Emerging Technologies

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Research, development, and adoption of innovative, but not widely adopted, tools and technologies for leveraging and enhancing the value of healthcare and clinical data. This can include use of existing or immature technologies in innovative ways.


Working Group Overview

New challenges in regulatory science and drug, biologic, and device development provide new opportunities for recognizing and leveraging new or emerging technologies and computational tools or underutilized existing technologies. Initiated at the 2013 PhUSE Annual CSS, the Emerging Technologies working group provides a forum for determining interest in specific computational science topics, tools, technologies, and approaches.

This Emerging Technologies working group will be an open, transparent forum for sharing pre-competitive means of applying new technologies and is being challenged with creation of well-defined collaborative projects that will describe, prioritize, assess, and assist advancement of these opportunities. Possible topics include (but are not limited to) semantic web applications, analysis metadata, modeling, simulation, and “The Cloud”. Projects incorporating these topics might include prioritization, development, and piloting for feasibility and value.

Leadership Team

Current Leadership Team

Name Role Organization E-mail
Andy Richardson ET&T Co-lead Independent andy.richardson@phuse.eu
DJ Chhatre ET&T Co-lead Gilead Dhananjay.Chhatre@gilead.com
Sam Hume CDISC Liaison CDISC shume@cdisc.org

Current Projects

Project Description Recruiting New Members
Cloud Adoption in the Life Sciences Industry Cloud technology and its use of multi-tennant app solutions are increasing the capabilities of Life Sciences solutions and reducing IT infrastructure costs through the sharing of infrastructure and investment cross-industry. The goal of this work stream has been to provide a practical, usable framework to overcome those barriers. Through the use of this framework, it is envisaged that the barriers to adoption by pharma of cloud-based technology will be addressed. Yes
Data Visualisations for Clinical Data The FDA Guidance on a Risk-Based Approach to Monitoring (August 2013) opened the door to using scientifically founded monitoring solutions as alternatives to 100% source verification of clinical data. Individual companies have proposed a range of opportunities to look at the applicability of data visualization within the pharmaceutical environment that addresses cross-domain questions and insight associated with RBM. Yes
Investigating the use of FHIR in Clinical Research Increasing interest in eSource keeps the issue of data integration between Research Systems (EDC, CTMS, CDMS, etc) and healthcare systems (EHR, etc) as a consistent want for Sponsors clinical investigators and Regulators. The new PhUSE project 'Evaluating the Use of FHIR in Clinical Research' will look at how the HL7 FHIR standard could be used as a fundamental part of the clinical trial process in the future. Yes
BlockChain Technology Introduce Blockchain and describe how it works. Pre-requisites to adopt Blockchain. Understand the qualities of Blockchain relevant to the Pharma setting and the example of use cases and applications. Provide high level analysis of Pros/Cons of BlockChain in Pharma and Healthcare. Yes
Going Translational with Linked Data This project builds on the successful completion of the "Clinical Trials Data as RDF" project where four SDTM domains (DM, VS, EX, TS) were modeled in RDF, and the ontologies used to create RDF instance data.Existing domains will be broadened to include non-clinical concepts, thus extending the impact of the project further along the data lifecycle. A minimum of two additional domains will be added, starting with AE (and non-clinical AE equivalent observations). Yes
Key Performance Indicators & Metrics Collecting, tracking and evaluating data on an ongoing basis can provide organizations with credible, compelling information when communicating with key decision-makers and stake-holders.The PhUSE Data Science & KPI Metric Reporting Group has been working to establish a set of common Data Reporting Metrics which are more detailed than industry wide metrics, therefore, allowing a greater level of granularity in our project reporting, and business process management Yes


Machine Learning/Artificial Intelligence The most popular buzz word nowadays in the technology world is 'Machine Learning' (ML) and Artificial Intelligence (AI). Most economists and business experts foresee ML & AI changing every aspect of our lives in the next 10 years through automating and optimizing processes such as self-driving vehicles, online recommendations on Netflix and Amazon, fraud detection in banks, image and video recognition, natural language processing, question answering machines (e.g., IBM Watson) and many more. Yes


Open Source Technologies in Clinical Research Open Source Technologies in Clinical Research aims to provide guidance to the use of open source technologies in regulatory environments within the pharmaceutical industry, including but not limited to R and Python. Our intent is to be a repository of knowledge for: Use Cases, Implementation and validation guidance, Best Practices. Our goal is to broaden the acceptance and general level of comfort with these technologies in the industry to assist in increasing their level of adoption Yes
Real World Evidence The increasing interest/pressure to including results based on real world data as part of regulated clinical trial submissions and similar research initiatives is testing how current regulatory and good practice requirements can incorporate these results into submissions. Whilst specific projects have sucessfully addressed these issues, RWe guidelines or points for consideration are not yet formalised. PhUSE members are centrally involved in the management and analysis of these data, and best practice guidance for dealing with RWE data would assist members in this space. Yes


Projects on Hold

Project Description
ODM4 Submissions The Operational Data Model (ODM) standard has been the CDISC standard format for data exchange since 2000. Define-XML and Dataset-XML are ODM extensions supporting the transport of CDISC dataset metadata and data, respectively. Define-XML is now a required part of a regulatory submission. However, despite using Define-XML to submit dataset metadata, other data and metadata required for submissions are submitted in different file formats that adversely restrict data representation, machine-readability options, and the ability to validate submissions.


Webinar Presentations


PhUSE CSS 2017

The following projects are meeting at 2017 CSS

The Agenda is as follows: