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Institute of Continuing Education (ICE)

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This course is not open for online applications. If you would like to enquire about this course please do so using the 'Ask a question' button.

The PGDip in Healthcare Data is a continuation of the Postgraduate Certificate [PGCert] in Healthcare Data and provides a progression route to the one-year Masters [MSt] in Healthcare Data: Informatics, Innovation and Commercialization. It is a one-year, part-time Master’s-level course resulting in 60 FHEQ Level-7 credits and the University of Cambridge award.

Cambridge is a world-leading centre for innovation in electronic patient and clinical trial data. This is underpinned by an extensive and vibrant community of clinicians, researchers, entrepreneurs, and commercial and public sector organisations. There is a recognised shortage of the appropriate technical and practical skills in the workforce to effectively utilise the opportunities presented by healthcare data.

This course has been designed to meet the skills gap in the management, handling and utilisation of healthcare data and to develop individuals confident in using healthcare date for innovative and/or commercial applications.

Course delivery: We plan to deliver our postgraduate and MSt qualifications in-person in the academic year 2021-22. Please note that this will be reviewed in line with the latest public health guidance available at the time. If required, to ensure the health and safety of students, we may look to utilise alternative teaching formats and will contact students if we expect changes to the course delivery.

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Unit details

Course Units and Teaching Weeks

Dates below are provisional and may be subject to change.

Unit 3: Finding Relationships (20 Credits)

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.

Teaching week: tbc October 2021.

Unit 4: Healthcare Systems Improvement (20 Credits)

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

Teaching week: tbc Jan 2022, Madingley Hall

Unit 5: Medical Technology Innovation and Commercialization

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

Teaching week: tbc April 2022, Madingley Hall

To note: Students will have already studied the units below as part of the Postgraduate Certificate in Healthcare Data and Informatics:

  • Unit 1: Research Skills, Governance, and Innovation, 20 credits
  • Unit 2: Data Structures, Storage, and Queries, 40 credits.

Unless otherwise stated, teaching and assessment for ICE courses are in English. If your first language is not English, please refer to our Information for Applicants pages for further guidance.

Course dates

01 Oct 2021 to 31 Jul 2022

Course duration

1 Year

Apply by

15 Jul 2021

Course fee

Home: £5,152
Overseas: £9,452

Course director

Academic director

Venue

Various locations
Cambridge
United Kingdom

Qualifications / Credits

60 credits at Master of Studies

Course code

HDPGD601