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2) There is a risk that our skill sets and capabilities become outdated and external forces strongly influence the Industry without us having a say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.
Research materials from this Working Group will be published on Squarespace
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|
|Data Science|| According to Google Trends, the word “Data Science” is currently at a peak interest for worldwide searches. Companies like Uber or Amazon have built entire business models using data science methodology. The healthcare sector has also seen new players especially in consumer devices, which increased healthy lifestyles applying advanced analytics methods. Digital medicine outperforms already traditional medicine in terms of yearly growth.
The pharmaceutical industry is also adapting to these new technologies. For example, FDA approved or cleared devices (eg. activity trackers, etc.) are used mainly for exploratory purposes in clinical trials and create huge volumes of data. We can create valuable insights when we connect these new data sources to clinical data and apply Data Science methods like Machine Learning, Deep Learning or Artificial Intelligence.
This project will support people to familiarise with Data Science and all its various branches. We will provide a Data Science training repository, where we will consolidate available training material around topics like Machine Learning, Artificial Intelligence, Deep Learning, Quantitative and Advanced Analytics. We will also provide use cases of Data Science in the healthcare sector and we will also look into other industries. A learning path for members of our industry will be provided to dive deeper into the topic of Data Science to educate the future.
|Team Meetings||Days||GMT Timings|
|Working Group Leads||Mondays||14:00 - 14:30|
|Project Co-Leads||Bi-Weekly Mondays||14:30 - 15:00|
|Data Engineering||Bi-Weekly Wednesdays||16:00 - 17:00|
|Design Thinking||1st Monday||16:00 - 16:30|
|Data Science||Bi-Weekly Fridays||14:00 - 15:00|
If you would like to join any of the sub-team meetings, please contact Wendy@phuse.eu
Working Group Leads
|Ian Fleming||Working Group Lead||Medidata||Ian.Fleming@phuse.eu|
|James McDermott||Working Group Lead||Achieve Intelligenceemail@example.com|
|Wendy Dobson||PhUSE Project Manager||PhUSEfirstname.lastname@example.org|
Working Group Updates
Objectives and Timelines
List proposed project deliverables and timelines.
|Selection of Repository||Q22018|
|Logistics and Communications||Q22018|