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Clinical research innovation hub walking deck v12
- 1. Paul Slater Ryan Tubbs Accelerating Innovation in Clinical Research
- 2. Problem Patients are delayed access to best available treatments âSites donât recruit enough patientsâ âPatients drop out of trialsâ âToo many protocol amendmentsâ âTrials are too expensiveâ âRecruitment takes too longâ âNot enough clear guidance around new paradigmsâ âCannot connect to other systemsâ âCRCs are overburdenedâ âLack of reproducibilityâ âLack of diversityâ
- 3. Focus Areas Digitize the Study Establish an end-to-end flow of data from study objectives through study reporting Maximize the value of research data Enhance access to high quality data and enrich data quality through artificial intelligence Meet patient needs as research modernizes Reduce patient burden, improve benefits to patients from research and expand access to clinical research
- 4. Patients are delayed access to best available treatments
- 5. High Performance, Scalable, Flexible Globally Available and Compliant across Regions Contextual Security Controls, with Proof of Data Integrity Accesses Diverse Health Data Sources Enables Data Sharing Across the Value Chain Provides the basis for further waves of innovation Platform Attributes
- 6. Transformation Architecture 6 Limited Data Sharing Research Presentation Research Services Data Enrichment Study Conduct Research Analytics Regulatory Reporting Study Design Authoring Review Patient Recruitment Supply Chain Management Contract Management Quality Management Pharmacovigilance Monitoring Financial Management Clinician Marketing PI Recruitment Patient Engagement Patient Management Consent Trials Awareness Clinician Engagement Data Maintenance Data Analytics Annotation Data Ingestion EDC Digitization CRFs RCT Data Real World Data Historical Trials Data Population Data Operational Data MicrosoftDrivenTech Enablement PartnerDrivenResearchApplications ⢠Costs and time to market are increasing ⢠Technologies are a reflection of business processes that go back decades ⢠Combination of manually captured and digitized data ⢠Minimal standardization ⢠Minimal connection between research and clinical care ⢠Many sites do not recruit patients ⢠Minimal connection to rest of value chain ⢠Double-data entry common ⢠Requirements: Existing systems in place Performance Management Current State
- 7. Transformation Architecture 7 Data Sharing Portal Research Presentation Research Services Data Enrichment Study Conduct Research Analytics Regulatory Reporting Study Design Authoring Review Hybrid Trials Assisted Recruitment Supply Chain Management Enhanced Contract Mgmt. Quality Management Pharmacovigilance Monitoring Financial Management Clinician Marketing PI Recruitment Patient Engagement Patient Management E-consent Trials Matching Clinician Engagement Research Modelling Data Maintenance Data Analytics Annotation Data Ingestion EDC Normalization Digitization EDC-EHR CRFs RCT Data Real World Data Historical Trials Data Population Data Operational Data MicrosoftDrivenTech Enablement PartnerDrivenResearchApplications ⢠Current systems supplemented to deliver incremental improvements ⢠New point solutions to improve site management, recruitment and retention ⢠Improved standardization and digitization that allow for some modelling ⢠Easier data sharing to comply with open science requirements ⢠Requirements: Existing systems in place Performance Management Wave 1 Current State
