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Telemedicine

Telemedicine is the remote delivery of healthcare services, including diagnosis and treatment, using telecommunications technologies.
It allows healthcare providers to evaluate, diagnose and treat patients without the need for an in-person visit.
Telemedicine can improve access to medical services, especially for patients in remote or underserved areas, and reduce the tiem and cost of travel for both patients and providers.
It encompasses a variety of applications and technologies, such as videoconferencing, remote monitoring, and mobile health, that can be used to provide clinical services.
Telemedicine has the potential to enhance the quality, accessibility and cost-effectiveness of medical care, though its efficacy and implemtnation challenges continue to be studied.

Most cited protocols related to «Telemedicine»

1
Development of Telemedicine Usability Questionnaire
A literature review was conducted to identify existing questionnaires that have been widely used in the evaluation of telemedicine and computer/information technology. Identified questionnaires that were used as models for the TUQ were primarily from two fields: telemedicine and computer and information technology.
In the field of telemedicine the following questionnaires were identified: the Telemedicine Satisfaction Questionnaire (TSQ) (Yip et al., 2003 (link)), Telemedicine Patient Questionnaire (TMPQ) (Demeris et al., 2000 (link), 2004 (link)), and Telemedicine Satisfaction and Usefulness Questionnaire (TSUQ) (Bakken, 2009). Telemedicine questionnaires focus on three factors of usability: usefulness, satisfaction, and interaction quality between patient and clinician over telemedicine technology. The TSQ clearly addresses the three usability factors central to telehealth. For example, it includes items unique to telemedicine such as audio and video quality. TSQ is a questionnaire designed specifically for telemedicine systems. TSQ was also designed for traditional interactive videoconferencing systems such as Polycom or Cisco Tandberg. One main difference between traditional videoconferencing systems and new generation computer-based systems is that the former type of system does not have a user interface that clinicians and patients interact with, which is the case with computer-based systems such as VSee. The traditional videoconferencing systems are usually setup by a technician, and the user (patient and clinician) does not need to know how to setup and interact with the system. This means that the TSQ lacks the items related to interface quality that are important for computer/software-based telehealth. However, because items of the TSQ so clearly address the usability factors central to telehealth, it was identified as a primary source of questionnaire items for the TUQ.
In the field of information and computer technology the following questionnaires were identified: the Technology Acceptance Model (TAM) (Davis 1993 ), and the IBM Post-Study System Usability Questionnaire (PSSUQ) developed by Lewis (1995) (link). The TAM (Davis 1993 ) describes the relationships between perceived qualities of system usage, affective attitude, and behavioral responses to the system. This questionnaire is used widely in the business information arena. We derived questions related to the usability factors of usefulness and ease of use from the TAM. The PSSUQ measures system usability via a multitude of aspects, including system function, information and interface quality, to users’ satisfaction level. The evaluation covers the standards of effectiveness, efficacy and satisfaction (Lewis, 1995 (link)). From the PSSUQ, we derived items for ease of use, interface quality, reliability, and satisfaction.
PARMANTO B., LEWIS AN J.R., GRAHAM K.M, & BERTOLET M.H. (2016). Development of the Telehealth Usability Questionnaire (TUQ). International Journal of Telerehabilitation, 8(1), 3-10.
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Publication 2016
Patients Satisfaction Telemedicine
2
Implementing Novel Healthcare Technologies
Articles describing technology implementation frameworks and their applications were eligible if they (1) studied a technology that was perceived as new by intended users, (2) aimed (through the uptake and use of the technology) to improve service efficiency, or patient or client outcomes in health or social care; and (3) offered some kind of conceptual or theoretical framework. We were particularly interested in patient-facing technologies such as telehealth, but we also assessed other frameworks (eg, for health information systems) for transferable insights.
We began by selecting relevant studies from our hermeneutic literature review of telehealth in heart failure (covering 32 previous systematic reviews and 60 additional articles, including many that covered conditions beyond heart failure) [13 (link)]. We searched the reference lists of key studies [33 (link)-41 (link)]; we also put their titles into Google Scholar to identify 160 articles (surprisingly few) that had cited them subsequently, and manually screened these titles for relevance. We chose this “ancestry and snowballing” approach because initial database searching proved neither sensitive nor specific [42 (link)].
