Disparities in Initial Usage of Direct-to-Patient Teledermatology: A Retrospective Observational Study

Abstract

Background:

While telehealth has potential to enhance patients’ access to care, uneven implementation may limit its impact. My VA Images (MVAI), a clinician-gated, patient-facing asynchronous teledermatology application developed by the Department of Veterans Affairs (VA) was piloted at three VA facilities, and then sequentially offered to groups of seven facilities every 3 months for 1 year. We characterized patients who were exposed to and used MVAI and we identified associated characteristics.

Methods:

We used VA’s mobile health and administrative databases to examine MVAI activity and dermatology patient demographics over a 17-month period during 2019 and 2020.

Results:

At 11 VA facilities, 494 established dermatology patients were invited to a follow-up visit using MVAI. Invitees were more likely than Non-Invitees to be White and urban, have shorter drive times to specialty care, and have more outpatient and dermatology visits before and after MVAI use. Forty-eight percent of Invitees successfully used MVAI and were most likely to do so in the 8–30 day interval. Successful Users were more likely than Unsuccessful Users to be younger and have a dermatology visit 1 year after MVAI use. Patients requiring multiple attempts before successfully using MVAI were older than initially Successful Users, though younger than Unsuccessful Users.

Conclusions:

Not all patients benefited equally during the initial rollout of MVAI. Disparities in VA where other forms of patient-facing telehealth are already common illustrate challenges for health care organizations in equitably implementing direct-to-patient teledermatology. Specific groups may benefit from targeted outreach or support.

Keywords

equity telemedicine asynchronous teledermatology mobile health Veterans direct-to-consumer digital health health literacy age rurality race

Introduction

Teledermatology can be an effective method for providing skin evaluation and care to patients. In asynchronous teledermatology, patients are typically photographed at the clinic, and the digital skin images are electronically shared with the dermatologist who reads the case. Commonly used by nondermatologist health care providers to consult with dermatologists, asynchronous teledermatology can also be used by dermatologists to evaluate or follow-up with their own patients directly.^1,2 Direct-to-patient teledermatology, where patients take their own digital photos and share them from home is another type of asynchronous teledermatology with potential patient benefits, including increased flexibility in timing of evaluations, eliminating the need to travel to a clinic for photography, and increased patient self-efficacy.³ However, direct-to-patient teledermatology’s effectiveness also depends on its ability to reach broad patient populations, particularly those that are chronically underserved.⁴

In an effort to augment options to access health care, including dermatology, the Department of Veterans Affairs (VA) developed a direct-to-patient asynchronous mobile web-based application (app)—My VA Images (MVAI)—to provide care to Veterans at home with no additional patient-incurred costs. Patients are first identified and then invited by a clinician before using MVAI. Identifying a patient involves confirming the patient is willing to have a remote visit and has an eligible government-authenticated account and a mobile device or computer/camera allowing email receipt and photo capture/sharing. Inviting a patient requires that a VA clinician or designee uses the clinician-facing portal—Virtual Care Manager (VCM)—to order the MVAI invitation.⁵ The order triggers an invitation email containing a link to MVAI that is sent to the patient (Invitee) at the prescribed follow-up time, which at the inviting clinician’s discretion can be anywhere from hours to months in the future. At the designated follow-up time, the Invitee is typically given 1 week to use MVAI and is sent multiple email reminders, each with a link to the web-based app. When used for teledermatology, MVAI prompts the patient to enter their skin history since their last dermatology visit, including medications used, and then to capture specific skin images requested by the clinician.⁶

Failure to complete the above steps by the prescribed due date results in a final notification to the patient (Unsuccessful User) and to the ordering clinician, and expiration of the MVAI invitation. If the patient successfully completes the MVAI tasks (Successful User), the dermatologist is notified by email or may monitor VCM for patient submissions and then uses VCM to review the patient-submitted data, writes a note for the electronic health record, and writes a message to the patient, which can include the assessment as well as any management or therapy instructions. Finally, the patient is notified by email to read the dermatologist’s comment in MVAI.^5,6

The eligibility requirements as well as sequential steps—from receiving email notifications to reading the dermatologist’s assessment and instructions—required to successfully use MVAI mean that some patients are better candidates for direct-to-patient teledermatology than others. For VA and other health care systems that offer direct-to-patient teledermatology, patient access to appropriate technology, as well as the opportunity to use it for care are not universal and can determine if a patient benefits.^7–9 Concerns have been raised that teledermatology may actually increase health care access disparities.^10–14 Studies documenting disparities in virtual dermatology care have largely focused on the experiences during the COVID-19 pandemic and have found mixed results.^9,15–22 Understanding if patients differentially decide to use technology and whether they successfully use different types of digital health technology may help identify and address implementation challenges for VA as well as other health care organizations that offer similar technology to their patients.

