Course Units and Teaching Weeks
Please note that dates are provisional and may be subject to change.
Unit 1: Research Skills, Governance and Innovation (20 Credits)
Teaching week: Monday 2nd – Friday 6th October 2023 (tbc)
Unit 1 provides the landscape to understand the breadth of patient level data in the healthcare and economic landscape in the UK and globally. It provides knowledge of the technical, legal, and ethical infrastructure which guides all research, commercial development, and healthcare quality improvement. Furthermore, it introduces key concepts from subsequent parts of the course to allow students to develop their thinking around systems engineering, statistics and data visualisation, and innovation. Students will be taught by a faculty of experts from genomics, clinical medicine, informatics, statistics, business, and engineering. Masterclass sessions will use case studies to examine the impact of healthcare data.
Content
Indicative content for this module includes:
• An introduction to healthcare data and its importance
• Overview of local, national and global initiatives for healthcare data application
• Principles of good data management / stewardship
• Open vs closed data and the FAIR principles
• Governance of healthcare data use
• Principles of quality healthcare data research
• Basic data manipulation (data visualisation)
• An introduction to systems engineering approaches using healthcare data
• An introduction to innovation and commercialization
Learning Outcomes
By the end of the units the participants should be able to:
• Describe the breadth of healthcare data available and the potential for its use in clinical innovation
• Discuss appropriate data management requirements for a healthcare data set, including storage and access
• Apply appropriate ethical and governance guidelines in the acquisition and use of healthcare data
• Plan a basic piece of research on a healthcare dataset
• Outline how the results of a piece of research can be communicated to appropriate groups to support implementation of change
• Know the basic principles of the systems engineering approach and routes to innovation and commercialisation
Unit 2: Data Structures, Storage and Queries (40 Credits)
Teaching weeks:
Week 1: Monday 15th – Friday 19th January 2024
Week 2: Monday 15th – Friday 19th April 2024
Unit 2 is a 40-credit unit, the largest in the programme, and delivers all of the health informatics training needed for students to be able to independently design and execute queries of raw electronic patient record data.
The practical aspects of the unit will focus on the Epic system but the theoretical components will take a platform agnostic approach to covering data structures and healthcare database design. Students completing this unit will be competent in the use of the programming and scripting languages which are used globally to analyse healthcare data. Faculty will be drawn from clinical informaticians, researchers, and commercial sector software experts. Masterclasses will explore the practical aspects of patient level data extraction and analysis.
Content
Critical awareness of the wider implications, relationships, and impact of healthcare data (25%)
• How are populations and diseases reflected in datasets?
• Where and how does healthcare data impact policy and infrastructure development?
• How do hospitals and other organisations use data?
• What opportunities does healthcare data offer hospitals and other organisations?
Data and database structures, storage, quality, access and governance (25%)
• How is data stored, in what ways do databases differ?
• How do trial registries, clinical research, and audit databases differ from electronic patient records?
• Where is data stored, particularly in the UK health and research sectors?
• What is the appropriate governance surrounding access for healthcare data?
• Documentation standards, data quality and the implications for interoperability and secondary use.
Data-extraction and curation (50%)
• Converting research and quality improvement questions into database queries
• Writing and executing SQL database queries and related quality control
• How is a dataset ideally constructed for subsequent analysis?
Learning outcomes
By the end of this module participants should be able to:
• Describe the various types / properties / structure / usage of multiple types of patient-level and aggregated data used in the field of healthcare
• Describe the framework within which datasets are described, mandated / notified, implemented and reported in the NHS.
• Describe the UK governance framework relating to the use of personal data in healthcare
• Describe the differences between terminologies and classifications and their usage
• Describe an approach to data stewardship and proper curation in the management of healthcare data
• Describe the elements which underpin meaningful and safe interoperability in the context of personal healthcare data
• Evaluate a request for data, demonstrating an understanding of all of the factors / aspects to be considered including governance, structure / quality and extraction methodology
• Formulate a high-level approach to a database query from a specific data (research / audit etc.) question
• Write appropriate SQL queries and extract data from a normalised database
• Demonstrate the ability to transform / curate extracted data in preparation for more detailed analysis
Unit 3: Finding Relationships (20 Credits)
Teaching week: tbc October 2024, Madingley Hall
The ability to visualise results of healthcare data research and quality improvement projects is essential yet is rarely taught. Moreover, design theory and practice is uncommonly included within health informatics courses. Unit 3 is an entirely novel, innovative approach to teaching statistics and data visualisation as it applies to healthcare data and will allow the clear presentation and explanation of novel information arising from patient level data projects. The unit will use real, healthcare data datasets to develop understanding of practical statistics primarily using R. Students will also be taught design and visualisation theory and practice and tools to enhance their ability to present results from large datasets in clear, interesting, and visually appealing ways. Faculty will be drawn from statisticians, data scientists, genomic scientists, and graphic design experts.
