Beneficios Clínicos y Declaraciones de Rendimiento

Esta sección describe los beneficios clínicos previstos del dispositivo y las declaraciones de rendimiento que sustentan cada beneficio. Cada beneficio clínico está respaldado por métricas de rendimiento específicas validadas a través de estudios clínicos.

Identificadores de Beneficios Clínicos

Cada beneficio clínico se identifica con un código único de 3 caracteres utilizado de manera consistente en las Instrucciones de Uso, el expediente técnico, la evaluación clínica y la documentación de gestión de riesgos. La tabla siguiente define cada código y su descripción de beneficio clínico correspondiente.

Code	Clinical Benefit Description
7GH	The device improves the accuracy of healthcare professionals in the diagnosis of dermatological conditions across a broad spectrum of clinical presentations, including rare diseases and lesions suspicious for skin cancer. This has a positive impact on patient management and health outcomes related to diagnosis, enabling more appropriate clinical decision-making, earlier identification of rare conditions, and, in cases of suspected malignancy, reducing the risk of delayed diagnosis and the need for unnecessary invasive procedures.
5RB	The device measures the degree of involvement of disease objectively, quantitatively, and reproducibly. This increases the precision of healthcare providers during the monitoring of patients. This has a positive impact on patient management and outcomes related to the monitoring of patients and treatment.
3KX	The device improves the precision of healthcare professionals in managing dermatological care pathways, encompassing referral decisions, resource allocation, and clinical assessment in remote care settings. This has a positive impact on patient management and outcomes related to the diagnosis and monitoring of patients, resulting in reduced waiting times for specialist consultation, improved adequacy of referrals, and expanded access to dermatological assessment across in-person and remote care settings.

Cómo Leer las Declaraciones de Rendimiento

Cada beneficio clínico listado a continuación está respaldado por métricas de rendimiento de estudios de validación clínica. Al leer los datos de rendimiento, tenga en cuenta lo siguiente:

• Los códigos de estudio (p. ej., IDEI_2023, BI_2024) identifican el estudio de validación clínica que generó la métrica. Los detalles bibliográficos completos de cada estudio — incluyendo título, investigadores principales, centros de investigación, tamaño de muestra, período del estudio y estado de publicación — se proporcionan en la sección Estudios de Validación Clínica.
• El dispositivo siempre produce una distribución de probabilidad a través de todas las categorías CIE-11 validadas para cada imagen procesada. El dispositivo no diagnostica condiciones específicas; proporciona una representación de distribución interpretativa de las posibles categorías CIE para apoyar la toma de decisiones clínicas. La lista completa de categorías CIE-11 cubiertas por el dispositivo se especifica en la sección Finalidad prevista.
• La población del estudio mostrada para cada métrica (p. ej., "Multiple conditions", "Rare diseases", "Melanoma") indica el contexto clínico en el que se realizó el estudio de validación — es decir, la composición de imágenes utilizadas en el estudio. El mecanismo de salida del dispositivo es idéntico independientemente de la condición presentada: cada imagen recibe la misma distribución de probabilidad CIE-11 completa. Sin embargo, el beneficio clínico realizado por el profesional sanitario varía según el contexto clínico, porque la precisión diagnóstica basal difiere entre categorías de condiciones. Por ejemplo, los profesionales sanitarios tienen menor precisión basal para condiciones dermatológicas raras, por lo que la mejora atribuible al dispositivo es proporcionalmente mayor en ese contexto.
• Las métricas de rendimiento miden la mejora en la precisión diagnóstica, la precisión de derivación o la evaluación de gravedad del profesional sanitario al utilizar la salida distribucional del dispositivo, en comparación con el rendimiento sin el dispositivo.