Previous work has described the implementation of MVAI from the perspectives of staff and the VA health care organization as a whole.⁵ Here, our primary objective is to identify demographic characteristics of established dermatology patients who were invited to use MVAI (Invitees) and compare them to those who were not (Non-Invitees); secondarily, we identify factors associated with successful utilization. We find differences in who received MVAI invitations and in who successfully used the app.

Methods

PATIENT SAMPLE

VA’s Office of Connected Care introduced the option of using MVAI for dermatology at 3 pilot facilities beginning in May 2019, and to 28 facilities in cohorts of 7 facilities every 3 months starting July 2019.²³ Facility assignment to a cohort was randomized. Two of three pilot facilities and 9 of 28 subsequent facilities ultimately adopted MVAI as a follow-up option to be offered at the discretion of their dermatologists; established dermatology patients from these facilities invited to use MVAI were included for study and classified as Invitees, Successful Users, and Unsuccessful Users. Of the nine facilities that decided to use MVAI, two were part of the first cohort, three were part of the second cohort, one was part of the third cohort, and three were part of the fourth cohort. One pilot site and the 19 other facilities did not adopt MVAI and did not contribute any data. For a comparison group (Non-Invitees), 3,853 dermatology outpatients who were not invited to use MVAI during the same time frame were randomly chosen from the 11 participating facilities such that their distribution from these facilities matched that of MVAI users.

DATA AND ANALYSIS

From May 2019 through September 2020, we collected data from VA’s mobile health database to monitor MVAI usage and supplemented it with encounter and demographic data (age, sex, race, marital status, location, and VA facility) from VA’s Corporate Data Warehouse. We compared demographic data of MVAI Invitees, which included initially Successful Users, Successful Users requiring ≥2 attempts to succeed, and Unsuccessful Users, to Non-Invitees. We included a health care utilization variable, considered another type of patient health characteristic, as (1) the number of outpatient care instances before and after a patient’s MVAI visit or before and after a visit chosen at random for non-users and (2) the total number of dermatology encounters in the year before and following the MVAI encounter, excluding private care visits paid for by the VA. We conducted bivariate analyses to examine the sociodemographic and health characteristics of our patient sample, stratified by the MVAI visit success statuses. We used chi-square, t-test, Wilcoxon signed-rank, and analysis of variance to test differences among Veteran patients with specific characteristics and invitation or success status; differences with p-values <0.05 were interpreted as significant.

Results

CHARACTERISTICS OF MVAI INVITEES

During May 2019–September 2020, a total of 494 Veteran patients at 11 facilities received requests to have at least one remote follow-up visit using MVAI ( Table 1 ); these patients are henceforth referred to as Invitees. Invitees were not significantly different in age, sex, or ethnicity from Non-Invitees (patients with dermatology outpatient visits at the same facilities who were not invited to use MVAI). In comparison with Non-Invitees, Invitees were more likely to be White, urban and have a shorter drive time to specialty care. Invitees also had more total outpatient and dermatology visits in the year before and after their remote MVAI visit. In a separate analysis, Invitees averaged more post-MVAI in-person encounters (2 ± 2 visits) than Non-Invitees (1 ± 3 visits, p < 0.001).

Table 1.

