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      Factors to Effective Telemedicine Visits During the COVID-19 Pandemic: Cohort Study

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          Abstract

          Background

          With COVID-19 there was a rapid and abrupt rise in telemedicine implementation often without sufficient time for providers or patients to adapt. As telemedicine visits are likely to continue to play an important role in health care, it is crucial to strive for a better understanding of how to ensure completed telemedicine visits in our health system. Awareness of these barriers to effective telemedicine visits is necessary for a proactive approach to addressing issues.

          Objective

          The objective of this study was to identify variables that may affect telemedicine visit completion in order to determine actions that can be enacted across the entire health system to benefit all patients.

          Methods

          Data were collected from scheduled telemedicine visits (n=362,764) at the University of Miami Health System (UHealth) between March 1, 2020 and October 31, 2020. Descriptive statistics, mixed effects logistic regression, and random forest modeling were used to identify the most important patient-agnostic predictors of telemedicine completion.

          Results

          Using descriptive statistics, struggling telemedicine specialties, providers, and clinic locations were identified. Through mixed effects logistic regression (adjusting for clustering at the clinic site level), the most important predictors of completion included previsit phone call/SMS text message reminder status (confirmed vs not answered) (odds ratio [OR] 6.599, 95% CI 6.483-6.717), MyUHealthChart patient portal status (not activated vs activated) (OR 0.315, 95% CI 0.305-0.325), provider’s specialty (primary care vs medical specialty) (OR 1.514, 95% CI 1.472-1.558), new to the UHealth system (yes vs no) (OR 1.285, 95% CI 1.201-1.374), and new to provider (yes vs no) (OR 0.875, 95% CI 0.859-0.891). Random forest modeling results mirrored those from logistic regression.

          Conclusions

          The highest association with a completed telemedicine visit was the previsit appointment confirmation by the patient via phone call/SMS text message. An active patient portal account was the second strongest variable associated with completion, which underscored the importance of patients having set up their portal account before the telemedicine visit. Provider’s specialty was the third strongest patient-agnostic characteristic associated with telemedicine completion rate. Telemedicine will likely continue to have an integral role in health care, and these results should be used as an important guide to improvement efforts. As a first step toward increasing completion rates, health care systems should focus on improvement of patient portal usage and use of previsit reminders. Optimization and intervention are necessary for those that are struggling with implementing telemedicine. We advise setting up a standardized workflow for staff.

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          Most cited references25

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          Fitting Linear Mixed-Effects Models Usinglme4

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            ROCR: visualizing classifier performance in R.

            ROCR is a package for evaluating and visualizing the performance of scoring classifiers in the statistical language R. It features over 25 performance measures that can be freely combined to create two-dimensional performance curves. Standard methods for investigating trade-offs between specific performance measures are available within a uniform framework, including receiver operating characteristic (ROC) graphs, precision/recall plots, lift charts and cost curves. ROCR integrates tightly with R's powerful graphics capabilities, thus allowing for highly adjustable plots. Being equipped with only three commands and reasonable default values for optional parameters, ROCR combines flexibility with ease of usage. http://rocr.bioinf.mpi-sb.mpg.de. ROCR can be used under the terms of the GNU General Public License. Running within R, it is platform-independent. tobias.sing@mpi-sb.mpg.de.
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              Patient Characteristics Associated With Telemedicine Access for Primary and Specialty Ambulatory Care During the COVID-19 Pandemic

              Key Points Question What sociodemographic factors are associated with higher use of telemedicine and the use of video (vs telephone) for telemedicine visits for ambulatory care during the coronavirus disease 2019 (COVID-19) pandemic? Findings In this cohort study of 148 402 patients scheduled for primary care and medical specialty ambulatory telemedicine visits at a large academic health system during the early phase of the COVID-19 pandemic, older age, Asian race, non-English language as the patient’s preferred language, and Medicaid were independently associated with fewer completed telemedicine visits. Older age, female sex, Black race, Latinx ethnicity, and lower household income were associated with lower use of video for telemedicine care. Meaning This study identified racial/ethnic, sex, age, language, and socioeconomic differences in accessing telemedicine for primary care and specialty ambulatory care; if not addressed, these differences may compound existing inequities in care among vulnerable populations.
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                Author and article information

                Contributors
                Journal
                JMIR Med Inform
                JMIR Med Inform
                JMI
                JMIR Medical Informatics
                JMIR Publications (Toronto, Canada )
                2291-9694
                August 2021
                27 August 2021
                27 August 2021
                : 9
                : 8
                : e27977
                Affiliations
                [1 ] University of Miami Miller School of Medicine Miami, FL United States
                [2 ] Department of Otolaryngology University of Miami Miller School of Medicine Miami, FL United States
                [3 ] Department of Surgery University of Miami Miller School of Medicine Miami, FL United States
                [4 ] Department of Medicine University of Miami Miller School of Medicine Miami, FL United States
                Author notes
                Corresponding Author: Kristin Nicole Gmunder kgmunder@ 123456med.miami.edu
                Author information
                https://orcid.org/0000-0001-7529-4929
                https://orcid.org/0000-0002-6234-7865
                https://orcid.org/0000-0002-2210-069X
                https://orcid.org/0000-0003-4317-2438
                Article
                v9i8e27977
                10.2196/27977
                8404776
                34254936
                4176426e-7507-4e17-b3be-94ba7acc1c10
                ©Kristin Nicole Gmunder, Jose W Ruiz, Dido Franceschi, Maritza M Suarez. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 27.08.2021.

                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 Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.

                History
                : 15 February 2021
                : 14 March 2021
                : 5 June 2021
                : 10 July 2021
                Categories
                Original Paper
                Original Paper

                telemedicine,covid-19,patient portals,delivery of health care,telehealth,pandemic,digital health

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