Sarah Bell, RN, MSN, MHA, Calvin D. Lawrence, MSc, Seth Dobrin, PhD, William Cherniak, MD MPH CCFP(EM) DABFM, Dr. Fernando De La Peña Llaca, PhD, MSc, Jefferson G. Fernandes, MD, MSc, PhD, MBA, Aditi U. Joshi MD, MSc, FACEP, Bilal Naved MD PhD (Candidate), Geoffrey Rutledge, MD,PhD, FACMI
Healthcare is rapidly evolving, particularly in the realm of digital health. When we consider the future of digital healthcare, it is impossible to ignore the vast potential of artificial intelligence (AI) and the profound impact it will have on the healthcare industry. This momentum of change has accelerated, particularly since the onset of the COVID-19 pandemic, and is largely attributable to workforce shortages and an increased demand for healthcare services. These circumstances have given rise to a unique scenario, compelling health-care to harness AI for various applications.The integration of AI in healthcare necessitates a comprehensive and rigorous approach to ensure accuracy and safety, acknowledging the inherent risks to patient care and safety when used improperly. When imple-menting care models that rely on AI for decision-making, it is imperative to establish meticulous workflows that emphasize human guidance in model development and allow models to adapt and learn from input data. In addition to prioritizing accuracy and safety, equal emphasis should be placed on the implementation of robust measures to protect patients from potential cybersecurity threats posed by data breaches. AI’s advantages extend beyond healthcare institutions, as patients will also experience a transformation in the way they receive care. Harnessing AI will empower patients to establish stronger connections with their health data and gain access to unique insights that are not readily available in traditional care models. These enhanced connections will enable patients to collaborate more effectively with their healthcare teams and receive care that is tailored to their specific needs.
{"title":"Near-Term Digital Health Predictions: A Glimpse into Tomorrow’s AI-driven Healthcare","authors":"Sarah Bell, RN, MSN, MHA, Calvin D. Lawrence, MSc, Seth Dobrin, PhD, William Cherniak, MD MPH CCFP(EM) DABFM, Dr. Fernando De La Peña Llaca, PhD, MSc, Jefferson G. Fernandes, MD, MSc, PhD, MBA, Aditi U. Joshi MD, MSc, FACEP, Bilal Naved MD PhD (Candidate), Geoffrey Rutledge, MD,PhD, FACMI","doi":"10.30953/thmt.v8.452","DOIUrl":"https://doi.org/10.30953/thmt.v8.452","url":null,"abstract":"Healthcare is rapidly evolving, particularly in the realm of digital health. When we consider the future of digital healthcare, it is impossible to ignore the vast potential of artificial intelligence (AI) and the profound impact it will have on the healthcare industry. This momentum of change has accelerated, particularly since the onset of the COVID-19 pandemic, and is largely attributable to workforce shortages and an increased demand for healthcare services. These circumstances have given rise to a unique scenario, compelling health-care to harness AI for various applications.The integration of AI in healthcare necessitates a comprehensive and rigorous approach to ensure accuracy and safety, acknowledging the inherent risks to patient care and safety when used improperly. When imple-menting care models that rely on AI for decision-making, it is imperative to establish meticulous workflows that emphasize human guidance in model development and allow models to adapt and learn from input data. In addition to prioritizing accuracy and safety, equal emphasis should be placed on the implementation of robust measures to protect patients from potential cybersecurity threats posed by data breaches. AI’s advantages extend beyond healthcare institutions, as patients will also experience a transformation in the way they receive care. Harnessing AI will empower patients to establish stronger connections with their health data and gain access to unique insights that are not readily available in traditional care models. These enhanced connections will enable patients to collaborate more effectively with their healthcare teams and receive care that is tailored to their specific needs.","PeriodicalId":320236,"journal":{"name":"Telehealth and Medicine Today","volume":"94 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139232082","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}
In June 2022, President Biden and Vice President Harris released their blueprint for addressing the maternal health crisis, stating their “vision for the future is that the United States will be considered the best country in the world to have a baby.” Currently, it is one of the worst among industrialized countries despite the US spending nearly double the average amount on healthcare per capita. The US is amidst a maternal mortality crisis, particularly for Black and American Indian/Alaska Native pregnant people, with more than 80% of the deaths preventable. Telehealth in obstetrics has the potential to reach pregnant people who are not currently being served by the medical system and to improve rates of severe maternal morbidity and mortality; however, more research is needed to understand and monitor its equity, costs, and optimal usage. Extant research shows that telehealth can produce a small positive effect for certain obstetric health outcomes, but these research findings are rarely stratified by pregnant patients’ demographics. To prevent the perpetuation of existing health inequities, gaps in obstetric telehealth research will need to be addressed. Key knowledge gaps for researchers and policy makers include outcomes, access, satisfaction by patients and providers, potential time savings for patients, and health system cost savings. Implementation of equitable obstetric coverage of telehealth services requires clarity from private and public payers for inter-state provisions of care, liability and risk, and service and payment parity.Note: While we use pregnant and postpartum people where possible here to recognize that not all pregnant people identify as women, we occasionally use maternal to reflect terminology used in federal, state, and local data.
