Bénéfices cliniques et performances revendiquées

Cette section décrit les bénéfices cliniques prévus du dispositif et les performances revendiquées qui étayent chaque bénéfice. Chaque bénéfice clinique repose sur des indicateurs de performance spécifiques, validés dans le cadre d'études cliniques.

Identifiants des bénéfices cliniques

Chaque bénéfice clinique est identifié par un code unique de 3 caractères, utilisé de manière cohérente dans la notice d'utilisation, la documentation technique, l'évaluation clinique et la documentation de gestion des risques. Le tableau ci-dessous définit chaque code et la description du bénéfice clinique correspondant.

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.

Comment lire les performances revendiquées

Chaque bénéfice clinique présenté ci-dessous est étayé par des indicateurs de performance issus d'études de validation clinique. Lors de la lecture des données de performance, tenez compte des éléments suivants :

• Les codes d'étude (par exemple IDEI_2023, BI_2024) identifient l'étude de validation clinique dont est issu l'indicateur. Les informations bibliographiques complètes de chaque étude, notamment le titre, les investigateurs principaux, les sites d'investigation, la taille de l'échantillon, la période de l'étude et le statut de publication, sont fournies dans la section Études de validation clinique.
• Pour chaque image traitée, le dispositif fournit toujours une distribution de probabilités sur l'ensemble des catégories CIM-11 validées. Le dispositif ne pose pas de diagnostic d'affections spécifiques ; il fournit une représentation interprétative, sous forme de distribution, des catégories CIM possibles, afin d'aider à la prise de décision clinique. La liste complète des catégories CIM-11 couvertes par le dispositif est précisée dans la section Destination.
• La population de l'étude indiquée pour chaque indicateur (par exemple « Affections multiples », « Maladies rares », « Mélanome ») précise le contexte clinique dans lequel l'étude de validation a été menée, c'est-à-dire la composition des images utilisées dans l'étude. Le mécanisme de production des résultats du dispositif est identique quelle que soit l'affection présentée : chaque image reçoit la même distribution de probabilités complète sur la CIM-11. Toutefois, le bénéfice clinique obtenu par le professionnel de santé varie selon le contexte clinique, car l'exactitude diagnostique de base diffère d'une catégorie d'affections à l'autre. Par exemple, les professionnels de santé ont une exactitude de base plus faible pour les affections dermatologiques rares ; l'amélioration attribuable au dispositif est donc proportionnellement plus importante dans ce contexte.
• Les indicateurs de performance mesurent l'amélioration de l'exactitude diagnostique, de la précision de l'adressage ou de l'évaluation de la sévérité par le professionnel de santé lorsqu'il utilise le résultat du dispositif présenté sous forme de distribution, par rapport à ses performances sans le dispositif.

Définitions des indicateurs et terminologie

Par souci de clarté, les définitions suivantes sont utilisées pour les indicateurs de performance :

• Exactitude top-K : mesure la fréquence à laquelle le diagnostic correct (le standard de référence clinique) figure parmi les K prédictions de plus forte probabilité fournies par l'algorithme.
• Exactitude top-1 : la prédiction n'est considérée comme réussie que si le diagnostic le plus probable (la prédiction classée en première position) généré par l'algorithme correspond exactement au diagnostic correct.
• Exactitude top-3 : la prédiction est considérée comme réussie si le diagnostic correct figure à n'importe quelle position parmi les trois premières prédictions fournies par l'algorithme.
• Exactitude top-5 : la prédiction est considérée comme réussie si le diagnostic correct figure parmi les cinq premières prédictions fournies par l'algorithme.
• ASC (aire sous la courbe ROC, AUC) : mesure la capacité du résultat du dispositif à distinguer deux classes (par exemple malin et non malin). Elle est utilisée pour le sous-critère de malignité du bénéfice 7GH, car la question clinique porte sur la discrimination entre présentations malignes et non malignes, pour laquelle l'AUC est l'indicateur méthodologiquement approprié. L'AUC et l'exactitude top-1 mesurent des aspects différents d'un même résultat de classification sous-jacent et ne sont pas interchangeables.

Bénéfices cliniques

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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