JMIR Publications

ABSTRACT

Background:

The increasing incidence and financial burden of skin cancer place immense pressure on the UK's National Health Service (NHS). Systemic challenges, including dermatologist shortages and long waiting lists, complicate timely assessment of skin lesions for patients under the Urgent Suspected Cancer pathway. While teledermatology offers an innovative solution compared to traditional face-to-face appointments, standard teledermatology models still face limitations in addressing healthcare access barriers. Community-based decentralised models may reduce such barriers, but the cost and operational impact of such specific models remain largely under-researched.

Objective:

This study assessed the impact on financial cost to the NHS and patient waiting times at the Northern Care Alliance NHS Foundation Trust by comparing a community-based teledermatology model utilising Pathpoint eDerma against the Trust's standard-of-care for patients in the urgent suspected skin cancer pathway.

Methods:

This study utilised an ambidirectional design involving two distinct analyses. The Cost Comparison Analysis (CCA) compared costs incurred under the teledermatology model (intervention arm, N=563) against the Trust’s standard care, represented by a synthetic comparator arm (N=4011). The Discrete Event Simulation (DES) modeled the operational impact on patient waiting times over a one-year period. Data for the intervention arm were collected prospectively from December 2022 to May 2023 for CCA and up to November 2023 for DES, while comparator data were collected retrospectively from September 2021 to December 2022. Publicly available resource costs were incorporated to ensure the robustness of our analyses.

Results:

The community-based teledermatology model demonstrated significant improvements in both cost to the NHS and patient waiting times. The CCA revealed a mean cost saving of £45 per referral (95% CI £22 to £60). This cost-savings is achieved through the reduction of proportion of patients requiring a full diagnostic biopsy, and time-savings in face-to-face clinics and administration. Furthermore, the DES demonstrated that, on average, the teledermatology pathways decreased the time to reach a clinical diagnosis by 9.90 days (95% CI 9.64 to 10.16 days); communicate a diagnosis to patients by 54.18 days (95% CI 50.76 to 57.61 days); and reach histopathological diagnosis by 62.80 days (95% CI 59.76 to 65.83 days) compared to standard care.

Conclusions:

The implementation of the community-based teledermatology model is a highly effective, cost-efficient strategy that significantly shortens patient journeys. The intervention has successfully expedited the initial triage phase, but our study identified the histopathology process as the next major systemic constraint that could deter further pathway efficiency. Achieving timely diagnosis for all patients, including those requiring diagnostic biopsies, will necessitate continued strategic investment in innovative technologies to accelerate this downstream process. Clinical Trial: Not applicable.