PUN108 Clinical Informatics for Intelligent Healthcare
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | PUN108 |
|---|---|
| Assumed Knowledge: | Principles of an evidence-based approach to practice is assumed knowledge |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $2,174 |
| Pre-2021 CSP student contribution | $1,703 The pre-2021 commonwealth supported place (CSP) contribution amount only applies to students enrolled in a course prior to 2021. To learn more, visit our Understanding your fees page. |
| Domestic tuition unit fee | $3,720 |
| International unit fee | $5,472 |
Unit Outline: Semester 2 2026, Kelvin Grove, Internal
| Unit code: | PUN108 |
|---|---|
| Credit points: | 12 |
| Assumed Knowledge: | Nil |
| Coordinator: | Amina Tariq | a.tariq@qut.edu.au |
Overview
There is an increasing demand across the healthcare service spectrum for professionals with the ability to critically analyse data and make evidence-based decisions that are suitable for the context of their healthcare organisation(s). This unit supports career readiness for emerging roles in clinical informatics, digital health, data governance, and AI-enabled healthcare service improvement. It responds to the growing demand for professionals who can evaluate, justify and communicate technology and AI-enabled solutions in contemporary health systems.
In this unit, you will discover the clinical informatics principles, tools and technologies which can be used in to support a wide range of decisions ranging from day-to-day health service delivery (including clinical care) to the long-term strategic developments. This unit addresses the core components of major national and international certification programs such as the Certified Health Information Australasia (CHIA).
Learning Outcomes
On successful completion of this unit you will be able to:
- Apply data literacy concepts to design a contextually appropriate data management approach (es) for driving quality improvement in health care services.
- Critically analyse and contextualise informatics principles, data governance insights and ethical considerations specific to healthcare settings in a culturally safe manner.
- Evaluate the effectiveness and utility of complex technologies, e.g. clinical dashboards and AI applications, to improve decision making in contemporary healthcare settings.
- Communicate evidence-informed recommendations on AI-enabled healthcare solutions that adhere to ethical and professional standards.
- Design effective collaboration processes to address data management challenges in digital and AI-enabled healthcare environments, with consideration of inclusive practice.
Content
This unit consists of the following major areas of study:
- Core concepts: data, information, and knowledge and life cycle of data within the context of health systems
- Sources of health information, including the various data repositories held in hospitals, specialized health units and government agencies.
- Examining relationship between various types of health information, associated governance principles and ethical considerations
- The importance of working with a diverse range of interdisciplinary and inter-professional stakeholders to inform quality improvement initiatives and utilising culturally safe communication.
- The role of data in quality improvement: why is managing data an essential part of quality improvement and what are the basic techniques for using data to support quality improvement efforts.
- Artificial Intelligence (AI) for clinical intelligence in Healthcare: Different AI applications and types of technologies including generative AI applications that are used to deliver clinical intelligence in real world health settings
- Using data for evidence-based decision making: Various ways of data visualization and how they are used to communicate with diverse stakeholders in healthcare.
- Leading data analytics and AI projects: explore the required technological and clinical competencies to deliver successful data analytic projects.
Learning Approaches
In this unit, you will learn by engaging in the following:
- lectures (synchronous and self-paced)
- tutorials
- online readings and learning materials
The design of learning activities are drawn from situated learning theory, which will provide students opportunities to engage in authentic learning and real-world assessment tasks. The unit will be delivered via blended learning; which allows students to experience the majority of unit material and lectures online, combined with synchronous lectures and tutorials scheduled throughout the semester. The lectures and tutorials will provide an opportunity to consolidate learning via practical case-driven discussions and other learning activities. Students will engage in structured peer review and collaborative critique activities to strengthen their ability to communicate clinical informatics insights and refine evidence-based recommendations.
The Canvas site provides embedded support for learning through the provision of writing resources and practice quizzes. Face to face and online learning is conducted in such a way that there is peer and academic feedback. Writing resources include links and videos about referencing, finding articles, paraphrasing, and academic integrity. The online quizzes are designed as a self-test to help students study throughout semester and they do not count toward final grade.
