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![Demonstrates the “Initiate Study Build & Study
Build Design”
The Solution
Matteo logs in to the new platform to retrieve Alzheimer’s Study
– 3172020 and initiate the Study Design process.
Matteo builds the Protocol Title of the study based on hyperlinks
to prepopulated libraries and templates. He then defines the
Study Objective as follows: “To assess the effect of [study
intervention #1] on the ADAS-COG and CIBIC + scores at Week
[X] in participants with Mild to Moderate Alzheimer’s Disease.”
Matteo next enters the Study Masking Information,
Intervention, and Arms. For these categories, he chooses
Double Blind, Xanomeline, and High Dose respectively.
Matteo can select the Objectives and Endpoints for the study
using the standards managed and governed in the Objectives and
Endpoints libraries. Matteo selects the appropriate Objectives,
Objective Level and related Endpoints. For each Endpoint, the
appropriate visit Timeframes, Units and Biomedical Concepts are
selected from controlled standard metadata terms.](https://image.slidesharecdn.com/transceleratehackathon04192020fornn-200420140341/85/Transcelerate-hackathon-04192020_for-nn-70-320.jpg)
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- 1. Digitizing Clinical StudyDesign April 2020 TransCelerate Hackathon
- 2. Ryan Tubbs Project Lead VasuRanganathan Intelinotion Gerald Kukko Lead Solution Architect, Intelinotion Tianna Umann Blockchain Architect, Microsoft Brent Groom Innovation Lead, Microsoft Brian Nikonow Insight UX Architect Michelle Hendrickson Insight, UX Lead
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- 5. Today's Headline -EXTRA! EXTRA! READ ALL ABOUT IT "TransCelerate Digital Data Flow Solutions Platform enables the building of Global Clinical Study Design in 3 minutes! “
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- 11. User Story User Guide R1 –Study Build Navigation UG-1 R2 – Data Access UG-2 R3 – Export Protocol Content UG-3 L1 – Manage Elements UG-L1 L2 – Manage Element Values UG-L2 L3 – Manage Element Relationships UG-L3 L4 – Manage Value Relationships UG-L4 L5 – Library Import UG-L5 User Story User Guide P0 – New Study UG-P0 P1 – General Protocol Info UG-P1 P2 – Study Objectives UG-P2 P3 – Study Endpoints UG-P3 P4 & P5 – Mandatory Elements and Manage Schedule UG-P4/5 P6 – Version Study UG-P6 Extra Credit - Generate a Study Design Document in 3 minutes EC-1
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- 32. Art of thePossible Looking toward the future…
- 33. Our Future StateVision We hope you enjoyed test-driving our prototype! This interactive UI represents our unique vision for creating a standards-based end to end digital clinical trials workflow that is automated, seamless, and accelerates critical life-saving medicines to market – starting with Alzheimer's disease. We believe the clinical trials industry is at a critical juncture. Now, more than ever, emerging technologies have the potential to transform the way drug trials are run. Just like taking the keys to a Porsche, technology is accelerating faster than ever and disrupting wide swaths of industry. One compelling new technology that has surfaced in recent years is Blockchain. Blockchain consists of a series of blocks in which each consists of a cryptographic hash of the previous block, a timestamp, and transaction data. By requiring a match between the user and the encrypted hash, this “distributed ledger” provides an immutable source of truth that is safe and hackproof. While Blockchain originated in the financial industry, a growing number of other applications are emerging across industries, such as in real estate, insurance, and asset management. Imagine for a moment the possibility of a Blockchain controlled Clinical Trial workflow that provides participants with immediate visibility into all study metadata, including protocol titles, objectives, amendments, import/export query logs, and more. Take it further and consider a Blockchain-controlled IRB board consent process: Clinical Study protocol is revised, and study site request a change to their consent. The change request is logged on the blockchain and triggers a notification to the IRB board The IRB board approves the consent change. The approval is logged on the blockchain. This triggers an event that notifies all the stakeholders of the change in the consent. Blockchain holds vast potential to transform clinical trials research. If you enjoyed our prototype, then you’ll love our vision of a digital ledger-based clinical trial workflow that accelerates the marketing of life-saving medicines.
