Emerging Trends & Technologies

Initiated in 2013, this Working Group aims to provide an open, transparent forum for cooperatively sharing and investigating how new technologies, tools and approaches can support clinical data science. The projects are selected with the aim of assessing, understanding, describing, instructing and offering guidance about how new technologies can be used in the development of new medicines. The projects range in scope from investigations into new technological innovations and how they can be used, through to guidance and training for new and developing standards, as well as metrics and benchmarking initiatives to support successful operational implementation. 

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Blockchain Technology

This project introduces blockchain, describes how it works and discusses the pre-requisites for adopting blockchain. Members explore the qualities of blockchain relevant to the pharma setting, looking at examples of use cases and applications. Collectively, project members provide high-level analyses of the pros and cons of blockchain in pharma and healthcare.

Cloud Adoption in the Life Sciences Industry

Cloud technology and its use of multi-tenant app solutions is 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 Working Group project is to provide a practical, usable framework to address the barriers pharma face in adopting cloud-based technology.

Data Visualizations 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 visualisation within the pharmaceutical environment that addresses cross-domain questions and insight associated with RMB.

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 nonclinical concepts, thus extending the impact of the project further along the data life cycle. A minimum of two additional domains will be added, starting with AE (and nonclinical AE equivalent observations).

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 Working Group project Investigating the Use of FHIR in Clinical Research looks at how the HL7 FHIR standard could be used as a fundamental part of the clinical trial process in the future.

Key Performance Indicators & Metrics

Collecting, tracking and evaluating data on an ongoing basis can provide organisations with credible, compelling information when communicating with key decision-makers and stakeholders. The PhUSE Key Performance Indicators & Metrics Working Group project 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.

Machine Learning/Artificial Intelligence 

The most popular buzzwords nowadays in the technology world are '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 optimising processes. This project explores its application to the pharmaceutical industry. Its goal is first to introduce ML & AI since they are foreign to many programmers and statisticians in the pharmaceutical industry. This project will help them to start compiling ML & AI education materials. Secondly, the project explores how ML & AI can foster innovative approaches in data-driven research and drug development, personalised medicines, faster drug discovery and many more.

 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  

 

 

 

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