Feedback on Learning and Assessment
Feedback on the activities conducted in tutorials will form the basis of the formative assessment. In addition, written and oral feedback associated with the marking of assessments will constitute formative assessment for the unit. Students will engage in structured peer review and collaborative critique activities to strengthen their ability to communicate clinical informatics insights and refine evidence-based recommendations
Assessment
Overview
There are two assessments in this unit.
1. Applied Informatics Project: You will be required to develop an informatics project plan. This will draw on approaches to planning data and communication management for implementing informatics driven quality improvement initiatives in healthcare settings.
2. AI Suitability Report: You will be required to assess whether AI is suitable for a real healthcare problem by creating and applying your own contextualized evidence-based framework that considers clinical informatics principles, stakeholder needs, and professional standards.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Applied Clinical Informatics Project
You will take on the role of the clinical informatics manager (s) to develop the data management and communications plan for a quality improvement initiative in a real-world healthcare context. The plan will address a diversity of perspectives that will include the healthcare organisation, clinical stakeholders and patients. You will also need to select an appropriate audience and develop a culturally safe communications plan. You will have the opportunity to receive feedback and discuss any questions in relation to the assessments during the tutorial sessions scheduled throughout the semester.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
Assessment: AI Suitability Report
This assessment is designed to help you evaluate whether artificial intelligence is suitable for addressing a real healthcare problem. Part 1: You will first deliver a three-minute pitch that outlines your chosen problem, a potential AI solution, and the key questions in your suitability framework. You will participate in a structured peer feedback activity during tutorials following the pitch presentation. You are expected to reflect on peer feedback and incorporate relevant improvements into the final written AI suitability report. Part 2: You will then complete a written report where you develop and apply your AI suitability framework, analyse stakeholder and ethical considerations, and provide evidence-based recommendations. Together, these two parts assess your ability to integrate clinical informatics knowledge with practical decision-making about AI in healthcare.
Part 1 (3 minute pitch) assignment is not eligible for the 48-hour late submission period and assignment extensions. Part 2 (Report ) is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Risk Assessment Statement
There are no out of the ordinary risks in this unit apart from those associated with substantial computer-based work.
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
ACHSM Master Health Service Management Competency Framework 2022
A.a): Action - Communication
Relates to: Applied Clinical Informatics Project
Relates to: Applied Clinical Informatics Project, AI Suitability Report
Relates to: Applied Clinical Informatics Project
A.b): Action - Relationship Management
Relates to: Applied Clinical Informatics Project
Relates to: Applied Clinical Informatics Project
A.g): Action - Digital Management
Relates to: Applied Clinical Informatics Project, AI Suitability Report
Relates to: AI Suitability Report
Relates to: Applied Clinical Informatics Project, AI Suitability Report
Relates to: Applied Clinical Informatics Project, AI Suitability Report
Relates to: Applied Clinical Informatics Project, AI Suitability Report
Relates to: Applied Clinical Informatics Project, AI Suitability Report
E.b): Enabling - Impact and Influence
Relates to: Applied Clinical Informatics Project
Relates to: Applied Clinical Informatics Project
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.PU70 Graduate Certificate in Digital Health Leadership and Management
- Apply a holistic understanding of how digital data, information and knowledge are managed for clinical care, research and health policy and planning. [KNOWLEDGE, SKILLS AND PRACTICE]
Relates to: ULO1 - Take leadership in identifying the challenges, opportunities and potential of disruptive technologies, and evaluate their impact on models of healthcare delivery. [SKILLS, PRACTICE AND KNOWLEDGE]
Relates to: ULO1, ULO2, ULO3 - Critically analyse socio-technical, ethical and political issues associated with the implementation and use of digital information systems in healthcare. [VALUES, DISPOSITIONS]
Relates to: ULO2, ULO3, ULO4, ULO5 - Lead the management of technologies in multi-disciplinary teams across diverse stakeholders at all stages of the health information system life cycle. [SKILLS AND PRACTICE]
Relates to: ULO1, ULO2, ULO4, ULO5
PU87 Master of Health Management and Leadership
- Design innovative and strategic responses to health leadership and management challenges to improve consumer, community, organisational and system level outcomes.
Relates to: Applied Clinical Informatics Project - Apply operational management skills to plan, organise and supervise internal organisational processes required for achieving high performance.
Relates to: Applied Clinical Informatics Project - Formulate strategies for culturally safe and inclusive approaches to optimise health system design and service delivery.
