Outline

Overview

Healthcare systems intimately coupled to ICT have been at the forefront of many of the medical advances in modern society in the past decade. As is already the case in many other service-driven sectors, it is widely recognised that a key approach to solve some of the healthcare challenges is to harness and further ICT innovations. This unit is designed to help fill a massive technology talent gap where one of the biggest IT challenges in history is in the technology transformation of healthcare. The unit will consist of weekly lectures, a set of group discussions (tutorials) and practical lab sessions. The contents will offer students the opportunity to develop IT knowledge and skills related to all aspects of Enterprise Healthcare Information Systems. Key Topics covered include: Health Information System e. g. , Picture Archiving and Communication Systems (PACS) and Radiology IS; Electronic Health Records / Personal Health Records; Health data management; Healthcare Transactions; Health Statistics and Research; Decision Support Systems including Image-based systems; Cost Assessments and Ethics / Privacy; TeleHealth / eHealth; Cases studies with Australian Hospitals. Guest lecturers from the healthcare industry will be invited. The core of student's assessments will be based on individual research reports (topics related to the current industry IT needs), software / practical assignment and quizzes.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	Enrolment in a thesis unit. INFO4001 or INFO4911 or INFO4991 or INFO4992 or AMME4111 or BMET4111 or CHNG4811 or CIVL4022 or ELEC4712 or COMP4103 or SOFT4103 or DATA4103 or ISYS4103
Prohibitions ?	INFO5306
Assumed knowledge ?	ISYS2120
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Jinman Kim, jinman.kim@sydney.edu.au

Type	Description	Weight	Due	Length
Supervised exam ?	Final exam Type C: Short Q/As Follow School’s default minimum pass requirement	55%	Formal exam period	2 hours
Outcomes assessed:
Tutorial quiz	Quiz Mid-term quiz	10%	Week 07 Due date: 13 Sep 2023 at 21:00 Closing date: 13 Sep 2023	n/a
Outcomes assessed:
Assignment	Assignment 1: research report and presentation Written Assignment (week 9) and Presentation (week 11-13)	25%	Week 11 Due date: 20 Oct 2023 at 23:59 Closing date: 20 Oct 2023	n/a
Outcomes assessed:
Assignment	Assignment 2: Practical Assignment Practical Assignment	10%	Week 13 Due date: 03 Nov 2023 at 23:59 Closing date: 03 Nov 2023	n/a
Outcomes assessed:
= hurdle task ?

Assessment summary

A statement regarding late penalties will display on the unit outline above any qualifying information you choose to include. Click the ? icon to see the statement.
Late penalties should be modified in accordance with the Assessment Procedures 2011.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to enterprise health information systems, digital health and healthcare fundamentals	Lecture (2 hr)
Week 01	The rise of Technology in healthcare IS	Tutorial (1 hr)
Week 02	Healthcare Information System Fundamentals; Data Standards; Electronic Health Records / Personal Health Records; Case study: Future outlook for healthcare IS	Lecture (2 hr)
Week 02	Personal Health Record Technologies	Tutorial (1 hr)
Week 03	Picture archiving and communication system (PACS) and cloud PACS; Case Study: PACS Experience	Lecture (2 hr)
Week 03	Understanding Cloud PACS	Tutorial (1 hr)
Week 04	Personalised Healthcare - Wellbeing (including Wearable Tech and Smart Medical Homes)	Lecture (2 hr)
Week 04	Evolution of Wearable technologies	Tutorial (1 hr)
Week 05	Telehealth Technologies and Mobile Health (mHealth) Case study: Nepean Telehealth and Technology Centre	Lecture (2 hr)
Week 05	Telehealth adoption during the pandemic	Tutorial (1 hr)
Week 06	Ethics, regulations, governance and data security	Lecture (1 hr)
	Developing an Ethics application	Tutorial (1 hr)
	Quiz	Computer laboratory (1 hr)
Week 07	Managing Healthcare IS (Strategic planning; Tech management; Acquisition)	Lecture (2 hr)
Week 07	Accessing and Acquiring value in Healthcare IS	Tutorial (1 hr)
Week 08	Healthcare Data Analysis Healthcare Data Statistics	Lecture (2 hr)
Week 08	Introduction to the Lab series; Lab1 - Enterprise health data analysis I	Computer laboratory (1 hr)
Week 09	Clinical Decision Support Systems Machine Learning in Healthcare Case study: AI DSS	Lecture (2 hr)
Week 09	Lab 2– Enterprise health data analysis II	Computer laboratory (1 hr)
Week 10	Healthcare Technology Evaluation	Lecture (2 hr)
Week 10	Lab 3– Enterprise health data analysis III	Computer laboratory (1 hr)
Week 11	Healthcare Information and Data Visualisation; Case study: Advanced image visualisation	Lecture (2 hr)
Week 11	Assignment 1B Presentations	Tutorial (1 hr)
Week 12	Healthcare Data Linkage and Integration; Case study: Data linkage	Lecture (2 hr)
Week 12	Assignment 1B Presentations	Tutorial (1 hr)
Week 13	UoS Review	Lecture (2 hr)
Week 13	Assignment 1B Presentations	Tutorial (1 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. understand the practices and principles of current health information systems (HIS) used in the healthcare sector
LO2. design and analyse HIS in the hospital and also in research studies
LO3. demonstrate awareness of evolving HIS technologies including Telehealth and Cloud PACS
LO4. ability to use library databases (hardcopy and electronic) of books, journals, conference proceedings, research reports, etc., related to health information systems
LO5. present ideas and make arguments effectively
LO6. obtain knowledge associated with the use of technology in healthcare that impacts the society.
LO7. Understand how to use evidence based medicine to evaluate clinical evidences from literature

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

More labs have been added to ensure the highest quality for off-line teaching

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

INFO4406: Enterprise Healthcare Information Systems

Semester 2, 2023 [Normal evening] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect