INMR96-Digital Health and Data Analytics

Module Provider: Business Informatics, Systems and Accounting
Number of credits: 20 [10 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2023/4

Module Convenor: Prof Vicky Weizi Li
Email: weizi.li@henley.ac.uk

Type of module:

Summary module description:

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. 

Aims:

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. 

Assessable learning outcomes:

On completion of the module students should be able to: 

• Describe key digital technology, information systems and data sources and types in healthcare 


• Analyse healthcare data and develop insights to inform decisions in healthcare service delivery, hospital operation and clinical quality management.  


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


• Understand data standards, coding types, hospital management and healthcare quality metrics and indicators in UK and international context 


• 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 management 

Additional outcomes:

Outline content:

This module will cover the following areas: 

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


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


• Information structures, standards and coding system, quality indicators for healthcare delivery and hospital operational management 


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


• 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 and internationally. 

Brief description of teaching and learning methods:

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

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

The assessment consists of coursework only. 

Assessment will consist of a written coursework assignment (100%). Students will be required to analyse health datasets and/or design digital health solution as well as complete one report of 5,000 words. The assignment can be based on given datasets and case studies. Submission term: summer term, week 37.  

Formative assessment methods:

All lectures will indicate the core material with an introduction to the topics. These are followed by workshops 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.  

Penalties for late submission:

Penalties for late submission of course work will be in accordance with University policy. 

Assessment requirements for a pass:

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

This assessment of the module can be awarded a pass, pass with merit; pass with distinction, or fail. 

Pass criteria - To pass this module, the student must demonstrate their overall understanding of digital health concepts, data standards and digital technologies; and apply suitable machine learning and data analytics methods in healthcare scenarios. 

Distinction criteria - To achieve distinction, the student must demonstrate their in-depth understanding of digital health concepts, data standards and digital technologies; and also exhibit original creativity in problem solving and methodological development in machine learning and data analytics for suitable healthcare scenario and critical evaluation of analytics results. 

Reassessment arrangements:

Reassessment will be by resubmitting the failed coursework. 

Additional Costs (specified where applicable):

Last updated: 30 March 2023