Having obtained few hits, we extended our search to the wider literature by tracking our original 2004 diffusion of innovations review [1 (link)]; we manually screened the titles and abstracts of over 4500 publications that had cited it. We did the same with 8 other highly cited reviews on the broader topic of innovation in health care [4 (link)-9 (link),27 (link),43 (link)] (around 3000 additional hits), using progressive focusing to limit the dataset. We favored authoritative reviews and added selected primary studies (characterized by strong theory, naturalistic methods, and rich detail, and including a focus on technology implementation). Where articles cited a specific theory, we obtained the original article describing that theory.
We used a simple data extraction form to summarize key aspects of each study (both theoretical and empirical). Using the hermeneutic (interpretive) methodology described in detail previously [13 (link)], we combined the findings of primary studies and previous reviews to generate a preliminary list of domains, potential interactions, and theoretical mechanisms.
Greenhalgh T., Wherton J., Papoutsi C., Lynch J., Hughes G., A'Court C., Hinder S., Fahy N., Procter R, & Shaw S. (2017). Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies. Journal of Medical Internet Research, 19(11), e367.
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Publication 2017
Congestive Heart Failure Diffusion of Innovation Patients Telemedicine
3
Developing a New eHealth Self-Management Model
The Theory Derivation process was used to bring together the related eHealth concepts and to grasp the relatively new phenomenon of using eHealth tools for the self-management of chronic illness [56 ]. Theory derivation is a structured set of procedures where one chooses a parent theory or model that is used to guide the development of a new model or theory supported by a comprehensive understanding of the current literature [56 ]. In this paper, the CCM was carefully examined and supporting components were extrapolated for the development of a new model. Additionally, a methodical review of a wide range of literature was conducted. A draft framework was then developed and expanded by a continued review of new literature and evaluation of the established and new components of the revised model.
A thorough review of the published literature since 2000 was conducted using CINAHL, Medline, OVID, EMBASE, PsychINFO, Science Direct, and selected “grey” literature including government reports, industry reports, legislation, etc. The review involved using the search terms “CCM or Chronic Care Model” AND “eHealth” and then we searched the specific identified components of eHealth and Chronic disease self-management support (Virtual communities”, “Virtual health communities”, “e-Communities”, “on-line communities”, social networking”, “Telemedicine”, Telehealth”, “Internet use for health”, “mHealth”, “Electronic health records”, “Personal health records”, “Patient portals”, “User training”, “Technology”, “Chronic Illness”, “Chronic disease”, and “Self-management support”). Selection criteria included review papers, randomized controlled trials, cohort studies, cross-sectional studies, and qualitative studies. The researchers independently identified papers based on framework, design, sample, measures, and fit with self-management support and chronic illness. The CCM was carefully studied in the literature and then key components of the current CCM were used to provide a framework for new model construction.
Gee P.M., Greenwood D.A., Paterniti D.A., Ward D, & Miller L.M. (2015). The eHealth Enhanced Chronic Care Model: A Theory Derivation Approach. Journal of Medical Internet Research, 17(4), e86.
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Publication 2015
Disease, Chronic Grasp Long-Term Care Mobile Health Parent Self-Management Telehealth Telemedicine
4
Barriers to Telemedicine Adoption: Systematic Review
We conducted a systematic review of the literature by extracting data from the Cumulative Index of Nursing and Allied Health Literature (CINAHL) and PubMed (MEDLINE) research databases. Searches were performed between 6 and 10 June 2016. The keywords used for the research in this study were barriers, adoption, implementation, telemedicine, tele care, telecare, tele health, telehealth, mobile health, mHealth, m-Health, eHealth, and e-Health. The terms used in the searches were slightly different between the two databases, primarily because the two databases index differently. Figure 1 illustrates the search process with inclusion and exclusion criteria. As depicted, the exact search phrase in CINAHL was ‘((Barriers) AND (Adoption OR Implementation)) AND (“Telemedicine” OR “Tele care” OR “Mobile health” OR “eHealth” OR “mhealth” OR “m-health” OR “e-health” OR “Telecommunication” OR “telehealth” OR “Self care”)’. In PubMed (MEDLINE), all the sub-terms used in the CINAHL search were already nested under telemedicine in the PubMed Medical Subject Headings (MeSH) of telemedicine. Boolean operators and quotation marks were used in the search process to capture variations in the lexicon and to identify the desired intersection of telemedicine and barriers.

Literature search with inclusion and exclusion criteria.

