Using Implementation Science to Understand Teledermatology Implementation Early in the COVID-19 Pandemic: Cross-sectional Study

Briggs, Shanelle M.; Lipoff, Jules B.; Collier, Sigrid

doi:10.2196/33833

Cited by 7 publications

(6 citation statements)

References 15 publications

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“…Moreover, reducing the steps to connect, providing simple instructions, and offering audio-only calls may help patients with low technology literacy effectively use RTTD [ 18 •, 55 ]. In contrast, in populations where technology is not a limiting factor, there is some evidence that RTTD models with video visits and supplemental uploaded images may optimize patient preferences for direct interaction with providers and dermatologists’ ability to accurately diagnose and treat skin conditions [ 9 , 10 ••, 18 •].…”

Section: Discussionmentioning

confidence: 99%

“…Lastly, hybrid models merge advantageous aspects of both SF and RTTD services. For example, studies on the uptake of TD during the COVID-19 pandemic found that RTTD with stored digital photos was perceived by dermatologists to be the most feasible and accurate model of care [ 9 , 10 ••]. Modality preferences may be influenced by practice structures and features of the local health care system, including different reimbursement rates for TD models.…”

Section: Overview Of Models For Teledermatology Delivery: Rural Usa U...mentioning

confidence: 99%

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Teledermatology in Rural, Underserved, and Isolated Environments: A Review

2022

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Purpose of Review Summarize the current evidence for teledermatology in rural, underserved, and isolated environments including its use during the current COVID-19 pandemic. Recent Findings Teledermatology is a reliable and cost-effective tool that can reduce face-to-face visits and improve the timeliness of care for medically underserved populations. Recent studies have shown many additional benefits of teledermatology, including improving patients’ health outcomes and increasing local providers’ knowledge of dermatologic conditions. Despite these benefits, many low-income and rural populations lack access to digital technology and high-speed internet, limiting the reach of telemedical services. Summary Overall, barriers in access to care are unique across the globe, and thus teledermatology interventions should address and adapt to the needs of the local patient population. Certain strategies, such as implementing simple, SF models, using standardized TD consult templets, and providing real-time information technology support could potentially mitigate disparities and improve the effectiveness of TD programs in underserved areas.

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Section: Discussionmentioning

confidence: 99%

Section: Overview Of Models For Teledermatology Delivery: Rural Usa U...mentioning

confidence: 99%

Teledermatology in Rural, Underserved, and Isolated Environments: A Review

2022

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“…A cross-sectional study surveying the Association of Professors of Dermatology observed that most respondents (89%, 31/35) found the implementation of SAF images alongside video or phone calls the most feasible for teledermatology visits [ 47 ]. Of those who were most ready for teledermatology implementation, all respondents indicated they would continue to use teledermatology after the pandemic [ 47 ]. Havele et al [ 48 ] reviewed 1110 pediatric dermatology video visits and 89 SAF consultations with surveys embedded into every web-based encounter.…”

Section: Discussionmentioning

confidence: 99%

“…However, it remains unclear how SAF teledermatology will continue to be integrated into dermatologic practice. A cross-sectional study surveying the Association of Professors of Dermatology observed that most respondents (89%, 31/35) found the implementation of SAF images alongside video or phone calls the most feasible for teledermatology visits [ 47 ]. Of those who were most ready for teledermatology implementation, all respondents indicated they would continue to use teledermatology after the pandemic [ 47 ].…”

