Skip to main content
QMSQMS
QMS
  • Welcome to your QMS
  • Quality Manual
  • Procedures
  • Records
  • Legit.Health Plus Version 1.1.0.0
  • Legit.Health Plus Version 1.1.0.1
  • Legit.Health version 2.1 (Legacy MDD)
  • Legit.Health US Version 1.1.0.0
  • Legit.Health Utilities
  • Licenses and accreditations
  • Applicable Standards and Regulations
  • BSI Non-Conformities
    • Technical Review
    • Clinical Review
      • Round 1
        • Item 0: Background & Action Plan
        • Item 1: CER Update Frequency
        • Item 2: Device Description & Claims
        • Item 3: Clinical Data
        • Item 4: Usability
        • Item 5: PMS Plan
        • Item 6: PMCF Plan
        • Item 7: Risk
        • completed-tasks
          • task-3b10-legacy-pms-document-hierarchy-refactor
          • task-3b11-sme-coverage-subspecialty-documentation
          • task-3b12-phase-1-exploratory-per-bucket-c-feature
          • task-3b13-man-2025-cep-cip-completeness
          • task-3b14-ifu-integration-requirements-verification
          • task-3b4-mrmc-dark-phototypes
          • task-3b6-surrogate-endpoint-literature-review
            • Appraisal log — CRIT1–7 rolling table
            • Do we need this task?
            • Integration map — propagation of the surrogate-endpoint validity review
            • references
              • diagnostic-accuracy
              • referral-optimisation
                • Armstrong 2018 — Online vs in-person care for psoriasis: equivalency RCT
                • Chuchu 2018 — Cochrane review: teledermatology for diagnosing skin cancer in adults
                • Eminović 2009 — Cluster RCT: teledermatology reduces dermatology referrals
                • Finnane 2017 — Teledermatology for the diagnosis and management of skin cancer: systematic review
                • Giavina-Bianchi 2020 — Teledermatology reduces referrals and waiting times (São Paulo, 30,976 patients)
                • Jain 2021 — AI tool for skin-condition diagnosis by PCPs and NPs in teledermatology
                • Moreno-Ramirez 2007 — Store-and-forward teledermatology in skin-cancer triage (Seville, 2,009 teleconsultations)
                • Snoswell 2016 — Cost-effectiveness of store-and-forward teledermatology: systematic review
                • Whited 2013 — Clinical-course outcomes: store-and-forward teledermatology vs. conventional consultation RCT
              • severity-assessment
            • Research prompts — external deep-research tools
            • Surrogate-Endpoint Validity in Dermatology AI — Structured Literature Review
          • task-3b7-icd-per-epidemiological-group-vv
          • task-3b8-safety-confirmation-column-definition
          • task-3b9-legacy-pms-conclusions-into-plus-pms-plan
        • Coverage matrix
        • resources
        • Task 3b-5: Autoimmune and Genodermatoses Triangulated-Evidence Package
      • Evidence rank & phases
      • Pre-submission review of R-TF-015-001 CEP and R-TF-015-003 CER
  • Pricing
  • Public tenders
  • Trainings
  • BSI Non-Conformities
  • Clinical Review
  • Round 1
  • completed-tasks
  • task-3b6-surrogate-endpoint-literature-review
  • references
  • referral-optimisation
  • Giavina-Bianchi 2020 — Teledermatology reduces referrals and waiting times (São Paulo, 30,976 patients)

Giavina-Bianchi 2020 — Teledermatology reduces referrals and waiting times (São Paulo, 30,976 patients)

Citation​

Giavina-Bianchi M, Santos AP, Cordioli E. Teledermatology reduces dermatology referrals and improves access to specialists. eClinicalMedicine. 2020 Nov 21;29–30:100641. DOI: 10.1016/j.eclinm.2020.100641. PMID 33437950.

Study design and population​

Cross-sectional retrospective real-world implementation study. 30,976 individuals with 55,624 skin lesions triaged via store-and-forward teledermatology; São Paulo municipal public-health system; July 2017–July 2018.

Reported metrics​

  • 53 % of all triaged cases managed entirely within primary care (avoided specialist referral)
  • 43 % referred to in-person dermatologist; 4 % referred directly to biopsy
  • 78 % reduction in mean waiting time for in-person dermatologist appointment — from 6.7 months to 1.5 months

Surrogate-to-outcome linkage​

Largest contemporary real-world quantification of waiting-time reduction and referral-filtering surrogates in a public-health setting. The 78 % waiting-time reduction maps directly onto the Conic 2018 surgical-delay → mortality-hazard gradient for suspected malignancies (4 % direct-biopsy route). Anchors the quantitative care-pathway claim in Domain 3.

CRIT1–7 appraisal​

CriterionScoreJustification
CRIT1 Relevance3Direct — teledermatology referral-filtering and waiting-time surrogates.
CRIT2 Methodology2Very large retrospective real-world cohort; municipal public-health system.
CRIT3 Reporting2Point estimates reported; 95 % CIs not all stated.
CRIT4 Applicability2Brazil public-health context; partial EU-transferability.
CRIT5 Evidence weight1Retrospective cross-sectional (highest-quality real-world evidence at this scale).
CRIT6 Risk of bias2Retrospective; single health system; no clinical outcome follow-up; selection bias in teledermatology uptake.
CRIT7 Contribution3Core quantitative anchor — most-cited modern number for teledermatology waiting-time reduction.

Aggregate: strong.

Limitations and notes​

Brazil public-system context; no patient-outcome follow-up; selection bias in referral uptake.

Strength as anchor​

Strong — the canonical modern quantitative anchor for waiting-time reduction in teledermatology. Used widely in HTA reports including NICE / NIHR evaluations.

Previous
Finnane 2017 — Teledermatology for the diagnosis and management of skin cancer: systematic review
Next
Jain 2021 — AI tool for skin-condition diagnosis by PCPs and NPs in teledermatology
  • Citation
  • Study design and population
  • Reported metrics
  • Surrogate-to-outcome linkage
  • CRIT1–7 appraisal
  • Limitations and notes
  • Strength as anchor
All the information contained in this QMS is confidential. The recipient agrees not to transmit or reproduce the information, neither by himself nor by third parties, through whichever means, without obtaining the prior written permission of Legit.Health (AI Labs Group S.L.)