Postgraduate Diploma (Flexible) in Healthcare Data: Informatics, Innovation and Commercialization

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The deadline for booking a place on this course has passed. Please use the 'Ask a Question' button to register your interest in future or similar courses.

Please note that this course is only open to those who have successfully completed and passed the Postgraduate Certificate in Healthcare Data and Informatics at ICE. If you have not, and wish to apply for another of our Healthcare Data courses, please look at our Postgraduate Certificate, Postgraduate Diploma, or MSt pages.

The PGDip (Flex) 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.

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Overview

The programme has been developed by a network of partners with unrivalled experience in healthcare informatics to create a world-leading training environment.

Aims of the course

The programme will develop individuals with the necessary knowledge and skills to be able to understand and critically evaluate electronic healthcare data in a manner compatible with innovative commercialisation opportunities

The course will:

Provide professionally relevant teaching and learning of the knowledge and skills at the forefront the successful understanding and utilisation of electronic healthcare data.
Develop healthcare data experts with the necessary expertise, and originality of application, to pursue and expand their roles in the rapidly evolving environment of healthcare data.
Promote a comprehensive understanding of the practical and ethical considerations relevant to healthcare data, informatics, innovation and commercialisation.
Ensure a systems-based approach to the critical analysis and development of improvements in healthcare systems.
Provide work-relevant learning around the current problems, best-practice, challenges and potential solutions in the use of healthcare data.
Instil a critically reflective approach to the processes involved in the development of innovative and commercially viable opportunities arising from healthcare data.
Create a professional network of like-minded individuals as leaders in the field of healthcare data commercialisation.
Provide students with the skills and knowledge to execute the entire process from initial curiosity-driven healthcare data queries, through to data analysis and visualization, healthcare systems analysis, change management, and developing skills for data entrepreneurship, commercialisation, and digital marketing in a work-relevant and practical manner.

Teaching & Assessment

Teaching

This is a part-time course designed to fit with the demands of full-time employment. The course is delivered through a combination of face-to-face sessions requiring attendance in Cambridge, self-directed learning and supported through a virtual online environment [VLE].

It is structured across the following three units of work:

Unit 3: Finding relationships and data visualisation, 20 credits;

Unit 4: Healthcare systems improvement, 20 credits;

Unit 5: Medical technology innovation and commercialization, 20 credits;

There are approximately 30 hours of teaching for each of Units 3, 4 and 5. This teaching takes the form of seminars and classes, lectures, practicals and small group teaching.

Each 20 credits of study is roughly equivalent to 200 hours of study which will consist of face to face teaching, blended, and self-directed learning. This is an indicative amount and it is recognised that individuals may engage in greater or lesser amounts of study for each unit.

Learning outcomes

The programme provides the skills and knowledge needed to convert digital health data within electronic patient records, trial registries, and audit networks into effective research, service improvement tools which may have commercial applicability.

In particular the over-arching learning outcomes are:
Knowledge and understanding

To enhance the students’ systematic knowledge and critical understanding of the diverse range of healthcare data produced, the way this is stored and handled and the problems this presents.
To develop students’ ability to evaluate and to critically compare healthcare data sets in order to make value-based judgements on the most appropriate analytical and visualisation approaches to use.
To enable students to describe and critique the functional and organisational structure of healthcare datasets.
To provide a comprehensive understanding of the skills necessary to enable students’ to critically review and interpret healthcare data in the context of developing decision support tools, systemic change, and potentially novel commercial leads.
To create an enquiring perspective to enable critical and evaluative discussion that extends student understanding of key ethical issues in the acquisition and use of healthcare data.
To develop students’ ability to evaluate best practices in the use of healthcare data and apply appropriate methods to their own work.
To enable students to understand and apply the academic and professional theories and applications of change management, innovation, commercialisation, and entrepreneurship to the utilisation of healthcare data.
To enhance the students’ systematic knowledge and critical understanding of the importance, relevance, breadth and applicability of healthcare data in the modern world.
To develop students’ ability to critically evaluate subject matter.
To enable students to describe and critique key analytical, visualisation, and commercialisation techniques and approaches relevant to healthcare data.