{"title":"Telehealth for Equitable Obstetric Care","authors":"Sabrina Movitz, MS, Rachel Mayer Ediger, Alison Dingwall, Yvonne Butler Tobah","doi":"10.30953/thmt.v8.443","DOIUrl":"https://doi.org/10.30953/thmt.v8.443","url":null,"abstract":"In June 2022, President Biden and Vice President Harris released their blueprint for addressing the maternal health crisis, stating their “vision for the future is that the United States will be considered the best country in the world to have a baby.” Currently, it is one of the worst among industrialized countries despite the US spending nearly double the average amount on healthcare per capita. The US is amidst a maternal mortality crisis, particularly for Black and American Indian/Alaska Native pregnant people, with more than 80% of the deaths preventable. Telehealth in obstetrics has the potential to reach pregnant people who are not currently being served by the medical system and to improve rates of severe maternal morbidity and mortality; however, more research is needed to understand and monitor its equity, costs, and optimal usage. Extant research shows that telehealth can produce a small positive effect for certain obstetric health outcomes, but these research findings are rarely stratified by pregnant patients’ demographics. To prevent the perpetuation of existing health inequities, gaps in obstetric telehealth research will need to be addressed. Key knowledge gaps for researchers and policy makers include outcomes, access, satisfaction by patients and providers, potential time savings for patients, and health system cost savings. Implementation of equitable obstetric coverage of telehealth services requires clarity from private and public payers for inter-state provisions of care, liability and risk, and service and payment parity.Note: While we use pregnant and postpartum people where possible here to recognize that not all pregnant people identify as women, we occasionally use maternal to reflect terminology used in federal, state, and local data.","PeriodicalId":320236,"journal":{"name":"Telehealth and Medicine Today","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139272582","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}
Dr Haleema Yezdani, Avneesh Khare MBBS, MD, DNB, MNAMS, PDCC, MBA, Dr. M A Maluk Mohamed
Panelists discuss the clinical aspects of remote patient monitoring (RPM) in telehealth and clinical enhancements including technologies such as translational AI, and the benefits of RPM on acute and chronic medical conditions. Questions include What is RPM and how is it beneficial in chronic disease management? How is Telemedicine different from RPM? What is the role of AI in RPM? How has RPM taken over and changed the life of people? Have patients at large accepted the concept of RPM?
{"title":"Remote Patient Monitoring in India","authors":"Dr Haleema Yezdani, Avneesh Khare MBBS, MD, DNB, MNAMS, PDCC, MBA, Dr. M A Maluk Mohamed","doi":"10.30953/thmt.v8.430","DOIUrl":"https://doi.org/10.30953/thmt.v8.430","url":null,"abstract":"Panelists discuss the clinical aspects of remote patient monitoring (RPM) in telehealth and clinical enhancements including technologies such as translational AI, and the benefits of RPM on acute and chronic medical conditions. \u0000Questions include \u0000 \u0000What is RPM and how is it beneficial in chronic disease management? \u0000How is Telemedicine different from RPM? \u0000What is the role of AI in RPM? \u0000How has RPM taken over and changed the life of people? \u0000Have patients at large accepted the concept of RPM? \u0000","PeriodicalId":320236,"journal":{"name":"Telehealth and Medicine Today","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130918406","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}
Bharath Perugu, MBA, Varun Wadhwa, BS, Jin Kim, ME, Jenny Cai, BS (Candidate), Audrey Shin, BS (Candidate), Amar Gupta, MBA/PhD
Objective: In this article, the authors review the progress in healthcare interoperability from 2010 to 2023 from an international perspective. Countries covered here include the Republic of China (Taiwan), the Republic of Croatia, the Republic of Estonia, and the Commonwealth of Australia. Interoperability, in the context of this paper, is “the ability to share information across time and space from multiple devices, sources, and organizations,” as defined by the Institute of Electrical and Electronic Engineers (IEEE). This is followed by recommendations for future work toward improving the standardization of heterogeneous data in the healthcare setting. Methodology: A literature review was conducted on established interoperability standards and systems in healthcare based on information obtained from journal publications, government, academy reports, published materials, and publicly available websites. Examples of specific interoperability efforts and an evaluation of their feasibility were conducted at three levels of healthcare interoperability, as defined by the National Academy of Medicine: 1) inter-facility (macro-tier) interoperability, 2) intra-facility (meso-tier) interoperability, and 3) Point-of-Care (micro-tier) interoperability. The authors assessed the adoption levels of each standard by looking at factors that support or limit its systemic adoption. Estimations on the number of users—medical professionals and patients—for each system were made in instances where verifiable data are available. Results: Presented here is an evaluation of four interoperability parameters: 1) device/equipment interoperability, 2) compatibility issues, 3) involved organizations, and 4) migration and conversion issues. Conclusions: Despite many parallel ongoing efforts to improve the standardization of healthcare information in the mobile devices, Internet of Things (IoT), and electronic health records (HER) sectors, there remains space for improvement. The recent development of the Trusted Exchange Framework and Common Agreement (TEFCA) framework has significantly reduced the friction of data exchange in many healthcare contexts. In addition, funding architectures for mediating data between separate healthcare organizations, or middleware architectures, may also be an effective strategy for consolidating healthcare data and improving information exchange.
