HLTHINFO 730 : Healthcare Decision Support Systems
Medical and Health Sciences
2020 Semester Two (1205) (15 POINTS)
Course Prescription
Course Overview
- 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?
- 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
- 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
Capabilities Developed in this Course
Capability 1: | Disciplinary Knowledge and Practice |
Capability 2: | Critical Thinking |
Capability 3: | Solution Seeking |
Capability 4: | Communication and Engagement |
Capability 5: | Independence and Integrity |
Capability 6: | Social and Environmental Responsibilities |
Learning Outcomes
- Apply a structured process of knowledge engineering to derive logical specifications for decision support from clinical practice guidelines written for humans (Capability 1.1, 1.2 and 3.1)
- Design (no programming required) a prototype rule-based decision support system (Capability 3.1, 3.2 and 5.2)
- Critically evaluate a decision support system in terms of provenance, agreement with practice guidelines, usability, fit to clinical workflow and maintainability (Capability 2.1, 5.1 and 6.1)
- Conduct independent research on technology concepts related to decision support systems (Capability 1.1, 2.2 and 5.2)
- Participate in informed discussion of decision support system concepts (using online means) (Capability 4.1, 4.2 and 5.2)
- Appreciate the role of decision support systems in healthcare, including tele-monitoring (Capability 4.1, 4.2 and 6.2)
- Discuss international standards development work in clinical guideline/knowledge representation and decision support system design (Capability 4.1, 5.1 and 6.1)
- Contribute to the design and successful implementation of healthcare decision support systems (Capability 3.1, 4.2 and 6.2)
Assessments
Assessment Type | Percentage | Classification |
---|---|---|
Domain Modelling short video presentation | 25% | Individual Coursework |
DSS Prototype | 40% | Individual Coursework |
Evaluation Report | 35% | Individual Coursework |
3 types | 100% |
Assessment Type | Learning Outcome Addressed | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |||
Domain Modelling short video presentation | ||||||||||
DSS Prototype | ||||||||||
Evaluation Report |
Learning Resources
Course Contacts
Workload Expectations
This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.
For this course, you can expect 24 hours of lectures, a 12 hour tutorial, 24 hours of reading and thinking about the content and 90 hours of work on assignments and/or test preparation.
Other Information
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.
Digital Resources
Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).
Please remember that the recording of any class on a personal device requires the permission of the instructor.
Academic Integrity
The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.
Inclusive Learning
All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.
Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website at http://disability.auckland.ac.nz
Special Circumstances
If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.
If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page: https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.
This should be done as soon as possible and no later than seven days after the affected test or exam date.
Student Feedback
At the end of every semester students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions.
Your feedback helps teachers to improve the course and its delivery for future students.
Class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.
Student Charter and Responsibilities
The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter (https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html).
Disclaimer
Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.
In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.