When the above-mentioned keywords were used, 226 articles from CINAHL Complete and 241 articles from PubMed were obtained. The articles were filtered using the publication dates ranging from the year 2011 to 2016 to evaluate the most recent barriers in implementing telemedicine and telehealth. The inclusion criteria used for PubMed were: free full-text, English language, and humans, focusing mainly on articles from MEDLINE. The inclusion criteria from the CINAHL-Complete database were: full text, English language, humans, academic journals, and references available. We excluded MEDLINE from CINAHL complete because the search criteria for PubMed included only MEDLINE articles. After applying the filters to both the PubMed and CINAHL Complete databases, the search was narrowed down to 56 and 10 articles, respectively. A literature matrix was created to list all the articles; the articles were then divided between reviewers so that at least two reviewers screened each abstract. Reviewers used a spreadsheet to compile their recommendations on whether the article was germane to this review, and a consensus meeting was called to share notes. Articles deemed not germane by at least two reviewers were excluded, and the articles for which the authors’ recommendations conflicted were discussed to reach consensus. The references from the remaining articles were visually scanned to identify common studies that were not already captured. This process added one additional article. Through our process, 30 articles were selected for the systematic literature review. These 30 articles were divided between the reviewers so that at least two reviewers read each article and made notes to identify barriers. A second consensus meeting was called to compare notes and to reach agreement on the barriers identified. We identified barriers by country and organized them into several bar charts organized by frequency of occurrence in the literature.
Scott Kruse C., Karem P., Shifflett K., Vegi L., Ravi K, & Brooks M. (2016). Evaluating barriers to adopting telemedicine worldwide: A systematic review. Journal of Telemedicine and Telecare, 24(1), 4-12.
Publication 2016
Homo Mobile Health Telehealth Telemedicine
5
ECHO Model for HCV Treatment in Underserved Areas
Using state-of-the-art telehealth technology, ECHO trains and supports primary care providers from underserved areas to develop knowledge and self-efficacy so they can deliver best practice care for complex health conditions like chronic HCV. At each of these ECHO partner sites, participants include a lead clinician (a physician, nurse practitioner, or physician's assistant) as well as a nurse or medical assistant who will help manage patient care. None of the community practice sites had treated HCV patients before joining the ECHO network.
Community providers take part in weekly HCV clinics, called “Knowledge Networks” by joining a videoconference or calling into a teleconference line. (See online supplement at www.nejm.org ) The providers present their cases by sharing patient medical histories, lab results, treatment plans, and questions about best practices and individual challenges. UNMHSC specialists from the fields of hepatology, infectious diseases, psychiatry, and pharmacology provide advice and clinical mentoring during these clinics. Working together, the community providers and specialists manage patients following evidence-based protocols. These case-based discussions are supplemented with short didactic presentations by inter-disciplinary experts to improve content knowledge.
This case-based approach creates a “Learning Loop“ which builds deep knowledge, skills and self-efficacy in several ways. Longitudinal co-management of patients with specialists allows community providers to practice their expanded knowledge and skills in a manner that builds self-efficacy in handling real-world situations with their actual patients, while ensuring that they follow best practices as they learn. Learning from other community-based providers with similar challenges and patient profiles is facilitated through shared case management decision making.
There are currently 16 community sites and 5 prisons that deliver HCV treatment using the ECHO model. Since ECHO's inception in 2003 there has been over 5,000 case presentations and 800 patients treated. We conducted a prospective cohort study to assess the safety and efficacy of ECHO model-based treatment in comparison to university clinic-based HCV treatment. Our hypothesis was that when HCV treatment is delivered using the ECHO model it is as effective as that provided on-site at the AMC.
Arora S., Thornton K., Murata G., Deming P., Kalishman S., Dion D., Parish B., Burke T., Pak W., Dunkelberg J., Kistin M., Brown J., Jenkusky S., Komaromy M, & Qualls C. (2011). Outcomes of Hepatitis C Treatment by Primary Care Providers. The New England journal of medicine, 364(23), 10.1056/NEJMoa1009370.
Publication 2011
Case Management Communicable Diseases Disease, Chronic ECHO protocol Infantile Neuroaxonal Dystrophy Nurses Patients Physicians Practitioner, Nurse Primary Health Care Safety Specialists Telemedicine

Most recents protocols related to «Telemedicine»

