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WHYZE 健康.pdf

上传人: Fl****zo 编号:724392 2025-07-01 19页 1.04MB

1、WHYZE HealthCan we transform RWE into actionable health outcomes?(in Ireland)Prof Frank Sullivan-CMO Co-Founder 6thFebruary 2025page2 2024 All Rights ReservedWhat is Real World EvidenceCentral role of clinical trials in healthcare improvementReal world data:Health information collated outside of cli

2、nical trialsWhat is an Actionable Health Outcome?Results that provide clear medical/personal decision-making utility.Providing specific actions for prevention/dx/txCentral role of data managementE.G.PSA,HbA1c leading to changes in lifestyle/testing etcpage3 2024 All Rights ReservedClinical Trial Res

3、ultsReal World OutcomesHandpicked patientsControlled EnvironmentNarrow timeframepage4 2024 All Rights ReservedBridging the Gap Between Clinical Trials and Real-World OutcomesHighly structuredLess structured?ActionableHigh patient numbersSampleVariability in AdministrationShort-Term observationsRelia

4、nt on data/SearchingExpensiveEHR regulations and existing deployment focusSelected cohortLess reliant on scientific tools for interpretation-BiostatisticsSmall patient numbersLong-Term EffectivenessError-prone,biasData siloing and fragmentationClinical TrialsReal World Evidencepage5 2024 All Rights

5、ReservedThe WHYZE ApproachWHYZE unifies data from patients,providers,and Life Science companies to transform Real-World Evidence(RWE)into real-world outcomes.Condition-specificEfficientAffordableReal World Evidence(RWE)To transform intoReal-world Outcomespage6 2024 All Rights ReservedUse CaseProstat

6、e Cancerpage7 2024 All Rights ReservedProstate CancerIn Irelandpage8 2024 All Rights ReservedProstate brachytherapy outcomes study(Blackrock Health)page9 2024 All Rights ReservedSurvival Curvepage10 2024 All Rights ReservedRisk Factors page11 2024 All Rights ReservedSimilarity Gradepage12 2024 All R

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本文主要介绍了一家名为WHYZE Health的公司,他们利用真实世界证据(RWE)转化为实际的健康结果。关键点如下: 1. 真实世界数据:在临床试验之外收集的健康信息。 2. 可操作的健康结果:提供明确医疗/个人决策效用,指导预防/诊断/治疗的具体行动。 3. 数据管理:中心角色是连接临床试验和真实世界结果。 4. WHYZE方法:统一患者、提供商和生命科学公司的数据,专注于前列腺癌和乳腺癌。 5. AI平台:利用约300万患者的结构化数据集,识别约66,000名前列腺癌患者,应用AI/ML工具回答具体问题。 6. 解决方案:通过AI驱动的试验加速器,普及研究,提高患者筛选和招募效率。 核心数据引用:约66,000名前列腺癌患者,25年的数据;300万患者的结构化数据集。
"如何实现临床外的健康决策?" "WHYZE Health怎样加速癌症研究?" "真实世界数据如何助力医疗改进?"
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