Reviewing: S-CE-S2-1

Analytics

Information:

Module information

Details

Title
Analytics
Type
Specialist
Module code
S-CE-S2-1
Credits
10
Requirement
Compulsory

Aim of this module

This module will prepare the trainees for the management, analysis and communication of technical, operational and clinical data in their professional practice.

Work-based content

Competencies

# Learning outcome Competency Action
# 1 Learning outcome 1,2 Competency

Create a relational database from available departmental data sources

OR

Modify an offline copy of an existing database incorporating new data tables

Develop data input forms or methods to populate tables, queries to extract subsets of the data, and reports to output data in a readable/viewable form

 Action View
# 2 Learning outcome 1,4 Competency

Use different methods to map a pathological, clinical or operational process

 Action View
# 3 Learning outcome 1,2 Competency

Use statistical techniques to evaluate the strength of relationships between elements in a clinical and operational process and present a summary

 Action View
# 4 Learning outcome 2 Competency

Compare competing clinical or operational measurements using appropriate statistical techniques and present the results and conclusions

 Action View
# 5 Learning outcome 2,3 Competency

Review and critique selected parameters (elements) and statistical methods used in a real world clinical or operational setting

 Action View
# 6 Learning outcome 1,2 Competency

Present a visualisation of a complex data set to stakeholders to support an explanation of findings from a mapping or statistical analysis

 Action View
# 7 Learning outcome 4 Competency

Use Computer Aided Design and Finite Element Analysis (FEA) to optimise the design of a mechanical part. Including the following:

  • Drawings of mechanical part using CAD
  • Functional analysis
  • FEA analysis
 Action View
# 8 Learning outcome 4 Competency

Simulate a prototype electronic circuit including:

  • Filtering and amplification phases
  • Analogue-to-digital conversion and digital display
  • Verification of circuit design including individual stages
  • Validation of simulated circuit performance
 Action View
# 9 Learning outcome 4 Competency

Apply and evaluate the application of a model that describes a physiological system which incorporates a system of differential equations

 Action View
# 10 Learning outcome 1 Competency

Using a scripted data processing language create a script to demonstrate:

  • Variables
  • Data structures
  • Pattern matching
  • Loops
  • Conditionals
 Action View

Assessments

Complete 2 Case-Based Discussions

Complete 2 DOPS or OCEs

Direct Observation of Practical Skills Titles

  • Run a physiological simulation and explain it to a clinical colleague.
  • Debug or modify software.
  • Demonstrate the influence of filter design on circuit performance.

Observed Clinical Event Titles

  • Explain a “mapping” of a process to a colleague.
  • Discuss statistics used in a clinical or operational team with the multidisciplinary team.
  • Demonstrate a CAD and FEA solution to stakeholders.

Learning outcomes

# Learning outcome
1

Explore and explain relationships between elements in a system.
2

Select, perform and critique statistical analyses and interpretations on clinical and operational datasets.
3

Synthesise and present statistical analyses in clinical or operational reports, articulating premises, assumptions, conclusions and caveats, and communicate findings to multidisciplinary colleagues.
4

Create and evaluate mechanical, electronic and physiological models using a range of methodologies.

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. Select and generate statistics associated with the status and change in status of the individual patient or other entity.
  2. Navigate and map complex systems using appropriate tools.
  3. Analyse complex physiological, operational, electronic and mechanical systems through the use of modelling software.

Indicative content

  • The statistics of clinical and operational measurements:
    • Estimates of precision in continuous and categorical variables.
      • Minimal detectable differences.
      • Intra-class correlation.
    • Level of agreement statistics for continuous and categorical variables.
    • Introduction to Monte Carlo simulations.
    • Regression of continuous and categorical variables.
    • Original hypotheses and test statistics.
    • Visualisation of statistical data.
    • Statistical reporting.
  • Improving understanding by systems mapping:
    • Knowledge domains and the Cynefin framework (Known, Knowable, Complex, and Chaotic).
    • Review of mapping methodologies for analysis of systems.
      • g. Directed acyclic graphs
    • Introduction to mapping tools.
  • Introduction to systems of differential equations in medicine and biology:
    • Mathematical representation of physiological processes (modelling).
    • Numerical methods for simulation.
    • Parameter optimisation.
    • Sensitivity analysis.
  • Medical Electronic Circuit Design:
    • Analogue circuit design.
      • The properties of passive circuit components.
      • The properties of active circuit components.
      • Application of the golden rules.
      • The instrumentation amplifier.
      • Analogue to digital conversion.
    • Digital circuit design.
      • Boolean algebra.
      • Basic digital operations (gates).
      • Microprocessors.
      • Programmable microcontrollers.
  • Modelling and simulation of mechanical systems in health care.
    • Mechanics refresher.
    • Physical and biological behaviours of clinical materials.
    • Introduction to CAD.
    • Introduction to FEA.
  • Introduction to structured programming.
  • Principles of relational databases.

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

  1. Observe differential equation model in practice.
  2. Visit another service applying a statistical model to observe and appreciate different practices.
  3. Visit industry using simulations to implement design in manufacture.
  4. Visit a commercial data science organisation involved in healthcare applications to appreciate their use of machine learning and other higher order statistical methods.

Module assigned to

Specialties

Specialty code Specialty title View
Specialty code SPE3-4-22 Specialty title Clinical Engineering [2022] Action View
Specialty code SPE3-4-23 Specialty title Clinical Engineering [2023] Action View
Specialty code SPE3-4-24 Specialty title Clinical Engineering [2024] Action View
Specialty code SPE3-4-25 Specialty title Clinical Engineering [2025] Action View
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