Module information
Details
- Title
- Data and Security
- Type
- Specialist
- Module code
- S-CSC-S4-1
- Credits
- 15
- Requirement
- Compulsory
Aim of this module
This module aims to provide the trainee with experience of the practical security and governance arrangements associated with the use and management data in a clinical environment, including the development and quality assurance of artificial intelligence (AI) data models.
Work-based content
Competencies
| # | Learning outcome | Competency | Action |
|---|---|---|---|
| # 1 | Learning outcome 1 |
Competency
Audit documentation for a clinical system or key clinical infrastructure and make suitable recommendations |
Action View |
| # 2 | Learning outcome 1, 2 |
Competency
Review and identify the data protection and cyber security risks of a clinical system or key clinical infrastructure, develop appropriate mitigations and update the safety case report |
Action View |
| # 3 | Learning outcome 2,3 |
Competency
Implement data protection and cyber security measures to mitigate risks identified for a clinical system |
Action View |
| # 4 | Learning outcome 1,2,3 |
Competency
Create a safety case report for a new system or system under development, applying the principles of “data protection by design” |
Action View |
| # 5 | Learning outcome 1,3,4 |
Competency
Undertake an information assurance audit of digital clinical and non-clinical data across a patient pathway and report the findings |
Action View |
| # 6 | Learning outcome 5 |
Competency
Complete data integrity checks for a healthcare dataset, report the findings and recommendations for actions |
Action View |
| # 7 | Learning outcome 6 |
Competency
Advise a healthcare professional on data encryption in the following conditions:
|
Action View |
| # 8 | Learning outcome 7 |
Competency
Advise a healthcare professional on de-identification of data |
Action View |
| # 9 | Learning outcome 9 |
Competency
Construct complex SQL queries against an information system in a clinical environment |
Action View |
| # 10 | Learning outcome 8 |
Competency
Design a simple relational database |
Action View |
| # 11 | Learning outcome 9 |
Competency
Extract, prepare and process clinical data for further analysis and reporting |
Action View |
| # 12 | Learning outcome 8 |
Competency
Undertake administration of a database |
Action View |
| # 13 | Learning outcome 10 |
Competency
Clean and prepare a healthcare dataset for an AI study and make recommendations for appropriate AI algorithms by undertaking Exploratory Data Analysis (EDA) |
Action View |
| # 14 | Learning outcome 10 |
Competency
Select, train and optimise an AI model on a healthcare dataset |
Action View |
| # 15 | Learning outcome 10 |
Competency
Validate and interpret the output of an AI model and present findings to an audience of peers |
Action View |
Assessments
Complete 3 Case-Based Discussions
Complete 3 DOPS or OCEs
Direct Observation of Practical Skills Titles
- Transfer data across a hospital network and demonstrate data integrity through the use of appropriate checks.
- Perform de-identification of a clinical dataset and confirm the de-identification has been applied as expected.
- Prepare and execute a SQL query against an information system to answer a clinical or operational question.
- Perform a database backup and confirm that the operation has been performed successfully.
Observed Clinical Event Titles
- Explain the flow of clinical information across a patient pathway, describing the data format, transfer mechanism and processing performed at each step.
- Describe the data protection and cyber security risks of a clinical system, along with the measures in place to mitigate these.
- Explain the validation and interpretation of a machine learning model to a non-expert.
- Explain how encryption is used to protect data in transit and at rest for a specific clinical system.
Learning outcomes
| # | Learning outcome |
|---|---|
| 1 | Review and prepare appropriate documentation for clinical information systems. |
| 2 | Review and identify data protection and cyber security risks for clinical information systems and develop and implement appropriate mitigation strategies. |
| 3 | Apply the principles of “data protection by design” to new and existing clinical information systems. |
| 4 | Undertake information assurance audits, applying relevant legislation and guidance to clinical data flows. |
| 5 | Confirm the integrity of clinical datasets. |
| 6 | Evaluate encryption schemes for data at rest or in transit and advise healthcare professionals on appropriate use. |
| 7 | Critically appraise de-identification processes and advise healthcare professional on their application. |
| 8 | Design, implement and administer database systems. |
| 9 | Prepare reports against information systems through the development and execution of complex and multi-staged SQL queries. |
| 10 | Design, develop, train and validate AI models using alphanumeric and imaging datasets. |
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
On successful completion of this module the trainee will be able to:
- Apply integrative knowledge of databases and be able to develop a database structure to meet a clinical need.
- Critically implement SQL and data mining strategies on large data sets.
- Critically appraise data from large clinical data sets.
Indicative content
- Database management and data mining:
- The relational model of data
- Implementation of relational databases
- Advanced SQL programming
- Query optimisation
- Concurrency control and transaction management
- Database performance tuning
- Distributed relational systems and data replication
- Columnstore/data warehousing database engines
- Document-oriented databases (e.g. Lucene)
- Security considerations
- Data mining
- Large data set methodologies
- Database standards and standards for interoperability and integration
- Data analysis and presentation
Clinical experiences
Important information
Clinical experiential learning is the range of activities trainees may undertake in order to gain the experience and evidence to demonstrate their achievement of module competencies and assessments. The list is not definitive or mandatory, but training officers should ensure, as best training practice, that trainees gain as many of these clinical experiences as possible. They should be included in training plans, and once undertaken they should support the completion of module assessments and competencies within the e-portfolio.
Activities
- Attend a departmental meeting where a data protection or a cyber security incident is discussed.
- Observe a cyber-security assessment of a clinical information system being undertaken.
- Attend a general management or clinical governance meeting where reports and dashboards generated from clinical information system are being discussed.
- Observe the administrator of a clinical information system undertake basic database management tasks.
- Observe a complex SQL query being developed by an experienced systems administrator.
- Attend a meeting where the use or potential use of AI models is discussed with healthcare professionals.
Module assigned to
Specialties
| Specialty code | Specialty title | View |
|---|---|---|
| Specialty code SBI1-2-22 | Specialty title Clinical Scientific Computing [2022] | Action View |
| Specialty code SBI1-2-23 | Specialty title Clinical Scientific Computing [2023] | Action View |
| Specialty code SBI1-2-24 | Specialty title Clinical Scientific Computing [2024] | Action View |
| Specialty code SBI1-2-25 | Specialty title Clinical Scientific Computing [2025] | Action View |