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- 35. The Underlying Magicof Contoso’s Digital Data Flow (with Real User Scenarios) Contoso is a biopharma company seeking to accelerate time to market for an Alzheimer drug that alleviates typical symptoms associated with the early onset of the disease. Clinical trial study methods have long been stuck in the past, and therefore the company is passionately committed to revamping the traditional document-based approach to introducing new drugs to market. The biggest challenge is how to implement efficiency and transparency through a trusted single source of truth for all the required documentation and approvals within the clinical trial lifecycle process. The second is to establish patient trust in order to improve recruitment, participation, and decrease the drop out in clinical trials. Contoso has identified the primary obstacles that stand in the way of creating a more streamlined, efficient, and time-saving approach to introducing its Alzheimer drug to market. Study documentation is fragmented and spread out across multiple data throughout the company’s protocol design team. The industry itself faces a general public mistrust due to increasing reports of data fraud in the pharma industry, past misconduct around safety, and lack of transparency into the outcomes of clinical trial studies. Lot of regulation and documentation and this technology can help . . .use Andreas’ information <this needs to be reworded>
- 36. Intermediaries Increase cost and reducedirect contact with consumers Traditional methods for establishing trust increase friction Manual Verification Time consuming and error prone Turning point (New solution) After more than a year of focused research, Contoso is excited to introduce an enterprise solution it believes will revolutionize clinical trials workflows through the key tenants of trust, digitization, and innovation. The solution is based on a seamless trusted data layer that enables all ecosystem members to generate, share, and audit information in a way that guarantees compliance and authenticity in the clinical research trials lifecycle process. The technology is based on Blockchain, and even though Contoso anticipates some initial pessimism due to association with the Bitcoin cryptocurrency, the company is confident that the pharma industry will quickly recognize the advantages blockchain offers the clinical trials workflow. Most notably, blockchain brings to the entire clinical research trial ecosystem a transparent single source of immutable truth around organizational process, validity of study data, and near real time visibility into the process to all the participants.
- 37. Scenario #1 Rajesh isthe Pharma Project Manager for Contoso’s new Alzheimer’s study. With many years of experience in clinical trials research, Rajesh has been tasked with identifying a solution for a workflow gap analysis that was done as part of the pharma’s digital transformation plan targeted to reduce the time to market for new drugs. Rajesh interviews the teams who work on the end to end clinical trials lifecycle for drug research. He also meets with the core group that generates the study protocol and associated assets for submission of the clinical trial research project to the government health and compliance authorities. Rajesh’s gap analysis reveals several areas for improvement. There are two key areas that the study protocol design team says consistently slows down their workflow. One is a lack of visibility into the work being done by others on the same clinical trial. The other is poor communication and visibility into study and protocol amendment changes.
- 38. Scenario #1 Rajesh’s managementhas encouraged him to think outside of the box for solutions instead of building upon existing workflow processes and legacy technology. After considerable research and talking with several pharma industry innovation thought leaders, Rajesh has put together a proposal for a combined business and technology solution. His proposal addresses immediate gaps around visibility and communication for the study team. It also enables a technology solution that can be reused and scaled to meet other requirements for transformation to accelerate time to market for life saving therapies.
- 39. Scenario #1 The immediatesolution for visibility and communication regarding amendments will leverage blockchain technology to enable near real time visibility into amendments to the protocol. It will also give the team full transparency into the clinical trial lifecycle along with the ability to accelerate the overall workflow. Furthermore, regulatory, study sites, and other ecosystem stakeholders involved in the clinical trial will gain access to the end to end study data artifacts, approvals, and reports. The added benefit of the solution is the team is able to integrate blockchain as part of their existing infrastructure instead of a rip and replace solution.
