Digital Health Software Project (COMP3820)

Course overview

This course will help you learn the basic fundamentals of digital health technology through hands-on experience, and through exposure to the real-world use of data and informatics in the health industry. The course includes a MOOC component that provides a broad introduction to healthcare informatics. There will also be a number of guest lecturers from industry talking about the current digital health landscape. A major part of the course is composed of a group software development project building a FHIR app for use in a clinical (or clinical education) setting, with real-world clients. FHIR (Fast Healthcare Interoperability Resources) is the new standard for sharing health data that is revolutionising how clinicians and patients are interacting with digital technology and has been adopted by major global IT companies (e.g., Apple, Google, Microsoft, etc.) and digital health vendors (e.g., Cerner, EPIC, etc.). Be at the forefront of healthcare application development by learning about and developing new FHIR and SMART-on-FHIR applications.

COMP3820 introduces students to interoperability standards for exchanging electronic healthcare data via the FHIR (Fast Healthcare Interoperability Resources) platform. As a software project development course, teams of students work together to design and develop a web-based application using FHIR.

Course Changes in Response to Previous Student Feedback

Changes in 2024: to address feedback, the assessments have been streamlined and more spread out over the semester. The MOOC has also been updated.

Course requirements

Assumed background

Students are assumed to have some background in object-oriented programming and have completed a team-based course. Strong programming skills (in any language) will be an advantage. It is also advantageous to have some basic knowledge of web and/or mobile technologies and prior exposure to user interface design. It is highly recommended that students have web development skills (HTML, CSS, JavaScript), including the use of RESTful web services.

Prerequisites

You'll need to complete the following courses before enrolling in this one:

(CSSE2002 or CSSE7023 or COMP2140 or CSSE2310 or CSSE7231) and (DECO2500 or DECO7250 or BIOE6901)

Recommended prerequisites

We recommend completing the following courses before enrolling in this one:

DECO2800 or DECO7280 or DECO3800 or DECO7380

Incompatible

You can't enrol in this course if you've already completed the following:

COMP3000 in 2018 or 2019 or 2020

Course contact

Course staff

Timetable

The timetable for this course is available on the UQ Public Timetable.

Additional timetable information

The timetable for COMP3820 is 4C, which is split into 2 x 2-hour studio-like sessions per week. Students are expected to attend both sessions. Guest speakers will be scheduled according to speaker availability and as such may occur during any timetabled session. Speaker talks will only be recorded by permission of the guest speakers. We ask that you respect that they do this for confidentiality reasons and that you do not make any recordings of your own. Sessions where a guest speaker has not been scheduled will not be recorded.

This is a 2-unit course. Under University policy, a total workload of approximately 10 – 12 hours per week (including class contact time) is expected for satisfactory progress. The two sessions per week will total 4 hours. Therefore, in addition to attending classes, students should expect to spend at least approx. 6-8 hours for at-home study and work on assessment. Please note that these are guidelines only and actual time spent on the course may vary.

Aims and outcomes

To teach the basic fundamentals of digital health technology through hands-on experience, and through exposure to the real-world use of data and informatics in the health industry. The course includes a MOOC component that provides a broad introduction to healthcare informatics. There will also be a number of guest lecturers from industry talking about how digital health is used in their work. A major part of the course is composed of a group software project building a FHIR app for use in a clinical (or clinical education) setting, with real-world clients. FHIR (Fast Healthcare Interoperability Resources) is the new standard for sharing health data that is revolutionising how clinicians and patients are interacting with digital technology and has been adopted by major global IT companies (e.g. Apple, Google, Microsoft, etc.) and digital health vendors (e.g. Cerner, EPIC, etc.). Be at the forefront of healthcare application development by learning about and developing new FHIR and SMART-on-FHIR applications in collaboration with CSIRO’s Australian e-Health Research Centre, Georgia Tech University (USA) and UQ’s Faculty of Medicine and Faculty of Health and Behavioural Sciences.

Learning outcomes

After successfully completing this course you should be able to:

Assessment

Assessment summary

A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.