MVAI Invitees and Non-Invitees^a

CHARACTERISTICS	INVITEES	NON-INVITEES	p-VALUE	TEST
N	494	3,853
Age
Mean (SD.)	66 (14)	67 (15)	0.06	t-test
<45	52 (10)	398 (10)	0.1	χ²
45–64	152 (31)	1020 (26)
65+	290 (59)	2,435 (63)
Male Sex	422 (85)	3,393 (88)	0.09	χ²
Ethnicity
Hispanic/Latino	12 (2)	92 (2)	0.97	χ²
Non-Hispanic/Latino	457 (93)	3,575 (93)
Unknown	25 (5)	186 (5)
Race
White	358 (72)	2,560 (66)	0.007	χ²
Non-White	136 (28)	1293 (34)
Black	88 (18)	885 (23)
Other	48 (10)	408 (11)
Location
Urban	373 (76)	2,718 (70)	0.02	χ²
Rural	121 (24)	1,135 (30)	0.02	χ²
Mean Drive Time, min. (SD)	44 (32)	54 (42)	<0.001	t-Test
Median number of total outpatient visits
pre-MVAI (IQR)	25 (16, 45)	21 (11, 37)	<0.001	Wilcoxon
post-MVAI	32 (20, 53)	26 (15, 45)	<0.001	Wilcoxon
Number pre-MVAI dermatology visits
Median (IQR)	1 (0.2)	1 (1,2)	0.04	Wilcoxon
0 visits	81 (16)	1043 (27)	<0.001	χ²
1 visit	248 (50)	1388 (36)
≥2 visits	165 (33)	1422 (37)
Number post-MVAI dermatology visits
Median (IQR)	2 (1, 4)	0 (0, 1)	<0.001	Wilcoxon
0 visits	81 (16)	1,935 (50)	<0.001	χ²
1 visit	112 (23)	956 (25)
≥2 visits	301 (61)	962 (25)

^aUnless otherwise indicated, numbers in parentheses represent the percentage of each characteristic in the entire group of Invitees or Non-Invitees.

IQR, interquartile range; SD, Standard deviation.

CHARACTERISTICS OF SUCCESSFUL MVAI USERS

Overall, 48% of Invitees successfully followed up using MVAI ( Table 2 ). Successful Users were on average 5 years younger than Unsuccessful Users. Patients aged <45 and 45–64 had more success than failure, while the majority of those aged ≥65 were Unsuccessful Users. There were no significant differences based on race or ethnicity, marital status, drive time, and rurality (some data not shown). On the other hand, similar to MVAI Invitees overall, differences were observed in post-MVAI dermatology utilization. In contrast to Unsuccessful Users, Successful Users had a higher median number of post-MVAI dermatology encounters, and a higher proportion of these users had at least one dermatology visit within 1 year after their MVAI use. The number of prior dermatology visits as well as the number of pre or post-MVAI total outpatient visits were not associated with successful usage (data not shown).

Table 2.

MVAI Users by Completion Status^a

CHARACTERISTICS	SUCCESSFUL USERS	UNSUCCESSFUL USERS	p-VALUE	TEST
N (% of total)	239 (48)	255 (52)
Age
Mean (SD.)	63 (15)	68 (13)	0.0008	t-test
Median (IQR)	66 (52, 76)	71 (60, 77)	0.0018	Wilcoxon
<45	21 (9)	8 (3)	0.003	χ²
45–64	96 (40)	79 (31)
65+	122 (51)	168 (66)
Race
White	166 (70)	192 (75)	0.15	χ²
Non-White	73 (31)	63 (25)	0.15	χ²
Mean Drive time, min. (SD)	45 (37)	43 (28)	0.5	t-Test
Location
Urban	181 (76)	192 (75)	0.9	χ²
Rural	58 (24)	63 (25)	0.9	χ²
Number post-MVAI dermatology visits
Median (IQR)	3 (1, 5)	1 (0, 3)	<0.001	Wilcoxon
0 Visits	6 (2)	75 (29)	<0.001	χ²
1 Visit	59 (25)	53 (21)
≥2 Visits	174 (73)	127 (50)

Unless otherwise indicated, numbers in parentheses represent the percentage of each characteristic in the entire group of Successful or Unsuccessful Users.

The timing of the patients’ follow-up evaluation using MVAI also influenced successful completion. Earlier follow-up intervals had the highest success rates for completing an evaluation, with 8–29 days associated with the most successful use rates and 60 days or more associated with the least ( Fig. 1 ).

Fig. 1.

Time-dependence of MVAI user success rates. Successful MVAI use rates were monitored as a function of the invited follow-up interval. Follow-up interval is defined as the number of days from the ordering of the MVAI invitation to the day the patient successfully followed-up with MVAI. MVAI, my VA images.

