Telemedicine and Digital Health Applications in Vascular Surgery

Lareyre, Fabien; Chaptoukaev, Hava; Kiang, Sharon C.; Chaudhuri, Arindam; Behrendt, Christian‐Alexander; Zuluaga, María A.; Raffort, Juliette

doi:10.3390/jcm11206047

Cited by 21 publications

(6 citation statements)

References 91 publications

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“…Such procedures were successfully reported in the field of vascular surgery during the COVID-19 pandemic [ 19 ]. A broader use of teleproctoring could be of benefit for remote areas and non-experienced centers [ 7 ]. Notably, the appropriate technical support is essential [ 19 ].…”

Section: Discussionmentioning

confidence: 99%

“…Telemedicine methods are crucial in promoting equal access to healthcare in remote regions. The ICT tools employed in telemedicine services enable access to high specialization, provide support for chronic condition management, and ensure continuity of care through multidisciplinary collaboration, serving as a vital resource for urgent healthcare services [ 6 , 7 , 8 ]. In recent years, there have been many telemedicine initiatives, but too often, they are traced back to experiments, prototypes, and projects and ultimately characterized by high rates of failure [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

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A RAND/UCLA-Modified VAS Study on Telemedicine, Telehealth, and Virtual Care in Daily Clinical Practice of Vascular Medicine

Pillon,

Gomatou,

Dimakakos

et al. 2024

JCM

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Background: Telemedicine is increasingly used in several fields of healthcare, including vascular medicine. This study aimed to investigate the views of experts and propose clinical practice recommendations on the possible applications of telemedicine in vascular medicine. Methods: A clinical guidance group proposed a set of 67 clinical practice recommendations based on the synthesis of current evidence and expert opinion. The Telemedicine Vascular Medicine Working Group included 32 experts from Europe evaluating the appropriateness of each clinical practice recommendation based on published RAND/UCLA methodology in two rounds. Results: In the first round, 60.9% of clinical practice recommendations were rated as appropriate, 35.9% as uncertain, and 3.1% as inappropriate. The strongest agreement (a median value of 10) was reached on statements regarding the usefulness of telemedicine during the 2019 coronavirus disease (COVID-19) pandemic, its usefulness for geographical areas that are difficult to access, and the superiority of video calls compared to phone calls only. The lowest degree of agreement (a median value of 2) was reported on statements regarding the utility of telemedicine being limited to the COVID-19 pandemic and regarding the applicability of teleconsultation in the diagnosis and management of abdominal aortic aneurysm. In the second round, 11 statements were re-evaluated to reduce variability. Conclusions: This study highlights the levels of agreement and the points that raise concern on the use of telemedicine in vascular medicine. It emphasizes the need for further clarification on various issues, including infrastructure, logistics, and legislation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A RAND/UCLA-Modified VAS Study on Telemedicine, Telehealth, and Virtual Care in Daily Clinical Practice of Vascular Medicine

Pillon,

Gomatou,

Dimakakos

et al. 2024

JCM

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show abstract

“…The “digital health” area is expanding quickly and uses digital tools to enhance population health, patient outcomes, and healthcare administration [ 44 , 45 , 46 , 47 , 48 ].…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications

Borna,

Maniaci,

Haider

et al. 2023

Healthcare

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Electronic health record (EHR) systems collate patient data, and the integration and standardization of documents through Health Information Exchange (HIE) play a pivotal role in refining patient management. Although the clinical implications of AI in EHR systems have been extensively analyzed, its application in HIE as a crucial source of patient data is less explored. Addressing this gap, our systematic review delves into utilizing AI models in HIE, gauging their predictive prowess and potential limitations. Employing databases such as Scopus, CINAHL, Google Scholar, PubMed/Medline, and Web of Science and adhering to the PRISMA guidelines, we unearthed 1021 publications. Of these, 11 were shortlisted for the final analysis. A noticeable preference for machine learning models in prognosticating clinical results, notably in oncology and cardiac failures, was evident. The metrics displayed AUC values ranging between 61% and 99.91%. Sensitivity metrics spanned from 12% to 96.50%, specificity from 76.30% to 98.80%, positive predictive values varied from 83.70% to 94.10%, and negative predictive values between 94.10% and 99.10%. Despite variations in specific metrics, AI models drawing on HIE data unfailingly showcased commendable predictive proficiency in clinical verdicts, emphasizing the transformative potential of melding AI with HIE. However, variations in sensitivity highlight underlying challenges. As healthcare’s path becomes more enmeshed with AI, a well-rounded, enlightened approach is pivotal to guarantee the delivery of trustworthy and effective AI-augmented healthcare solutions.

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“…In addition, various applications for patients with vascular diseases are currently being developed to enable telemedicine including telecoaching, telesurveillance, or telemonitoring. 3 These applications can provide support assistance for follow up, medication management, or engagement of patients for lifestyle changes such as promoting physical activity or health diet. 9 As an example, various telemedicine enhanced exercise programs have been developed for patients with LEAD to enhance education and patient adherence.…”

Section: Natural Language Processing As Virtual Assistants For Patientsmentioning

confidence: 99%

“…Health data are diverse and heterogeneous, including physiological, biological, imaging, administrative, billing data, and medical notes, as well as social and environmental data that can be collected through the use of digital devices. 2 , 3 They include structured data, defined by a standardised format and structure (i.e., diagnosis codes using standardised classification, biological values with pre-defined units, and also unstructured data, which do not have standardised organisation and formatting (i.e., clinical notes or reports). Much of the knowledge and medical information is still stored in free text format.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Review of Natural Language Processing (NLP) in Vascular Surgery

Lareyre,

Nasr,

Chaudhuri

et al. 2023

EJVES Vascular Forum

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Telemedicine and Digital Health Applications in Vascular Surgery

Cited by 21 publications

References 91 publications

A RAND/UCLA-Modified VAS Study on Telemedicine, Telehealth, and Virtual Care in Daily Clinical Practice of Vascular Medicine

A RAND/UCLA-Modified VAS Study on Telemedicine, Telehealth, and Virtual Care in Daily Clinical Practice of Vascular Medicine

Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications

Comprehensive Review of Natural Language Processing (NLP) in Vascular Surgery

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