This paper is in the following e-collection/theme issue:

Teledermatology (61) JMIR Theme Issue: COVID-19 Special Issue (2452) Systemic Racism and Racial Bias in Health Care and Society (85) Telehealth and Telemonitoring (1671) Equity and Digital Divide (218)

Published on in Vol 6 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/43983, first published .
Demographic Data Associated With Digital Inequity Reported in Patient-to-Provider Teledermatology Studies in the United States From 2011 to 2021: Scoping Review

Demographic Data Associated With Digital Inequity Reported in Patient-to-Provider Teledermatology Studies in the United States From 2011 to 2021: Scoping Review

Demographic Data Associated With Digital Inequity Reported in Patient-to-Provider Teledermatology Studies in the United States From 2011 to 2021: Scoping Review

Authors of this article:

John Miller1 Author Orcid Image ;   Patrick Ioffreda1 Author Orcid Image ;   Shannon Nugent1 Author Orcid Image ;   Elizabeth Jones2 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

    Review

    1Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, United States

    2Department of Dermatology and Cutaneous Biology, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, United States

    *these authors contributed equally

    Corresponding Author:

    Elizabeth Jones, MD

    Department of Dermatology and Cutaneous Biology

    Sidney Kimmel Medical College

    Thomas Jefferson University

    833 Chestnut Street

    Philadelphia, PA, 19107

    United States

    Phone: 1 215 955 6680

    Email: elizabeth.jones@jefferson.edu


    Background: Patient-to-provider teledermatology relies on a patient’s access to technology to ensure a successful visit. However, access to broadband internet and technology varies across populations in the United States—leading to the digital divide. While teledermatology has been recognized as a model to improve access, little is known about how often demographic data associated with digital inequity are captured in studies.

    Objective: Given the expansion of teledermatology over the past decade, we sought to determine how often demographic data associated with digital inequity are reported in patient-to-provider teledermatology studies.

    Methods: A scoping literature review search was conducted using the search term teledermatology in the following databases: PubMed, Embase, and the Cochrane Database of Systematic Reviews. All studies published between December 31, 2011, and December 31, 2021, that evaluated patient-to-provider teledermatology were eligible.

    Results: In total, 1412 publications describing teledermatology were identified, of which 46 met the inclusion criteria. Race or ethnicity was the most frequently reported demographic characteristic (28/46, 61%). However, only 41% (19/46) of studies were representative of race or ethnicity, defined as including >20% nonwhite participants. Studies rarely reported the number of participants greater than 65 years of age (14/46, 30%), preferred language (9/46, 20%), income (6/46, 13%), highest level of education (5/46, 11%), or access to a device (4/46, 9%). Studies conducted after the onset of the COVID-19 pandemic were significantly more likely to report preferred language (9/25, 36% vs 0%; P=.002) and appeared more likely to report other demographic data associated with digital inequity, without reaching statistical significance (P>.05).

    Conclusions: Demographic data associated with digital inequity are rarely reported in patient-to-provider teledermatology studies to date. These studies frequently lack adequate representation of racial and ethnic minorities. With increased calls for equitable representation in dermatology studies, future teledermatology studies can improve the reporting of race and ethnicity and consider demographic data associated with digital inequity as an important criterion in research design.

    JMIR Dermatol 2023;6:e43983

    doi:10.2196/43983

    Keywords

    teledermatology; equity; inequity; scoping review; digital divide; dermatology; racial minority; ethnic minority; digital inequity; patient care; COVID-19 


    Unlike traditional in-person care, patient-to-provider teledermatology relies on a patient’s access to a compatible device and broadband internet to ensure a successful visit. However, access to broadband internet and technology varies across populations in the United States—creating what is referred to as the digital divide [1,2]. Populations less likely to report a broadband internet subscription include those with less formal education or lower income, racial and ethnic minorities, persons greater than 65 years of age, and residents of rural communities [3].

    Demographics that report less access to broadband internet and technology recorded lower telehealth utilization rates during the COVID-19 pandemic. For instance, older age and persons who reported a non-English language as their primary language were associated with fewer completed telemedicine visits [4-6]. Inequity in access to telemedicine care exists. However, it is unknown whether demographic data associated with digital inequity are frequently reported in teledermatology studies.