- 40. Scenario #1 The immediatesolution for visibility and communication regarding amendments will leverage blockchain technology to enable near real time visibility into amendments to the protocol. It will also give the team full transparency into the clinical trial lifecycle along with the ability to accelerate the overall workflow. Furthermore, regulatory, study sites, and other ecosystem stakeholders involved in the clinical trial will gain access to the end to end study data artifacts, approvals, and reports. The added benefit of the solution is the team is able to integrate blockchain as part of their existing infrastructure instead of a rip and replace solution. Report Patient Enrollment Data Collection Data Analysis Protocol Design Validation SOA EDC Protocol SAPCSR MetaData Ecosystem Amendment Reporting Visibility and Notification IRB Consent Change Request CRO Protocol Revision/ Amendment Stakeholder Network Consensus 1 2 3 4 A trusted shared, single source of data truth enables visibility of all stakeholders into process enabling efficiency in the revision, reporting, and approval of study trial documents. Modern technologies such as blockchain and automated study event notifications work together to accelerate the clinical research trial process. 1. Clinical Study protocol is revised, and study site request a change to their consent. 2. The change request is logged on the blockchain and triggers a notification to the IRB board 3. The IRB board approves the consent change. 4. The approval is logged on the blockchain which triggers an event that notifies all the stakeholders of the change in the consent which enables them to update their internal systems documentation
- 41. Scenario #2 Lydia, aclinical data scientist at Contoso, is leading the phase III clinical research trial submission for the new Alzheimer’s drug study. The company recently deployed a new solution called blockchain to accelerate the study design and protocol creation and provide more transparency and data sharing. The solution also enables her team to produce deliverables on activities that previously required input from the study design team. The new workflow now allows teams across the clinical trial stakeholder’s ecosystem to work in parallel. This new system not only allows users to quickly enter required trial information into the CT.gov website, it also offers seamless submission of the CTSD information to the appropriate health authority. Blockchain opens up the possibility of near real-time visibility, removes upstream data dependencies, while enabling parallel authoring to efficiently sync report and other data component deliverables . Thanks to blockchain technology, team authors can now share components and reports in a much more secure environment. Ecosystem Parallel Activities Trial Summary Report Report Patient Enrollment Data Collection Data Analysis Protocol Design Validation CT.gov Updates SOA EDC Protocol SAPCSR
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- 43. AAD Connect HTTPSOAuth 2.0 Web Mobile ERP CRM ECM Indexing/ Search Service Azure AD Container Registry Azure Key Vault Blob Storage Service (GRS) Database Service Integration Service DB Backup Service Core App Service Audit Service Caching Service Notification Service Client App Service File Storage Service Co-Authoring & Collab. Review Service App DB Service Content Proc. Service Task Mgmt. & WF Service
- 44. Sponsor Operational Data Sponsor Clinical Data External (Public) StudyData Real World Data Additional Sources Sponsor Global Libraries Sponsor Protocol Templates Protocol CSR SAP EDC CTMS PPM IRT eTMF Additional Systems DDF Compatible System Existing (non-compatible) Systems Objectives Endpoints SOA Etc.. Simulation Machine Learning Etc.. Study Builder Build Components Analysis Components Core Components: Versioning, Workflow, Security Data Mapping (CDM to System) Deployment Engine Study Design Repository (Stored using Common Data Model) Documents DDF Solution Components Study Design Data Sources Downstream Clinical and Operational Systems StudyDesignAPIs(CDM) Instance of Study Design Repository • Versioned repository of all study designs. • Based on DDF common datamodel (CDM) – for storage and exchange • Contains additional metadata for audit trail and version history • Built-in security to support data privacy Historical and Actual Data
- 45. Cost Estimation Consistency Checking Study Benchmarks SearchPatient Tech KB Search PSoC Eligibility Assessment Patient Assessment Why Today’s state requires manual entry of same information into multiple systems spanning Sponsors, and clinical study partners What Optimize study processes through automation of manual and repetitive processes, connecting systems to drive improved study design and data quality and reduced cycle times
- 50. AI & ML Enablement Smart Capabilities Content ManagementAI From our meeting… Discover opportunities to use ML to add auto- population for content and template segments Discover opportunities to intelligently classify and categorize content to improve content retrieval & searchability Educate and demo end- to-end ML & AI tools in Azure, including Cognitive Services APIs like Text Analytics or AutoML