Definiciones de Métricas y Terminología

Para garantizar la claridad, se utilizan las siguientes definiciones para las métricas de rendimiento:

• Precisión Top-K: Mide con qué frecuencia el diagnóstico correcto (el estándar de referencia clínica) aparece entre las K predicciones con mayor probabilidad proporcionadas por el algoritmo.
• Precisión Top-1: La predicción se considera exitosa únicamente si el diagnóstico más probable (la primera predicción) generado por el algoritmo coincide exactamente con el diagnóstico correcto.
• Precisión Top-3: La predicción se considera exitosa si el diagnóstico correcto está incluido en cualquier lugar de las tres predicciones con mayor probabilidad.
• Precisión Top-5: La predicción se considera exitosa si el diagnóstico correcto aparece entre las cinco predicciones con mayor probabilidad.
• AUC (Área Bajo la Curva ROC): Mide la capacidad de la salida del dispositivo para discriminar entre dos clases (p. ej., maligno frente a no maligno). Se utiliza para el subcriterio de malignidad dentro del Beneficio 7GH, ya que la pregunta clínica es la discriminación entre presentaciones malignas y no malignas, para lo cual el AUC es la métrica metodológicamente apropiada. El AUC y la precisión Top-1 miden aspectos diferentes de la misma salida de clasificación subyacente y no son intercambiables.

Beneficios Clínicos

7GH The device improves the accuracy of healthcare professionals in the diagnosis of dermatological conditions across a broad spectrum of clinical presentations, including rare diseases and lesions suspicious for skin cancer. This has a positive impact on patient management and health outcomes related to diagnosis, enabling more appropriate clinical decision-making, earlier identification of rare conditions, and, in cases of suspected malignancy, reducing the risk of delayed diagnosis and the need for unnecessary invasive procedures.