Twenty-two percent of Successful Users required multiple MVAI orders before eventually succeeding ( Table 3 ). These patients were older (66 years) than those who needed only one attempt (63 years) but slightly younger than Unsuccessful Users (68 years). Ethnicity (data not shown) as well as sex, race and the rurality of invited users were again not significantly related to success or to the likelihood of requiring multiple attempts. However, patients who were ultimately successful did have a significantly higher number of dermatology visits after their MVAI visit compared with Unsuccessful Users.

Table 3.

MVAI Users Requiring Multiple Attempts^a

CHARACTERISTICS	SUCCESSFUL USERS		UNSUCCESSFUL USERS	P-VALUE	TEST
CHARACTERISTICS	1 ATTEMPT	≥2 ATTEMPTS	ALL	P-VALUE	TEST
N (% OF TOTAL)	187 (38)	52 (10)	255 (52)
Age
Mean (SD)	63 (15)	66 (14)	68 (13)	0.001	ANOVA
Median (IQR)	64 (51, 76)	69 (54, 76)	71 (60, 77)	0.002	Wilcoxon
<45	27 (14)	5 (4)	8 (3)	0.01	χ²
45–64	68 (36)	20 (38)	79 (31)
65+	92 (49)	30 (58)	168 (66)
Sex—Male	151 (81)	47 (90)	224 (88)	0.06	χ²
Race
White	131 (70)	35 (67)	192 (75)	0.3	χ²
Non-White	56 (30)	17 (33)	63 (25)	0.3	χ²
Location
Urban	147 (79)	34 (65)	192 (75)	0.15	χ²
Rural	40 (21)	18 (35)	63 (25)	0.15	χ²
Number pre-MVAI dermatology visits
Median (IQR)	1 (1, 2)	1 (0, 2)	1 (1, 2)	0.5	Wilcoxon
0 visits	25 (13)	16 (31)	40 (16)	0.02	χ²
1 visit	103 (55)	18 (35)	127 (50)
≥2 visits	59 (32)	18 (35)	88 (35)
Number post-MVAI dermatology visits
Mean (SD)	3 (3)	4 (3)	2 (3)	<0.001	ANOVA
Median (IQR)	3 (1, 5)	3 (2, 5)	1 (0, 3)	<0.001	Wilcoxon
0 visits	5 (3)	1 (2)	75 (29)	<0.001	χ²
1 visit	49 (26)	10 (19)	53 (21)
≥2 visits	133 (71)	41 (79)	127 (50)

Unless otherwise indicated, numbers in parentheses represent the percentage of each characteristic in the identified group of MVAI invitees.

Where patients received care was related to the likelihood of completing their MVAI visit. Patients at two facilities were more likely to complete their MVAI visit (54% and 30%) than those at the 9 other facilities (16% p < 0.001). One of these facilities had the highest share of Successful Users requiring multiple attempts (44%) while the other had more initially Successful Users (60% p < 0.001). Facility differences persisted in multivariate models examining usage.

Discussion

Direct-to-patient telehealth technologies offer patients the potential of enhanced access to health care with increased convenience, privacy and reduced cost. However, not all patients may be able to take advantage of such technologies, and concern has been expressed that benefits may not be evenly distributed.^11–14,24 We observed that initial implementation of asynchronous direct-to-patient telehealth did not evenly reach all Veteran patient demographic groups, and some groups benefitted more than others. While the data is for a specific app developed by VA for Veterans, all health care organizations have criteria for participating in direct-to-patient telehealth that, as with VA’s, are not intentionally designed to favor particular demographic groups. Our results highlight that unintended disparities can nevertheless emerge and point to several factors that existed for MVAI that may also be generally relevant for direct-to-patient teledermatology.

RACE

In contrast to prior reports of racial disparities in teledermatology,^{16,18–20,25–27} our results focus specifically on asynchronous direct-to-patient teledermatology and are strengthened by comparison with a matched group of Non-Invitees. MVAI’s explicit requirement for patients to be first identified and then invited, allows our results to distinguish the opportunity to use MVAI from successful usage itself. We observed that, compared with Non-Invitees, White patients were over-represented among Invitees, though race was not associated with successful use of MVAI. It is unclear whether White patients were more likely to be offered MVAI, to be eligible to use it, or to accept the offer. Others have reported racial differences in internet access and in reluctance to share images of sensitive body parts, which may help to explain the racial differences found.²⁸ Further research will be needed to determine if racial differences reflect other underlying disparities.

RURALITY

We originally anticipated that rural patients and others with longer drive times to VA specialty clinics such as dermatology would most benefit from an app that facilitated remote access to care.²⁹ Other studies have similarly mentioned the benefits of teledermatology for rural patients.^12,30 Surprisingly, MVAI Invitees were over-represented by urban patients and those with shorter drive times to specialty care. Similarly, a study of video and phone teledermatology visits found greater urban than rural usage.³¹ Rural Veterans have been reported to have lower interest in mobile apps, financial limitations and connectivity issues and may benefit from programs that increase access to equipment and broadband with added strategies to encourage interest.^32–34 Regarding successful usage, we found no difference, suggesting that rural patients have no additional barriers to actual app usage after they have been invited and have agreed to use the app.

AGE

We found no difference in age between Invitees and Non-Invitees, suggesting that older patients were willing to try MVAI, which is consistent with reports that use of digital health among seniors has been increasing.^35,36 Nevertheless, older age was associated with a lack of success using MVAI, similar to reports that older age was associated with a lack of submission of photos to augment phone or video teledermatology visits as well as with reduced usage of self-monitoring apps or direct-to-patient video teledermatology.^19,20,37,38 Lack of technological expertise among older adults may explain some of this age difference in successful usage.^39–41 Successful users requiring two or more attempts were older than the initially successful users, suggesting that older patients have persistence and may benefit from additional assistance^42,43 to increase digital literacy to successfully complete remote visits.^44–46

HEALTH SYSTEM UTILIZATION

Direct-to-patient teledermatology may benefit health care system operations by freeing up in-person appointments.⁴⁷ However, we observed that Invitees actually utilized overall and dermatology-specific health services more than Non-Invitees. Similarly, Successful Users had more dermatology appointments than Unsuccessful Users 1 year after MVAI usage. These findings ironically suggest that an app that promises increased dermatology access was used most by those with more frequent access already. A prior study examining ambulatory care similarly found higher health care utilizers associated with video compared with audio visits.⁴⁸ Previous studies of Veterans have reported worse health associated with use of VA health-related apps, which may explain higher usage.^49,50 Further research will be needed to determine whether high health care utilizers have more complex skin disease requiring more frequent dermatology visits, or are preferentially seeking more care.

ADDITIONAL FACTORS

Site-specific factors may play a role in successful usage; the facility where the user received care affected their success, with two facilities having proportionally more Successful Users than the other nine facilities. Although patient demographics at these facilities differed, facility differences persisted in multivariate models, suggesting organizational factors (e.g., provider influence, the presence of champions or the use of residents) influenced usage differences.^5,42,43,49

Last, the most successful users of MVAI were those who were requested to follow up between 8 and 30 days. The reasons are unclear, but it is possible that Veterans did not have enough time to use the app in shorter time windows and the importance of using the app diminished with longer follow-up times.

Limitations

First, our findings are based on Veteran dermatology patients at specific VA facilities who may not necessarily be representative of the general dermatology patient population, though they do appear in many cases to confirm or augment results in non-Veteran populations. Second, MVAI is an app designed by VA and is not available to non-Veterans. However, it likely shares some features with non-VA direct-to-patient teledermatology apps that present comparable benefits and challenges to Veteran and non-Veteran patients alike.

Conclusions

While direct-to-patient telehealth technologies have potential to increase health care access for all, certain Veteran demographic groups had reduced access to or successful use of an asynchronous teledermatology mobile app. In some cases, pre-existing gaps were accentuated. Understanding and addressing disparities among patient groups may help to optimize the effectiveness of direct-to-patient teledermatology for health care organizations.

Authors’ Contributions

Conceptual: D.H.O., J.D.W., M.A.W., R.P.L., H.A.K., G.L.J., A.L.G., D.C.M., and S.B.P. Data acquisition: D.J.S., O.L., R.P.L., A.L.G., and S.B.P. Data analysis: D.J.S., R.P.L., S.B.P., D.C.M., A.L.G., E.D.Z., and D.H.O. Writing, reviewing, editing, and article preparation: D.H.O., S.B.P., J.D.W., D.C.M., D.J.S., R.P.L., E.D.Z., A.L.G., M.A.W., G.L.J., H.A.K., O.L., and I.J.M.