- 8. Transformation Architecture 8 Business Planning Recommendations Data Sharing Portal Sponsor Exec Dashboards Research Presentation Research Services Data Enrichment Study Conduct Realtime Research Analytics Regulatory Reporting Population AI Study Design Authoring Review Adaptations Hybrid Trials POC Trials Automated Recruitment Supply Chain Management Enhanced Contract Mgmt. Performance Management Quality Management Pharmacovigilance Monitoring Financial Management Journey Visualization Research Network Patient Engagement Telemedicine for Trials Patient Management E-consent Personalized Trial Options Connected Research/Care Clinician Engagement Research Portfolio New Studies Research Modelling Machine Learning Data Maintenance Big Data Analytics Automated Annotation Data Ingestion HL7-FHIR Other Cognitive ServicesNLP Automated Normalization Standardization DICOM CRFs Social Media Connectors RCT Data Real World Data Historical Trials Data Population Data Operational Data Common Data Platform MicrosoftDrivenTech Enablement PartnerDrivenResearchApplications IoMT ⢠Digital End-to-end representation of the study ⢠Common data architecture that provides deeper data insights ⢠AI used to provide study recommendations, research portfolio recommendations, and to enrich data sources ⢠Data sourced from clinical care settings ⢠End-to-end executive insight into research process ⢠Requirements: Standardized data architecture, regulatory approval of AI systems. Wave 1 Wave 2 Current State
- 9. Transformation Architecture 9 Business Planning Recommendations Realtime Researcher Portal Realtime Clinician PortalRealtime Patient Portal Sponsor Exec Dashboards Research Presentation Research Services Data Enrichment Study Conduct Realtime Research Analytics Realtime research APIs Realtime reporting APIs Population AI Study Design Authoring Review Adaptations Hybrid Trials POC Trials Automated Recruitment Digital Supply Chain Smart Contracts Performance Management Quality Management Automated Pharmacovigilance Automated Monitoring Financial Management Journey Visualization Research Network Patient Engagement Telemedicine for Trials Patient Driven Data Sharing E-consent Personalized Trial Options Integrated Research/Care Clinician Engagement Research Portfolio New Studies Virtual Research Simulation Engine Machine Learning Data Maintenance Big Data Analytics Automated Annotation Data Ingestion HL7-FHIR Other Cognitive ServiceNLP Automated Normalization Standardization Social Media Connectors RCT Data Real World Data Historical Trials Data Population Data Operational Data Common Data Platform MicrosoftDrivenTech Enablement PartnerDrivenResearchApplications Wave 1 Wave 2 Wave 3 ⢠Research fully integrated into clinical care ⢠Continuous gathering of evidence on safety, efficacy and patient value ⢠Real-time reporting available to patients, clinicians, researchers and regulators ⢠Data insights available across full value chain, from discovery, through commercialization ⢠Requirements: Full adoption of data architecture, including enrichment layer, regulatory approval of real- time reporting methods Current State DICOM IoMT
- 10. Waves of Transformation Incremental improvements to current systems. (Currently underway) Dramatic improvements through rearchitecting systems and infusing artificial intelligence. Transformative improvements to proving safety, efficacy and patient value in real time.
- 11. Options for Clinical Research Innovation Partnership
- 12. We thrive on Open Innovation
- 14. Digitize the study Planned use of standards Unstructured Protocol eDC Build Data Capture Data Storage and Review Digital Protocol Collect Aggregate Analyze Analysis Analysis Data and Results Study Reports DefineDefine CDASH Controlled Terminologies Define.xml TAUGS ODM XML SDM-XML ODM XML DATASET XML DEFINE XML SDTM ADaM
- 15. Data captured in a research setting Disconnected research, non-digitized data, terminated studies, social media, folklore EMR, genomics, wearables, smart medical devices, relevant digitized studies Make Data Visible Extract meaning from unstructured data Enrich clinically relevant data
- 16. Meeting Patient Needs as Research Modernizes Every patient who can benefit from research should have the option to participate. The burden of participating in research should be minimized. The benefits of clinical research should flow to clinical care settings. The administrative burden on clinicians conducting research should be minimized. Informed consent should be truly informed.