    Demographic data associated with digital inequity should be included in patient-to-provider teledermatology studies to ensure conclusions that reflect all populations. We conducted a scoping review of patient-to-provider teledermatology studies conducted in the United States to characterize how often patients vulnerable to the digital divide are represented. Given the expansion of teledermatology during the COVID-19 pandemic, we also compared how often demographic data associated with digital inequity were reported in studies conducted prior to and after the declaration of the pandemic.


    Study Design

    A scoping review search was performed on PubMed, Embase, and the Cochrane Database of Systematic Reviews from December 31, 2011, through December 31, 2021. We used the search term teledermatology. This study is registered with the International Prospective Register of Systematic Reviews (CRD42022325030) and followed the relevant portions of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.

    Eligibility Criteria

    Studies were included if they evaluated an asynchronous or synchronous model of patient-to-provider teledermatology. Both quantitative and qualitative studies evaluating teledermatology were included to represent the broad scope of research. Only studies conducted in the United States were included. No restrictions were placed on the age or gender of study participants. Telephone-based studies were excluded as these encounters involve separate technology and resources unrelated to measures of broadband connectivity and access to a device.

    Article Selection and Data Extraction

    Two reviewers (PI and JM) sequentially evaluated titles, abstracts, and then the full text of all publications based on the inclusion criteria. Studies were assessed based on whether they reported demographic data associated with digital inequity as described in a national report by the Pew Research Center [3]. These included participant income, highest level of education, race or ethnicity, number of participants greater than 65 years of age, and residents of rural communities [3]. Studies were defined as unrepresentative of race and ethnicity if they included less than 20% nonwhite participants, consistent with the methodology of a previous study by Chen et al [7].

    Data Analysis and Synthesis

    The primary outcome was the frequency of reported demographic characteristics associated with digital inequity. The secondary outcome was the likelihood of reporting demographic characteristics associated with digital inequity in studies conducted prior to versus during the COVID-19 pandemic. The Fisher exact test was used to analyze the secondary outcome. Analysis was performed using Stata software (version 17.0; StataCorp LLC). P<.05 was considered statistically significant.


    Selection of Articles

    Among 1412 potential studies, 46 satisfied the inclusion criteria [8-53]. Figure 1 shows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart illustrating the publication selection process. The full list of included studies and general study characteristics are presented in Multimedia Appendix 1.

    ‎
    Figure 1. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart for publication selection.

    Demographic Data Associated With Digital Inequity

    Demographic data associated with digital inequity reported in patient-to-provider teledermatology studies are presented in Table 1. Studies most often reported race or ethnicity (28/46, 61%), but a representative sample was present in only 41% (19/46). Of 46 studies, 14 (30%) reported the number of participants greater than 65 years of age. Studies infrequently reported preferred language, income, highest level of education, or access to a device (Table 1). No studies reported participant-reported internet quality or the number of rural residents included.

    Table 1. Demographic data reported in patient-to-provider teledermatology studies.
    Characteristic reportedStudies (N=46), n (%)
    Race or ethnicity28 (61)
    Representative of racial and ethnic minoritiesa19 (41)
    Number of participants >65 years of age14 (30)
    Preferred language9 (20)
    Income6 (13)
    Highest level of education5 (11)
    Access to a device4 (9)
    Internet quality0 (0)
    Number of rural residents0 (0)

    aStudies were representative of racial and ethnic minorities if they included >20% nonwhite participants.

    Demographic Data Reported and COVID-19

    Demographic data reported in patient-to-provider teledermatology studies conducted before (n=21) and after (n=25) the declaration of the COVID-19 pandemic are presented in Table 2. Studies conducted after the onset of the COVID-19 pandemic appeared more likely to report race or ethnicity, number of participants older than 65 years, income, highest level of education, and access to a device, without reaching statistical significance. Studies conducted during the COVID-19 pandemic were significantly more likely to report the participant’s preferred language (9/25, 36% vs 0%; P=.002). Studies conducted before and after the declaration of the COVID-19 pandemic were representative of racial and ethnic minorities at comparable rates.

    Table 2. Demographic data reported in patient-to-provider teledermatology studies conducted before and after the declaration of the COVID-19 pandemic.
    Characteristic reportedStudies conducted before COVID-19 (n=21), n (%)Studies conducted after COVID-19 (n=25), n (%)P value
    Race or ethnicity11 (52)17 (68).22
    Representative of racial and ethnic minoritiesa9 (43)10 (40).54
    Number of participants >65 years of age4 (19)10 (40).11
    Preferred language0 (0)9 (36).002
    Income2 (10)4 (16).42
    Highest level of education1 (5)4 (16).23
    Access to a device1 (5)3 (12).37

    aStudies were representative of racial and ethnic minorities if they included >20% nonwhite participants.