Estimated Magnitude of Benefit

• 97.06% Specificity:
  • 6EP 97.06%. Study: IDEI_2023 (Multiple malignant conditions). User Group: Dermatologists.
• 97.06% Negative predictive value(NPV):
  • V2U 97.06%. Study: IDEI_2023 (Multiple malignant conditions). User Group: Dermatologists.
• 97.00% Area under the ROC curve(AUC):
  • FIQ 97.00%. Study: IDEI_2023 (Multiple malignant conditions). User Group: Dermatologists.
• 96.00% Negative predictive value(NPV):
  • 7ZI 96.00%. Study: DAO_Derivación_O_2022 (Multiple malignant conditions). User Group: Primary care practitioners.
• 93.95% Top-5 sensitivity:
  • ZM8 93.95%. Study: MC_EVCDAO_2019 (Melanoma). User Group: Dermatologists.
• 93.53% Specificity:
  • R9P 93.53%. Study: DAO_Derivación_O_2022 (Multiple malignant conditions). User Group: Primary care practitioners.
• 92.47% Positive predictive value(PPV):
  • 9G4 92.47%. Study: MC_EVCDAO_2019 (Multiple malignant conditions). User Group: Dermatologists.
• 90.72% Specificity. Weighted average across the following studies:
  • B4N 90.72%. Study: SAN_2024 (Multiple conditions). User Group: Dermatologists.
  • YIO 90.72%. Study: SAN_2024 (Multiple conditions). User Group: Dermatologists.
• 90.32% Top-3 sensitivity:
  • T3S 90.32%. Study: MC_EVCDAO_2019 (Melanoma). User Group: Dermatologists.
• 89.83% Area under the ROC curve(AUC):
  • 9OD 89.83%. Study: MC_EVCDAO_2019 (Multiple malignant conditions). User Group: Dermatologists.
• 89.29% Top-3 accuracy:
  • 7V9 89.29%. Study: IDEI_2023 (Multiple conditions). User Group: Dermatologists.
• 89.29% Top-5 accuracy:
  • 1S5 89.29%. Study: IDEI_2023 (Multiple conditions). User Group: Dermatologists.
• 87.50% Sensitivity:
  • GS5 87.50%. Study: IDEI_2023 (Multiple malignant conditions). User Group: Dermatologists.
• 87.50% Positive predictive value(PPV):
  • PZD 87.50%. Study: IDEI_2023 (Multiple malignant conditions). User Group: Dermatologists.
• 87.08% Specificity. Weighted average across the following studies:
  • QX8 89.91%. Study: PH_2024 (Multiple conditions). User Group: Primary care practitioners.
  • VCN 89.91%. Study: PH_2024 (Multiple conditions). User Group: Primary care practitioners.
  • FBJ 84.15%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners.
  • N2E 84.15%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners.
• 86.84% Specificity. Weighted average across the following studies:
  • FEH 86.84%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners, Dermatologists.
  • VFV 86.84%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners, Dermatologists.
• 86.00% Specificity:
  • VFY 86.00%. Study: MC_EVCDAO_2019 (Multiple malignant conditions). User Group: Dermatologists.
• 85.00% Area under the ROC curve(AUC):
  • 6U1 85.00%. Study: MC_EVCDAO_2019 (Melanoma). User Group: Dermatologists.
• 84.22% Top-5 accuracy:
  • EYP 84.22%. Study: MC_EVCDAO_2019 (Multiple conditions). User Group: Dermatologists.
• 84.20% Area under the ROC curve(AUC):
  • EAC 84.20%. Study: DAO_Derivación_PH_2022 (Multiple malignant conditions). User Group: Primary care practitioners.
• 81.50% Area under the ROC curve(AUC):
  • DX7 81.50%. Study: DAO_Derivación_O_2022 (Multiple malignant conditions). User Group: Primary care practitioners.
• 81.00% Top-1 accuracy:
  • JFM 81.00%. Study: MC_EVCDAO_2019 (Melanoma). User Group: Dermatologists.
• 80.88% Sensitivity:
  • BRI 80.88%. Study: MC_EVCDAO_2019 (Multiple malignant conditions). User Group: Dermatologists.
• 80.00% Specificity:
  • 4JY 80.00%. Study: MC_EVCDAO_2019 (Melanoma). User Group: Dermatologists.
• 77.86% Top-1 accuracy. Weighted average across the following studies:
  • GU0 70.87%. Study: BI_2024 (Multiple conditions). User Group: Dermatologists.
  • 47J 70.87%. Study: BI_2024 (Multiple conditions). User Group: Dermatologists.
  • LXJ 82.14%. Study: IDEI_2023 (Multiple conditions). User Group: Dermatologists.
  • 8V3 86.93%. Study: SAN_2024 (Multiple conditions). User Group: Dermatologists.
  • UC7 86.93%. Study: SAN_2024 (Multiple conditions). User Group: Dermatologists.
• 76.41% Sensitivity. Weighted average across the following studies:
  • A76 73.90%. Study: BI_2024 (Multiple conditions). User Group: Dermatologists.
  • CUC 73.90%. Study: BI_2024 (Multiple conditions). User Group: Dermatologists.
  • Q5G 85.08%. Study: SAN_2024 (Multiple conditions). User Group: Dermatologists.
  • 3GH 85.08%. Study: SAN_2024 (Multiple conditions). User Group: Dermatologists.
• 75.69% Top-3 accuracy:
  • 6R7 75.69%. Study: MC_EVCDAO_2019 (Multiple conditions). User Group: Dermatologists.
• 75.10% Top-1 accuracy. Weighted average across the following studies:
  • GZS 68.78%. Study: BI_2024 (Multiple conditions). User Group: Primary care practitioners.
  • 31Q 68.78%. Study: BI_2024 (Multiple conditions). User Group: Primary care practitioners.
  • CZR 81.85%. Study: PH_2024 (Multiple conditions). User Group: Primary care practitioners.
  • F16 81.85%. Study: PH_2024 (Multiple conditions). User Group: Primary care practitioners.
  • 5XF 89.92%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners.
  • R7X 89.92%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners.
• 74.45% Sensitivity. Weighted average across the following studies:
  • 37G 71.23%. Study: BI_2024 (Multiple conditions). User Group: Primary care practitioners.
  • 19H 71.23%. Study: BI_2024 (Multiple conditions). User Group: Primary care practitioners.
  • HUG 83.15%. Study: PH_2024 (Multiple conditions). User Group: Primary care practitioners.
  • JZ1 83.15%. Study: PH_2024 (Multiple conditions). User Group: Primary care practitioners.