- 17. Focus Areas
Hinweis der Redaktion
- Talk track Open Iâm not here to sell you anything which is a bit unorthodox for this event Iâm here looking for customers and partners who share our POV and want to Think Big. Start Small. Act Fast with us Iâd like to share what weâve been up to and see if there are any shared interests Success for me is a deeper conversation and exploration of what we can do together to drive transformation Aside: some companies live on the bleeding edge some are fast followers and some are laggards. All are viable business models. Iâm hoping to get a feel for which you are and what interest you have to build âwithâ a technology company. Traditionally we havenât served Pharma well â we just sold product to IT Iâm sure our technology has been valued, but there we were leaving so much on the table Our own transformation revealed to us who we really are â a platform â and with that how we can better serve our customers through problem-centricity Platforms can intrinsically do an infinite number of things â itâs only through deep exploration and maniacal focus on a problem that you can assemble the platform capabilities in a meaningful way to solve the problem Gave birth to problem-centricity (which we applied to clinical research) Research â our deep exploration into clinical research: Revealed the problem hierarchy Led us to a POV on where clinical research is headed (and how technology will influence it) Transformation is needed and existing solutions wonât get us there Belief that Microsoft can play a role And we have an advantage â we can take a greenfield approach without risk of cannibalizing an existing solution We also arenât looking to compete in life sciences but rather to enable the transformation How we make money (consumption) is an aligned incentive to transformation Universe â we took our time building out the first 3 quadrants prior to getting into any serious talks with Pharma We needed to build our POV and beliefs and test them We needed to figure out what our role is (if any) We needed to see if we could rally incumbents and newcomers and build a groundswell of belief And we needed to start making tangible progress â not just talk the talk, but walk the walk Now we are ready to have serious conversations with those that feel the pain most acutely â Sponsors and conductors (Pharmas, CROs, AMCs) Close (reiterate open) Is there a particular focus area that you want to partner on to build something? What aspects of the reference architecture are most interesting? Is there a partner solution that you are interested in? happy to broker a connection May I share our POV whitepaper with you? Next year we expect it will be our partners here instead of us Pharma Goal: Build something with us (and our partners) Allow us to conduct ethnographic experiments Key points Industry transformation Our investment in MS Healthcare and CRIH Focus areas Reference architecture CRO Goal: Instill a sense of urgency â disrupt or be disrupted Do they want to be a partner or a traditional customer Key points Industry transformation â CRO model being disrupted (misaligned incentives)
- The CDISC 360 Project seeks to implement a prototype that demonstrates the CDISC standards as linked metadata driven by automation across the end-to-end clinical research data lifecycle. CDISC 360 will demonstrate the feasibility of standards-based metadata-driven automation for a substantially improved efficiency, consistency, and re-usability experience using the CDISC standards across the clinical research data lifecycle.
- One aspect of patient centricity is ensuring that patients who can benefit from clinical research have the option to participate in it. Thereâs an increasing consensus in the research community that clinical research is not only essential to advancing medical science but is also associated with better health outcomes for many study participants. This has led to the emergence of Clinical Research as a Care Option (CRAACO) as a serious topic of discussion among researchers and healthcare professionals. As technology is used to facilitate more effective recruitment, it is vital that the right patients are being matched to studies, balancing the need for a diverse patient population with the requirement for patients to make an informed choice on whether participating is the right choice for them. Ultimately we can use technology to ensure that every patient who will benefit from research has the option to participate, and every patient is making a fully informed choice on the risks and benefits to themselves and society. Virtual and hybrid trials present their own challenges. These trials may present fewer burdens in terms of participation, but in many cases patients still have interests that need to be represented, particularly in terms of how their data is used and reused. Technology systems will need to be designed that are highly user-centric, giving participants a clear understanding of the intended primary and secondary uses of their data, alongside the risks of misuse (including reidentification of deidentified data). This will need to be provided both ahead of time and in real time, allowing patients to provide truly informed consent. The RWE and CRAACO trends, when considered together, reveal that the lines between clinical research and clinical care are becoming increasingly blurred. Trial designs are emerging that may combine claims data, EMR data, and data from wearables. New operating models are emerging that are increasingly direct-to-patient, or where much of the research is being performed in a clinical care setting. In addition, data from (for example) off-label prescribing, could be of huge benefit to clinical research as a whole if captured consistently. These changes can make it more difficult to represent the needs of patients in all circumstances. Our goal is to make it possible for patients to aid in clinical research simply by participating in healthcare, and to ensure that the benefits of clinical research flow to clinical care much more efficiently than is currently the case. But as we facilitate this change, we must ensure that patients participating in all aspects of the healthcare system are fully informed of the downstream effects of their participation.