Assessment details

Individual Essay

Mode

Written

Category

Essay/ Critique

Weight

20% (Individual)

Due date

6/09/2024 4:00 pm

Learning outcomes

L01

Task description

Submission guidelines

You should submit a single electronic file via the Turnitin Assignment Handler in Blackboard on or before the due date/time. Please ensure that you include a completed and signed coversheet to the beginning of your work before it is submitted. Your submission must be a single word-processed document that contains your assignment, including coversheet. You should submit the word processor file (e.g. Microsoft Word (.doc/.docx), Pages (.pages), etc. formats only. PDF is acceptable but is not the preferred format).

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.

Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 7-14 days where the earlier time frame applies if no extensions.

Late submission

A penalty of 1 grade for each 24 hour period from time submission is due will apply for up to 7 days. After 7 days you will receive a mark of 0.

Team FHIR Project

• Hurdle
• Identity Verified
• Team or group-based
• In-person

Mode

Oral, Product/ Artefact/ Multimedia, Written

Category

Project

Weight

45% (Team)

Due date

In-class Team Project Presentation 24/10/2024 2:00 pm

Team Project Final Report and Codebase 5/11/2024 4:00 pm

Team Project Video 11/11/2024 4:00 pm

Task description

Hurdle requirements

Submission guidelines

Submission for each component includes:

• Team Project Presentation: In-Class (all team members must attend in-person)
• Final Report: Your team should submit a single electronic file via the Turnitin Assignment Handler in Blackboard on or before the due date/time. Please ensure that a completed and signed coversheet is attached to the beginning of the work before it is submitted. The submission must be a single word-processed document that contains your team’s assignment, including coversheet. A word processor file should be submitted (e.g. Microsoft Word (.doc/.docx), Pages (.pages), etc. formats only. PDF is acceptable but is not the preferred format). Only one member of the team is required to submit on behalf of everyone.
• Final Codebase: GitHub
• Team Project Video: Blackboard

Deferral or extension

You cannot defer or apply for an extension for this assessment.

This course uses team-based assessment where the team assignment is developed and iterated on with students needing to act on regular formative feedback over the semester. This is to support the development of genuine reflective practice in the assessment and enable students to learn skills required in standard industry design practice. If teams encounter extraordinary difficulties in meeting a deadline, they should contact the course coordinator in advance of the due date. In all cases, teams should submit a version of the assignment by the deadline. All team submissions received after the deadline will be subject to the late penalty specified. 

Late submission

Late submission penalty for each component includes:

• Team Project Presentation: 100% late penalty (no grace period)
• Final Report & Codebase and Team Project Video: A penalty of 1 grade for each 24 hour period from time submission is due will apply for up to 7 days. After 7 days you will receive a mark of 0.

Online Activities

• Hurdle
• Online

Mode

Activity/ Performance

Category

Tutorial/ Problem Set

Weight

Pass/Fail (Individual)

Due date

25/10/2024 4:00 pm

Learning outcomes

L01

Task description

Hurdle requirements

Submission guidelines

Once you have completed (and passed) all activities in the MOOC, you must upload a screenshot of your progress page to Blackboard on or before the due date/time. You must include the “Grade Summary” information and your username in your screenshot. The task will only be deemed as completed once this screenshot has been uploaded to Blackboard. Failure to upload the screenshot to Blackboard will result in the task being deemed a “Fail”.

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.

Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 7-14 days where the earlier time frame applies if no extensions.

Late submission

You will receive a mark of 0 if this assessment is submitted late.

Individual Portfolio

• Hurdle

Mode

Written

Category

Portfolio

Weight

35% (Individual)

Due date

8/11/2024 4:00 pm

Learning outcomes

L01, L06

Task description

Hurdle requirements

Submission guidelines

You should submit a single electronic file via the Turnitin Assignment Handler in Blackboard on or before the due date/time. Please ensure that you include a completed and signed coversheet to the beginning of your work before it is submitted. Your submission must be a single word-processed document that contains your assignment, including coversheet. You should submit the word processor file (e.g. Microsoft Word (.doc/.docx), Pages (.pages), etc. formats only. PDF is acceptable but is not the preferred format).

Deferral or extension

You may be able to apply for an extension.

The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.

Marked assignments with feedback and/or detailed solutions with feedback will be released to students within 7-14 days where the earlier time frame applies if no extensions.

Late submission

A penalty of 1 grade for each 24 hour period from time submission is due will apply for up to 7 days. After 7 days you will receive a mark of 0.

Course grading

Full criteria for each grade is available in the Assessment Procedure.