1
Retrospective Cohort Study of IHS Teleophthalmology Program
This was a retrospective cohort study using deidentified medical record data obtained during routine clinical operations of the IHS teleophthalmology program at 75 primary care clinics distributed among 20 states. The IHS serves enrolled members of federally recognized tribes. The study was reviewed and approved by the IHS institutional review board at Phoenix Indian Medical Center under the exempt process. Written informed consent from participants was not required or obtained.
Details regarding the teleophthalmology program’s origins, protocols, distribution, and outcomes have been previously described.13 (link) Briefly, the program evaluates patients from participating primary care clinics. It is a validated American Telemedicine Association Category 3 program and its graders identify the Early Treatment Diabetic Retinopathy Study (ETDRS)–defined clinical levels of DR and diabetic macular edema (DME) severity.13 (link),14 (link),15 (link) Graders are certified and licensed optometrists who render a diagnosis using standardized protocols. The program currently recommends that patients receive annual DR examinations.
Before selecting the analytic cohort for this study, we defined a baseline period of January 1, 2015, to December 31, 2015, and a follow-up period of January 1, 2016, to December 31, 2019. Eligible patients had at least 1 IHS teleophthalmology examination with the program in both periods. Additionally, eligible patients were 20 years or older and had no evidence of DR or had mild nonproliferative DR (NPDR; ETDRS levels 10, 14, 15, 20) in the baseline period. Patients with severe/very severe NPDR (ETDRS levels 53 a-e), proliferative DR (PDR; ETDRS levels 61, 65, 71, 75, 81, 85), and/or any level of DME are referred out of the teleophthalmology program to specialty eye care; therefore, these patients were excluded. Referral recommendations of patients with moderate NPDR (ETDRS levels 35, 43, 47) are dependent on risk factors; therefore, these patients were also excluded. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines.
Fonda S.J., Bursell S.E., Lewis D.G., Clary D., Shahon D, & Cavallerano J. (2023). Incidence and Progression of Diabetic Retinopathy in American Indian and Alaska Native Individuals Served by the Indian Health Service, 2015-2019. JAMA Ophthalmology, 141(4), 366-375.
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Publication 2023
Diabetic Retinopathy Diagnosis Edema, Macular Ethics Committees, Research Optometrist Patients Physical Examination Primary Health Care Telemedicine Tribes
2
Cost-Effectiveness of AI-Assisted Telemedicine
The ICER and ICUR were calculated as the difference in the total costs between the AI-assisted and manual grading telemedicine screening divided by the difference in the total years without blindness and the QALYs between the 2 conditions, respectively. Values for the AI-assisted screening cohort minus those for the manual grading screening cohort, which were set as the baseline, were calculated as the differences.
Lin S., Ma Y., Xu Y., Lu L., He J., Zhu J., Peng Y., Yu T., Congdon N, & Zou H. (2023). Artificial Intelligence in Community-Based Diabetic Retinopathy Telemedicine Screening in Urban China: Cost-effectiveness and Cost-Utility Analyses With Real-world Data. JMIR Public Health and Surveillance, 9, e41624.
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Publication 2023
Blindness, Bilateral Telemedicine
3
Accuracy of AI-Assisted Telemedicine Screening
The accuracy of AI-based telemedicine screening was extracted from published studies specific to the AI-assisted screening model conducted in Shanghai based on the current dominant architecture of convolutional neural networks (Multimedia Appendix 10) [25 (link)]. Briefly, the sensitivity was 80.47% (95% CI 75.07%-85.14%) and the specificity was 97.96% (95% CI 96.75%-98.81%) for STDR [25 (link)]. In our screening program, 2 experienced ophthalmologists were employed to make the diagnoses based on the retinal images. Furthermore, the accuracy of the manual grading–based telemedicine screening was assumed to be 100%, which was in accordance with the DR diagnosis criteria [8 (link),9 (link),26 (link),27 (link)]. However, as described in some other studies, since trained graders instead of ophthalmologists performed the grading and diagnosis [5 (link),7 (link)] in the sensitivity analysis, we adopted the accuracy range of the manual grading based on the Singaporean study (Multimedia Appendix 3) [7 (link)].
Lin S., Ma Y., Xu Y., Lu L., He J., Zhu J., Peng Y., Yu T., Congdon N, & Zou H. (2023). Artificial Intelligence in Community-Based Diabetic Retinopathy Telemedicine Screening in Urban China: Cost-effectiveness and Cost-Utility Analyses With Real-world Data. JMIR Public Health and Surveillance, 9, e41624.
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Publication 2023
Diagnosis Hypersensitivity Ophthalmologists Retina Telemedicine
4
Compliance with Referral for STDR Assessment
Compliance with referral to specialized ophthalmic hospitals or tertiary hospitals for a full examination among patients screened for signs of STDR was assumed to be 50.4% for manual grading–based telemedicine screening, according to our investigation in Shanghai [23 (link)]. However, compliance with AI-based telemedicine screening was unclear. Because only 1 published study suggested that adopting an AI-assisted diagnosis model in DR screening may impact the participants’ adherence to ophthalmic care [24 (link)], the evidence is insufficient. Therefore, we assumed that compliance with referral in AI-based telemedicine screening was the same as that in manual grading–based telemedicine screening, while we set a wide range (±25%) for sensitivity analysis (Multimedia Appendix 3).
Lin S., Ma Y., Xu Y., Lu L., He J., Zhu J., Peng Y., Yu T., Congdon N, & Zou H. (2023). Artificial Intelligence in Community-Based Diabetic Retinopathy Telemedicine Screening in Urban China: Cost-effectiveness and Cost-Utility Analyses With Real-world Data. JMIR Public Health and Surveillance, 9, e41624.
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Publication 2023
Diagnosis Hypersensitivity Patients Telemedicine
5
Cost-Effectiveness Analysis of AI-Based Diabetic Retinopathy Screening
TreeAge Pro (TreeAge Software) was used to build a decision-analytic Markov model to compare the actual cost, effectiveness, and utility of manual grading telemedicine screening and AI-based assessment for DR (Multimedia Appendices 1-4). The incremental cost-effectiveness ratio (ICER) and incremental cost-utility ratio (ICUR) were calculated as the primary results. The effectiveness was defined as years without blindness per 100,000 people with DM, and the utility was evaluated by quality-adjusted life years (QALYs). Although all residents with DM could participate in our community-based screening, the majority were older people [15 (link),16 (link)]; therefore, a hypothetical cohort of community residents with DM was followed in the model from the age of 65 years through a total of 30 one-year Markov cycles [5 (link)]. The characteristics of the simulated cohort were extracted using the Shanghai Digital Eye Disease Screening Program (Table 1).
Individuals were enrolled as healthy (free from DR) or unhealthy (experiencing DR) and could die due to any reason. According to the English National Screening Program for Diabetic Retinopathy, a Markov model was constructed that included non-STDR, STDR, and DME [5 (link),15 (link)-17 (link)]. The category was assigned based on the DR grade in the worse eye. During each 1-year cycle, an individual had a risk of progressing to the more severe stage or staying in the same stage. However, the model does not allow returning to an earlier stage even with treatment because of the nature of the disease. Moreover, the treatment can only decrease the probability of progression to the next stage. The prevalence of DR, the incidence of DR (including STDR and DME), transition probabilities, characteristics of DR screening tests, referral and treatment compliance, utility, mortality, and other relevant parameters were collected from published studies specific to Shanghai, other cities in China, and other Asian regions, as well as unpublished data sources (eg, Shanghai Digital Eye Disease Screening Program). The costs of screening, ocular examinations, and treatment were all derived from a real-world eye disease screening program in Shanghai and the unified health care service pricing of the Shanghai Municipal Health Commission. The parameters used in the basic analysis and the ranges used in the sensitivity analyses are listed in detail in Multimedia Appendices 1-4.
Lin S., Ma Y., Xu Y., Lu L., He J., Zhu J., Peng Y., Yu T., Congdon N, & Zou H. (2023). Artificial Intelligence in Community-Based Diabetic Retinopathy Telemedicine Screening in Urban China: Cost-effectiveness and Cost-Utility Analyses With Real-world Data. JMIR Public Health and Surveillance, 9, e41624.
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Publication 2023
Asian Americans Diabetic Retinopathy Disease Progression Eye Disorders Hypersensitivity Physical Examination Telemedicine Vision Visually Impaired Persons