    Principal Findings

    The findings of this study highlight that demographic data associated with digital inequity are infrequently included in patient-to-provider teledermatology studies. With the explosion of telemedicine during the pandemic, our study noted a promising trend toward the inclusion of patient demographic information associated with digital inequity regarding race or ethnicity, participants older than 65 years, income, highest level of education, and access to a device. Preferred language was the only demographic characteristic that was significantly more likely to be reported in teledermatology studies following the pandemic onset. Even so, an overall lack of reporting of demographic data associated with digital inequity persisted.

    Demographic Data Associated With Digital Inequity

    Demographic data associated with digital inequity may be an indicator of the likelihood to complete teledermatology visits. Patients greater than 65 years of age, those who reported a non-English language as their primary language, and participants with lower income were less likely to complete teledermatology visits compared to their counterparts [5,6,8,9]. Other studies noted patients older than 65 years and those reporting lower income were less satisfied with the visit experience [10-12]. Demographic data associated with digital inequity should be reported more often to inform inclusive research design and ensure generalizable conclusions.

    The deficiencies in racial and ethnic representation identified in this review are consistent with those reported in dermatology clinical trials over the past decade [7]. It is critical to capture race and ethnicity data in patient-to-provider teledermatology studies as Black race and Latinx ethnicity were associated with lower use of video-based telemedicine during the pandemic [4]. Future teledermatology studies can improve the reporting of race and ethnicity and should consider this important criterion in research design.

    Demographic Data Reported and COVID-19

    Our study noted a trend toward the inclusion of demographic data associated with digital inequity in studies conducted after the COVID-19 pandemic compared to before. Dermatologists quickly adopted and expanded their teledermatology programs at the onset of the pandemic [54]. The sheer increase in the volume of care due to a lack of access to alternatives and improved reimbursement likely drove a wider demographic reach in the patient population. This trend coincides with increased calls for equitable inclusion in dermatology studies [55] and studies highlighting how teledermatology may exacerbate digital inequity [4,56-60]. As a result, lack of access combined with increased awareness of social inequities in care possibly encouraged researchers to be more intentional in seeking broader demographic variables to ensure their teledermatology programs represented underserved and underrepresented communities. Despite the positive trend, reporting of inclusive demographic data is insufficient to date and can be improved upon in future research design.

    Limitations

    This study is limited in its design. The studies included in our review had variable primary outcomes. Of note, our study did not determine whether a study’s primary outcome correlated with the inclusion of demographic data associated with digital inequity. It is also possible that studies captured demographic data associated with digital inequity in their methods but did not report these findings. This trend is consistent with medical studies’ failure to report sociodemographic variables consistently [61]. Studies will be unable to correct health inequity if these variables are not reported more consistently [61]. Lastly, this study only reflects demographic reporting trends in studies conducted in the United States.

    Conclusion

    This study highlights the need for equitable representation of racial and ethnic minorities and improved reporting of demographic data associated with digital inequity in patient-to-provider teledermatology studies over the past decade. With increased calls for equitable representation in dermatology studies, future teledermatology studies can improve reporting of race and ethnicity and consider the inclusion of demographic data associated with digital inequity an important criterion in research design. Professional and telehealth societies can help drive change by creating guidelines that reflect the value of reporting demographic data associated with digital inequity. Continued research and education can increase awareness of the digital divide’s impact on patient care and help optimize teledermatology’s potential to serve vulnerable populations.

    Conflicts of Interest

    None declared.

    ‎
    Multimedia Appendix 1

    General characteristics of included studies.

