Faculty of Medical and Health Sciences

HLTHINFO 730 - Healthcare Decision Support Systems

15 Points

Semester 2


Familiarises students with the main developments of decision support systems in healthcare. The theoretical concepts and the technology including data mining, clinical decision support systems, diagnostic systems and decision support in managed care are outlined. Ethical issues are also addressed.


Restriction: POPLHLTH 730

Course information

Goals of the Course

The general goals of the course are:

  •  An understanding of the concepts and principles of decision support systems and associated theory, nature, scope and limitations
  • Examination of types of decision support systems, their limitations and their contributions to evidence-based healthcare
  • Critical evaluation of the design, implementation and use of decision support systems in different healthcare settings

Learning Outcomes 

Following the course students should be able to:

  • Apply a structured process of knowledge engineering to derive logical specifications for decision support from clinical practice guidelines written for humans
  • Design (no programming required) a prototype rule-based decision support system
  • Critically evaluate a decision support system in terms of provenance, agreement with practice guidelines, usability, fit to clinical workflow and maintainability
  • Conduct independent research on technology concepts related to decision support systems
  • Participate in informed discussion of decision support system concepts (using online means)
  • Appreciate the role of decision support systems in healthcare, including tele-monitoring
  • Discuss international standards development work in clinical guideline/knowledge representation and decision support system design
  • Contribute to the design and successful implementation of healthcare decision support systems

Content Outline 

The course provides students with a theoretical and practical background in healthcare decision support. The sequence of course topics is as follows:

  • Foundations 
    •    Intro to role and motivation for automated decision support systems in healthcare
    • Encoding clinical information – formally representing facts about healthcare
    • Ontology, data warehousing, data linkage and data mining – how do they all relate?
  • Knowledge engineering 
    • Decision trees (as a representation, and learning them automatically from data)
    • Production rule systems (IF-THEN systems, our main practical focus) and Case Based Reasoning (learning from past similar cases )
    • Probability and Fuzzy Logic
    • Evidence Based Medicine and Clinical Practice Guideline representations
  • Applications 
    • Historical successes – and what makes for a success?
    • Enabling architectures – how to make it work and integrate with healthcare systems
    • Monitoring systems and mobile computing – some key rising areas
    • Evaluation – how to tell if a CDSS will be effective

Learning and Teaching 

The course is divided into online modules, each with a series of supporting resources, including lectures, readings from the international research literature and other activities (e.g., software tutorials and focus questions for online discussion). All materials are accessed via the Web and online discussions/peer feedback will be heavily used. Online participation and constructive criticism to other students are taken into consideration during marking. Course readings are available via the University Library's website. This is an online course with optional face to face sessions; one at the beginning of the semester and another one at the end of the semester. These will be recorded and put up on CANVAS for those who cannot attend the sessions.

Learning Resources 

The course book provides references to illustrative readings organised according to the learning module sequence. Students should study the key readings, which together with the other learning activities, will reinforce understanding and increase analytical skills.

There is no set text for this course but the following books are on desk call at the Tāmaki Library:

  • Van de Velde R, Degoulet P. Clinical Information Systems: A Component-based Approach, Springer-Verlag, N.Y. 2003
  • Australian National Electronic Decision Taskforce, Electronic Decision Support for Australia’s Health Sector, Commonwealth Department of Health and Human Service, ACT, Australia. 2002
  • Shortliffe EH, et al (ed.), Medical Informatics: Computer Applications in Health Care and Biomedicine, 2nd edition, Springer, 2000
  • Slee VN, Slee DA, Schmidt HJ. The Endangered Medical Record: Ensuring its Integrity in the Age of Informatics. St. Paul, Minn: Triaga Press, 2000
  • Smith J, "An Overview of Health Information Management Systems", in Smith J, Health Management Information Systems, Open University, 2000
  • van Bemmel J.H., Musen, M.A. (editors) Handbook of Medical Informatics. AW Houten, Netherlands: Bohn Stafleu Van Loghum ; Heidelberg, Germany : Springer Verlag, 1997

Students should also consult journals such as the International Journal of Medical Informatics and Journal of the American Medical Informatics Association


Assignment 1 Domain Modelling short video presentation 25%

Assignment 2 DSS Prototype (2000 word report) 40%

Assignment 3 Evaluation Report (2500 word report) 35% 

Programme and Course Advice 

This course is for students enrolled a Postgraduate Diploma in Health Sciences in the specialisation Health Informatics or in the Effective Practice or Health Services Research pathways in the public health programme.

Suitable for those who are currently employed in the health and IT sectors.

It does not require programming skills.

Course Coordinator

Course Administrator