Module information
Module details
- Title
- Introduction to Business Intelligence
- Type
- Rotation
- Module code
- S-HI-R2
- Credits
- 10
- Phase
- 1
- Requirement
- Compulsory
Aim of this module
This module will introduce trainees to the strategies and technologies used by NHS organisations for the reporting and analytics of service information to create Business Intelligence. This includes the development of data quality dashboards, performance management, processing of financial and commissioning information and use of data warehouse applications.
Work-based content
Training activities
| # | Learning outcome | Training activity | Type | Action |
|---|---|---|---|---|
| # 1 | Learning outcome 1,2,3,6 |
Training activities
Attend a Business Intelligence performance meeting where data is presented and reflect on the meeting |
Type OTA | Action View |
| # 2 | Learning outcome 1,2,3,6 |
Training activities
Shadow a data analyst preparing data from different sources and reflect on their role |
Type OTA | Action View |
| # 3 | Learning outcome 1,2,3,6 |
Training activities
Observe the procedure for collecting, processing and reporting data, and reflect on the process |
Type OTA | Action View |
| # 4 | Learning outcome 1,2,3,6 |
Training activities
Follow patient activity data through the information analysis pathway and reflect on the journey |
Type OTA | Action View |
| # 5 | Learning outcome 1,3,4,6 |
Training activities
Explore a new work request or innovation being considered by Business Intelligence and reflect on the impact this will have on practice |
Type OTA | Action View |
| # 6 | Learning outcome 1,3,5,6 |
Training activities
Review the requirements of the Freedom of Information Act and reflect on the involvement of Business Intelligence in meeting those requirements |
Type OTA | Action View |
| # 7 | Learning outcome 1,2,3,6 |
Training activities
Observe how clinical activity is coded into the commissioning data set and reflect on the process |
Type OTA | Action View |
| # 8 | Learning outcome 1,2,3,6 |
Training activities
Shadow a data analyst identifying the appropriate analytical techniques and reflect on their role |
Type OTA | Action View |
| # 9 | Learning outcome 1,2,3,6 |
Training activities
Observe how data is extracted for a use case/question for a particular user, research or requirement the question and reflect on the process |
Type OTA | Action View |
| # 10 | Learning outcome 1,2,3,6 |
Training activities
Observe preparation and reporting of financial activity data being prepared including commissioning data and reflect on the process |
Type OTA | Action View |
Assessments
Complete 1 Case-Based Discussion
Complete 1 DOPS or OCE
Direct Observation of Practical Skills Titles
- Perform database queries across multiple tables to extract data for analysis.
- Produce data for performance dashboard and reporting.
- Select an appropriate tool for data analysis.
Observed Clinical Event Titles
- Select and use an appropriate tool for data visualisation.
Learning outcomes
| # | Learning outcome |
|---|---|
| 1 | Explore the contribution of Business Intelligence to patient care. |
| 2 | Identify the roles and relationships of the multidisciplinary team in Business Intelligence. |
| 3 | Describe the working practices of Business Intelligence. |
| 4 | Examine the impact of innovation in Business Intelligence. |
| 5 | Review the effect of legislation in Business Intelligence. |
| 6 | Compare and contrast the practice of Business Intelligence with other specialties. |
Clinical experiences
Activities
There is no clinical experiential learning associated with this module.
Academic content (MSc in Clinical Science)
Important information
The academic parts of this module will be detailed and communicated to you by your university. Please contact them if you have questions regarding this module and its assessments. The module titles in your MSc may not be exactly identical to the work-based modules shown in the e-portfolio. Your modules will be aligned, however, to ensure that your academic and work-based learning are complimentary.
Learning outcomes
The academic content of this module will support the work-based activities, and provide a trusted environment in which to learn about the individual components required to perform modern information engineering with industry standard tools used in the healthcare service.
On successful completion of this module the trainee will be able to:
- Explain the different data sources used to enhance ‘the business’, the different data types within each, and the pros and cons of using and managing each.
- Discuss how database systems/data management and modern software processes contribute to the patient care pathways and the provision of high quality and safe patient care.
- Demonstrate a working and practical hands-on knowledge of core programming concepts in a modern programming language.
- Critically evaluate data management technologies including their strengths and limitations.
- Appraise technologies/tools to manage, extract and visualise data, and be able to justify their usage for a given scenario.
Indicative content
- Health analytics pipeline, software development and the project management process
- Building systems that integrate with the internet and databases
- Programming concepts and languages
- Introduction to programming in SQL (syntax, use cases, examples, workflow, etc.)
- Introduction to programming in python (including control structures, data types, structures, importing languages, implementing algorithms, and linkage to other technologies)
- Use of a python development platform, and use of specific libraries/APIs providing data extraction, management, analysis and graphical display of data
- Data and data modelling
- Database management using relational databases, use of SQL, and up-to-date models/technologies (e.g., graph models and Neo4j)
- Sources of data across the healthcare service used to address healthcare improvement challenges and providing business intelligence
- Data visualisation
- Principles of data visualisation
- Tools to produce visualisations and dashboards (e.g., Tableau)