Abstract

Background: Access to dermatology remains a significant problem in the Department of Veterans Affairs (VA), particularly during the COVD-19 pandemic. To address this need, VA will deploy an asynchronous teledermatology mobile app-My VA Images-which allows new dermatology patients to securely submit history and photos of their skin for evaluation. The app may also eventually provide a conduit for patients to submit skin images at will for analysis and triage by artificial intelligence (AI)-powered computer vision to a dermatologist. Significance: This project addresses the following gaps: 1) The impact of direct-to-patient teledermatology on access to dermatology and on the satisfaction with such care by both patients and health care providers has not been systematically studied; 2) Currently no AI-powered computer vision tool has been developed and validated for patient-generated images; 3) The readiness of large healthcare organizations, such as VA, and their stakeholders to engage in direct-to-patient teledermatology and AI is unknown. Innovation and Impact: Two related innovations will be tested: 1) Direct-to-patient teledermatology for new patients and 2) Evaluation of patient-submitted skin images by AI-powered computer vision. These separately have the potential to transform remote access to expert skin care in VA and together are potentially synergistic. At the conclusion of the project, we anticipate having a systematic understanding of how direct-to-patient technologies perform and of the operational gaps that will need to be addressed by VA before these technologies can be implemented enterprise-wide. The goal is to establish a critical scholarly and operational foundation to safely move toward a transformative vision where Veterans will no longer be tied to a fixed time and place for care, but instead will have the choice of self-directed, convenient and rapid access to expert-level dermatology care wherever and whenever they need it. Specific Aims: 1. Assess the impact of direct-to-patient teledermatology on access and health system utilization. 2. Assess, refine and augment computer-assisted evaluation of patient-submitted images. 3. Assess readiness of VA and Veterans' acceptance to implement direct-to-patient care. Methodology: Aim 1 will use a Type I hybrid pragmatic study design to compare the impact of the direct-to- patient teledermatology intervention relative to usual in-person and usual consultative teledermatology referrals, measuring access chiefly by data from VA's Central Data Warehouse. Aims 1 and 3 will measure patient satisfaction and readiness for change using survey instruments and interviews. Aim 2 will include both testing, training and refinement of the AI-powered computer vision and measure concordance with dermatologists. Population: Veterans referred to Dermatology at three VA medical facilities. Intervention: Eligible and medically appropriate patients will be offered the option to submit history and images to Dermatology using the My VA Images app. Comparison: The intervention will be compared to usual care (in- person and consultative teledermatology) groups. We will also compare two AI-powered computer vision models with dermatologist diagnoses. Outcomes over a 5-year period: 1) Multiple measures of temporal and geographic access to dermatologic care; 2) Patient satisfaction; 3) Concordance of AI with dermatologist diagnoses; 4) Organizational and patient-readiness for remote and computer-assisted dermatologic care; and 5) Implementation and sustainability of the direct-to-new patient teledermatology process. Next Steps/Implementation: Successful completion of the project will provide VA’s Office of Connected Care and other offices tasked with enhancing access to specialty care with critical data that will justify further expansion of the direct-to-patient asynchronous teledermatology program. The project will also provide VA with critical data to evaluate the role of AI-powered computer vision in future remote care strategies.

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DRA: Health Systems, Other Conditions

DRE: Diagnosis, TRL - Applied/Translational

Keywords: Clinical Diagnosis and Screening, Telemedicine/Telehealth

MeSH Terms: None at this time.