- 52. Constant advocate forend user. Acts as voice of user. Understand existing ecosystems. Evaluate development abilities. Satisfy business requirements Establish key performance indicators Help define MVP and future road map
- 53. Empathize Ideate Define • Understand thebase requirements • Identify current pain points • Establish personas (who is using this product and why?) • Evaluate similar existing products to set baseline • Keep users at the heart of all activities • Define the problem based on research and not assumptions • Establish road map and user journey map • Create backlog of requirements based on MVP • Establish future goals for post MVP • Construct a saleable navigation solution • Group content to align with user’s mental model • Begin to explore visual language • Build wireframes to review with subject matter experts Prototype Validate • Build out flow document in Sketch to view how all pages interact with one another • Constructed InVision (high fidelity) prototype to test functionality with users • Evaluate how designs will align with development efforts and back end services Repeat • Conducted various levels of user testing ranging from actual developed coded to prototypes • Task-driven testing allows users to control the experience to gauge ease of use • User acceptance testing ensures design has fulfilled provided user stories
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- 55. Technical Feasibility Vs Customer Value Thisactivity compares the development effort against the benefit to the user for all features. It helps the team establish what an MVP looks like and provides focus on what needs to be specific tasks. Technical Feasibility Customer Value HighLow Low High User Stories MVP
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- 57. Industry Standards &Best Practices Expected Behaviors & Recognizable Patterns Focus on Information Architecture Reduce User Cognitive Load Ensured we used functionality that aligns with user’s mental model. Helps encourage adoption and increases retention. Allows the user to quickly navigate in a logical flow. Presenting the user with grouped content and populating drop downs with relevant content
- 58. Clear visual indicatorsalerts user to what is required. Form field title’s are persistent at all times eliminating confusion. Selection summaries provide immediate feedback showing the user the selections made. “Help text” assists user in knowing what type of action they need to perform. (Select from drop down or populate with free text) Presenting the user with efficient enabled vs disabled state ”call-to-actions” or buttons informs them when the section is completed.
- 59. Provide the userwith a “safety net” for potentially destructive actions. Align with the knowledge of green means “go” and red means “stop” makes it clear to the user the impact of each button. Easy control for adding and removing selections Operating within a modal suggests to the user that they have not navigated deep within any flow The user has the ability to expand open text fields to view additional text as apposed scrolling within a small window.
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- 62. Contoso Pharma isa fictitious biopharmaceutical company we created to bring our story to life. We're about to take you on a journey where you’ll meet Frank, Jane, Matteo, and Ingrid – the four actors responsible for rolling out Contoso Pharma’s new Study Builder and Design Repository. Contoso Pharma seeks to introduce their first Alzheimer drug to the public in over 17 years. Their previous drug failed to get approved after over 10 years in development and at a cost of over $2 billion USD. In order to close the gap in time and money in bringing its Alzheimer drug to market, Contoso Pharma realizes it needs to rethink the traditional methodologies surrounding how clinical trial studies are designed and delivered. To address this concern Contoso Pharma has elected to use a new, innovative Study Builder and Design Repository that streamlines the process. Back to Nav
- 63. Demonstrates the “MasterLibrary of Study Design Elements” The Problem Frank is a clinical content steward who works within the Clinical Standards Group at Contoso Pharma. Frank’s responsibility is to interact with various investigators across a number of institutional boards, including IRB and FDA, to develop the terminology and standards required for a comprehensive search strategy across multiple medical sources. He must continually verify the quality and integrity of data submitted for review, ensure it follows all industry standards, and be exacting enough to call out data inconsistencies and gaps in information. Frank is flustered by the cumbersome and archaic nature of his job – managing communications and emails across all players, checking online sources, cross-checking paper-based documentation. With modern technology, Frank dreams there must be a better way to “digitize” and automate the content management process for clinical trials research The Turning Point Frank gets a call from Matteo, Clinical Director, at Contoso Pharma who informs him that they are adopting a new, innovative Study Builder and Digital Repository that will make the process seamless. Frank is skeptical, but excited.
