Melanoma Detected Through Teledermatology Versus In-Person Visits.
Erik L JaklitschVrusha K ShahBrandon SmithAshima AgarwalJeffrey ChenAnna SweeneyJoseph C EnglishLaura K FerrisPublished in: Telemedicine journal and e-health : the official journal of the American Telemedicine Association (2023)
Background: Early detection of melanoma improves survival; however, patients face long wait times to receive dermatology care. Teledermatology (TD) is a promising tool to optimize access to care and has shown promise for the identification of malignancies but has not been well studied for melanoma. We evaluated the utility of TD as a triage tool to allow high-risk lesions of concern to be seen more expeditiously. Methods: Patient sociodemographic factors and histological characteristics of 836 melanomas biopsied between March 2020 and November 2022 in the University of Pittsburgh Medical Center health system were retrospectively evaluated, stratified by initial appointment type of TD versus in-person visit. Results: Patients first seeking care through teledermatology had shorter wait times to initial evaluation ( p < 0.001) and eventual biopsy ( p < 0.001), and these melanomas had higher Breslow thickness ( p < 0.001), were more ulcerated ( p = 0.002), invasive ( p = 0.001), and of a more aggressive subtype ( p = 0.007) than those initially evaluated in-person. TD was also utilized by a higher proportion of younger ( p = 0.001) and non-white ( p = 0.03) patients who identified their own lesion ( p < 0.001). Conclusions: TD may be a strategy to improve melanoma outcomes by providing an accessible avenue of expedited care for high-risk lesions associated with worse clinical prognosticators of disease.
Keyphrases
- skin cancer
- healthcare
- end stage renal disease
- palliative care
- newly diagnosed
- quality improvement
- chronic kidney disease
- ejection fraction
- prognostic factors
- mental health
- peritoneal dialysis
- type diabetes
- pain management
- adipose tissue
- case report
- skeletal muscle
- ultrasound guided
- machine learning
- insulin resistance
- big data