目的:本文从国际视角回顾了2010 - 2023年医疗互操作性的进展。这里涵盖的国家包括中华民国(台湾)、克罗地亚共和国、爱沙尼亚共和国和澳大利亚联邦。互操作性，在本文的上下文中，是“从多个设备、来源和组织跨时间和空间共享信息的能力”，正如电气和电子工程师协会(IEEE)所定义的那样。接下来是对未来工作的建议，以改善医疗保健环境中异构数据的标准化。方法:根据从期刊出版物、政府、学术报告、出版材料和公开网站获得的信息，对医疗保健领域已建立的互操作性标准和系统进行了文献综述。根据美国国家医学院(National Academy of Medicine)的定义，在医疗保健互操作性的三个级别上进行了具体互操作性工作示例及其可行性评估:1)设施间(宏观层)互操作性，2)设施内(中观层)互操作性，以及3)医疗点(微观层)互操作性。作者通过观察支持或限制其系统采用的因素来评估每个标准的采用水平。在有可核实数据的情况下，对每个系统的用户(医务专业人员和病人)人数进行了估计。结果:这里给出了四个互操作性参数的评估:1)设备/设备互操作性，2)兼容性问题，3)涉及的组织，以及4)迁移和转换问题。结论:尽管在移动设备、物联网(IoT)和电子健康记录(HER)领域有许多正在进行的改进医疗信息标准化的并行努力，但仍有改进的空间。最近开发的可信交换框架和共同协议(TEFCA)框架大大减少了许多医疗保健环境中数据交换的摩擦。此外，为独立医疗保健组织之间的数据中介提供资金的体系结构或中间件体系结构也可能是整合医疗保健数据和改进信息交换的有效策略。
{"title":"Surmounting Barriers to Healthcare Data and Information: International Case Studies","authors":"Bharath Perugu, MBA, Varun Wadhwa, BS, Jin Kim, ME, Jenny Cai, BS (Candidate), Audrey Shin, BS (Candidate), Amar Gupta, MBA/PhD","doi":"10.30953/thmt.v8.428","DOIUrl":"https://doi.org/10.30953/thmt.v8.428","url":null,"abstract":"Objective: In this article, the authors review the progress in healthcare interoperability from 2010 to 2023 from an international perspective. Countries covered here include the Republic of China (Taiwan), the Republic of Croatia, the Republic of Estonia, and the Commonwealth of Australia. Interoperability, in the context of this paper, is “the ability to share information across time and space from multiple devices, sources, and organizations,” as defined by the Institute of Electrical and Electronic Engineers (IEEE). This is followed by recommendations for future work toward improving the standardization of heterogeneous data in the healthcare setting.\u0000Methodology: A literature review was conducted on established interoperability standards and systems in healthcare based on information obtained from journal publications, government, academy reports, published materials, and publicly available websites. Examples of specific interoperability efforts and an evaluation of their feasibility were conducted at three levels of healthcare interoperability, as defined by the National Academy of Medicine: 1) inter-facility (macro-tier) interoperability, 2) intra-facility (meso-tier) interoperability, and 3) Point-of-Care (micro-tier) interoperability. The authors assessed the adoption levels of each standard by looking at factors that support or limit its systemic adoption. Estimations on the number of users—medical professionals and patients—for each system were made in instances where verifiable data are available.\u0000Results: Presented here is an evaluation of four interoperability parameters: 1) device/equipment interoperability, 2) compatibility issues, 3) involved organizations, and 4) migration and conversion issues.\u0000Conclusions: Despite many parallel ongoing efforts to improve the standardization of healthcare information in the mobile devices, Internet of Things (IoT), and electronic health records (HER) sectors, there remains space for improvement. The recent development of the Trusted Exchange Framework and Common Agreement (TEFCA) framework has significantly reduced the friction of data exchange in many healthcare contexts. In addition, funding architectures for mediating data between separate healthcare organizations, or middleware architectures, may also be an effective strategy for consolidating healthcare data and improving information exchange.","PeriodicalId":320236,"journal":{"name":"Telehealth and Medicine Today","volume":"572 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134237711","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}
Objective This paper provides a review of the landscape of interoperability efforts in healthcare from 2010 to 2023, in the US and abroad. Interoperability, in the context of this paper, is “the ability to share information across time and space from multiple devices, sources, and organizations”, as defined by the IEEE (Institute of Electrical and Electronic Engineers). This review is followed by recommendations for future work toward improving the standardization of heterogeneous data in the healthcare setting. Methodology and Results A literature review was conducted on established interoperability standards and systems in healthcare, based on information obtained from journal publications, government, academy reports, published materials, and publicly available websites. The review emphasizes four interoperability parameters: device/equipment interoperability, compatibility issues, involved organizations, and migration and conversion issues. It evaluates adoption levels for each standard, evaluating factors supporting and/or limiting systemic adoption. Estimations on the number of users – both medical professionals and patients – for each system were made in instances where verifiable data were available. Examples of specific interoperability efforts and an evaluation of their feasibility were conducted at three levels of healthcare interoperability, as defined by the National Academy of Medicine: Inter-facility (macro-tier) interoperability, Intra-facility (meso-tier) interoperability, and Point-of-care (micro-tier) interoperability. Conclusions Despite many parallel ongoing efforts to improve the standardization of healthcare information, in the mobile devices, IoT, and EHR sectors, there is still more space for improvement. The recent development of the TEFCA framework has greatly reduced the friction of data exchange in many healthcare contexts. In addition, funding architectures for mediating data between separate healthcare organizations, or middleware architectures, may also be an effective strategy for consolidating healthcare data and improving information exchange.