Skills and other attributes

To develop a data query and follow this with appropriate application of analytical and visualisation tools.
To define, develop and implement the process of change management in a healthcare context.
To demonstrate systematic knowledge of the skills and methodologies necessary to: understand healthcare data in a repository; to perform appropriate analytical interpretation of the data; to apply appropriate visualisation tools and techniques; to identify innovative commercial potential in a critically evaluative manner, under their own initiative.
To present an appropriate understanding of, and technical competence in, the analytical and visualisation methods required for understanding healthcare data and for engaging downstream user groups and stakeholders.
To design, prepare, implement and evaluate a business case and digital marketing strategy for the innovative commercialisation of healthcare data.

Feedback

This is a part-time course with a combination of face-to-face delivery and self-directed learning designed to be compatible with full-time work.

Students will receive formative (does not contribute to the final mark) and peer-based feedback throughout the course, along with tutor provided feedback on the submitted summative assignments, which contribute to the final mark.

Assessment

Essays, projects & written papers

Units 3-5 will use summative assessment approaches designed to ensure experiential learning and work- based real-life relevance. Each Unit shall be examined by assignments of 3000-4000 words, or assignments deemed their equivalent by Degree Committee, except where other methods of assessment are published in individual Unit descriptions.

Approaches may include, but are not limited to: critical analysis of case-studies, assessment of evidence-based portfolios, assessment of work and sector relevant group presentations and projects, short answer questions, essays, and the ability to handle, analyse and visualise unseen datasets.

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: 10th - 14th October 2022.

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: 23rd - 27th January 2023

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: 24th - 28th April 2023

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.

Requirements

Academic requirement

The Postgraduate Diploma (flexible) in Healthcare Data: Informatics, Innovation and Commercialization one-year programme is only open to those who have successfully completed and passed the Postgraduate Certificate in Healthcare Data and Informatics.

Language requirement

All applicants are required to demonstrate competency in English at a high level before commencing the programme; students whose first language is not English must be able to satisfy the requirements below.

IELTS Academic: Overall score of 7.0 (a minimum of 7.0 in Listening, Writing and Speaking; 6.5 in Reading)
TOEFL Internet: Overall score of 100 (a minimum of 25 in each individual component)
CAE: Grade A or B (with at least 193 in each individual element) plus a Language Centre assessment
CPE: Grade A, B, or C (with at least 200 in each individual element)

Please be aware that students require the use of a laptop for the duration of the course.

Fees

The fees for 2022 entry will be £5,301 for Home students and £9,501 for overseas students.

The fee status of EU nationals for 2021 entry onwards will be International by default until the UK government announce further details and amendments of the current regulation. EU students should regularly check for updates at: www.cam.ac.uk/eu

Other costs

Students will be expected to cover the application fee (£75 online) and any costs of travel, accommodation and subsistence during the teaching weeks in Cambridge.

Applications and FAQs

Below you will find answers to the most frequently asked question or FAQs. Please do read through these and you should find the answers to most questions.

However, if you still cannot find what you are looking for then please click on, the ‘ASK A QUESTION’ link at the top right of this page to send the course team a message and we will respond as soon as possible.

When is the deadline for applications?

Applications will be accepted online until 30th June 2022. For more information about applying, click here.

Am I suitable for the PG Diploma in Healthcare Data?

In addition, all applicants are required to demonstrate a high degree of competency in English. Students whose first language is not English must satisfy the following requirements:

IELTS Academic: Overall score of 7.0 (a minimum of 7.0 in Listening, Writing and Speaking; 6.5 in Reading)
TOEFL Internet: Overall score of 100 (a minimum of 25 in each individual component)
CAE: Grade A or B (with at least 193 in each individual element), plus a Language Centre assessment
CPE: Grade A, B, or C (with at least 200 in each individual element)

Finally, students require the use of a laptop for the duration of the course.

Final decisions regarding admissions can only be made on the full and final applications and the offer of places will depend on the strength of applicants in any one year.

Do I need a strong background in maths, statistics or computer programming?