    DOCX File , 21 KB
    1. Bakhtiar M, Elbuluk N, Lipoff JB. The digital divide: how COVID-19's telemedicine expansion could exacerbate disparities. J Am Acad Dermatol 2020 Nov;83(5):e345-e346 [FREE Full text] [CrossRef] [Medline]
    2. Okoye SM, Mulcahy JF, Fabius CD, Burgdorf JG, Wolff JL. Neighborhood broadband and use of telehealth among older adults: cross-sectional study of national survey data linked with census data. J Med Internet Res 2021 Jun 14;23(6):e26242 [FREE Full text] [CrossRef] [Medline]
    3. Perrin A. Mobile technology and home broadband 2021. Pew Research Center. 2021 Jun 03.   URL: https://www.pewresearch.org/internet/2021/06/03/mobile-technology-and-home-broadband-2021/ [accessed 2022-07-22]
    4. Eberly LA, Kallan MJ, Julien HM, Haynes N, Khatana SAM, Nathan AS, et al. Patient characteristics associated with telemedicine access for primary and specialty ambulatory care during the COVID-19 pandemic. JAMA Netw Open 2020 Dec 01;3(12):e2031640 [FREE Full text] [CrossRef] [Medline]
    5. Eberly LA, Khatana SAM, Nathan AS, Snider C, Julien HM, Deleener ME, et al. Telemedicine outpatient cardiovascular care during the COVID-19 pandemic: bridging or opening the digital divide? Circulation 2020 Aug 04;142(5):510-512 [FREE Full text] [CrossRef] [Medline]
    6. Duan GY, Ruiz De Luzuriaga AM, Schroedl LM, Rosenblatt AE. Disparities in telemedicine use during the COVID-19 pandemic among pediatric dermatology patients. Pediatr Dermatol 2022 Jul;39(4):520-527 [FREE Full text] [CrossRef] [Medline]
    7. Chen V, Akhtar S, Zheng C, Kumaresan V, Nouri K. Assessment of changes in diversity in dermatology clinical trials between 2010-2015 and 2015-2020: a systematic review. JAMA Dermatol 2022 Mar 01;158(3):288-292. [CrossRef] [Medline]
    8. Miller RC, Stewart CR, Lipner SR. Retrospective study of trends in dermatology telemedicine and in-person visits at an academic center during COVID-19. J Am Acad Dermatol 2021 Mar;84(3):777-779 [FREE Full text] [CrossRef] [Medline]
    9. Blundell AR, Kroshinsky D, Hawryluk EB, Das S. Disparities in telemedicine access for Spanish-speaking patients during the COVID-19 crisis. Pediatr Dermatol 2021 Jul;38(4):947-949. [CrossRef] [Medline]
    10. Yeroushalmi S, Millan SH, Nelson K, Sparks A, Friedman AJ. Patient perceptions and satisfaction with teledermatology during the COVID-19 pandemic: a survey-based study. J Drugs Dermatol 2021 Feb 01;20(2):178-183. [CrossRef] [Medline]
    11. Su MY, Smith GP, Das S. Trends in teledermatology use during clinic reopening after COVID-19 closures. J Am Acad Dermatol 2021 Apr;84(4):e213-e214 [FREE Full text] [CrossRef] [Medline]
    12. Kaunitz G, Yin L, Nagler AR, Sicco KL, Kim RH. Assessing patient satisfaction with live-interactive teledermatology visits during the COVID-19 pandemic: a survey study. Telemed J E Health 2022 Apr;28(4):591-596. [CrossRef] [Medline]
    13. Hamad J, Fox A, Kammire MS, Hollis AN, Khairat S. Evaluating the experiences of new and existing teledermatology patients during the COVID-19 pandemic: cross-sectional survey study. JMIR Dermatol 2021 May 5;4(1):e25999 [FREE Full text] [CrossRef] [Medline]
    14. Pearlman RL, Le PB, Brodell RT, Nahar VK. Evaluation of patient attitudes towards the technical experience of synchronous teledermatology in the era of COVID-19. Arch Dermatol Res 2021 Nov 05;313(9):769-772 [FREE Full text] [CrossRef] [Medline]
    15. Afanasiev OK, Hung DY, Yan S, Huang SJ, Cho BK. COVID-19: a catalyst for innovative hybrid teledermatology workflows to increase access and improve patient care at a large group practice. J Am Acad Dermatol 2021 Jul;85(1):206-209 [FREE Full text] [CrossRef] [Medline]
