Back Module: S-CSC-S4

Data and Security

Module information

Module details

Title
Data and Security
Type
Specialist
Module code
S-CSC-S4
Credits
15
Phase
3
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

Training activities

# Learning outcome Training activity Type Action
# 1 Learning outcome 1 Training activities

Audit documentation for a clinical system or key clinical infrastructure and make suitable recommendations

 Type DTA Action View
# 2 Learning outcome 1, 2 Training activities

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

 Type DTA Action View
# 3 Learning outcome 2,3 Training activities

Implement data protection and cyber security measures to mitigate risks identified for a clinical system

 Type DTA Action View
# 4 Learning outcome 1,2,3 Training activities

Create a safety case report for a new system or system under development, applying the principles of “data protection by design”

 Type DTA Action View
# 5 Learning outcome 1,3,4 Training activities

Undertake an information assurance audit of digital clinical and non-clinical data across a patient pathway and report the findings

 Type DTA Action View
# 6 Learning outcome 5 Training activities

Complete data integrity checks for a healthcare dataset, report the findings and recommendations for actions

 Type DTA Action View
# 7 Learning outcome 6 Training activities

Advise a healthcare professional on data encryption in the following conditions:

  • “At rest”
  • In transfer
 Type DTA Action View
# 8 Learning outcome 7 Training activities

Advise a healthcare professional on de-identification of data

 Type DTA Action View
# 9 Learning outcome 9 Training activities

Construct complex SQL queries against an information system in a clinical environment

 Type ETA Action View
# 10 Learning outcome 8 Training activities

Design a simple relational database

 Type DTA Action View
# 11 Learning outcome 9 Training activities

Extract, prepare and process clinical data for further analysis and reporting

 Type ETA Action View
# 12 Learning outcome 8 Training activities

Undertake administration of a database

 Type DTA Action View
# 13 Learning outcome 10 Training activities

Clean and prepare a healthcare dataset for an AI study and make recommendations for appropriate AI algorithms by undertaking Exploratory Data Analysis (EDA)

 Type DTA Action View
# 14 Learning outcome 10 Training activities

Select, train and optimise an AI model on a healthcare dataset

 Type DTA Action View
# 15 Learning outcome 10 Training activities

Validate and interpret the output of an AI model and present findings to an audience of peers

 Type DTA 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.

Clinical experiences

Clinical experiences help you to develop insight into your practice and a greater understanding of your specialty's impact on patient care. Clinical experiences should be included in your training plan and you may be asked to help organise your experiences. Reflections and observations from your experiences may help you to advance your practice and can be used to develop evidence to demonstrate your awareness and appreciation of your specialty.

Activities

  1. Attend a departmental meeting where a data protection or a cyber security incident is discussed.
  2. Observe a cyber-security assessment of a clinical information system being undertaken.
  3. Attend a general management or clinical governance meeting where reports and dashboards generated from clinical information system are being discussed.
  4. Observe the administrator of a clinical information system undertake basic database management tasks.
  5. Observe a complex SQL query being developed by an experienced systems administrator.
  6. Attend a meeting where the use or potential use of AI models is discussed with healthcare professionals.

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:

  1. Apply integrative knowledge of databases and be able to develop a database structure to meet a clinical need.
  2. Critically implement SQL and data mining strategies on large data sets.
  3. 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

Module assigned to

Specialties

Specialty code Specialty title Action
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
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