Section: Discussionmentioning

confidence: 99%

Store-and-Forward Images in Teledermatology: Narrative Literature Review

Jiang¹,

Flynn²,

Kwock³

et al. 2022

JMIR Dermatol

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Background Store-and-forward (SAF) teledermatology uses electronically stored information, including patient photographs and demographic information, for clinical decision-making asynchronous to the patient encounter. The integration of SAF teledermatology into clinical practice has been increasing in recent years, especially during the COVID-19 pandemic. Despite this growth, data regarding the outcomes of SAF teledermatology are limited. A key distinction among current literature involves comparing the quality and utility of images obtained by patients and trained clinicians, as these metrics may vary by the clinical expertise of the photographer. Objective This narrative literature review aimed to characterize the outcomes of SAF teledermatology through the lens of patient- versus clinician-initiated photography and highlight important future directions for and challenges of the field. Methods A literature search of peer-reviewed research was performed between February and April 2021. Key search terms included patient-initiated, patient-submitted, clinician-initiated, clinician-submitted, store-and-forward, asynchronous, remote, image, photograph, and teledermatology. Only studies published after 2001 in English were included. In total, 47 studies were identified from the PubMed electronic database and Google Scholar after omitting duplicate articles. Results Image quality and diagnostic concordance are generally lower and more variable with patient-submitted images, which may impact their decision-making utility. SAF teledermatology can improve the efficiency of and access to care when photographs are taken by either clinicians or patients. The clinical outcomes of clinician-submitted images are comparable to those of in-person visits in the few studies that have investigated these outcomes. Coinciding with the onset of the COVID-19 pandemic, asynchronous teledermatology helped minimize unnecessary in-person visits in the outpatient setting, as many uncomplicated conditions could be adequately managed remotely via images captured by patients and referring clinicians. For the inpatient setting, SAF teledermatology minimized unnecessary contact during dermatology consultations, although current studies are limited by the heterogeneity of their outcomes. Conclusions In general, photographs taken by trained clinicians are higher quality and have better and more relevant diagnostic and clinical outcomes. SAF teledermatology helped clinicians avoid unnecessary physical contact with patients in the outpatient and inpatient settings during the COVID-19 pandemic. Asynchronous teledermatology will likely play a greater role in the future as SAF images become integrated into synchronous teledermatology workflows. However, the obstacles summarized in this review should be addressed before its widespread implementation into clinical practice.

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“…As video quality is not sufficient for assessing skin disease, patients are often asked to submit photos (eg, of their lesion or rash). 2 Most clinical photo-taking applications, including those used at Stanford Healthcare, primarily rely on the patient's judgment for submitting adequate quality photos. However, even when given instructions, patients frequently take photos of insufficient quality for clinical use.…”

mentioning

confidence: 99%

Development and Clinical Evaluation of an Artificial Intelligence Support Tool for Improving Telemedicine Photo Quality

Chiou

Novoa

et al. 2023

JAMA Dermatol

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ImportanceTelemedicine use accelerated during the COVID-19 pandemic, and skin conditions were a common use case. However, many images submitted may be of insufficient quality for making a clinical determination.ObjectiveTo determine whether an artificial intelligence (AI) decision support tool, a machine learning algorithm, could improve the quality of images submitted for telemedicine by providing real-time feedback and explanations to patients.Design, Setting, and ParticipantsThis quality improvement study with an AI performance component and single-arm clinical pilot study component was conducted from March 2020 to October 2021. After training, the AI decision support tool was tested on 357 retrospectively collected telemedicine images from Stanford telemedicine from March 2020 to June 2021. Subsequently, a single-arm clinical pilot study was conducted to assess feasibility with 98 patients in the Stanford Department of Dermatology across 2 clinical sites from July 2021 to October 2021. For the clinical pilot study, inclusion criteria for patients included being adults (aged ≥18 years), presenting to clinic for a skin condition, and being able to photograph their own skin with a smartphone.InterventionsDuring the clinical pilot study, patients were given a handheld smartphone device with a machine learning algorithm interface loaded and were asked to take images of any lesions of concern. Patients were able to review and retake photos prior to submitting, so each submitted photo met the patient’s assumed standard of clinical acceptability. A machine learning algorithm then gave the patient feedback on whether the image was acceptable. If the image was rejected, the patient was provided a reason by the AI decision support tool and allowed to retake the photos.Main Outcomes and MeasuresThe main outcome of the retrospective image analysis was the receiver operator curve area under the curve (ROC-AUC). The main outcome of the clinical pilot study was the image quality difference between the baseline images and the images approved by AI decision support.ResultsOf the 98 patients included, the mean (SD) age was 49.8 (17.6) years, and 50 (51%) of the patients were male. On retrospective telemedicine images, the machine learning algorithm effectively identified poor-quality images (ROC-AUC of 0.78) and the reason for poor quality (blurry ROC-AUC of 0.84; lighting issues ROC-AUC of 0.70). The performance was consistent across age and sex. In the clinical pilot study, patient use of the machine learning algorithm was associated with improved image quality. An AI algorithm was associated with reduction in the number of patients with a poor-quality image by 68.0%.Conclusions and RelevanceIn this quality improvement study, patients use of the AI decision support with a machine learning algorithm was associated with improved quality of skin disease photographs submitted for telemedicine use.

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Using Implementation Science to Understand Teledermatology Implementation Early in the COVID-19 Pandemic: Cross-sectional Study

Cited by 7 publications

References 15 publications

Teledermatology in Rural, Underserved, and Isolated Environments: A Review

Teledermatology in Rural, Underserved, and Isolated Environments: A Review

Store-and-Forward Images in Teledermatology: Narrative Literature Review

Development and Clinical Evaluation of an Artificial Intelligence Support Tool for Improving Telemedicine Photo Quality

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