  • KPM 76.53%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners.
  • 2W5 76.53%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners.
• 74.07% Specificity:
  • CH0 74.07%. Study: PH_2024 (Rare diseases). User Group: Primary care practitioners.
• 73.90% Sensitivity. Weighted average across the following studies:
  • 5IT 71.94%. Study: BI_2024 (Multiple conditions). User Group: Primary care practitioners, Dermatologists.
  • ASM 71.94%. Study: BI_2024 (Multiple conditions). User Group: Primary care practitioners, Dermatologists.
  • 5UM 80.64%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners, Dermatologists.
  • S2C 80.64%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners, Dermatologists.
• 73.79% Top-1 sensitivity:
  • T1S 73.79%. Study: MC_EVCDAO_2019 (Melanoma). User Group: Dermatologists.
• 73.73% Top-1 accuracy. Weighted average across the following studies:
  • 9D7 69.36%. Study: BI_2024 (Multiple conditions). User Group: Primary care practitioners, Dermatologists.
  • ZKC 69.36%. Study: BI_2024 (Multiple conditions). User Group: Primary care practitioners, Dermatologists.
  • YJC 88.78%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners, Dermatologists.
  • ME3 88.78%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners, Dermatologists.
  • M2A 65.07%. Study: MAN_2025 (Multiple conditions). User Group: Primary care practitioners, Dermatologists.
• 70.56% Top-1 accuracy. Weighted average across the following studies:
  • KW3 78.57%. Study: IDEI_2023 (Multiple conditions). User Group: Dermatologists.
  • WGP 55.00%. Study: MC_EVCDAO_2019 (Multiple conditions). User Group: Dermatologists.
• 67.89% Negative predictive value(NPV):
  • Z96 67.89%. Study: MC_EVCDAO_2019 (Multiple malignant conditions). User Group: Dermatologists.
• 57.14% Sensitivity:
  • LU4 57.14%. Study: DAO_Derivación_O_2022 (Multiple malignant conditions). User Group: Primary care practitioners.
• 51.85% Specificity:
  • 5W2 51.85%. Study: PH_2024 (Rare diseases). User Group: Primary care practitioners.
• 44.44% Sensitivity:
  • REV 44.44%. Study: PH_2024 (Rare diseases). User Group: Primary care practitioners.
• 42.00% Positive predictive value(PPV):
  • 0L2 42.00%. Study: DAO_Derivación_O_2022 (Multiple malignant conditions). User Group: Primary care practitioners.
• 31.28% Sensitivity. Weighted average across the following studies:
  • 8PG 34.00%. Study: BI_2024 (Rare diseases). User Group: Primary care practitioners.
  • 6YW 22.22%. Study: PH_2024 (Rare diseases). User Group: Primary care practitioners.
• 30.39% Specificity:
  • ZGT 30.39%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners, Dermatologists.
• 29.80% Sensitivity:
  • NK7 29.80%. Study: BI_2024 (Rare diseases). User Group: Primary care practitioners, Dermatologists.
• 25.61% Top-1 accuracy. Weighted average across the following studies:
  • JBB 28.30%. Study: BI_2024 (Rare diseases). User Group: Primary care practitioners.
  • I7Y 16.66%. Study: PH_2024 (Rare diseases). User Group: Primary care practitioners.
• 24.70% Top-1 accuracy:
  • DII 24.70%. Study: BI_2024 (Rare diseases). User Group: Primary care practitioners, Dermatologists.
• 22.25% Sensitivity. Weighted average across the following studies:
  • 81T 23.76%. Study: BI_2024 (Multiple conditions). User Group: Primary care practitioners.
  • 09O 14.60%. Study: PH_2024 (Multiple conditions). User Group: Primary care practitioners.
  • 7YC 24.95%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners.
• 22.22% Top-1 accuracy:
  • Z90 22.22%. Study: PH_2024 (Rare diseases). User Group: Primary care practitioners.
• 21.87% Sensitivity. Weighted average across the following studies:
  • 02A 20.08%. Study: BI_2024 (Multiple conditions). User Group: Primary care practitioners, Dermatologists.
  • A84 28.03%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners, Dermatologists.
• 20.71% Top-1 accuracy. Weighted average across the following studies:
  • O4L 19.65%. Study: BI_2024 (Multiple conditions). User Group: Primary care practitioners.
  • 6KX 18.15%. Study: PH_2024 (Multiple conditions). User Group: Primary care practitioners.
  • AR8 27.00%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners.
• 20.70% Specificity. Weighted average across the following studies:
  • VEF 11.90%. Study: PH_2024 (Multiple conditions). User Group: Primary care practitioners.
  • 9YX 29.80%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners.
• 18.20% Sensitivity:
  • TG6 18.20%. Study: BI_2024 (Rare diseases). User Group: Dermatologists.
• 17.47% Top-1 accuracy. Weighted average across the following studies:
  • MRT 16.73%. Study: BI_2024 (Multiple conditions). User Group: Primary care practitioners, Dermatologists.
  • 61I 20.00%. Study: SAN_2024 (Multiple conditions). User Group: Primary care practitioners, Dermatologists.
  • M2N 23.27%. Study: MAN_2025 (Multiple conditions). User Group: Primary care practitioners, Dermatologists.
• 15.80% Top-1 accuracy:
  • 4KO 15.80%. Study: BI_2024 (Rare diseases). User Group: Dermatologists.
• 11.03% Sensitivity. Weighted average across the following studies:
  • W8N 9.96%. Study: BI_2024 (Multiple conditions). User Group: Dermatologists.
  • BHO 14.70%. Study: SAN_2024 (Multiple conditions). User Group: Dermatologists.
• 9.53% Top-1 accuracy. Weighted average across the following studies:
  • 6FT 9.25%. Study: BI_2024 (Multiple conditions). User Group: Dermatologists.
  • O0B 10.50%. Study: SAN_2024 (Multiple conditions). User Group: Dermatologists.
• 8.37% Specificity:
  • N50 8.37%. Study: SAN_2024 (Multiple conditions). User Group: Dermatologists.