{"title":"Pragmatic Approaches to Interoperability – Surmounting Barriers to Healthcare Data and Information Across Organizations and Political Boundaries","authors":"Bharath Perugu, MBA, Varun Wadhwa, BS, Jin Kim, ME, Jenny Cai, BS (Candidate), Audrey Shin, BS (Candidate), Amar Gupta, MBA, PhD","doi":"10.30953/thmt.v8.421","DOIUrl":"https://doi.org/10.30953/thmt.v8.421","url":null,"abstract":"Objective\u0000This paper provides a review of the landscape of interoperability efforts in healthcare from 2010 to 2023, in the US and abroad. Interoperability, in the context of this paper, is “the ability to share information across time and space from multiple devices, sources, and organizations”, as defined by the IEEE (Institute of Electrical and Electronic Engineers). This review is followed by recommendations for future work toward improving the standardization of heterogeneous data in the healthcare setting.\u0000 \u0000Methodology and Results\u0000A literature review was conducted on established interoperability standards and systems in healthcare, based on information obtained from journal publications, government, academy reports, published materials, and publicly available websites. The review emphasizes four interoperability parameters: device/equipment interoperability, compatibility issues, involved organizations, and migration and conversion issues. It evaluates adoption levels for each standard, evaluating factors supporting and/or limiting systemic adoption. Estimations on the number of users – both medical professionals and patients – for each system were made in instances where verifiable data were available. \u0000 \u0000Examples of specific interoperability efforts and an evaluation of their feasibility were conducted at three levels of healthcare interoperability, as defined by the National Academy of Medicine: Inter-facility (macro-tier) interoperability, Intra-facility (meso-tier) interoperability, and Point-of-care (micro-tier) interoperability.\u0000 \u0000Conclusions\u0000Despite many parallel ongoing efforts to improve the standardization of healthcare information, in the mobile devices, IoT, and EHR sectors, there is still more space for improvement. The recent development of the TEFCA framework has greatly reduced the friction of data exchange in many healthcare contexts. In addition, funding architectures for mediating data between separate healthcare organizations, or middleware architectures, may also be an effective strategy for consolidating healthcare data and improving information exchange.","PeriodicalId":320236,"journal":{"name":"Telehealth and Medicine Today","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115758829","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}
Francis X. Campion, MD, FACP, Aanchal Mathur, MS, Beata Konczewski, MS
Importance: This very large claims data analysis documents lower rates of hospitalization among patients receiving telehealth during a skilled nursing facility stay compared to patients who did not receive telehealth. These findings from care during the COVID-19 pandemic support the call for telehealth to become a routine option even after the public health emergency has ended. Objective: The COVID-19 Telehealth Impact Study was designed to describe the natural experiment of telehealth adoption during the pandemic. This focused analysis can assist program development for the large population of patients cared for in skilled nursing facilities. Design, Setting, Participants: In March 2020, the MITRE Corporation and Mayo Clinic founded the COVID-19 Healthcare Coalition (C19HCC) and Telehealth Impact Study to respond to the pandemic. We report trends using a data set of 668,533 patients cared for in skilled nursing facilities (SNF) between January 2020 and March 2021. Main Outcomes and Measures: We compared rates of hospitalization within 60 days of admission to the skilled nursing facility for patients who received telehealth and those who did not receive telehealth during their SNF stay. We analyzed subgroups of patients based upon the clinical diagnoses for which they received professional services from physicians and other providers. Results: Of the 668,533 patients meeting criteria for an admission to a SNF facility, 97,204 (14.5%) had one or more telehealth visits. 40.1% of patients received one TH visit per 30 days during their SNF stay; 9.4% received ten or more TH visits per 30 days. Patients in all six diagnostic cohorts experienced significantly lower rates of hospitalization compared to patients who received no telehealth despite having a higher average disease burden measured by the Charlson Comorbidity Index. Absolute rates of hospitalization rates dropped between 1.25% (dementia) and 1.87% (orthopedic) for clinical groups. The relative rates of hospitalization dropped between 22% (cardiovascular) and 33% (dementia). Conclusions and Relevance: This study underscores the potential benefits of using virtual care in skilled nursing facilities to lower rates of hospitalization and improve outcomes. We encourage continued study of the use digital health services for patients in post-acute settings and promotion of insurance coverage in the years to come as new best practices emerge.