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More about "Telemedicine"

Telemedicine, also known as telehealth or e-health, is the remote delivery of healthcare services using telecommunications technologies.
It allows healthcare providers to evaluate, diagnose, and treat patients without the need for an in-person visit.
This can improve access to medical services, especially for patients in remote or underserved areas, and reduce the time and cost of travel for both patients and providers.
Telemedicine encompasses a variety of applications and technologies, such as videoconferencing, remote monitoring, and mobile health (mHealth).
These can be used to provide a wide range of clinical services, including consultations, diagnoses, and even surgical procedures.
Telemedicine has the potential to enhance the quality, accessibility, and cost-effectiveness of medical care, though its efficacy and implementation challenges continue to be studied.
Researchers can utilize platforms like PubCompare.ai to optimize their telemedicine research protocols for enhanced reproducibility and accuracy.
These AI-powered tools can help locate the best protocols from literature, preprints, and patents, while providing AI-driven comparisons to identify the most effective solutions.
This can boost telemedicine research and development, leveraging cutting-edge technologies like SAS version 9.4, Stata version 16, Nikon D80 cameras, and SPSS version 26.
Whilethe efficacy of telemedicine continues to be studied, it has already demonstrated the ability to improve access to healthcare, reduce costs, and enhance the quality of care, making it an increasingly important component of modern healthcare systems.