- 64. Demonstrates the “MasterLibrary of Study Design Elements” The Solution Frank starts using the system and quickly finds that Libraries can be generated for creation and management of specific design elements in the clinical trial lifecycle. Metadata tags associated with the Library can be configured at the time of Library creation as Mandatory or Optional, so that any objects created or imported into the Library will be required to be tagged with these metadata values. Frank sees that various components managed in the Library can be configured for “Type of Reuse” such as “As Is” or “Verbatim, “Repurpose” or “Derivative.” This design enables downstream users to optionally edit them to provide additional control of standard terms and content based on the Library level controls placed on the design elements. This will help control consistency and quality of standardized data and content – while making Frank’s job much easier. Frank sets up a number of relevant reusable libraries for the Study Design elements that are tagged by applicable metadata such as Therapeutic area, including libraries for: • Objectives, End Points • Inclusion, Exclusion Criteria • Protocol Title
- 65. Demonstrates the “MasterLibrary of Study Design Elements The Resolution Frank can’t believe how easy and powerful this solution is! He is particularly excited about the ability to import from external sources into the digital repository (such as TransCelerate libraries or CDISC). This will enable sponsors from regulatory agencies to login to the system and maintain and update the specific libraries across any number of Therapeutic areas, Indications, and other study parameters. Frank feels he is now better equipped to ensure that data will flow seamlessly from these design elements into the protocol and other relevant documents, as well as export for downstream systems such as CTMS. Frank calls up Matteo and says: “I’m sold!”
- 66. Demonstrates the “StudyBuild – Navigation” Feature The Problem Jane is the lead clinical scientist for the study. On the previous Alzheimers study, her and her team experienced months of setbacks and delays. She recalls having to triangulate between various compliance and regulatory agencies, patient rights, and an avalanche of paperwork. Completing the design swiftly and with predictability has always eluded her. She knows all to well that the processes to search, find, and verify sources requires considerable time to copy and paste from PDFs into the study trial templates. This quality control process is often very cumbersome and time consuming, not to mention considerably prone to errors. The Turning Point To address their key pain points and improve efficiencies, Jane’s manager, Matteo, has been leading an initiative on how to create a new streamlined process. She learns that the digital Study Builder will enable her team to build a clinical study with predefined agency/institutional or company defined templates and libraries based on a single source of truth. For example, the builder inherits correct metadata values from the Product such as Compound Number, Therapeutic Area, and Indication. It also is designed to prepopulate relevant data from a large number of institutional databases, saving the manager considerable amounts of time from cross-checking and copy/pasting sources Even before launching into this project, Jane has high hopes about the potential time savings this platform will introduce into her clinical trials workflow. But she also has some degree of apprehension. She hopes this works!
- 67. Demonstrates the “StudyBuild – Navigation” Feature The Solution Jane logs into the Study Builder, follows the directions, and navigates to the Business Object Navigator. She selects “New” from the drop-down to start building the study. The Study Builder is pre-populated with properties from the name of her experimental product “Wonderdrug!” Jane proceeds to fill in the specific metadata required: • Protocol Number • EudraCT Number • IND Number • Study Phase
- 68. Demonstrates the “StudyBuild – Navigation” Feature The Resolution Jane creates a new Clinical Trial study called Alzheimer’s Study – 3172020 and saves it in the database. Jane is impressed with how fast and seamless this went compared to the old “cut and paste” approach.