    16. Cline A, Kim C, Deitz M, Rizzo A, Thagirisa A, Asrani F, et al. Real-world direct-to-patient teledermatology in a low-income, elderly population. J Am Acad Dermatol 2021 Jul;85(1):e19-e20. [CrossRef] [Medline]
    17. Vasavda C, Tang O, Kwatra SG, Ho BK, Grossberg AL. Asymmetric patterns of patient access to in-person and teledermatologic health care during the COVID-19 pandemic. J Am Acad Dermatol 2023 Jan;88(1):167-169 [FREE Full text] [CrossRef] [Medline]
    18. Gu L, Diaz SM, Lipner SR. Retrospective study of acne telemedicine and in-person visits at an academic center during the COVID-19 pandemic. J Cosmet Dermatol 2022 Jan 19;21(1):36-38 [FREE Full text] [CrossRef] [Medline]
    19. Cline A, Kim C, Berk-Krauss J, Kazemi A, Ginsberg D, Sarriera-Lazaro CJ, et al. Bridging the digital divide: The use of text message-enabled video in the public safety-net setting. J Am Acad Dermatol 2021 Aug;85(2):e97-e99. [CrossRef] [Medline]
    20. Franciosi EB, Tan AJ, Kassamali B, O'Connor DM, Rashighi M, LaChance AH. Understanding the impact of teledermatology on no-show rates and health care accessibility: a retrospective chart review. J Am Acad Dermatol 2021 Mar;84(3):769-771 [FREE Full text] [CrossRef] [Medline]
    21. Havele SA, Fathy R, McMahon P, Murthy AS. Pediatric teledermatology: a retrospective review of 1199 encounters during the COVID-19 pandemic. J Am Acad Dermatol 2022;87(3):678-680 [FREE Full text] [CrossRef] [Medline]
    22. Krueger S, Leonard N, Modest N, Flahive J, Guilarte-Walker Y, Rashighi M, et al. Identifying trends in patient characteristics and visit details during the transition to teledermatology: experience at a single tertiary referral center. J Am Acad Dermatol 2021 Dec;85(6):1592-1594. [CrossRef] [Medline]
    23. Pannu S, Nguyen BM, Yang FC, Rosmarin D. Predictors of patient experience with teledermatology in setting of COVID-19 pandemic in a single medical center. Int J Dermatol 2021 May 31;60(5):626-627. [CrossRef] [Medline]
    24. Moore B, Washington A, Butt M, Silva C, Green B, Helm M. Patient satisfaction of real-time teledermatology: a cross-sectional survey. Int J Dermatol 2022 Feb 26;61(2):e69-e71. [CrossRef] [Medline]
    25. Das S, Su MY, Kvedar JC, Smith GP. Asynchronous telemedicine for isotretinoin management: a direct care pilot. J Am Acad Dermatol 2022 Jan;86(1):184-186. [CrossRef] [Medline]
    26. Drugge E, Sarac R, Fay P, Drugge R. Semi-automated total body photography supports robust delivery of skin cancer monitoring services during the SARS-COV2/COVID-19 pandemic. Dermatol Online J 2021 Apr 15;27(4) [FREE Full text] [Medline]
    27. Johnson A, Shukla N, Halley M, Nava V, Budaraju J, Zhang L, et al. Barriers and facilitators to mobile health and active surveillance use among older adults with skin disease. Health Expect 2021 Oct 30;24(5):1582-1592 [FREE Full text] [CrossRef] [Medline]
    28. Hekman D, Maldonado S, Wolverton J, Rahnama-Moghadam S. Video visits for isotretinoin patients are perceived as safe, effective, and convenient. Dermatol Online J 2021 Apr 15;27(4) [FREE Full text] [Medline]
    29. Asabor EN, Bunick CG, Cohen JM, Perkins SH. Patient and physician perspectives on teledermatology at an academic dermatology department amid the COVID-19 pandemic. J Am Acad Dermatol 2021 Jan;84(1):158-161 [FREE Full text] [CrossRef] [Medline]
    30. Kazi R, Evankovich MR, Liu R, Liu A, Moorhead A, Ferris LK, et al. Utilization of asynchronous and synchronous teledermatology in a large health care system during the COVID-19 pandemic. Telemed J E Health 2021 Jul 01;27(7):771-777. [CrossRef] [Medline]
    31. McGee JS, Reynolds RV, Olbricht SM. Fighting COVID-19: early teledermatology lessons learned. J Am Acad Dermatol 2020 Oct;83(4):1224-1225 [FREE Full text] [CrossRef] [Medline]