Ethical Considerations

The study was approved by Institutional Review Boards (IRBs) at University of California at San Francisco and at VA facilities at Durham, Providence, and Boston.

Consent to Participate

The requirement for informed consent to participate was not required as the data were administratively collected by the VA.

Data Availability

These data are available upon reasonable request and in accordance with federal law.

Disclaimer

The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs, the United States Government or other institutions with which authors are affiliated.

Footnotes

Disclosure Statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding Information

The research, authorship, and publication were supported by the U.S. Department of Veterans Affairs’ Health Systems Research Service, Office of Research and Development, Awards HX002450, HX002961, and HX-17-007 to D.H.O. and M.A.W.

References

1. Peracca

, Jackson

, Weinstock

, et al. Implementation of teledermatology: Theory and practice. Curr Derm Rep 2019;8(2):35–45; doi: 10.1007/s13671-019-0252-2

2. Mehta

, Armstrong

. Teledermatology: Practice models. In: Teledermatology Updates in Clinical Dermatology. ( English

., III ed.) Springer: Cham, Switzerland; 2023; pp. 9–17.

3. Flaten

, St Claire

, Schlager

, et al. Growth of mobile applications in dermatology - 2017 update. Dermatol Online J 2018;24(2):13030.

4. Hadeler

, Beer

, Nouri

. The influence of teledermatology on health care access and equity. J Am Acad Dermatol 2021;84(4):e219–e220; doi: 10.1016/j.jaad.2020.12.036

5. Peracca

, Lachica

, Lamkin

, et al. Implementation of direct-to-patient mobile teledermatology in VA. J Gen Intern Med 2024;39(Suppl 1):97–105; doi: 10.1007/s11606-023-08480-1

6. Peracca

, Fonseca

, Lachica

, et al. Organizational readiness for patient-facing mobile teledermatology to care for established Veteran patients in the United States. Telemed J E Health 2023;29(1):72–80; doi: 10.1089/tmj.2022.0009

7. Hadeler

, Beer

, Nouri

. Teledermatology: Improving access or widening healthcare disparities? J Drugs Dermatol 2020;19(12):1248; doi: 10.36849/jdd.2020.5693

8. Linggonegoro

, Williams

, Hlobik

, et al. Inequities in technology access and digital health literacy among patients with dermatologic conditions: Cross-sectional analysis of the national health interview survey. JMIR Dermatol 2024;7:e51511; doi: 10.2196/51511

9. Blundell

, Kroshinsky

, Hawryluk

, et al. Disparities in telemedicine access for Spanish‐speaking patients during the COVID‐19 crisis. Pediatr Dermatol 2021;38(4):947–949; doi: 10.1111/pde.14489

10.

10. Wang

, Trinidad

, Lawrence

, et al. Improved patient access and outcomes with the integration of an eConsult program (teledermatology) within a large academic medical center. J Am Acad Dermatol 2020;83(6):1633–1638; doi: 10.1016/j.jaad.2019.10.053

11.

11. Uscher-Pines

, Malsberger

, Burgette

, et al. Effect of teledermatology on access to dermatology care among Medicaid enrollees. JAMA Dermatol 2016;152(8):905–912.

12.

12. Maddukuri

, Patel

, Lipoff

. Teledermatology addressing disparities in health care access: A review. Curr Dermatol Rep 2021;10(2):40–47; doi: 10.1007/s13671-021-00329-2

13.

13. Miller

, Ioffreda

, Nugent

, et al. Demographic data associated with digital inequity reported in patient-to-provider teledermatology studies in the United States from 2011 to 2021: Scoping review. JMIR Dermatol 2023;6:e43983; doi: 10.2196/43983

14.

14. Shah

, Saliba

. Gender and ethnic disparities in teledermatology clinical trial participants: Cross-sectional analysis of ClinicalTrials.gov-registered trials. JMIR Dermatol 2023;6:e46031; doi: 10.2196/46031

15.

15. Franciosi

, Tan

, Kassamali

, et al. Understanding the impact of teledermatology on no-show rates and health care accessibility: A retrospective chart review. J Am Acad Dermatol 2021;84(3):769–771; doi: 10.1016/j.jaad.2020.09.019

16.

