PGCert Clinical Data Science / Course details

In order to make use of data and data based products and services in the clinical context, clinical data scientists need to be able to capture the requirements for data-driven and AI tools and systems. This involves aspects of working with teams to understand and model their existing work flows, gathering and understanding their requirements and representing these formally, using user centred and cooperative/participatory design methods to work with teams to create products and services that are needed and of real value to the end user. Once a system or tool has been designed and created or is to be procured, it also needs to be implemented and evaluated and supported in the clinical environment where it is intended to be used. This module aims to provide students with an overview of the process of capturing and presenting user requirements and implementing and evaluating systems in the clinical, health and social care environment.

The unit aims to:

• Provide students with methods to capture and represent user requirements
• To map and understand existing work flows and digital/data/AI driven implementation challenges
• Tools and techniques for the evaluation of data products and services
• Governance, ethical and legislative issues of implementing and evaluating data products and AI in the health and social care environment
• Awareness of the challenges surrounding access to data
• Working with users to develop and design systems using user centred and co-design principles
• Awareness of the challenges and opportunities for digital transformation in health and social care

This unit will cover the following indicative content:

• Methods for capturing and presenting user requirements
• Introduction to user centred design principles
• Introduction to co-design principles (PPIE, working with patients and other stakeholders)
• Evaluation of data driven and AI systems in the clinical environment
• Methods for capturing and representing existing workflows and processes
• Principles and models of change management
• Information governance and regulatory compliance
• Healthcare challenges and digital transformation opportunities
• Introduction to digital enablers (e.g. apps, genomics, robotics, cloud, AI, IoT, telemedicine, blockchain)
• Understanding people, process and culture
• Health and digital inequalities, impact and mitigation strategies
• Defining and monitoring performance of digital transformation projects

The unit will be delivered online making use of workshops, lectures, videos, interactive online activities, discussions,  real-world clinical case studies and group work.

LO1: Discuss policies and principles around digital transformation with reference to related reports and frameworks (e.g. Topol review, 'What good looks like', the NHS long term plan, Better care loop)

LO2: Describe the principles of user centred design (e.g. empirical measurement, iterative design, focus on user tasks)

LO3: Describe the principles and practices of co-design and Patient and Public Involvement and Engagement (PPIE)

LO4: Represent user/system requirements with formal methods (e.g. user stories, personas, storyboards, use case diagrams)

LO5: State the features of various digital enablers and how they can be used to improve patient care

LO6: Discuss the impact of digital inequalities on delivery of digital solutions

LO7: Evaluate different digital enablers for specific use cases/users (e.g. wearables, telemedicine, apps, etc.)

LO8: Appraise the need to tailor AI/digital solutions for specific clinical areas/uses

LO10: Discuss change management models and approaches

LO11: Define and measure success in digital transformation projects (e.g. costs, productivity, engagement/performance/usage metrics)

LO12: Analyse and apply frameworks to digital projects and systems (e.g. The NASSS framework)

LO13: Create diagrams to model existing workflows and systems

LO14: Access, extract and present information from knowledge bases to support evidence-based decision making

LO15: Present ideas and work in a verbal and/or written format

LO16: Work through the problem-solving cycle

LO17: Develop an analytical problem solving mind-set

Formative assessment and feedback to students is a key feature of the on-line learning materials for this unit and is provided through self-directed learning activities and feedback during synchronous tutorials and discussion boards.. 

• Butler-Henderson, K., Day, K., Gray, K (2021) The Health Information Workforce: Current and Future Developments. Switzerland: Springer
• Marx, E (2020) Healthcare Digital Transformation: How Consumerism, Technology and Pandemic are Accelerating the Future. New York: Taylor & Francis
• Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A'Court C, Hinder S, Fahy N, Procter R, Shaw S. Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies. J Med Internet Res 2017;19(11):e367 URL: https://www.jmir.org/2017/11/e367DOI: 10.2196/jmir.8775
• Wilson A, Saeed H, Pringle C, et al. Artificial intelligence projects in healthcare: 10 practical tips for success in a clinical environment. BMJ Health & Care Informatics 2021;28:e100323. doi: 10.1136/bmjhci-2021-100323
• Eleftheriou I, Embury S.M., Moden R, Dobinson P, Brass A. Data journeys: Identifying social and technical barriers to data movement in large, complex organisations, Journal of Biomedical Informatics 2018; 78:102-122, ISSN 1532-0464, https://doi.org/10.1016/j.jbi.2017.12.001. (https://www.sciencedirect.com/science/article/pii/S153204641730271X)

Employability skills

• Develop lay summaries of digital transformation projects
• Develop funding applications for digital transformation projects