Content
Indicative content for this module includes:
- An overview of data visualisation theory
- Development of visualisation critical skills
- Training in Python graphical modules including plotly, seaborn and plotnine
- An overview of classical statistical techniques from simple hypothesis testing through to generalised linear models and power analysis
- Training in Python statistical modules
- An overview of statistical reporting.
Learning Outcomes
By the end of the units the participants should be able to:
• To understand core aspects of visual design theory and use them to critically evaluate and construct data visualisations
• To utilise Python to create appropriate static visualisations of healthcare data
• To apply core statistical techniques to data using Python in order to identify statistically significant relationships and patterns
• To be able to produce a short, technically accurate statistical report for a small healthcare dataset
Unit 4: Healthcare Systems Improvement (20 Credits)
Teaching week: tbc Jan 2025, Madingley Hall
Healthcare faces considerable challenges. The complexity of the system mean that efforts to improve it often achieve only limited benefits and frequently have unforeseen consequences. Over the past two decades, there have been numerous calls to implement a systems approach to transform healthcare. Yet there has been no clear definition of what this might mean.
Engineers routinely use a systems approach to address challenging problems in complex projects and this allows them to work through the implications of each change for the project as a whole. They consider the layout of the system, defining all the elements and interconnections, to ensure that the whole system performs as required.
This module will apply a systems engineering approach to the process of data-driven change in healthcare environments allowing students to understand and measure the consequences of any change introduced due to analysis of complex healthcare datasets.
This unit will enable students to understand healthcare systems before making data-driven changes. This will allow students to become experts in balancing the differing needs of users, assessing risk, and then implementing system change and assessing effectiveness of that change within hospitals, pharmaceutical companies and health research charities.
Content
Indicative content for this module includes:
• Engineering Better Care – introducing the concept of a systems approach.
• Mapping Systems – describing the architecture and behaviour of systems.
• Managing Risk – delivering robust system risk assessment and evaluation.
• Enabling Creativity – facilitating the delivery of the ‘right’ systems solution.
• Improving Improvement – supporting a systems approach to improvement.
Learning Outcomes
By the end of the units the participants should be able to:
- Appreciate the importance of people, systems, risk and design perspectives within improvement.
- Understand the role of data-flow diagrams, influence maps and rich pictures in mapping systems.
- Understand the value of FMEA, SWIFT and bowtie methods in the management of system risk.
- Understand the use of requirements, morphological charts and measures to drive system design.
- Plan the application of a systems approach to a data-driven healthcare improvement project
Unit 5: Medical Technology Innovation and Commercialization
Teaching week: tbc April 2025, Madingley Hall
In Unit 5, we will look at a range of skills required for innovation. Firstly, we will examine the difference between “entrepreneurial” and “intrapreneurial” opportunities and the paths entrepreneurs and intrapreneurs need to walk. Secondly, we will consider the range of business models currently fashionable and interesting in the medtech data space, and how this range of business models may evolve over time. Finally, we will investigate the question of medtech innovation strategy—how medium and large players in the medtech space manage their innovation portfolios and the implications for individual innovators. By the end of the unit, students should have a good grasp of the choices in front of them in terms of commercialisation, and the critical success factors for successful innovation
Content
Indicative content for this module includes:
- Evaluating and ranking a range of ideas for innovations
- Applying their understanding of entrepreneurial project selection to a basic “pitch structure” for a venture
- Applying their understanding of corporate innovation portfolios to the preparation of an “investment case” for a project.
- Analysing a range of current business models in the space and assessing their likelihood of leading to market entry and market power
- Analysing a range of value propositions in the space and making recommendations for improving them
Learning Outcomes
By the end of the units the participants should be able to:
- Evaluate opportunities for innovation, considering customer, stakeholder and financial perspectives.
- Assess whether entrepreneurial or intrapreneurial approaches are more appropriate for a given opportunity
- Understand the financial underpinning of entrepreneurial ventures and how investors evaluate and select ventures to invest in
- Understand how corporate innovation strategies within medical technology and healthcare lead to the selection and development of a portfolio of innovation projects
- Deconstruct business models into their component pieces and understand the relationship of the business model to market entry and market power
- Construct and evaluate value propositions and understand the role of data within such value propositions
- Understand the critical components of an early stage entrepreneurial venture and the typical pathways to assembling them
- Understand the typical pathways to the initiation of a corporate innovation project and the navigation by individual project managers of the stages of such projects