- 69. Demonstrates the “InitiateStudy Build & Study Build Design” The Problem Now, let’s get to know Matteo, Director of Clinical Operations at Contoso. Matteo manages a staff of 20 people who are working on the Alzheimer drug clinical trial. Matteo is responsible for ensuring the successful completion of the trial protocols and that they operate according to the highest ethical and industry standards. Matteo is deeply concerned by the enormous time and cost of all the study design activities – generating clinical reports, connecting the right data to the correct protocols, and managing all upstream and downstream schedules, etc. Matteo would like nothing more than to create a Digital Data Flow – one that generates a “single source of truth” – for the end- to-end study process. The Turning point Matteo’s team has been working with TransCelerate to test out a new digital Study Builder and Design Repository. He’s eager to see it in action. His lead clinical scientist Jane has already kicked off the study named Alzheimer’s Study – 3172020. • • • •
- 70. Demonstrates the “InitiateStudy Build & Study Build Design” The Solution Matteo logs in to the new platform to retrieve Alzheimer’s Study – 3172020 and initiate the Study Design process. Matteo builds the Protocol Title of the study based on hyperlinks to prepopulated libraries and templates. He then defines the Study Objective as follows: “To assess the effect of [study intervention #1] on the ADAS-COG and CIBIC + scores at Week [X] in participants with Mild to Moderate Alzheimer’s Disease.” Matteo next enters the Study Masking Information, Intervention, and Arms. For these categories, he chooses Double Blind, Xanomeline, and High Dose respectively. Matteo can select the Objectives and Endpoints for the study using the standards managed and governed in the Objectives and Endpoints libraries. Matteo selects the appropriate Objectives, Objective Level and related Endpoints. For each Endpoint, the appropriate visit Timeframes, Units and Biomedical Concepts are selected from controlled standard metadata terms.
- 71. Demonstrates the “InitiateStudy Build & Study Build Design” The Resolution Matteo is impressed! Not only was this easy (and kind of fun) but he also has options for generating the study design: • Choose a new Design template as the starting point • Populated from a previous Study Design • Start from scratch, if user is defining the design elements for the first time Matteo chooses to select a template for the Alzheimer’s therapeutic area to use for the design, and next edits the Schedule of Assessments. This outlines the activities, a plan for administration of study treatment, and a list of assessments and procedures that are to be performed for the duration of the study.
- 72. Demonstrates “Document ProtocolGeneration for TransCelerate CPT” The Problem Meet Ingrid, the Medical Writer at Contoso Pharma. Ingrid oversees a staff of three other writers who are responsible for generating the company’s clinical studies and reports, including QC on all protocols, amendments, and administrative changes. On top of this, Ingrid must manage all upstream and downstream schedules for her team and guarantee that all content changes are imported to the company’s central database. Ingrid is stressed out because she spends so much time project managing her team that she hardly can keep up with content production, report generation, and QC. Most of the time it feels like she’s squeezing 25 hours into 24. It’d be great if she could leverage technology to create efficiencies in her content workflow and reduce authoring time while maintaining consistency and the same high standards. The Problem Ingrid has just learned from Matteo, her director, that the department a new digital Study Builder and Design Repository. They meet for a briefing and he shows her the new solution.
- 73. Demonstrates “Document ProtocolGeneration for TransCelerate CPT” The Solution Ingrid logs in to the new platform to start building out the auto insertion protocol for Alzheimer’s Study – 3172020. Her job is to ensure the study is reusing and auto-inserting the correct library content, and that the control of this content is based on the right library component policies. Ingrid must also verify that the design of each document maps correctly to the overall digital data flow. Ingrid edits the document protocol generation engine with templates that will be used to automate the Protocol Title for the Study Builder. By providing automated links to the Common Statistical Analysis Plan (SAP) and Common Clinical Study Report (CSR) templates, Study Managers will now be able to save considerable amounts of time in formulating titles to their studies.