{"title":"Impact of Telehealth on Hospitalization of Skilled Nursing Facility Patients During the COVID-19 Pandemic","authors":"Francis X. Campion, MD, FACP, Aanchal Mathur, MS, Beata Konczewski, MS","doi":"10.30953/thmt.v8.416","DOIUrl":"https://doi.org/10.30953/thmt.v8.416","url":null,"abstract":"Importance: This very large claims data analysis documents lower rates of hospitalization among patients receiving telehealth during a skilled nursing facility stay compared to patients who did not receive telehealth. These findings from care during the COVID-19 pandemic support the call for telehealth to become a routine option even after the public health emergency has ended. \u0000Objective: The COVID-19 Telehealth Impact Study was designed to describe the natural experiment of telehealth adoption during the pandemic. This focused analysis can assist program development for the large population of patients cared for in skilled nursing facilities. \u0000Design, Setting, Participants: In March 2020, the MITRE Corporation and Mayo Clinic founded the COVID-19 Healthcare Coalition (C19HCC) and Telehealth Impact Study to respond to the pandemic. We report trends using a data set of 668,533 patients cared for in skilled nursing facilities (SNF) between January 2020 and March 2021. \u0000Main Outcomes and Measures: We compared rates of hospitalization within 60 days of admission to the skilled nursing facility for patients who received telehealth and those who did not receive telehealth during their SNF stay. We analyzed subgroups of patients based upon the clinical diagnoses for which they received professional services from physicians and other providers. \u0000Results: Of the 668,533 patients meeting criteria for an admission to a SNF facility, 97,204 (14.5%) had one or more telehealth visits. 40.1% of patients received one TH visit per 30 days during their SNF stay; 9.4% received ten or more TH visits per 30 days. Patients in all six diagnostic cohorts experienced significantly lower rates of hospitalization compared to patients who received no telehealth despite having a higher average disease burden measured by the Charlson Comorbidity Index. Absolute rates of hospitalization rates dropped between 1.25% (dementia) and 1.87% (orthopedic) for clinical groups. The relative rates of hospitalization dropped between 22% (cardiovascular) and 33% (dementia). \u0000Conclusions and Relevance: This study underscores the potential benefits of using virtual care in skilled nursing facilities to lower rates of hospitalization and improve outcomes. We encourage continued study of the use digital health services for patients in post-acute settings and promotion of insurance coverage in the years to come as new best practices emerge.","PeriodicalId":320236,"journal":{"name":"Telehealth and Medicine Today","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134593874","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}
Bharath Perugu, MBA, Varun Wadhwa, BS, Jin Kim, ME, Jenny Cai, BS (Candidate), Audrey Shin, BS (Candidate), Amar Gupta, MBA/PhD
Objective: The authors review the progress in healthcare interoperability from 2010 to 2023 in the United States. Interoperability, in the context of this paper, is “the ability to share information across time and space from multiple devices, sources, and organizations”, as defined by the IEEE (Institute of Electrical and Electronic Engineers). This is followed by recommendations for future work toward improving the standardization of heterogeneous data in the healthcare setting. Methodology: A literature review was conducted on established interoperability standards and systems in healthcare based on information obtained from journal publications, government, academy reports, published materials, as well as publicly available websites. Examples of specific interoperability efforts and an evaluation of their feasibility were conducted at three levels of healthcare interoperability, as defined by the National Academy of Medicine: 1) inter-facility (macro-tier) interoperability, 2) intra-facility (meso-tier) interoperability, and 3) Point-of-Care (micro-tier) interoperability. An evaluation of four interoperability parameters: 1) device/equipment interoperability, 2) compatibility issues, 3) involved organizations, and 4) migration and conversion issues are presented. The evaluation assessed the adoption levels of each standard by looking at factors that support or limit its systemic adoption. Estimations on the number of users—medical professionals and patients—for each system were made in instances where verifiable data were available. Results: This review reveals that… Conclusions: Despite many parallel ongoing efforts to improve the standardization of healthcare information, in the mobile devices, Internet of Things (IoT), and electronic health record (HER) sectors, there remains space for improvement. The recent development of the Trusted Exchange Framework and Common Agreement (TEFCA) greatly reduces the friction of data exchange in many healthcare contexts. In addition, funding architectures for mediating data between separate healthcare organizations, or middleware architectures, might also be an effective strategy for consolidating healthcare data and improving information exchange.
目的:回顾2010年至2023年美国医疗互操作性的进展。互操作性，在本文的上下文中，是“从多个设备、来源和组织跨时间和空间共享信息的能力”，由IEEE(电气和电子工程师协会)定义。接下来是对未来工作的建议，以改善医疗保健环境中异构数据的标准化。方法:基于从期刊出版物、政府、学术报告、出版材料以及公开网站获得的信息，对医疗保健中已建立的互操作性标准和系统进行了文献综述。根据美国国家医学院(National Academy of Medicine)的定义，在医疗保健互操作性的三个级别上进行了具体互操作性工作示例及其可行性评估:1)设施间(宏观层)互操作性，2)设施内(中观层)互操作性，以及3)医疗点(微观层)互操作性。提出了四个互操作性参数的评估:1)设备/设备互操作性，2)兼容性问题，3)涉及的组织，以及4)迁移和转换问题。评估通过查看支持或限制其系统采用的因素来评估每个标准的采用水平。在有可核实数据的情况下，对每个系统的用户(医务专业人员和病人)人数进行了估计。结论:尽管在移动设备、物联网(IoT)和电子健康记录(HER)领域有许多并行的正在进行的努力来提高医疗信息的标准化，但仍有改进的空间。最近开发的可信交换框架和通用协议(TEFCA)大大减少了许多医疗保健环境中数据交换的摩擦。此外，为独立医疗保健组织之间的数据中介提供资金的体系结构或中间件体系结构也可能是整合医疗保健数据和改进信息交换的有效策略。
