데이터 와 기술 | Clinical Trial Innovation Summit 2020 공식 사이트 
 ( 데이터 와 기술 ) 
 Technology and data are at the forefront driving clinical trial decision - making . With further advancements in new technologies ( such as mobile devices and wearables ) and the rise of online communities , the pharma and biotech industries are poised to capitalize on these advancements to innovate existing clinical trial processes and systems . Learn more at this lively conference program at CHI ' s 9 th Annual Clinical Trial Innovation Summit . 
 전자 데이터 , 블록체인 , 적응 적 임상 시험 
 11 : 20 DMD Phase II and Phase III Study Lessons Learned with Various eData Collection 
 The Phase II study collected 12 types of eData , including activity monitoring ( wrists and ankles ) , ePRO , biomarkers , immunogenicity , imaging , labs and biopsy data . This presentation will review the process of how Pfizer decides on the data collection strategy through the data analysis . With > 70 % of the data collected in our trials coming direct from source , we need to change how we manage our data . This presentation will highlight how we work today and where we are going . 
 11 : 45 Simulating Patient Matching to Clinical Trials Using a Property Rights Blockchain : A Study in Applying Blockchains to Biomedical Data Processing 
 12 : 10 pm CO - PRESENTATION : Advances in Personalized Medicine : How Technology Based on Predictive Power Can Advance Clinical Trials 
 임상 시험 을 변화 시키 는 디지털 기술 
 2 : 10 PANEL DISCUSSION : Accelerating Adoption of Digital Biomarkers and RWE in Clinical Research 
 Reducing Friction in Adaption of RWE and Remote Sensors in Clinical Trials 
 임상시험 에 대한 AI 의 영향 
 8 : 50 Your Brain at Your Fingertips 
 9 : 05 CO - PRESENTATION : The Future is Now : Using Predictive Algorithms and Machine Learning to Identify the Best Sites for Your Studies 
 Industry reports estimate that by 2020 , medical data will double every 73 days and there could be $ 100 billion in annual savings by leaning on big data as well as the artificial intelligence and machine learning tools to process it . To test these new approaches for study planning , Sanofi partnered with Inato to deploy its algorithm across several studies in order to predict site performance . The algorithm was able to accurately tier sites by performance , which provides the opportunity to significantly reduce the number of sites needed to meet the trial timelines and reduce study costs . 
 9 : 20 PANEL DISCUSSION : AI for Clinical Trials : Adaptation , Hurdles and Advantages 
 Topics to be discussed : How do you see adaptation of AI in clinical trials from perspective of Patients , Health care providers , Regulatory agencies ? What are the biggest hurdles for implementation of novel tools based on AI and ML ? Apart from increased efficiency and lowering the costs , what other advantages does AI bring to the clinical trials and the various stakeholders ? Where do you see the biggest impact ? Can you share with us some of the concerns that you , or the people you are working with , may have in relation to the use of AI in clinical trials ? What will the clinical trials look like 5 years and 15 years from now ? 
 임상 개발 에서 의 RWD ( REAL WORLD DATA ) 
 10 : 50 Digital Transformation as It Seeks to Better Leverage Data across the Life Science Ecosystem 
 This presentation will discuss key applications of RWD such as : improving patient recruitment for clinical trials , creating synthetic control arms in clinical trials , fulfilling the regulatory requirements for post - approval Phase IV trials and uncovering new uses for existing drugs ( and potentially using RWD to accelerate label extensions ) . 
 11 : 15 Advancing Real - World Evidence to Incorporate Patient - Generated Health Data 
 The talk will highlight how patient - generated health data is the RWD that is more reflective of actual day - to - day health and outcomes . Patient - generated data helps us understand what works for whom , how much it works and when . For companies across the health care ecosystem , claims and EHR data have been bedrock data sources , but they are not sufficient to change how health is measured and how diseases are diagnosed , treated , and monitored .