| Module Code: |
H8HA |
|
Long Title
|
Healthcare Analytics
|
|
Title
|
Healthcare Analytics
|
| Module Level: |
LEVEL 8 |
| EQF Level: |
6 |
| EHEA Level: |
First Cycle |
| Module Coordinator: |
Anu Sahni |
| Module Author: |
Catherine Mulwa |
| Departments: |
School of Computing
|
| Specifications of the qualifications and experience required of staff |
Master’s degree or PhD in a computing or cognate discipline. May have industry experience also.
|
| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| # |
Learning Outcome Description |
| LO1 |
Discuss techniques for improving efficiency in a variety of settings (hospitals, primary care, and private sector) and the associated trade‐offs. |
| LO2 |
Conduct advanced data analysis tasks, including data preparation, inspection, cleansing and transformation with the goal of discovering useful information. |
| LO3 |
Design and develop optimisation and simulation models to evaluate and improve health care operations. |
| LO4 |
Effectively interpret model output to assess processes and outcomes of care and the potential impact of proposed changes on healthcare systems performance. |
| LO5 |
Critically evaluate healthcare models and systems (i.e. creative analysis of findings, demonstrate ability to synthesise data collected). |
| Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
|
| No recommendations listed |
Co-requisite Modules
|
| No Co-requisite modules listed |
| Entry requirements |
Learners should have attained the knowledge, skills and competence gained from stage 3 of the BSc (Hons) in Data Science
|
Module Content & Assessment
| Indicative Content |
|
Introduction to Healthcare Industry
Various constituents. The current state of healthcare – cost, process, structure, quality. Challenges . Latest development in this area. Impact of technology.
|
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Data Sources and Healthcare Analytics
Electronic Health Records, Imaging, Sensor data, Biomedical signals.. Common representations of data in health information systems (ICD, CPT). How Analytics Can Improve Decision Making . Existing quality/performance measurement frameworks (NQF, HEDIS). Applications of Healthcare Analytics . Attributes of high-performing healthcare systems. Components of Healthcare Analytics
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|
Healthcare Quality and Value
Overview of Healthcare QI . Common QI Frameworks in Healthcare . Working with QI Methodologies. Strategies for optimizing data quality. Querying tools and methods. Data preparation/transformation. Ethics, data ownership and privacy
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Data Quality and Governance
The Need for Effective Data Management . Data Quality . Data Governance and Management
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Working with Data
Data: The Raw Material of Analytics . Preparing Data for Analytics . Getting Started with Analysing Data
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Developing and Using Effective Indicators
Measures, Metrics, and Indicators . Using Indicators to Guide Healthcare. Improvement Activities
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Data Mining Healthcare Applications
Introduction. Association Analysis. Pattern Mining. Sensor Data Analysis. Terminology Acquisition and Management. Information Extraction. Discourse Interpretation. Text Mining Environments. Applications. Integration with Clinical Text Mining.
|
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Healthcare Optimisation
Modelling and simulation . Design space exploration. Simulated annealing. Multi-objective optimization. Resource allocation . Hospital staff scheduling Patient flow optimization.
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Healthcare Policies and Ethical Approval Procedure in Ireland
Understanding the benefits and significance of healthcare policies In Ireland. Ethical approval process e.g. Dataset privacy . .
|
| Assessment Breakdown | % |
|---|
| Coursework | 100.00% |
AssessmentsFull Time
| Coursework |
| Assessment Type: |
Continuous Assessment |
% of total: |
Non-Marked |
| Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4,5 |
| Non-Marked: |
Yes |
Assessment Description:
Ongoing weekly feedback on tutorial activities. |
|
| Assessment Type: |
Continuous Assessment |
% of total: |
40 |
| Assessment Date: |
n/a |
Outcome addressed: |
1,2 |
| Non-Marked: |
No |
Assessment Description:
The learner will be required to discuss techniques for improving efficiency in a variety of settings (i.e. hospitals, primary care, and private sector) and the associated trade‐offs.
Select a particular area in healthcare, find datasets, utilize data mining and machine learning techniques and perform data analyses tasks (i.e. data pre-processing, inspection, cleansing and transformation) with the goal of discovering useful information |
|
| Assessment Type: |
Project |
% of total: |
60 |
| Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4,5 |
| Non-Marked: |
No |
Assessment Description:
Specify, Design and develop an optimisation or simulation model to evaluate and improve healthcare operations. Based on developed model, the learner will be required to effectively interpret and communicate results of model output assess processes and outcomes of care. |
|
| No End of Module Assessment |
| Reassessment Requirement |
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
|
Reassessment Description
The repeat strategy for this module is a project. Learners will be afforded an opportunity to repeat the project at specified times throughout the year and all learning outcomes will be assessed in the repeat project.
|
NCIRL reserves the right to alter the nature and timings of assessment
Module Workload
| Module Target Workload Hours 0 Hours |
| Workload: Full Time |
| Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
| Lecture |
Classroom & Demonstrations (hours) |
24 |
Per Semester |
2.00 |
| Tutorial |
Other hours (Practical/Tutorial) |
24 |
Per Semester |
2.00 |
| Independent Learning |
Independent learning (hours) |
202 |
Per Semester |
16.83 |
| Total Weekly Contact Hours |
4.00 |
Module Resources
| Recommended Book Resources |
|---|
-
Yang, H. & Lee, E. K.. (2016), Healthcare Analytics: From Data to Knowledge to Healthcare Improvement, Wiley Series.
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Reddy, C. K. & Aggarwal, C. C.. (2015), Healthcare Data Analytics, Chapman and Hall/CRC.
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Strome, T.. (2013), Healthcare analytics for quality and performance improvement, Wiley & Sons.
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Story, P.. (2010), ) Dynamic Capacity Management for Healthcare: Advanced Methods and Tools for Optimization, CRC Press.
| | Supplementary Book Resources |
|---|
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Nadinia, D. & Melissa, L.. (2016), Foundations of Health Information Management, (4th ed).
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Hokey, M.. (2016), Global Business Analytics Models: Concepts and Applications in Predictive, Healthcare, Supply Chain, and Finance Analytics (FT Press Analytics).
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David, M.. (2010), Data Analytics in Healthcare Research: Tools and Strategies.
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Shilpa, B.. (2017), Business Intelligence in Healthcare with IBM Watson Analytics.
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Madsen, L.. (2012), Healthcare Business Intelligence: A Guide to Empowering Successful Data Reporting and Analytics, John Wiley & Sons.
| | This module does not have any article/paper resources |
|---|
| This module does not have any other resources |
|---|
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