{"title":"Surmounting Barriers to Healthcare Data and Information: Cases in Point, the U.S. Experience","authors":"Bharath Perugu, MBA, Varun Wadhwa, BS, Jin Kim, ME, Jenny Cai, BS (Candidate), Audrey Shin, BS (Candidate), Amar Gupta, MBA/PhD","doi":"10.30953/thmt.v8.425","DOIUrl":"https://doi.org/10.30953/thmt.v8.425","url":null,"abstract":"Objective: The authors review the progress in healthcare interoperability from 2010 to 2023 in the United States. Interoperability, in the context of this paper, is “the ability to share information across time and space from multiple devices, sources, and organizations”, as defined by the IEEE (Institute of Electrical and Electronic Engineers). This is followed by recommendations for future work toward improving the standardization of heterogeneous data in the healthcare setting.\u0000Methodology: A literature review was conducted on established interoperability standards and systems in healthcare based on information obtained from journal publications, government, academy reports, published materials, as well as publicly available websites. Examples of specific interoperability efforts and an evaluation of their feasibility were conducted at three levels of healthcare interoperability, as defined by the National Academy of Medicine: 1) inter-facility (macro-tier) interoperability, 2) intra-facility (meso-tier) interoperability, and 3) Point-of-Care (micro-tier) interoperability.\u0000An evaluation of four interoperability parameters: 1) device/equipment interoperability, 2) compatibility issues, 3) involved organizations, and 4) migration and conversion issues are presented. The evaluation assessed the adoption levels of each standard by looking at factors that support or limit its systemic adoption. Estimations on the number of users—medical professionals and patients—for each system were made in instances where verifiable data were available.\u0000Results: This review reveals that…\u0000Conclusions: Despite many parallel ongoing efforts to improve the standardization of healthcare information, in the mobile devices, Internet of Things (IoT), and electronic health record (HER) sectors, there remains space for improvement. The recent development of the Trusted Exchange Framework and Common Agreement (TEFCA) greatly reduces the friction of data exchange in many healthcare contexts. In addition, funding architectures for mediating data between separate healthcare organizations, or middleware architectures, might also be an effective strategy for consolidating healthcare data and improving information exchange.","PeriodicalId":320236,"journal":{"name":"Telehealth and Medicine Today","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131372544","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}
Karen A. Truitt, DO, Kogulavadanan Arumaithurai, MD, Nathan Young, DO
Abstract Introduction: We assessed patients’ satisfaction and perception of physician empathy after implementation of video telemedicine service for general neurology follow-up visits at two community spoke clinics that serve patients in rural areas located approximately 45 miles from a medical center hub. Methods: Consecutive patients who completed a telemedicine neurology follow-up visit from February 12, 2020 to January 13, 2021 at the spoke clinic in Red Wing, MN and from July 21, 2021 to January 21, 2022 at the spoke clinic in Austin, MN were asked to complete a paper-based survey at the conclusion of the telemedicine visit. The neurologist conducted the telemedicine visit from the medical center hub site in Rochester, MN or from their own home using the InTouch (Teladoc Health™) operating system mobile telehealth platform. All patients had previously completed an initial traditional face-to-face consultation at the spoke clinic with the same neurologist performing the follow-up telemedicine visit. Primary outcomes were Telemedicine Patient Satisfaction Measure and Consultation and Relational Empathy scores and mean total favorable survey responses. Results: 31 patients at our clinic in Red Wing, MN and 38 patients at our clinic in Austin, MN participated in telemedicine neurology follow-up visit, completed the survey, and were included in the final analysis. The mean Telemedicine Patient Satisfaction Measure scores (possible score of 12-60) were 55 (range 42-60), and for all items ‘agree’ or ‘strongly agree’ was rated on average 94% of the time. The mean Consultation and Relational Empathy scores (possible score of 10–50) were 44 (range 28–50), and for all items ‘very good’ or ‘excellent’ was rated on average 90% of the time. Both neurologists conducting telemedicine visits reported that this model of care improved work-life balance with reduced travel time. Discussion: We successfully implemented a telemedicine service for general neurology follow-up visits at two community spoke clinics serving patients in rural areas without compromising on perceived care. We were able to bridge the gap between patients’ needs for local care and physicians’ need for work-life balance. Patients’ perception of physician empathy and satisfaction with telemedicine neurology follow-up visits was high. This model of telemedicine avoids the barriers of limited internet access in rural areas and minimizes technology related anxiety that is often present in telemedicine visits to patients’ homes. This model allowed for high quality neurological examination with high resolution pan-tilt-zoom camera on a mobile platform, incorporation of vital signs, nursing support, and lab services that may have contributed to the patients’ and neurologists’ satisfaction. Our study supports our continuation and expansion of this TM model in our community clinic spoke sites and may help to improve access to neurological care for patients in rural areas. Keywords: Teleme