Additional course grading information

Identity-Verified Assessment with Hurdles (IVAH) Requirement

In order to meet IVAH requirements and pass the course, students must gain at least a Pass (+/-) for the Individual Portfolio assessment item AND must be able to demonstrate their active participation and contribution to the team’s output. Failure to meet this requirement will result in the final grade being capped at a 3, regardless of performance in other assessment items.

Teaching staff actively monitor individual participation throughout the semester during weekly contacts and through scheduled in-person activities. Based on this monitoring, teaching staff are aware of the degree to which students are actively participating and contributing to the team project. If students have not actively participated throughout, further evidence may be requested to assess the level of active participation. Students who are unable to demonstrate their active participation and contribution to the team’s output risk failing this course with an overall grade capped at 3 by not meeting this requirement. The Course Coordinator reserves the right to moderate team member grades in the event of varied contributions to team effort.

In order to pass the course and achieve a higher grade, students must gain at least a Pass (+/-) for the Individual Portfolio AND must be able to demonstrate their active participation and contribution to the team’s output AND pass the Online Activities assessment item. Failure to meet this requirement will result in the final grade being capped at a 4, regardless of performance in other assessment items.

The final grade for the course will be initially constructed from the individual grades according to assessment weightings. At the discretion of the course coordinator, final grades may be moderated.

EFFORT vs ACHIEVEMENT and Assessment

Achievement and proficiency in the skills relevant to a particular course are rewarded, which is in alignment with criteria-based assessment as used at the University of Queensland.

Effort on the part of students is required to attain the knowledge, experience, and skills necessary to be able to demonstrate achievement and proficiency in the learning objectives of particular courses.

Students should not, however, assume that effort and achievement are equal. Effort and achievement are not equivalent.

Overall, assessment does not reward effort, it measures and rewards achievement against the course criteria.

Blackboard/TurnItIn Submissions:

When submitting your assessment items through Blackboard/TurnItIn, please allow plenty of time for the submission to be uploaded as many students will be attempting the same task at the same time. Be sure to allow enough time for uploading of assessment files. It is YOUR responsibility to check that submissions have been uploaded correctly and ensure that you have received a digital receipt.

Supplementary assessment

Supplementary assessment is not available for some items in this course.

If you receive an overall grade of 3 in this course, then you will only be eligible for supplementary assessment if you have at least a passing grade (+/-) on the Team FHIR Project. Due to the design and development aspects of this team-based project over time, supplementary assessment will not be available to students who receive a failing grade on the Team FHIR Project (Approved by EAIT AD-A).

Additional assessment information

1.Use of Generative AI Tools and Open-Source Code for Assessment in this Course

This course is aimed at students understanding and being able to effectively develop software applications that meet the needs of people in different and novel contexts. This application to different and novel contexts requires a level of creativity and critical thinking as these skills play a pivotal role in supporting effective practice.

Assessment in this course have been designed to be challenging, authentic and complex. Whilst students may use Generative AI technologies and/or open-source code in some assessments, successful completion of assessment in this course will require students to critically engage in specific contexts and tasks for which artificial intelligence and open-source code will provide only limited support and guidance. Generative AI Tools, techniques and open-source code may be utilized as supportive elements. However, there are boundaries to your usage of Generative AI Tools and/or open-source code. Going beyond those boundaries amounts to an academic integrity issue. Further, you will need to acknowledge your use of Generative AI tools and/or open-source code in each assessment where you are permitted to use those tools (see below for more information on what is required for these acknowledgements). Failure to appropriately and completely acknowledge your use of Generative AI tools in an assessment also amounts

It is essential to recognise that the primary objectives of this course and the assessment that you will complete is for you to demonstrate your achievement of the learning objectives outlined, as well as UQ’s Graduate Attributes, which are relevant to this course.