    32. Yi JZ, Reynolds RV, Olbricht SM, McGee JS. Moving forward with teledermatology: operational challenges of a hybrid in-person and virtual practice. Clin Dermatol 2021 Jul;39(4):707-709. [CrossRef] [Medline]
    33. Edison KE, Fleming DA, Nieman EL, Stine K, Chance L, Demiris G. Content and style comparison of physician communication in teledermatology and in-person visits. Telemed J E Health 2013 Jul;19(7):509-514. [CrossRef] [Medline]
    34. Creighton-Smith M, Murgia RD, Konnikov N, Dornelles A, Garber C, Nguyen BT. Incidence of melanoma and keratinocytic carcinomas in patients evaluated by store-and-forward teledermatology vs. dermatology clinic. Int J Dermatol 2017 Oct 20;56(10):1026-1031. [CrossRef] [Medline]
    35. Parsi K, Chambers CJ, Armstrong AW. Cost-effectiveness analysis of a patient-centered care model for management of psoriasis. J Am Acad Dermatol 2012 Apr;66(4):563-570. [CrossRef] [Medline]
    36. Fathy R, Briker S, Rodriguez O, Barbieri JS. Comparing antibiotic prescription rates between in-person and telemedicine visits. J Am Acad Dermatol 2022 Aug;87(2):438-440 [FREE Full text] [CrossRef] [Medline]
    37. Chambers CJ, Parsi KK, Schupp C, Armstrong AW. Patient-centered online management of psoriasis: a randomized controlled equivalency trial. J Am Acad Dermatol 2012 Jun;66(6):948-953 [FREE Full text] [CrossRef] [Medline]
    38. O'Connor DM, Jew OS, Perman MJ, Castelo-Soccio LA, Winston FK, McMahon PJ. Diagnostic accuracy of pediatric teledermatology using parent-submitted photographs: a randomized clinical trial. JAMA Dermatol 2017 Dec 01;153(12):1243-1248 [FREE Full text] [CrossRef] [Medline]
    39. Kornmehl H, Singh S, Johnson MA, Armstrong AW. Direct-access online care for the management of atopic dermatitis: a randomized clinical trial examining patient quality of life. Telemed J E Health 2017 Sep;23(9):726-732. [CrossRef] [Medline]
    40. Armstrong AW, Johnson MA, Lin S, Maverakis E, Fazel N, Liu F. Patient-centered, direct-access online care for management of atopic dermatitis: a randomized clinical trial. JAMA Dermatol 2015 Feb 01;151(2):154-160. [CrossRef] [Medline]
    41. Armstrong AW, Chambers CJ, Maverakis E, Cheng MY, Dunnick CA, Chren M, et al. Effectiveness of online vs in-person care for adults with psoriasis: a randomized clinical trial. JAMA Netw Open 2018 Oct 05;1(6):e183062 [FREE Full text] [CrossRef] [Medline]
    42. Armstrong AW, Ford AR, Chambers CJ, Maverakis E, Dunnick CA, Chren M, et al. Online care versus in-person care for improving quality of life in psoriasis: a randomized controlled equivalency trial. J Invest Dermatol 2019 May;139(5):1037-1044 [FREE Full text] [CrossRef] [Medline]
    43. Ford AR, Gibbons CM, Torres J, Kornmehl HA, Singh S, Young PM, et al. Access to dermatological care with an innovative online model for psoriasis management: results from a randomized controlled trial. Telemed J E Health 2019 Jul;25(7):619-627 [FREE Full text] [CrossRef] [Medline]
    44. Fiks AG, Fleisher L, Berrigan L, Sykes E, Mayne SL, Gruver R, et al. Usability, acceptability, and impact of a pediatric teledermatology mobile health application. Telemed J E Health 2018 Mar;24(3):236-245. [CrossRef] [Medline]
    45. Khosravi H, Zhang S, Siripong N, Moorhead A, English Iii JC. Comparing acne follow-up: teledermatology versus outpatient dermatology visits. Dermatol Online J 2020 Apr 15;26(4) [FREE Full text] [Medline]
    46. Clark JJ, Snyder AM, Sreekantaswamy SA, Petersen MJ, Lewis BK, Secrest AM, et al. Dermatologic care of incarcerated patients: a single-center descriptive study of teledermatology and face-to-face encounters. J Am Acad Dermatol 2021 Dec;85(6):1660-1662. [CrossRef] [Medline]
    47. Gregory J, Taylor E, Liu Y, Love T, Raiciulescu S, Meyerle J. The burden of skin disease on deployed servicemembers. Mil Med 2019 Dec 01;184(11-12):889-893. [CrossRef] [Medline]