{"title":"Patient Satisfaction and Perception of Physician Empathy in Outpatient Community General Neurology Telemedicine","authors":"Karen A. Truitt, DO, Kogulavadanan Arumaithurai, MD, Nathan Young, DO","doi":"10.30953/thmt.v8.422","DOIUrl":"https://doi.org/10.30953/thmt.v8.422","url":null,"abstract":"Abstract \u0000Introduction: We assessed patients’ satisfaction and perception of physician empathy after implementation of video telemedicine service for general neurology follow-up visits at two community spoke clinics that serve patients in rural areas located approximately 45 miles from a medical center hub. \u0000Methods: Consecutive patients who completed a telemedicine neurology follow-up visit from February 12, 2020 to January 13, 2021 at the spoke clinic in Red Wing, MN and from July 21, 2021 to January 21, 2022 at the spoke clinic in Austin, MN were asked to complete a paper-based survey at the conclusion of the telemedicine visit. The neurologist conducted the telemedicine visit from the medical center hub site in Rochester, MN or from their own home using the InTouch (Teladoc Health™) operating system mobile telehealth platform. All patients had previously completed an initial traditional face-to-face consultation at the spoke clinic with the same neurologist performing the follow-up telemedicine visit. Primary outcomes were Telemedicine Patient Satisfaction Measure and Consultation and Relational Empathy scores and mean total favorable survey responses. \u0000Results: 31 patients at our clinic in Red Wing, MN and 38 patients at our clinic in Austin, MN participated in telemedicine neurology follow-up visit, completed the survey, and were included in the final analysis. \u0000The mean Telemedicine Patient Satisfaction Measure scores (possible score of 12-60) were 55 (range 42-60), and for all items ‘agree’ or ‘strongly agree’ was rated on average 94% of the time. The mean Consultation and Relational Empathy scores (possible score of 10–50) were 44 (range 28–50), and for all items ‘very good’ or ‘excellent’ was rated on average 90% of the time. \u0000Both neurologists conducting telemedicine visits reported that this model of care improved work-life balance with reduced travel time. \u0000Discussion: We successfully implemented a telemedicine service for general neurology follow-up visits at two community spoke clinics serving patients in rural areas without compromising on perceived care. We were able to bridge the gap between patients’ needs for local care and physicians’ need for work-life balance. Patients’ perception of physician empathy and satisfaction with telemedicine neurology follow-up visits was high. This model of telemedicine avoids the barriers of limited internet access in rural areas and minimizes technology related anxiety that is often present in telemedicine visits to patients’ homes. This model allowed for high quality neurological examination with high resolution pan-tilt-zoom camera on a mobile platform, incorporation of vital signs, nursing support, and lab services that may have contributed to the patients’ and neurologists’ satisfaction. Our study supports our continuation and expansion of this TM model in our community clinic spoke sites and may help to improve access to neurological care for patients in rural areas. \u0000Keywords: Teleme","PeriodicalId":320236,"journal":{"name":"Telehealth and Medicine Today","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121606655","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}
Introduction: With the onset of the COVID-19 pandemic in 2020, the utilization of telemedicine now offered an alternative diagnostic and treatment resource to providers in many areas of medicine including oncology and cancer genetics. This care option paired with genetic testing labs’ ability to send saliva-based DNA collection kits to patients, enabled our community hospital in Detroit to offer diagnostic testing without the patient coming to a healthcare setting for a host of reasons. Social determinants of health have been found to influence success with telehealth, and this study sought to analyze how successful telehealth cancer genetics care was throughout the Detroit Metro area. Methods: Patient demographics for in person visits six months before COVID were analyzed, and then compared with demographics of patients during the 2020-2021 pandemic period where visits were telehealth. Results: Pre-pandemic there were , 192 unique patients seen in person with the top three cities patients were from were Detroit (12.1%), Clinton Township (8.3%), and Saint Clair Shores (10.4%). During the pandemic, with telehealth as the major modality, the top three cities were Macomb (7.2%), Detroit (7%), and Clinton Township (7%). Detroit is in Wayne County, while St.Clair Shores and Clinton Township are in Macomb County. Per the US Census Bureau Macomb county has a median income of $64,641 and Wayne county has a median income of $49,359, and poverty level in Macomb county is 9.2% versus in Wayne the level is 20%. Conclusions: This paper outlines the challenges of initiating a telemedicine program in an urban community area and highlights the benefits of a concierge service in serving cancer patients who may have economic and historically poor perceived technologic abilities.