Assessment and use of Generative AI Tools and/or Open-Source Code

Use of Generative AI Tools and/or open-source code is permitted when completing the following assessment items within the boundaries outlined below:

• Online Activities
• Individual Essay
• Team FHIR Project
• Individual Portfolio

How you may use generative AI and/or open-source code in assessment

Generative AI technologies may be used in the completion of assignments within certain boundaries. Please note that this list is an example and is not exhaustive. Each student must take responsibility in understanding if the tools they have used fall under these broad categories (or similar) and provide appropriate acknowledgement:

• Graphics for visitor consumption: You may utilise Generative AI to produce graphic images for decorative purposes only.
• Support, Not Replace: Generative AI can be used to facilitate or enhance your communication, but it should not be used to replace your own communication practices in written communication.
• Adherence to Learning Objectives: Your work must clearly demonstrate your understanding of core concepts as stated in the learning objectives. Generative AI Tools can be used to facilitate or enhance your understanding but must not replace or diminish your understanding and ability to critically apply these concepts.
• Transparency, including open-source code/libraries and/or AI code generation tools, such as Copilot and others: You must clearly indicate any part of your work where Generative AI Tools and/or open-source code/libraries have been utilised and provide a brief rationale explaining why it was used and how it supported your completion of the assessment.
• Compliance with Ethical Guidelines: Ensure that the use of Generative AI Tools aligns with the course's ethical, social, cultural, and cybersecurity considerations.

Remember, the goal here is not to have the AI do your job, but to use AI as a tool to expand your knowledge, understanding and skills.

Where you cannot use generative AI and/or open-source code in assessment

Generative AI has the potential to automate certain aspects of the design and development of technologies to support for example usability and accessibility. However, in the context of this course, there are specific limitations to ensure that students' own creative thought and understanding are not compromised.

Students must NOT use Generative AI Tools and/or open-source code to:

1. Replace Creative Thought and Application: Generative AI Tools and/or open-source code must not be utilised to generate content that supplants your own creative process.
2. Automate Understanding: You must not rely on Generative AI Tools and/or open-source code to create or produce any content that requires you to understand and apply your knowledge.
3. Violate Ethical Guidelines: You must not employ Generative AI Tools and/or open-source code in a way that contradicts the ethical, social, cultural, colonial, and cybersecurity guidelines of the course.

These prohibitions are put in place to preserve the integrity of the learning experience and ensure that your work authentically reflects your understanding of and ability to critically engage with the essential components of the course.

Acknowledging your use of Generative AI

If you are using any form of Generative AI and/or open-source code for any assessment item, you must be transparent about that usage. Not being transparent is an academic integrity issue.

For example, if using any of following types of sources, full disclosure of how it was used, and proper acknowledgement/disclosure must be made. You must make it absolutely clear what is your teams and/or your own work. Please note that this list is an example and is not exhaustive. Each student must take responsibility in understanding if the tools they have used fall under these broad categories (or similar) and provide appropriate acknowledgement:

• If you use a Generative AI Tool to find out a fact, then you need to reference it.

For example, if you ask ChatGPT 'what causes climate change' or 'what are the ingredients of milk chocolate' and you use the answer it provides, then you reference that fact in this format:

In your assignment

Milk chocolate is made of cocoa butter, cocoa powder and sugar (OpenAI, 2024).

In your reference list or bibliography:

OpenAI. (2024). ChatGPT (Dec 20 version) [Large language model]. [link to website]

• If you use a Generative AI Tool to do other things, then you need to acknowledge it.

When you acknowledge use of, for example, ChatGPT you need to explain how you used ChatGPT and the answers it gave you. For example, if you used it to help you structure an essay, you would put an acknowledgement/disclose statement like this at the start of your essay:

I acknowledge and disclose the use of ChatGPT ([link to website]) to provide me with ideas on how to structure this essay. The prompts used and the response from ChatGPT are included in Appendix xx. The output from these prompts was a structure in the form of headings and details of what should be included under each of those headings. This suggested structure was used as a basis for writing this essay and none of the output was copied and pasted directly into this essay with the exception of some headings.

Note, that you need to include all prompts and answers in an appendix. The suggested ways to do this are to include a link to your chat or to print the webpage with your chat as a PDF and include it in the appendix. You should add in the date/s of that chat.

• Grammarly and similar tools

Grammarly is also Generative AI because it provides things like suggestions on making sentences simpler. So, you also need to acknowledge the use of Grammarly as well as any similar tools. This is wording that you might use:

I acknowledge the use of Grammarly (link) to ensure grammatical correctness and to improve the clarity of parts of this assignment. I have installed the Grammarly plug-in for MS Word so the suggestions that Grammarly provides are not the result of any prompt but rather automatic suggestions.

• Translation software

Some translation software also uses Generative AI. Even if the translation software you use does not incorporate Generative AI, you need to acknowledge your use of that software. Important: you cannot solely rely on translation software, as it will vary in quality and will often misinterpret the context of words you have used so you must carefully check the produced version.

