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      Barriers to Use of Remote Monitoring Technologies Used to Support Patients With COVID-19: Rapid Review

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          Abstract

          Background

          The COVID-19 pandemic has acted as a catalyst for the development and adoption of a broad range of remote monitoring technologies (RMTs) in health care delivery. It is important to demonstrate how these technologies were implemented during the early stages of this pandemic to identify their application and barriers to adoption, particularly among vulnerable populations.

          Objective

          The purpose of this knowledge synthesis was to present the range of RMTs used in delivering care to patients with COVID-19 and to identify perceived benefits of and barriers to their use. The review placed a special emphasis on health equity considerations.

          Methods

          A rapid review of published research was conducted using Embase, MEDLINE, and QxMD for records published from the inception of COVID-19 (December 2019) to July 6, 2020. Synthesis involved content analysis of reported benefits of and barriers to the use of RMTs when delivering health care to patients with COVID-19, in addition to health equity considerations.

          Results

          Of 491 records identified, 48 publications that described 35 distinct RMTs were included in this review. RMTs included use of existing technologies (eg, videoconferencing) and development of new ones that have COVID-19–specific applications. Content analysis of perceived benefits generated 34 distinct codes describing advantages of RMTs, mapped to 10 themes overall. Further, 52 distinct codes describing barriers to use of RMTs were mapped to 18 themes. Prominent themes associated with perceived benefits included a lower burden of care (eg, for hospitals, health care practitioners; 28 records), reduced infection risk (n=33), and support for vulnerable populations (n=14). Prominent themes reflecting barriers to use of RMTs included equity-related barriers (eg, affordability of technology for users, poor internet connectivity, poor health literacy; n=16), the need for quality “best practice” guidelines for use of RMTs in clinical care (n=12), and the need for additional resources to develop and support new technologies (n=11). Overall, 23 of 48 records commented on equity characteristics that stratify health opportunities and outcomes, including general characteristics that vary over time (eg, age, comorbidities; n=17), place of residence (n=11), and socioeconomic status (n=7).

          Conclusions

          Results of this rapid review highlight the breadth of RMTs being used to monitor and inform treatment of COVID-19, the potential benefits of using these technologies, and existing barriers to their use. Results can be used to prioritize further efforts in the implementation of RMTs (eg, developing “best practice” guidelines for use of RMTs and generating strategies to improve equitable access for marginalized populations).

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          Most cited references69

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          The qualitative content analysis process.

          This paper is a description of inductive and deductive content analysis. Content analysis is a method that may be used with either qualitative or quantitative data and in an inductive or deductive way. Qualitative content analysis is commonly used in nursing studies but little has been published on the analysis process and many research books generally only provide a short description of this method. When using content analysis, the aim was to build a model to describe the phenomenon in a conceptual form. Both inductive and deductive analysis processes are represented as three main phases: preparation, organizing and reporting. The preparation phase is similar in both approaches. The concepts are derived from the data in inductive content analysis. Deductive content analysis is used when the structure of analysis is operationalized on the basis of previous knowledge. Inductive content analysis is used in cases where there are no previous studies dealing with the phenomenon or when it is fragmented. A deductive approach is useful if the general aim was to test a previous theory in a different situation or to compare categories at different time periods.
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            COVID-19 transforms health care through telemedicine: evidence from the field

            Abstract This study provides data on the feasibility and impact of video-enabled telemedicine use among patients and providers and its impact on urgent and non-urgent health care delivery from one large health system (NYU Langone Health) at the epicenter of the COVID-19 outbreak in the United States. Between March 2nd and April 14th 2020, telemedicine visits increased from 369.1 daily to 866.8 daily (135% increase) in urgent care after the system-wide expansion of virtual health visits in response to COVID-19, and from 94.7 daily to 4209.3 (4345% increase) in non-urgent care post expansion. Of all virtual visits post expansion, 56.2% and 17.6% urgent and non-urgent visits, respectively, were COVID-19-related. Telemedicine usage was highest by patients aged 20-44, particularly for urgent care. The COVID-19 pandemic has driven rapid expansion of telemedicine use for urgent care and non-urgent care visits beyond baseline periods. This reflects an important change in telemedicine that other institutions facing the COVID-19 pandemic should anticipate.
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              Applying an equity lens to interventions: using PROGRESS ensures consideration of socially stratifying factors to illuminate inequities in health.

              To assess the utility of an acronym, place of residence, race/ethnicity/culture/language, occupation, gender/sex, religion, education, socioeconomic status, and social capital ("PROGRESS"), in identifying factors that stratify health opportunities and outcomes. We explored the value of PROGRESS as an equity lens to assess effects of interventions on health equity.
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                Author and article information

                Contributors
                Journal
                JMIR Mhealth Uhealth
                JMIR Mhealth Uhealth
                JMU
                JMIR mHealth and uHealth
                JMIR Publications (Toronto, Canada )
                2291-5222
                April 2021
                20 April 2021
                20 April 2021
                : 9
                : 4
                : e24743
                Affiliations
                [1 ] Chronic Viral Illness Service Royal Victoria Hospital McGill University Health Centre Montréal, QC Canada
                [2 ] Department of Physical Therapy Faculty of Medicine University of Toronto Toronto, ON Canada
                [3 ] Centre for Outcomes Research and Evaluation Research Institute of the McGill University Health Centre Montréal, QC Canada
                [4 ] Centre de recherche du Centre Hospitalier de l’Université de Montréal Montréal, QC Canada
                [5 ] Département de gestion, évaluation et politique de santé École de santé publique de l'Université de Montréal Montréal, QC Canada
                [6 ] Department of Epidemiology and Biostatistics Faculty of Medicine McGill University Montréal, QC Canada
                [7 ] Division of Radiation Oncology McGill University Health Centre Montréal, QC Canada
                [8 ] Department of Family Medicine McGill University Montréal, QC Canada
                Author notes
                Corresponding Author: Bertrand Lebouché bertrand.lebouche@ 123456gmail.com
                Author information
                https://orcid.org/0000-0002-7519-5063
                https://orcid.org/0000-0003-0027-846X
                https://orcid.org/0000-0001-8364-7421
                https://orcid.org/0000-0002-0431-4889
                https://orcid.org/0000-0001-5180-8139
                https://orcid.org/0000-0002-7041-1556
                https://orcid.org/0000-0002-9341-1735
                https://orcid.org/0000-0002-1273-9393
                Article
                v9i4e24743
                10.2196/24743
                8059785
                33769943
                a1037585-1aee-4f65-999b-c41724c10829
                ©Elizabeth Houlding, Kedar K V Mate, Kim Engler, David Ortiz-Paredes, Marie-Pascale Pomey, Joseph Cox, Tarek Hijal, Bertrand Lebouché. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 20.04.2021.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

                History
                : 2 October 2020
                : 30 November 2020
                : 3 March 2021
                : 22 March 2021
                Categories
                Review
                Review

                remote monitoring,technology,covid-19, telehealth,asynchronous technology,synchronous technology,mhealth,monitoring,review,barrier,benefit,equity

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