- 74. Demonstrates “Document ProtocolGeneration for TransCelerate CPT” The Resolution Next, Ingrid builds in auto generation features into the remaining initial protocol design entities (Study Objectives, Endpoints, Assessments, Forms). These are based on the selected key attributes on the protocol record of Therapeutic Area, Indication, Study Phase, and Study Type. Ingrid will initially focus on auto generating TransCelerate CPT documents. However, in the near future she will be asked to migrate to auto-generating the draft eCRFs and other related downstream documents from a master set of common data elements. Additional industry standards on the agenda to be auto generated include CDISC, Clinical Trials.gov, EudraCT, and more. Ingrid sees many benefits of the auto-generation feature especially as it introduces Study Managers to early visualization of their work and facilitates early detection of errors or inconsistencies.
- 75. Demonstrates the “StudyDesign – Export” Feature The Problem Jocelyn is a biostatistician at Contoso Pharma and part of the team leading the Alzheimer drug study. She works on Jane’s team and is responsible for monitoring how the study is conducted and to ensure full integrity of the results for reporting and external validation. Much of Jocelyn’s time is spent writing research proposals and conveying her findings to pharma professionals and the broader scientific community. A painstaking component of Jocelyn’s work involves collecting, organizing, and cross-checking the study data against numerous medical databases to ensure her results are as accurate as possible. Much of this is process is very manual and tedious. The Turning Point Jocelyn is called to an important meeting with Jane and Matteo where she’s introduced to a new Study Builder and Design Repository. Could it be that her days of wrangling data are over??
- 76. Demonstrates the “StudyDesign – Export” Feature The Solution Jocelyn logs into Contoso’s new Study Builder and locates the saved Study Design components for the Alzheimer trial study initiated by Jane and built out by Matteo. The study can be exported in JSON or other formats for downstream uses to support the end-to-end digital dataflow. Jocelyn proceeds to publish the Study Design elements output in JSON format.
- 77. Demonstrates the “StudyDesign – Export” Feature The Resolution Jocelyn previews the JSON format export of the Study Design output. After a careful review, she is ready to import the data into the EDC and CTMS. Jocelyn is surprised by how easy it is to manage the Study Builder. She now has the ability to provide automated workflows to benefit other downstream users. Jocelyn has waited a long time for this and couldn’t be happier.
- 79. Scenario #2 Lydia, aclinical data scientist at Contoso, is leading the phase III clinical research trial submission for the new Alzheimer’s drug study. The company recently deployed a new solution called blockchain to accelerate the study design and protocol creation and provide more transparency and data sharing. The solution also enables her team to produce deliverables on activities that previously required input from the study design team. The new workflow now allows teams across the clinical trial stakeholder’s ecosystem to work in parallel. There are two distinct ways this new system improves efficiencies. One, it gives users the ability to quickly enter required trial information into the CT.GoV website. Second, is submission of the Clinical Trial Summary Dataset to the Health Authority. Lydia has reported to Contoso’s leadership that the drug trials timeline will become significantly faster thanks to automated gathering of all study trial artifacts. Also, this digital transformation means participant enrollments in the study can now progress much more seamlessly. Parallel authoring
- 80. Scenario #2 This newtechnology scenario demonstrates how near real- time visibility into the ecosystems work products can remove upstream data dependencies on deliverables and bring report and other data component deliverables into parallel. What’s more, this shows how visibility into the sponsor study project teams provides both parallel authoring of the trial summary and reporting to ct.gov. Thanks to blockchain technology, team authors can now share components and reports in a much safer and secure environment. <rewrite and combine paragraphs b & c>
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Editor's Notes
- #45 This is a core use case for single source of truth for versioning, exchange, audit trail, data privacy and data sharing Versioning = hashed on ledger Exchange = smart content conditions of data use (can also leverage azure data share) Audit = regulators/internal auditors can query the ledger for audit Data Sharing – see exchange Data Privacy – hash of data resides on the ledger + description of data (schema) controlled through a smart contract conditions, data resides in off chain repository wih ability to maintain privacy - eConsent