Outline

Overview

Digital health interventions and data science are increasingly used to address health challenges through a myriad of solutions from apps, augmented interfaces, clinician-facing decision-support systems, and new models of care such as telehealth. Candidates will work on a substantial research project in an area of specific interest applicable to digital health, health, or clinical data science. The project may include the analysis of an existing health related data set, a systematic review, a case study, health technology evaluation, clinical re-design, survey, or other projects deemed acceptable to the project partner and supervisor. Listed projects may be available for students to select if they fulfill the skills, pre-requisites, and interview requirements. Candidates with a current workplace-based project may apply for project partner approval if learning outcomes criteria are met. The candidate will enter a group or individual learning contract. The development of suitable methodologies and a substantive literature review will be the primary focus for Project A. This supports the focus for Project B; a scholarly work which may be a paper for publication or industry report, culminating in a presentation or seminar suitable for academic and/or professional audiences. Implementation science and modern project management techniques should be used where appropriate in projects.

Unit details and rules

Academic unit	Department of Medical Sciences
Credit points	6
Prerequisites ?	24 credit points of (HTIN5006 or HTIN5005 or HSBH5003 or BIDH5003 or BIDH5000 or COMP9001 or INFO5306 or HTIN5003 or COMP9103 or COMP5046 or COMP5048 or COMP5318 or COMP5424 or STAT5002 or STAT5003 or BMET9925 or BMET5933 or BMET5992 or IDEA9106 or CEPI5100 or BETH5204 or HPOL5014 or HPOL5012 or COMP5427)
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Assumed library information systems research skills and basic knowledge of health, health care and associated ethics and governance systems are required. Students must complete a pre-capstone knowledge screening quiz or interview which will identify recommended modules for their capstone. Please check the Canvas site for this unit for any information on further recommended resources, mandatory sessions and modules
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Adam Dunn, adam.dunn@sydney.edu.au
Lecturer(s)	Jinman Kim, jinman.kim@sydney.edu.au
	Audrey P. Wang (Digital Health), audrey.wang1@sydney.edu.au
	Adam Dunn, adam.dunn@sydney.edu.au

Assessment

The census date for this unit availability is 2 September 2024

Details
Criteria

Type	Description	Weight	Due	Length
Assignment	Project Proposal Project proposal	30%	Week 04 Due date: 19 Aug 2024 at 23:59	up to 2000 words
Outcomes assessed:
Assignment	Project Progress Report 1 Project Progress Report 1	20%	Week 06 Due date: 02 Sep 2024 at 23:59	up to 1000 words
Outcomes assessed:
Assignment	Interim Report Literature Review component of Dissertation	50%	Week 12 Due date: 21 Oct 2024 at 23:59	up to 3000 words
Outcomes assessed:
= hurdle task ?

Assessment summary

Detailed information for each assessment can be found on the corresponding Canvas site.

Assessment criteria

Assessment Grades

Result Code	Result Name	Mark Range	Description
HD	High distinction	85 - 100	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard
DI	Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard
CR	Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard
PS	Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard
FA	Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.
AF	Absent Fail	0 - 49	When you haven’t completed all assessment tasks or met the attendance requirements.

NB: A results incomplete will be assigned to BIDH5001 Digital Health and Data Science Project A, so students will need to complete BIDH5002 Digital Health and Data Science Project B before receiving results applied to both units.

Result Code	Result Name	Mark Range	Description
RI	Results incomplete	No mark	Temporarily used in cases where a result will remain incomplete for a longer period than the IC (Incomplete) result. You will be able to re-enrol with an RI result.

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

The Assessment Procedures 2011 provide that any written work submitted after 11:59pm on the due date will be penalised by 5% of the maximum awardable mark for each calendar day after the due date. If the assessment is submitted more than ten calendar days late, a mark of zero will be awarded. This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below: Simple extensions of up to 48 hours may be requested by emailing the unit coordinator Dr Audrey P Wang prior to the assignment due date. Should your request be approved you will receive an email with your new due date and time. Requests for longer extensions must be submitted through the Special Consideration system.

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.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week -01	Detailed information can be found on the corresponding Canvas site. This includes pre-screening knowledge quiz or interview, the Online Learning Environment: Literature Review Module, and modules that may be required by the project partner or supervisor to support the research project.	Independent study (12 hr)
Week 01	Introduction Workshop, which includes sessions discussing the research projects, interacting with supervisors and project partners, and detailed description of project proposal and progress report assessments.	Workshop (3 hr)
Week 04	Literature Review Workshop, which includes a lecture on review methods, and an interactive tutorial session guiding students on concept mapping, search strategies, and quality/reporting checklists.	Lecture and tutorial (3 hr)
Week 07	Check-in session on research projects and	Workshop (2 hr)
Weekly	Project based activities (weekly with supervisor)	Independent study (12 hr)

Attendance and class requirements

Please refer to Canvas for detailed information about attendance and class requirements including timetabled workshops and presentations.

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. Initiate, formulate and plan a research/ development project and objectives that addresses individual, organisational and systemic factors in complex health challenges
LO2. Demonstrate the application of critical inquiry and research skills by synthesising the relevant literature and results into a publication or industry report, and presentation
LO3. Critically reflect and evaluate the research outcomes, iterative and collaborative processes of the interdisciplinary project.
LO4. Develop, substantiate and articulate professional responsibilities by applying modern project management and implementation science frameworks.

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.

Updated the delivery to split the teaching across the semester more effectively, updated the dates for the assessments.

Additional information

Note that "results incomplete (or unit continuing)" will be assigned to BIDH5001 Digital Health and Data Science Project A, so students will need to complete BIDH5002 Digital Health and Data Science Project B before receiving results applied to both units.

Additional costs

Students will require a laptop. If they wish to conduct a healthcare provider-associated capstone project they may additionally require other checks and tests including criminal record check, vaccinations, personal protective equipment and other site-specific requirements. Costs will vary.

Site visit guidelines

Students who are accepted onto a healthcare related capstone project (including those with a placement component) must adhere to professional standards that are in keeping with Faculty of Medicine and Health - Professionalism Requirements Provisions 2022 policy and site-specific policies.

Work, health and safety

Detailed information is available in your relevant Canvas site,and is project and site dependent.

Students of MDHDS may have the opportunity to conduct a Capstone project closely tied to a healthcare provider or making use of health data if they fulfil the criteria to be accepted onto the project. Some of these projects have specific requirements, policies and WHS checks to fulfil including vaccinations, criminal record checks, prohibited employment declaration, health records and privacy information declarations, and Working with Children checks. Costs may be incurred by the student dependent on the project.

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

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Site visit guidelines

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

BIDH5001: Digital Health and Data Science Project A

Semester 2, 2024 [Supervision] - Westmead, 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

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Site visit guidelines

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect