Development and Evaluation of a Teledermatology Platform for Chronic Skin Disease Management

Thesis Abstract

Chronic skin diseases impose substantial burdens on patients and health systems, with significant barriers to timely access to dermatologic care, especially in remote and underserved populations. Teledermatology offers a scalable solution by enabling remote assessment, triage, and follow-up, yet existing platforms often lack interoperability with electronic health records, robust user-centered design, and rigorous evaluation of clinical outcomes, patient satisfaction, and cost-effectiveness. This study develops and evaluates a comprehensive teledermatology platform for chronic skin disease management that integrates image-based asynchronous consultations, synchronous video visits, patient-reported outcome measures, and decision-support tools aligned with evidence-based guidelines. The aim is to design, implement, and evaluate a teledermatology platform that improves access, timeliness, diagnostic accuracy, treatment adherence, and patient-reported quality of life for adults with chronic dermatologic conditions. The specific objectives are (1) to architect a modular teledermatology platform with interoperable APIs, secure data storage, and clinician and patient interfaces; (2) to assess feasibility, usability, and pilot performance in a real-world clinical setting with 350 eligible patients over 12 months; (3) to evaluate diagnostic concordance between teledermatology and in-person assessments using Cohen’s kappa and Bland–Altman analyses; (4) to determine the platform’s impact on wait times, follow-up rates, and care pathways through time-to-treatment and adherence analyses; (5) to analyze patient-reported outcomes, including dermatology-specific quality of life and satisfaction, using validated instruments; (6) to conduct a cost-effectiveness analysis from the payer and societal perspectives; and (7) to derive design recommendations and governance guidelines for scalable deployment. A mixed-methods design will be employed. The quantitative strand uses a prospective cohort study of 350 adult patients with chronic skin diseases recruited from three dermatology clinics. Data collection instruments include standardized image capture protocols, the DermQoL Instrument, the Skindex-16 for quality of life, the Patient Satisfaction Questionnaire Short Form (PSQ-18), the Telemedicine User Experience Questionnaire (TUEQ), and clinical metrics (diagnostic accuracy, time-to-treatment, adherence, and health service utilization). Diagnostic concordance will be assessed using Cohen’s kappa and intraclass correlation coefficients (ICCs) comparing teledermatology diagnoses and management plans with in-person gold standards. Time-to-treatment will be analyzed with Kaplan–Meier estimates and Cox proportional hazards models. Cost-effectiveness will be evaluated via incremental cost-effectiveness ratios (ICERs) and sensitivity analyses. The qualitative strand involves semi-structured interviews and focus groups with 20 dermatologists, 25 primary care providers, and 40 patients to explore usability, acceptability, perceived barriers, and facilitators. Thematic analysis will be conducted following Braun and Clarke’s approach, with deductive coding anchored to the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). A convergent mixed-methods design will integrate findings to explain quantitative outcomes and to identify domain-specific design improvements. Analytical techniques include regression analyses to identify predictors of diagnostic concordance and platform adoption, multilevel modeling to account for clustering within clinics, and thematic analysis for qualitative data. The theoretical frameworks guiding interpretation include the Technology Acceptance Model (TAM), the Diffusion of Innovations theory, and the Donabedian model for quality of care. Data governance and ethics will adhere to GDPR-equivalent standards, with informed consent, data de-identification, and a data safety monitoring plan. Validity and reliability will be established through pilot testing of instruments, inter-rater reliability checks for image assessments, and standardization of imaging protocols. Expected findings include superior or non-inferior diagnostic accuracy of teledermatology compared with in-person assessment for chronic conditions, reduced wait times by 40–60%, higher follow-up adherence, and improved patient-reported quality of life and satisfaction. The study aims to demonstrate cost-effectiveness under plausible payer thresholds and to identify platform design features that maximize clinician usability and patient engagement. The study’s contribution to knowledge includes evidence on the clinical efficacy, economic value, and user-centered design principles for teledermatology platforms in chronic disease management, along with practical guidelines for scalable implementation across diverse health systems. The main conclusion is that a carefully designed, interoperable teledermatology platform can enhance access to care, maintain or improve diagnostic quality, support treatment adherence, and generate favorable cost-effectiveness outcomes. Recommendations include adopting standardized imaging protocols, integrating decision-support and EHR interoperability, ensuring robust data privacy, and implementing ongoing training and governance to sustain adoption, with implications for policy and future research on long-term outcomes and integration with AI-driven diagnostic aids.