Means of Measure

Top-1 accuracySensitivityArea under the ROC curveSpecificityPositive predictive valueNegative predictive valueTop-3 accuracyTop-5 accuracyTop-1 sensitivityTop-3 sensitivityTop-5 sensitivity

Associated Performance Claims

MRT9D7ZKC02A5ITASMO4LGZS31Q81T37G19H6FTGU047JW8NA76CUCDIINK7JBB8PG4KOTG6EACDX7LU4R9P0L27ZILXJKW37V91S5FIQGS56EPPZDV2UWGP6R7EYP6U1JFMT1ST3SZM84JY9ODBRIVFY9G4Z966KXCZRF1609OHUGJZ1VEFQX8VCNI7YZ906YWREV5W2CH061IYJCME3A845UMS2CZGTFEHVFVAR85XFR7X7YCKPM2W59YXFBJN2EO0B8V3UC7BHOQ5G3GHN50B4NYIOM2NM2A

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5RB The device measures the degree of involvement of disease objectively, quantitatively, and reproducibly. This increases the precision of healthcare providers during the monitoring of patients. This has a positive impact on patient management and outcomes related to the monitoring of patients and treatment.

Estimated Magnitude of Benefit

• 81.50% Expert consensus(CUS). Weighted average across the following studies:
  • 3OA 80.00%. Study: COVIDX_EVCDAO_2022 (Multiple conditions). User Group: Dermatologists.
  • EZ1 83.00%. Study: COVIDX_EVCDAO_2022 (Multiple conditions). User Group: Dermatologists.
• 72.70% Accuracy against the expert-consensus gold standard:
  • LL5 72.70%. Study: AIHS4_2025 (Hidradenitis suppurativa). User Group: Dermatologists.
• 59.00% Correlation. Weighted average across the following studies:
  • JWQ 77.00%. Study: IDEI_2023 (Androgenetic alopecia). User Group: Dermatologists.
  • 284 47.00%. Study: IDEI_2023 (Androgenetic alopecia). User Group: Dermatologists.
  • 7TS 53.00%. Study: IDEI_2023 (Androgenetic alopecia). User Group: Dermatologists.
• 53.47% Unweighted Kappa. Weighted average across the following studies:
  • A1Q 73.97%. Study: IDEI_2023 (Androgenetic alopecia). User Group: Dermatologists.
  • 3OB 32.97%. Study: IDEI_2023 (Androgenetic alopecia). User Group: Dermatologists.

Means of Measure

Accuracy against the expert-consensus gold standardExpert consensusCorrelationUnweighted Kappa

Associated Performance Claims

LL53OAEZ1JWQA1Q2843OB7TS

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3KX The device improves the precision of healthcare professionals in managing dermatological care pathways, encompassing referral decisions, resource allocation, and clinical assessment in remote care settings. This has a positive impact on patient management and outcomes related to the diagnosis and monitoring of patients, resulting in reduced waiting times for specialist consultation, improved adequacy of referrals, and expanded access to dermatological assessment across in-person and remote care settings.

