INMR96: Digital Health and Data Analytics

Module code: INMR96

Module provider: Business Informatics, Systems and Accounting; Henley Business School

Credits: 20

Level: 7

When you'll be taught: Semester 2

Module convenor: Professor Vicky Weizi Li, email: weizi.li@henley.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: No

Talis reading list: Yes

Last updated: 19 November 2024

Overview

Module aims and purpose

This module focuses on knowledge and hands on skills to leverage information technology, data analytics and machine learning skills in healthcare service delivery, clinical research, hospital operational management and public health. The students will have blended learning experience including in-class lectures, machine learning and data analytics exercise as well as case studies from NHS hospitals, community and social care organisations, local authority and companies who provide digital health and data analytics solutions, in UK and internationally. Ìý

The module is to equip students with digital health knowledge and modern data analytics and machine learning skills to solve real-world problems. It is intended to enable students to understand, develop, apply and evaluate healthcare information technologies, digital transformation, data analytics and machine learning applications in healthcare related industries
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Module learning outcomes

By the end of the module, it is expected that students will be able to:Ìý

1. Describe key digital technology, information systems and data sources and types in healthcareÌý

2. Analyse healthcare data and develop insights to inform decisions in healthcareservice delivery, hospital operation and clinical quality management.Ìý

3. Apply machine learning and statistical methods to solve key problems in health and social care scenarios such as predicting risk and understanding patternsÌý

4. Understand data standards, coding types, hospital management and healthcare quality metrics and indicators in UK and international contextÌý

5. Design decision support system integrating with healthcare processes and systems (e.g. Electronic Patient Records) for service planning, operation and clinical decision support and hospital managementÌý

Module content

This module will cover the following areas:Ìý

1. Digital technology and IT applications to date such as Electronic patient records, clinical portals, tele-health systems and mobile healthcare.Ìý

2. Types of data that health and wellness systems collect and process to allow informed care decisions about individuals or populations.Ìý

3. Information structures, standards and coding system, quality indicators for healthcare delivery and hospital operational managementÌý

4. Machine learning and statistical models and tools to explore patterns and risk predictions and to solve healthcare decision making problems.Ìý

5. Case studies of decision support systems, machine learning and data analytics in NHS hospitals, community and social care organisations, local authority and companies who provide digital health and data analytics solutions, in UK andinternationally.Ìý

Structure

Teaching and learning methods

Teaching and learning methods includes face to face teaching, case study, group discussion, Python programming demonstration and exercise, independent reading and research.

Study hours

At least 30 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students will be required to obtain a mark of 50% on the coursework.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

All lectures will indicate the core material with an introduction to the topics. These are followed by seminars and tutorials where discussions and exercises on applying the methods and techniques into the given digital health and data analytics scenarios and case studies will be carried out. Feedback will be provided in the end of each workshop for improvements and further considerations. Ìý

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Reassessment

Additional costs