Educating For The Future
Methodologies and technologies utilized in the clinical development process have been largely unchanged since 2005. Externally, there have been immense advancements in technology and mindsets related to working with data that have had little impact in our industry. This Working Group sees this as a risk. The risks breakdown into 3 categories:
1) There is a risk of advancement happening outside of our domain that may expose us to threats that we are unable to protect ourselves against.
2) There is a risk that our skill sets and capabilities become outdated and external forces strongly influence the Industry without us having a say
3) There is a risk that we are missing opportunities to become more efficient and to use these advancements to deliver better value for patients
The goal of this Working Group is to develop frameworks by which to educate the PhUSE community at large. The frameworks will be designed to educate the community on the importance of topics where we feel we have gaps, the details of the topics themselves, and how they can be used to drive innovation in the industry.
We have now established 3 sub teams:
|Data Engineering||Huge efficiencies have been made in BioPharma companies over the last few decades, notably in the area of data capture (with moving to eCRFs) business process improvements, and data standardisation efforts by CDISC. However, the sector is increasingly competing on the basis of their analytical capabilities, which requires a centralised, combined, and, as much as possible, automated data environment to support these deeper insights. It is clear that data is R&D needs a major transformation; it is too siloed, fragmented and manually intensive, to be utilised effectively. This project will explore how established Data Engineering techniques, successfully deployed in other industries, could be utilised in our industry. From traditional data warehousing; to the arrival of the big data lake; with data marketplaces; ePRO and IoT; the challenge is on – to identify analytical value from all of these disparate data sources. The aims of this project are two-fold. Firstly, to gather the myriad resources available on traditional methods of data engineering, to provide a breadth of knowledge that could immediately bring benefit to our existing clinical data estate. This will be achieved by curating and organising content into an easy-to-use structure (such as Wiki). Secondly, to prepare us for the “big data tsunami” which is to arrive shortly in our sector, so that we can learn about the more thought-leading subjects in this area and help disseminate and share this information with the Data Science & AI/Machine Learning co-projects – a natural fit for these new types of data analysis – alongside the more tradition methods on the PhUSE Wiki||Yes|
|Design Thinking||Design Thinking is a process that promises to help tackle big ideas in a manageable and structured way. By definition, it is 'a formal method for practical, creative resolution of problems and creation of solutions'. Design thinking methods foster a way of thinking that reframes the problems and solutions we assume we have the answer to. It also puts heavy emphasis on experimentation, iteration, and user feedback to quickly develop solutions that derive maximum benefit for end users and the actual problems they are trying to resolve. Benefits of employing Design thinking can include: 1) a focus on the User, 2) leveraging collective expertise, 3) employing empathy, 4) creating value by solving real problems. Design Thinking is universally applicable because the methods are flexible, to may different types of problems encountered in diverse industries. This is relevant to the life sciences realm because the industry and regulatory bodies are fertile ground for identifying solution-based systems and processes that could benefit from thoughtful re-design. Participants in the PhUSE project would be expected to share their experience with relevant examples. Because not everyone can attend the D.School at Standard University, where design thinking is the core off the curriculum, this project would focus on identifying and collating resources in Design Thinking for consumption by the PhUSE community. Because the actual content is not house by the PhUSE project, this can include resources in many media formats, including, but not limited to: Books, Journal Articles, Podcasts, Webpages, Recordings etc. This will be achieved by curating and organising content into an easy-to-use structure and making it readily available and navigable technology.||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. ML is a game changer and it will also impact the pharmaceutical industry greatly; diagnosis in medical imaging, analysing the deluge of healthcare data, drug discovery, robotic surgery and personalised medicine for example. This project will explore its application to the pharmaceutical industry. Our goal is first to introduce ML & AI to the pharmaceutical industry. ML & AI are foreign to most programmers and statisticians in the pharmaceutical industry. This project will help them to start ML & AI educations materials. Secondly we will explore how ML & AI can foster innovative approaches in data-driven research and drug development, personalised medicines, faster drug discovery and many more.||Yes|
|Working Group Leads||Mondays||14:00 - 14:30|
|Project Co-Leads||Bi-Weekly Mondays||14:30 - 15:00|
|Data Engineering||Bi-Weekly Wednesdays||17:00 - 18:00|
|Machine Learning Project Leads||Last Friday||13:30 - 14:00|
|Design Thinking||1st Monday||16:00 - 17:00|
If you would like to join any of the sub-team meetings, please contact Wendy@phuse.eu
|Ian Fleming||Working Group Lead||Medidata||Ian.Fleming@phuse.eu|
|James McDermott||Working Group Lead||Ab Tartarusemail@example.com|
|Wendy Dobson||PhUSE Project Manager||PhUSEfirstname.lastname@example.org|
|Dante Di Tommaso||Participant||Protonmailemail@example.com|
|David Pressley||Participant||Clear Creek Analyticsfirstname.lastname@example.org|
|Guy Garrett||Participant||Achieve Intelligenceemail@example.com|
|Kevin Kyosun Lee||Participant||Industry||Kevin.Kyosun.Lee@gmail.com|
Working Group Updates
Objectives and Timelines
List proposed project deliverables and timelines.
|Selection of Repository||Q22018|
|Logistics and Communications||Q22018|
This section can document project activities or serve as a jumping off point to other pages in the project.