    48. Lamel S, Chambers CJ, Ratnarathorn M, Armstrong AW. Impact of live interactive teledermatology on diagnosis, disease management, and clinical outcomes. Arch Dermatol 2012 Jan 01;148(1):61-65. [CrossRef] [Medline]
    49. Bosanac S, Nguyen V, Bui D, Eisen D, Sivamani R. Randomized and controlled pilot study of the pragmatic use of mobile phone based follow up of actinic keratoses treated with topical 5-fluorouracil. Dermatol Online J 2018 Apr 15;24(4) [FREE Full text] [Medline]
    50. Rajda J, Seraly MP, Fernandes J, Niejadlik K, Wei H, Fox K, et al. Impact of direct to consumer store-and-forward teledermatology on access to care, satisfaction, utilization, and costs in a commercial health plan population. Telemed J E Health 2018 Feb;24(2):166-169. [CrossRef] [Medline]
    51. Wu X, Oliveria SA, Yagerman S, Chen L, DeFazio J, Braun R, et al. Feasibility and efficacy of patient-initiated mobile teledermoscopy for short-term monitoring of clinically atypical nevi. JAMA Dermatol 2015 May 01;151(5):489-496. [CrossRef] [Medline]
    52. Jacoby T, Woolard A, Chamoun S, Moy R. Asynchronous teledermatology assessment of young adult acne likely concordant with in-person evaluation. JDD 2021 Mar 01;20(4):432-435. [CrossRef]
    53. Pathipati AS, Ko JM. Implementation and evaluation of Stanford Health Care direct-care teledermatology program. SAGE Open Med 2016 Jul 12;4:2050312116659089 [FREE Full text] [CrossRef] [Medline]
    54. Kennedy J, Arey S, Hopkins Z, Tejasvi T, Farah R, Secrest AM, et al. Dermatologist perceptions of teledermatology implementation and future use after COVID-19: demographics, barriers, and insights. JAMA Dermatol 2021 May 01;157(5):595-597. [CrossRef] [Medline]
    55. Burgin S, Dlova NC, Ingram JR, Parish LC, Goldsmith LA. The essential role of dermatology publications in enhancing professional diversity, equity and inclusion. Skinmed 2021;19(5):332-333. [Medline]
    56. Gao JC, Zhang A, Gomolin T, Cabral D, Cline A, Marmon S. Accessibility of direct-to-consumer teledermatology to underserved populations. J Am Acad Dermatol 2022 Mar;86(3):e133-e134. [CrossRef] [Medline]
    57. Maddukuri S, Patel J, Lipoff JB. Teledermatology addressing disparities in health care access: a review. Curr Dermatol Rep 2021 Mar 12:1-8 [FREE Full text] [CrossRef] [Medline]
    58. Miyawaki A, Tabuchi T, Ong MK, Tsugawa Y. Age and social disparities in the use of telemedicine during the COVID-19 pandemic in Japan: cross-sectional study. J Med Internet Res 2021 Jul 23;23(7):e27982 [FREE Full text] [CrossRef] [Medline]
    59. Long V, Chandran NS. A glance at the practice of pediatric teledermatology pre- and post-COVID-19: narrative review. JMIR Dermatol 2022;5(2):e34228 [FREE Full text] [CrossRef] [Medline]
    60. Hooper J, Shao K, Feng H. Racial/ethnic health disparities in dermatology in the United States, part 1: overview of contributing factors and management strategies. J Am Acad Dermatol 2022 Oct;87(4):723-730. [CrossRef] [Medline]
    61. Orkin AM, Nicoll G, Persaud N, Pinto AD. Reporting of sociodemographic variables in randomized clinical trials, 2014-2020. JAMA Netw Open 2021 Jun 01;4(6):e2110700 [FREE Full text] [CrossRef] [Medline]

    ‎
    PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
    PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews

    Edited by R Dellavalle; submitted 01.11.22; peer-reviewed by F Kaliyadan, V Long; comments to author 25.12.22; revised version received 15.01.23; accepted 16.01.23; published 28.02.23

    Copyright

    ©John Miller, Patrick Ioffreda, Shannon Nugent, Elizabeth Jones. Originally published in JMIR Dermatology (http://derma.jmir.org), 28.02.2023.

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