Estimated Magnitude of Benefit

• 213.46% Reduction in the number of days. Weighted average across the following studies:
  • KPQ 84.37%. Study: DAO_Derivación_O_2022 (Multiple conditions). User Group: Primary care practitioners.
  • 1M1 56.00%. Study: DAO_Derivación_O_2022 (Multiple conditions). User Group: Primary care practitioners.
  • UGS 5 days. Study: DAO_Derivación_O_2022 (Multiple conditions). User Group: Primary care practitioners.
• 100.00% Expert consensus:
  • P30 100.00%. Study: COVIDX_EVCDAO_2022 (Multiple conditions). User Group: Dermatologists.
• 100.00% Expert consensus:
  • 8MV 100.00%. Study: SAN_2024 (Multiple conditions). User Group: Dermatologists.
• 80.00% Expert consensus:
  • VCT 80.00%. Study: DAO_Derivación_PH_2022 (Multiple conditions). User Group: Primary care practitioners.
• 74.01% Expert consensus. Weighted average across the following studies:
  • ZGP 50.00%. Study: COVIDX_EVCDAO_2022 (Multiple conditions). User Group: Dermatologists.
  • RND 100.00%. Study: COVIDX_EVCDAO_2022 (Multiple conditions). User Group: Dermatologists.
  • 3BD 67.00%. Study: COVIDX_EVCDAO_2022 (Multiple conditions). User Group: Dermatologists.
  • NVT 76.67%. Study: COVIDX_EVCDAO_2022 (Multiple conditions). User Group: Dermatologists.
  • LYP 87.00%. Study: SAN_2024 (Multiple conditions). User Group: Dermatologists.
• 74.00% Sensitivity. Weighted average across the following studies:
  • CST 74.00%. Study: DAO_Derivación_O_2022 (Multiple conditions). User Group: Primary care practitioners.
  • 6H0 74.00%. Study: DAO_Derivación_O_2022 (Multiple conditions). User Group: Primary care practitioners.
• 73.10% Adequacy of referrals during in-person care. Weighted average across the following studies:
  • DCH 78.60%. Study: DAO_Derivación_O_2022 (Multiple conditions). User Group: Primary care practitioners.
  • DZC 67.60%. Study: DAO_Derivación_O_2022 (Multiple conditions). User Group: Primary care practitioners.
• 67.00% Specificity. Weighted average across the following studies:
  • H4U 67.00%. Study: DAO_Derivación_O_2022 (Multiple conditions). User Group: Primary care practitioners.
  • 04D 67.00%. Study: DAO_Derivación_O_2022 (Multiple conditions). User Group: Primary care practitioners.
• 60.70% Reduction in the number of days:
  • IP4 60.70%. Study: PH_2024 (Multiple conditions). User Group: Primary care practitioners.
• 56.00% Increase in patients that can be managed remotely:
  • WL4 56.00%. Study: SAN_2024 (Multiple conditions). User Group: Dermatologists.
• 50.00% Adequacy of referrals during remote care. Weighted average across the following studies:
  • LHF 33.00%. Study: DAO_Derivación_O_2022 (Multiple conditions). User Group: Primary care practitioners.
  • 4BO 67.00%. Study: DAO_Derivación_O_2022 (Multiple conditions). User Group: Primary care practitioners.
• 49.00% Increase in patients that can be managed remotely:
  • WOI 49.00%. Study: PH_2024 (Multiple conditions). User Group: Primary care practitioners.
• 42.00% Reduction in the number of days:
  • V2J 42.00%. Study: SAN_2024 (Multiple conditions). User Group: Dermatologists.
• 38.00% :
  • D62 38.00%. Study: DAO_Derivación_O_2022 (Multiple conditions). User Group: Primary care practitioners.
• 25.00% Increase in the adequacy of referrals:
  • 8H5 25.00%. Study: DAO_Derivación_PH_2022 (Multiple conditions). User Group: Primary care practitioners.

Means of Measure

Expert consensusIncrease in the adequacy of referralsSensitivitySpecificityAdequacy of referrals during in-person careAdequacy of referrals during remote careReduction in the number of daysIncrease in patients that can be managed remotely

Associated Performance Claims

ZGPRNDP303BDNVT8H5VCTD62CST6H0H4U04DDCHDZCLHF4BOKPQ1M1UGSIP4WOIV2JWL4LYP8MV

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