16. Duan

, Ruiz De Luzuriaga

, Schroedl

, et al. Disparities in telemedicine use during the COVID‐19 pandemic among pediatric dermatology patients. Pediatr Dermatol 2022;39(4):520–527; doi: 10.1111/pde.14982

17.

17. Krueger

, Leonard

, Modest

, 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;85(6):1592–1594; doi: 10.1016/j.jaad.2020.11.040

18.

18. Lee

, Guo

, Wang

, et al. Differences in utilization of nonvideo telemedicine visits for dermatologic concerns in underserved populations during the COVID-19 pandemic. Telemed J E Health 2021;27(8):827–834; doi: 10.1089/tmj.2021.0128

19.

19. Jallow

, Ewulu

, Ajilore

, et al. Analyzing disparities in access to teledermatology compared with dermatology clinic visits before, during, and after COVID-19 quarantine. Clin Dermatol 2023;41(1):207–214; doi: 10.1016/j.clindermatol.2022.10.006

20.

20. Lamb

, Fitzsimmons

, Sevagamoorthy

, et al. Patient factors associated with teledermatology visit type and submission of photographs during the COVID-19 pandemic: Cross-sectional analysis. JMIR Dermatol 2022;5(4):e38694; doi: 10.2196/38694

21.

21. Lee

, Kassamali

, Shah

, et al. Differences in virtual care utilization for acne by vulnerable populations during the COVID-19 pandemic: A retrospective review. J Am Acad Dermatol 2021;85(3):718–719; doi: 10.1016/j.jaad.2021.05.008

22.

22. Zhu

, Dovigi

, English

3rd ., Association of socioeconomic factors with teledermatology usage. Telemed J E Health 2025;31(9):1160–1165; doi: 10.1089/tmj.2025.0085

23.

23. Done

, Oh

, Weinstock

, et al. VA telederm study: Protocol for a stepped-wedge cluster randomised trial to compare access to care for a mobile app versus a workstation-based store-and-forward teledermatology process. BMJ Open 2018;8(12):e022218; doi: 10.1136/bmjopen-2018-022218

24.

24. Badr

, Motulsky

, Denis

J-L

. Digital health technologies and inequalities: A scoping review of potential impacts and policy recommendations. Health Policy 2024;146:105122; doi: 10.1016/j.healthpol.2024.105122

25.

25. Kamat

, Chennareddy

, D’Ovidio

, et al. Disparities in the use of teledermatology during the COVID-19 pandemic lockdown in a pediatric dermatology practice. Telemed J E Health 2023;29(5):744–750; doi: 10.1089/tmj.2022.0221

26.

26. Xiang

, Neman

, Levine

, et al. Differences in patient characteristics for virtual and in-person outpatient dermatology visits: A retrospective cross-sectional analysis. Arch Dermatol Res 2024;316(7):422; doi: 10.1007/s00403-024-03169-9

27.

27. Patel

, Rundle

, Liu

, et al. Teledermatology may benefit marginalized populations: National and institutional trends during the COVID-19 pandemic. Dermatol Ther (Heidelb) 2023;13(3):827–834; doi: 10.1007/s13555-023-00900-8

28.

28. Leger

, Wu

, Haimovic

, et al. Patient perspectives on medical photography in dermatology. Dermatol Surg 2014;40(9):1028–1037; doi: 10.1097/01.DSS.0000452632.22081.79

29.

29. Lamkin

, Peracca

, Jackson

, et al. Using the RE-AIM framework to assess national teledermatology expansion. Front Health Serv 2023;3:1217829; doi: 10.3389/frhs.2023.1217829

30.

30. Peracca

, Jackson

, Lamkin

, et al. Implementing teledermatology for rural Veterans: An evaluation using the RE-AIM framework. Telemed J E Health 2021;27(2):218–226; doi: 10.1089/tmj.2020.0013

31.

31. Schroedl

, Duan

, De Luzuriaga

. Teledermatology during COVID-19: A comparison of video and telephone visits with patient-uploaded images at an urban academic medical center. Dermatol Online J 2022;28(1); doi: 10.5070/d328157057

32.