All prerequisite for course as listed on the course page of our website here. The course involves the study of statistics and includes some programming but there are no specific prerequisites in these areas and the faculty are expert at tailoring their teaching to different backgrounds and levels of expertise.

When can I expect to hear back on a decision?

Decisions will be made approximately 4-6 weeks after the relevant closing date.

Can I study individual Healthcare Data modules?

The Healthcare Data Programme is also offered as:
- Postgraduate Certificate 1 year, part-time: units 1 and 2, further details can be found here
- Master of Studies 2 years, part-time: units 1-5 and a dissertation, further details can be found here

When can I apply for the Healthcare Data course?

Applications for the MSt, PG Certificate and PG Diploma starting in the Michaelmas term (October) open early each year.

How is teaching delivered?

Postgraduate courses at ICE are usually taught using a mixture of face-to-face classes and online resources, designed to fit around the demands of life.

The programme consists of a combination of face-to-face teaching, blended and self-directed learning.

Is it possible to study the courses over a longer period of time?

The courses have been designed on the basis of continuous study and we recommend completing them as scheduled, unless there are unforeseen circumstances. If you are unable to complete the study as advertised, please contact the course team and they will be able to advise you of your options for completing your study.

Can I transfer credits from my current university?

We do not currently accept any transfer of credits from other universities.

What are the Fees?

The fees for 2022 entry will be £5,301 per annum for Home students and £9,501 per annum for overseas students. The fee status of EU nationals for 2021 entry onwards will be International by default until the UK government announce further details and amendments of the current regulation. EU students should regularly check for updates at: www.cam.ac.uk/eu. Students will be expected to cover the application fee (£75 online) and any costs of travel, accommodation and subsistence during residential sessions in Cambridge. Please be aware that students require the use of a laptop for the duration of the course.

Is there any funding available for Healthcare Data courses? How are fees payable?

Unfortunately there are currently no bursaries available for the Healthcare Data courses. It may be possible to secure funding through your employer or healthcare organisation.

Information about Bursaries at the Institute of Continuing Education (ICE) can be found here: www.ice.cam.ac.uk/bursaries The Graduate Admissions Office website provides information on funding administered centrally within the University and you may find the following link helpful :http://www.graduate.study.cam.ac.uk/finance/funding . Gates funding is not available for MSts. Once you have arranged any funding, payments can be scheduled through a payment plan, of up to four instalments per academic year.

What is my Fee Status?

Guidance on fee status, whether Home/EU or Overseas can be found on the University website here. Your fee status will determine the fee that you are liable to pay for your course of study. If in doubt or you require further advice regarding your fee status please contact admissions on this email address:
Ice.admissions@ice.cam.ac.uk

Related courses

The Healthcare Data Programme is also offered as:

Postgraduate Certificate 1 year, part-time: units 1 and 2, further details can be found here

Master of Studies 2 years, part-time: units 1-5 plus unit 6 a research dissertation, further details can be found here

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.

ASK A QUESTION

Course dates

01 Oct 2022 to 31 Jul 2023

Course duration

1 Year

Apply by

30 Jun 2022

Course fee

Home: £5,301
Overseas: £9,501

Course director

Dr Ronan O'Leary

Academic Directors, Course Directors and Tutors are subject to change, when necessary.

Venue

Various locations

Cambridge

United Kingdom

Qualifications / Credits

60 credits at Master of Studies

Course code

HDPGD601

Postgraduate Diploma (Flexible) in Healthcare Data: Informatics, Innovation and Commercialization

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Aims of the course

Teaching

Learning outcomes

Feedback

Assessment

Course Units and Teaching Weeks

Academic requirement

Language requirement

Fees

Other costs

When is the deadline for applications?

Am I suitable for the PG Diploma in Healthcare Data?

Do I need a strong background in maths, statistics or computer programming?

When can I expect to hear back on a decision?

Can I study individual Healthcare Data modules?

When can I apply for the Healthcare Data course?

How is teaching delivered?

Is it possible to study the courses over a longer period of time?

Can I transfer credits from my current university?

What are the Fees?

Is there any funding available for Healthcare Data courses? How are fees payable?

What is my Fee Status?

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Course duration

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