This is wording that you might use:

I acknowledge the use of SuperEasyTranslator (link) to support translating word from French into English. I wrote some sections of this report in French and then pasted this content into SuperEasyTranslator to produce a version in English. I copied and pasted this produced version into this assignment making small adjustments as I felt necessary.

• Open-source code

You must give proper credit to the creators of the open-source code that you use. You must follow these guidelines for using open-source code:

• Review the License: Understand the license under which the open-source code is distributed. Common licenses include MIT, Apache, GPL, etc. Each license has specific requirements for how you should acknowledge and use the code.
• Include Attribution: In your project's documentation (such as a README file and/or in your project report), include a section that lists the open-source projects you have used. Mention the name of the project, the author, and a link to the original source. This is wording that you might use:

This project uses the following open-source projects: - [Open-source Project Name](https://link.to/project) by Author Name

• Retain License Notices: If the license requires it, keep the original license notice and any other required notices in your project's code files and/or in your project report. For instance, you might include a copy of the license in a LICENSE file in your project.
• Include Copyright Notices: Some licenses require that you include copyright notices in your project files that use the open-source code. This often involves copying a specific block of text from the original source into your own files.
• Provide Modifications Details: If you have modified the open-source code, make a note of the modifications. This could be done in the comments within the code or in your project report.
• Follow Any Additional Requirements: Make sure to adhere to any other specific requirements outlined in the open-source license, such as providing a copy of the license or noting any changes made to the original code.

Acknowledgements/disclosure of the use of Generative AI Tools and/or open-source code should be placed towards the start of each submission.

2.Qualitative Grading System

A qualitative grading system is used in this course, whereby you will receive qualitative feedback on your work. Grades for assessment in this course will identify your work as being of a particular standard that fall within a meaningful UQ grade band:

• High Distinction
• Distinction
• Credit
• Pass
• Fail

Within each category, there may be a further qualifier of + or - to indicate that the work is at the upper or lower bounds of the category. For example, if work is assessed at the level of Distinction then it has been assessed overall to meet the quality described by the Distinction grade descriptor. Distinction- means that the work is assessed at Distinction level, but it is at the lower bound having just satisfied the requirements for Distinction. Distinction+ means that the work is assessed at Distinction, but while it is at the upper bound of this grade, it is missing key aspects that would lead it to be assessed at the next grade up.

Qualitative grading is the only method used in this course to grade work but in order to calculate a grade, each qualitative descriptor is coded to a numerical digit from 0 – 15 (see below). There are no % used in any calculation and the coding system does not correspond to a % value.

Qualitative Grade / Qualitative Grade Coding Digit maps to (>) UQ Grade Band

• High Distinction+ (15) > High Distinction (7)
• High Distinction (14) > High Distinction (7)
• High Distinction- (13) > High Distinction (7)
• Distinction+ (12) > Distinction (6)
• Distinction (11) > Distinction (6)
• Distinction- (10) > Distinction (6)
• Credit+ (9) > Credit (5)
• Credit (8) > Credit (5)
• Credit- (7) > Credit (5)
• Pass+ (6) > Pass (4)
• Pass (5) > Pass (4)
• Pass- (4) > Pass (4)
• Fail+ (3) > Fail (3)
• Fail (2) > Fail (2)
• Fail- (1) >Fail (1)
• X - no assessable work received (0) > X

The final grade for an assignment is calculated by assigning the qualitative grade its corresponding digit from the coding system and the weighting in a formula to arrive at a final grade. The final grade for the course uses the same principle (see below for examples).

Example 1

• Online Activities: PASS
• Individual Essay (20%): Fail+ (3)
• Team FHIR Project (45%): Pass- (4)
• Individual Portfolio (35%): Pass (5)
• Initial Grade Calculation: (3*0.2) + (4*0.45) + (5*0.35) = 4.15
• Final UQ Grade: Pass (4)

Example 2

• Online Activities: FAIL
• Individual Essay (20%): Credit- (7)
• Team FHIR Project (45%): Credit- (7)
• Individual Portfolio (35%): Credit+ (9)
• Initial Grade Calculation: (7*0.2) + (7*0.45) + (9*0.35) = 7.7
• Final UQ Grade: Pass (4)* (*Final grade capped at 4 due to failing Online Activities)