{"title":"Tale of Two Counties With the Same Telehealth Story: Experience with Cancer Genetics Testing During COVID-19 at a Community Hospital","authors":"Allison M Jay, Hannah Kelley, Susan M. Jay","doi":"10.30953/thmt.v8.382","DOIUrl":"https://doi.org/10.30953/thmt.v8.382","url":null,"abstract":"Introduction: With the onset of the COVID-19 pandemic in 2020, the utilization of telemedicine now offered an alternative diagnostic and treatment resource to providers in many areas of medicine including oncology and cancer genetics. This care option paired with genetic testing labs’ ability to send saliva-based DNA collection kits to patients, enabled our community hospital in Detroit to offer diagnostic testing without the patient coming to a healthcare setting for a host of reasons. Social determinants of health have been found to influence success with telehealth, and this study sought to analyze how successful telehealth cancer genetics care was throughout the Detroit Metro area. Methods: Patient demographics for in person visits six months before COVID were analyzed, and then compared with demographics of patients during the 2020-2021 pandemic period where visits were telehealth. Results: Pre-pandemic there were , 192 unique patients seen in person with the top three cities patients were from were Detroit (12.1%), Clinton Township (8.3%), and Saint Clair Shores (10.4%). During the pandemic, with telehealth as the major modality, the top three cities were Macomb (7.2%), Detroit (7%), and Clinton Township (7%). Detroit is in Wayne County, while St.Clair Shores and Clinton Township are in Macomb County. Per the US Census Bureau Macomb county has a median income of $64,641 and Wayne county has a median income of $49,359, and poverty level in Macomb county is 9.2% versus in Wayne the level is 20%. Conclusions: This paper outlines the challenges of initiating a telemedicine program in an urban community area and highlights the benefits of a concierge service in serving cancer patients who may have economic and historically poor perceived technologic abilities.","PeriodicalId":320236,"journal":{"name":"Telehealth and Medicine Today","volume":"299 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132850640","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}
F. Cascini, A. Pantovic, Y. Al-Ajlouni, Omar Al Ta'ani, G. Failla, A. Melnyk, P. Barach, W. Ricciardi
Background: The literature supporting telehealth management is growing accelerated by the COVID-pandemic. We hypothesize that there are risks of adverse events associated with telehealth interventions. Methods: A review of PubMed (including MEDLINE), Embase, ISI (Web of Science), VHL/GHL, Scopus, Science Direct, and PsycINFO was conducted for all adverse events associated with telehealth from January 1, 1960 to March 1, 2021. This systematic review and meta-analyses were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: Of 5,144 citations 78 published studies met criteria for quality evaluation and underwent full text abstraction including the qualitative synthesis. Of the 78 included studies 8 were included in the quantitative synthesis resulting in 2 meta-analyses. The results of the meta-analysis suggest that monitoring patients using telehealth techniques is associated with 40% lower mortality risks among patients suffering from heart failure, compared to those who received traditional care. The results of the random-effects meta-analysis showed the pooled relative risk of mortality to be 0.60, indicating that patients that underwent telemonitoring had a lower mortality risk compared with the patients that underwent usual care. Among patients with heart implants, patients who received telemonitoring had a 35% lower mortality risk compared to patients receiving traditional care. Conclusions: While RCTs of telehealth interventions demonstrate enhanced patient outcomes in a number of studies and pave the way to evidence-based practice, the heterogeneity of the research questions suggest an important need for more complementary studies with consistent outcome assessments.
背景:受新冠肺炎疫情影响，支持远程医疗管理的文献越来越多。我们假设存在与远程医疗干预相关的不良事件风险。方法:对PubMed(包括MEDLINE)、Embase、ISI (Web of Science)、VHL/GHL、Scopus、Science Direct和PsycINFO等网站1960年1月1日至2021年3月1日期间与远程医疗相关的所有不良事件进行回顾性分析。本系统评价和荟萃分析是根据系统评价和荟萃分析首选报告项目(PRISMA)指南进行的。结果:在5144篇引用中，78篇已发表的研究符合质量评价标准，并进行了全文摘要，包括定性综合。在纳入的78项研究中，有8项被纳入定量综合，导致2项荟萃分析。荟萃分析的结果表明，与接受传统护理的患者相比，使用远程医疗技术监测患者与心力衰竭患者死亡风险降低40%相关。随机效应荟萃分析结果显示，合并相对死亡率风险为0.60，表明接受远程监护的患者与接受常规护理的患者相比，死亡风险较低。在接受心脏植入的患者中，接受远程监护的患者比接受传统护理的患者死亡率低35%。结论:虽然远程医疗干预的随机对照试验在一些研究中证明了增强的患者结果，并为循证实践铺平了道路，但研究问题的异质性表明，迫切需要更多具有一致结果评估的补充性研究。
{"title":"Systematic Review Identifying Adverse Health Outcomes and Mortality Rates Associated with Telehealth","authors":"F. Cascini, A. Pantovic, Y. Al-Ajlouni, Omar Al Ta'ani, G. Failla, A. Melnyk, P. Barach, W. Ricciardi","doi":"10.30953/thmt.v8.415","DOIUrl":"https://doi.org/10.30953/thmt.v8.415","url":null,"abstract":"Background: The literature supporting telehealth management is growing accelerated by the COVID-pandemic. We hypothesize that there are risks of adverse events associated with telehealth interventions. \u0000Methods: A review of PubMed (including MEDLINE), Embase, ISI (Web of Science), VHL/GHL, Scopus, Science Direct, and PsycINFO was conducted for all adverse events associated with telehealth from January 1, 1960 to March 1, 2021. This systematic review and meta-analyses were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. \u0000Results: Of 5,144 citations 78 published studies met criteria for quality evaluation and underwent full text abstraction including the qualitative synthesis. Of the 78 included studies 8 were included in the quantitative synthesis resulting in 2 meta-analyses. The results of the meta-analysis suggest that monitoring patients using telehealth techniques is associated with 40% lower mortality risks among patients suffering from heart failure, compared to those who received traditional care. The results of the random-effects meta-analysis showed the pooled relative risk of mortality to be 0.60, indicating that patients that underwent telemonitoring had a lower mortality risk compared with the patients that underwent usual care. Among patients with heart implants, patients who received telemonitoring had a 35% lower mortality risk compared to patients receiving traditional care. \u0000Conclusions: While RCTs of telehealth interventions demonstrate enhanced patient outcomes in a number of studies and pave the way to evidence-based practice, the heterogeneity of the research questions suggest an important need for more complementary studies with consistent outcome assessments.","PeriodicalId":320236,"journal":{"name":"Telehealth and Medicine Today","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130088176","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}
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