on its promise to enhance outcomes.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141656531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Comments on Contemporary Uses of Machine Learning for Electronic Health Records 关于机器学习在电子健康记录中的当代应用的评论
North Carolina Medical Journal Pub Date : 2024-07-11 DOI: 10.18043/001c.120570
Jordan Bryan, Didong Li
{"title":"Comments on Contemporary Uses of Machine Learning for Electronic Health Records","authors":"Jordan Bryan, Didong Li","doi":"10.18043/001c.120570","DOIUrl":"https://doi.org/10.18043/001c.120570","url":null,"abstract":"Various decisions concerning the management, display, and diagnostic use of electronic health records (EHR) data can be automated using machine learning (ML). We describe how ML is currently applied to EHR data and how it may be applied in the near future. Both benefits and shortcomings of ML are considered.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141657045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
“Plan, Don’t Panic” – Adapting to AI in Health Care "计划，不要惊慌"--在医疗保健领域适应人工智能
North Carolina Medical Journal Pub Date : 2024-07-11 DOI: 10.18043/001c.120534
Peter J. Morris
{"title":"“Plan, Don’t Panic” – Adapting to AI in Health Care","authors":"Peter J. Morris","doi":"10.18043/001c.120534","DOIUrl":"https://doi.org/10.18043/001c.120534","url":null,"abstract":"","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141657957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","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 in Health Care: Ethical Considerations Tied to Privacy, Interpretability, and Bias 医疗保健中的机器学习：与隐私、可解释性和偏见相关的伦理考虑因素
North Carolina Medical Journal Pub Date : 2024-07-11 DOI: 10.18043/001c.120562
Thomas Hofweber, Rebecca L. Walker
{"title":"Machine Learning in Health Care: Ethical Considerations Tied to Privacy, Interpretability, and Bias","authors":"Thomas Hofweber, Rebecca L. Walker","doi":"10.18043/001c.120562","DOIUrl":"https://doi.org/10.18043/001c.120562","url":null,"abstract":"Machine learning models hold great promise with medical applications, but also give rise to a series of ethical challenges. In this survey we focus on training data, model interpretability and bias and the related issues tied to privacy, autonomy, and health equity.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141658502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Sarcoidosis Mortality in North Carolina: Role of Region, Race, and Other Sociodemographic Variables 北卡罗来纳州肉样瘤病死亡率：地区、种族和其他社会人口变量的作用
North Carolina Medical Journal Pub Date : 2024-06-05 DOI: 10.18043/001c.118578
D. Wambui, Gregory Kearney, Kevin O'Brien, Guy Iverson, Ogugua Ndili Obi
{"title":"Sarcoidosis Mortality in North Carolina: Role of Region, Race, and Other Sociodemographic Variables","authors":"D. Wambui, Gregory Kearney, Kevin O'Brien, Guy Iverson, Ogugua Ndili Obi","doi":"10.18043/001c.118578","DOIUrl":"https://doi.org/10.18043/001c.118578","url":null,"abstract":"There is regional variability in sarcoidosis mortality across the United States. North Carolina ranks highly in sarcoidosis-related mortality, especially among African Americans (AA). We sought to determine any regional variability of sarcoidosis-related mortality and the relationship to sociodemographic determinants of health in North Carolina. Counties in North Carolina were categorized into three distinct geographic regions: Western, Piedmont, and Eastern. Sarcoidosis deaths were stratified by region, race, and gender. We conducted a mapping and cluster analysis utilizing ArcGIS; Global and Local Moran’s I was used to determine the prevalence, spatial autocorrelation, and clustering of mortality vis-a-vis various sociodemographic variables, occupational/environmental exposures, and levels of atmospheric particulate matter less than 2.5 microns in size (PM2.5). Multivariate linear regression with exposure limited to the county level was used to determine the relationship between sarcoidosis mortality and the variables of interest. Eastern North Carolina (ENC) had the highest age-adjusted sarcoidosis mortality rate (1.16/100,000 versus 0.49/100,000 in Piedmont and 0.32/100,000 in the Western region) with statistically significant high-high mortality clusters (P < .001 for Global Moran’s I). Several sociodemographic and occupational factors (proportion of AA, obese adults, and individuals working in nature) were more prevalent in ENC. Region and proportion of AA were the significant mortality predictors in our multivariate analysis. This was a cross-sectional study with exposure limited to the county level. Associations do not imply causality and risks cannot be extrapolated to the individual level. There is regional variability of sarcoidosis mortality in North Carolina. Eastern North Carolina had the highest mortality with high-high mortality clusters.","PeriodicalId":39574,"journal":{"name":"North Carolina Medical Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141381905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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