Example 3

• Online Activities: PASS
• Individual Essay (20%): Distinction+ (12)
• Team FHIR Project (45%): Distinction (11)
• Individual Portfolio (35%): Fail+ (3)
• Initial Grade Calculation: (12*0.2) + (11*0.45) + (3*0.35) = 8.4
• Final UQ Grade: Pass (3)** (**Final grade capped at 3 due to failing Individual Portfolio)

Teamwork/Team Assessment

Teams working in industry are expected to plan to meet their deadlines and allow for contingencies and other issues as they arise through good team management and appropriate redundancy in workload allocation, which also includes covering for colleagues when they are unable to fully contribute due to short-term illness, etc. The same is expected of teams in this course and this will be the default expectation for team assessment in this course. If more extreme circumstances occur, they should in the first instance contact teaching staff before upcoming deadlines to discuss the situation.

When finalising grades for the teamwork items (i.e., Team FHIR Project), each team member by default will receive the same grade. Teaching staff actively monitor individual participation throughout the semester during weekly contacts. Based on this monitoring, teaching staff are aware of the degree to which students are actively participating and contributing to the team projects. If students have not actively participated throughout, further evidence may be requested to assess the level of active participation. Students who are unable to demonstrate their active participation and contribution to the team’s output risk failing this course with an overall grade capped at 3 by not meeting this requirement. An online MOOC will also be provided that provides resources and training for working in teams. Teams are expected to develop their own effective conflict resolution strategies to enable them to deal with situations where workload is not shared equally. All teams should produce various articles to document their teams progress, including:

1. Team Charter – This charter will set up the “ground rules” for your team: how you communicate, when you meet, and how you will resolve issues. It really is the guide to how your team is going to work. If problems arise, you should let your demonstrator know BUT make sure that you are following the processes outlined in the charter.
2. Weekly progress logs – Team progress should be evidenced at weekly checkpoints via progress logs. At a minimum, your progress logs should evidence:

• Items worked on from the previous week, including the team member(s) allocated to each task and their current state (e.g., not started, in progress, completed, etc.)
• Issues that arose and actions required (if any)
• Deliverables/plan to be completed by the next week, including team member(s) allocated to each task/deliverable

Team conflicts should be resolved through discussion and negotiation. If teams encounter a situation whereby there may be issues, there is a process that students should follow if their team experiences conflict:

1. In the first instance, teams should independently take appropriate steps to resolve internal issues. It is highly recommended that all team members complete the team working MOOC and if issues arise should be able to evidence how they have applied their knowledge from this MOOC into their group project (if required).
2. All members of the team need to be given the opportunity to work on the project. Issues should be discussed in a non-confrontational manner. It is not appropriate to re-do another team member’s contribution because of a subjective opinion. The team needs to discuss and work as a group to ensure everyone is happy with the group’s output.
3. Tasks/contributions should be accurately documented in meeting logs
4. If your team cannot resolve the issue yourselves, you must inform the teaching team as soon as possible so that they can be made aware of the situation and can assist you in addressing any issues within the team.
5. Formative team performance reviews will be conducted during the project process to allow teams and teaching staff the opportunity to identify areas of conflict, concern, and opportunity. These performance reviews will not impact on individual grades but may be used in cases of severe team disfunction to inform course coordinator action.

If the above criteria have been met and there are still issues within the team, the teaching team will do their best to assist in helping to resolve the conflict. The Course Coordinator reserves the right to moderate team member grades in the event of varied contributions to team effort.

Having Troubles?

If you are having difficulties with any aspect of the course material, you should seek help. Speak to the course teaching staff.

If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help, for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things).

Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.

Learning resources

You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.

Library resources

Find the required and recommended resources for this course on the UQ Library website.

Additional learning resources information

An edX Edge MOOC course titled “Digital Health on FHIR” is provided with this course and can be found in Blackboard. Students are expected to complete this MOOC in their own time over the course of the semester.

Learning activities

The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.

Policies and procedures

University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:

• Student Code of Conduct Policy
• Student Integrity and Misconduct Policy and Procedure
• Assessment Procedure
• Examinations Procedure
• Reasonable Adjustments - Students Policy and Procedure

Learn more about UQ policies on my.UQ and the Policy and Procedure Library.

School guidelines

Your school has additional guidelines you'll need to follow for this course:

• EECS School Guidelines for Students