Information and Knowledge Management (SBI125)

20 credits

Aim of this module

Trainees completing this module will be able to identify clinical concepts underlying the description of healthcare activity and represent them in a computable form. They will be able to represent a clinical decision in an appropriate formalism and be able to assess data quality. Students will understand what knowledge is, and how knowledge can be harnessed to improve the quality of clinical care. By the end of the module students will know the various forms in which clinical knowledge exists and can be accessed and shared, including the main principles underpinning clinical coding and the design of knowledge management systems. They will understand the ways in which knowledge is used in decision making, and how knowledge can be formally represented. In addition, trainees will have a sufficient understanding of the nature of health record information, and the ways in which such information might formally be represented and managed and shared electronically, to equip them to play an active role in the design, development, procurement, or adoption of electronic health record (EHR) systems in their future careers. Trainees will also know about the requirements for rich interoperability between EHR systems. This module will enable the trainee to identify clinical concepts underlying healthcare activity, including healthcare science and represent them in a computable form. They will represent a clinical decision in an appropriate formalism and assess the quality of the data. Trainees will apply their understanding of what knowledge is, and how knowledge can be harnessed to improve the quality and safety of clinical care. The trainee will formally represent information and knowledge and apply decision analysis techniques to the use of large NHS data sets to inform clinical and service decision making. 

  1. Examine a work based related dataflow and review how data are collected, analysed and used to support care, research and healthcare management within the organisation, including a healthcare science service.
  2. Examine how large NHS data sets are created and used to support research and critical evaluation of practice, make funding decisions and contribute to the formulation and delivery of plans and strategies for meeting health and social care.
  3. Analyse the strengths and weaknesses, from a data perspective, of activity-based funding mechanisms.
  4. Assess the strengths and weaknesses of SNOMED CT, ICD 10 and OPCS5, and present the analysis and the findings in a written report.
  5. Review the challenges involved in coding clinical encounters; assess what kind of granularity of coding is practical and present at a team or departmental meeting.
  6. Represent a healthcare problem as a decision tree, including, where possible, the views of patients, and use the decision tree to inform the solution to the healthcare or healthcare science problem.
Number Work-based learning outcome Title Knowledge
1 1, 2, 3, 4, 5, 6

Apply information governance principles and best practice in the workplace, including confidentiality.

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2 1

Examine a dataflow and review how data are collected, analysed and used to support care, research and health and care management within the organisation.

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3 1

Present your findings in a written report and verbally to your line manager and peers.

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4 2

Critically evaluate how large NHS data sets are created and used to support research and make funding decisions.

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5 2

Work with a clinical team and discuss the perceived strengths and weaknesses of large NHS data sets.

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6 2

Critically evaluate how data are used to support research and critical evaluation of practice.

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7 2

Present your findings in a written report and verbally to your line manager and peers.

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8 3

Analyse the strengths and weaknesses, from a data perspective, of activity-based funding mechanisms.

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9 3

Propose changes to the use of data in activity-based funding mechanisms within your local health environment.

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10 4

Assess the relative strengths and weaknesses of SNOMED CT, ICD 10 and OPCS5.

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11 4

Present the analysis and the finding in a written report, making judgements in relation to the strength of the evidence.

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12 5

Review the challenges involved in coding clinical encounters and include an assessment of the kind of granularity provided by each coding system.

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13 5

Write a short report summarising your findings and present the key messages at a team or departmental meeting.

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14 6

Represent a healthcare problem as a decision tree.

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15 6

Use the decision tree to develop new guidelines to solve the healthcare problem.

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You must complete
2 Case-based discussion(s)
3 of the following DOPS / OCEs
Assessment Title Type
Present the three main coding systems SNOMED CT, ICD 10 and OPCS 5 to a non coding scientific audience, with particular emphasis on the improvement of data quality and the uses of data DOPS
Present to healthcare professionals in your organisations the uses of healthcare data and why data quality is important. DOPS
Construct a decision tree to represent a healthcare problem DOPS
Undertake some clinical coding from patient notes and compare your results to that of the professional coders OCE

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

  1. Discuss the pathway from data collection to interpretation, analysis and end use.
  2. Evaluate the advantages and the technical, ethical and legal problems in using a range of health data for secondary purposes.
  3. Appraise languages, codes and classifications used in healthcare.
  4. Appraise the computational techniques used in the creation and maintenance of terminologies.
  5. Represent a healthcare problem in a computable formalism.
  6. Use a machine learning programme to derive a classification rule from a data set.
  7. Identify and appraise sources of knowledge used to support clinical decision making.
  8. Explain how information can be used to optimise effective decision making in health and social care and thus improve outcomes.
  9. Describe how large data sets are created and used to support planning and/or commissioning, research and funding decisions.
  10. Describe predictive modelling techniques and appraise their application in health and social care.

Indicative Content

  • Language of health
  • Coding and classifications
  • Knowledge representation/ontology
  • Data quality and assurance
  • National data sets and models
  • Business intelligence
  • Data mining
  • Decision support
  • Data and information analysis
  • Predictive modelling techniques
  • Reporting and presentation
  • Dataflows in the NHS, looking at how data are collected, analysed and used to support care, research and healthcare management
  • Strengths and weaknesses, from a data perspective, of activity-based funding mechanisms, e.g. Payment by Results
  • Strengths and weaknesses of SNOMED CT, ICD 10 and OPCS5
  • Review the challenges involved in coding clinical encounters, assess what kind of granularity of coding is practical
  • Difficulties in representing clinical concepts in a structured way, what it means to provide a logical definition of a concept
  • Different approaches to creating decision support systems from knowledge and from data, including the computerisation of guidelines and of decision trees
  • Simple mathematical approaches to machine learning and apply them to an example in healthcare
  • Challenges and solutions to the issues surrounding transparency
  • Currently available predictive models
  • Use of quantitative outcomes measures to assess organisational performance, and patient safety, quality and care, e.g. standardised mortality rates