32. Zulman

, Wong

, Slightam

, et al. Making connections: Nationwide implementation of video telehealth tablets to address access barriers in Veterans. JAMIA Open 2019;2(3):323–329; doi: 10.1093/jamiaopen/ooz024

33.

33. Connolly

, Miller

, Koenig

, et al. Veterans’ attitudes toward smartphone app use for mental health care: Qualitative study of rurality and age differences. JMIR Mhealth Uhealth 2018;6(8):e10748; doi: 10.2196/10748

34.

34. Ahuja

, Briggs

, Collier

. Teledermatology in rural, underserved, and isolated environments: A review. Curr Dermatol Rep 2022;11(4):328–335; doi: 10.1007/s13671-022-00377-2

35.

35. Levine

, Lipsitz

, Linder

. Trends in seniors’ use of digital health technology in the United States, 2011-2014. JAMA 2016;316(5):538–540; doi: 10.1001/jama.2016.9124

36.

36. Zhou

, Cho

, Shang

, et al. Use of digital health technology among older adults with cancer in the United States: Findings from a national longitudinal cohort study (2015-2021). J Med Internet Res 2023;25:e46721; doi: 10.2196/46721

37.

37. Cline

, Kim

, Deitz

, et al. Real-world direct-to-patient teledermatology in a low-income, elderly population. J Am Acad Dermatol 2021;85(1):e19–e20; doi: 10.1016/j.jaad.2021.01.038

38.

38. Carroll

, Moorhead

, Bond

, et al. Who uses mobile phone health apps and does use Matter? A secondary data analytics approach. J Med Internet Res 2017;19(4):e125; doi: 10.2196/jmir.5604

39.

39. Lam

, Lu

, Shi

, et al. Assessing telemedicine unreadiness among older adults in the United States during the COVID-19 pandemic. JAMA Intern Med 2020;180(10):1389–1391; doi: 10.1001/jamainternmed.2020.2671

40.

40. Wildenbos

, Jaspers

MWM

, Schijven

, et al. Mobile health for older adult patients: Using an aging barriers framework to classify usability problems. Int J Med Inform 2019;124:68–77; doi: 10.1016/j.ijmedinf.2019.01.006

41.

41. Fortugno

, Dellavalle

. Implementation of a patient-assisted teledermatology model in the Veteran Health Administration. Dermatol Online J 2019;25(11); doi: 10.5070/d32511046138

42.

42. Cimperman

, Brenčič

, Trkman

, et al. Older adults’ perceptions of home telehealth services. Telemed J E Health 2013;19(10):786–790; doi: 10.1089/tmj.2012.0272

43.

43. Johnson

, Shukla

, Halley

, et al. Barriers and facilitators to mobile health and active surveillance use among older adults with skin disease. Health Expect 2021;24(5):1582–1592; doi: 10.1111/hex.13229

44.

44. Becker

, Witkowski

, Ludzik

. Inequities in geriatric access to teledermatology and smartphone dermoscopy. Dermatol Reports 2023;15(2):9612; doi: 10.4081/dr.2023.9612

45.

45. Simpson

, Kovarik

. Effectively engaging geriatric patients via teledermatology. J Am Acad Dermatol 2020;83(6):e417–e418; doi: 10.1016/j.jaad.2020.05.118

46.

46. van Deursen

, van Dijk

. The first-level digital divide shifts from inequalities in physical access to inequalities in material access. New Media Soc 2019;21(2):354–375; doi: 10.1177/1461444818797082

47.

47. Bosanac

, Nguyen

, Bui

, et al. 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;24(4); doi: 10.5070/d3244039373

48.

48. Zachrison

, Yan

, Sequist

, et al. Patient characteristics associated with the successful transition to virtual care: Lessons learned from the first million patients. J Telemed Telecare 2023;29(8):621–631; doi: 10.1177/1357633X211015547

49.

49. Hogan

, Etingen

, Lipschitz

, et al. Factors associated with self-reported use of web and mobile health apps among US military Veterans: Cross-sectional survey. JMIR Mhealth Uhealth 2022;10(12):e41767; doi: 10.2196/41767

50.

50. Robinson

, Shimada

, Zocchi

, et al. Factors associated with Veteran self-reported use of digital health devices. J Gen Intern Med 2024;39(Suppl 1):79–86; doi: 10.1007/s11606-023-08479-8