Relates to: Applied Clinical Informatics Project - Critically analyse and manage the implementation, benefits and risks of contemporary and emerging health technologies.
Relates to: Applied Clinical Informatics Project, AI Suitability Report
Unit Outline: Semester 2 2026, Online
| Unit code: | PUN108 |
|---|---|
| Credit points: | 12 |
| Assumed Knowledge: | Nil |
Overview
There is an increasing demand across the healthcare service spectrum for professionals with the ability to critically analyse data and make evidence-based decisions that are suitable for the context of their healthcare organisation(s). This unit supports career readiness for emerging roles in clinical informatics, digital health, data governance, and AI-enabled healthcare service improvement. It responds to the growing demand for professionals who can evaluate, justify and communicate technology and AI-enabled solutions in contemporary health systems.
In this unit, you will discover the clinical informatics principles, tools and technologies which can be used in to support a wide range of decisions ranging from day-to-day health service delivery (including clinical care) to the long-term strategic developments. This unit addresses the core components of major national and international certification programs such as the Certified Health Information Australasia (CHIA).
Learning Outcomes
On successful completion of this unit you will be able to:
- Apply data literacy concepts to design a contextually appropriate data management approach (es) for driving quality improvement in health care services.
- Critically analyse and contextualise informatics principles, data governance insights and ethical considerations specific to healthcare settings in a culturally safe manner.
- Evaluate the effectiveness and utility of complex technologies, e.g. clinical dashboards and AI applications, to improve decision making in contemporary healthcare settings.
- Communicate evidence-informed recommendations on AI-enabled healthcare solutions that adhere to ethical and professional standards.
- Design effective collaboration processes to address data management challenges in digital and AI-enabled healthcare environments, with consideration of inclusive practice.
Content
This unit consists of the following major areas of study:
- Core concepts: data, information, and knowledge and life cycle of data within the context of health systems
- Sources of health information, including the various data repositories held in hospitals, specialized health units and government agencies.
- Examining relationship between various types of health information, associated governance principles and ethical considerations
- The importance of working with a diverse range of interdisciplinary and inter-professional stakeholders to inform quality improvement initiatives and utilising culturally safe communication.
- The role of data in quality improvement: why is managing data an essential part of quality improvement and what are the basic techniques for using data to support quality improvement efforts.
- Artificial Intelligence (AI) for clinical intelligence in Healthcare: Different AI applications and types of technologies including generative AI applications that are used to deliver clinical intelligence in real world health settings
- Using data for evidence-based decision making: Various ways of data visualization and how they are used to communicate with diverse stakeholders in healthcare.
- Leading data analytics and AI projects: explore the required technological and clinical competencies to deliver successful data analytic projects.
Learning Approaches
In this unit, you will learn by engaging in the following:
- lectures (synchronous and self-paced)
- tutorials
- online readings and learning materials
The design of learning activities are drawn from situated learning theory, which will provide students opportunities to engage in authentic learning and real-world assessment tasks. The unit will be delivered via blended learning; which allows students to experience the majority of unit material and lectures online, combined with synchronous lectures and tutorials scheduled throughout the semester. The lectures and tutorials will provide an opportunity to consolidate learning via practical case-driven discussions and other learning activities. Students will engage in structured peer review and collaborative critique activities to strengthen their ability to communicate clinical informatics insights and refine evidence-based recommendations.
The Canvas site provides embedded support for learning through the provision of writing resources and practice quizzes. Face to face and online learning is conducted in such a way that there is peer and academic feedback. Writing resources include links and videos about referencing, finding articles, paraphrasing, and academic integrity. The online quizzes are designed as a self-test to help students study throughout semester and they do not count toward final grade.
Feedback on Learning and Assessment
Feedback on the activities conducted in tutorials will form the basis of the formative assessment. In addition, written and oral feedback associated with the marking of assessments will constitute formative assessment for the unit. Students will engage in structured peer review and collaborative critique activities to strengthen their ability to communicate clinical informatics insights and refine evidence-based recommendations
Assessment
Overview
There are two assessments in this unit.
1. Applied Informatics Project: You will be required to develop an informatics project plan. This will draw on approaches to planning data and communication management for implementing informatics driven quality improvement initiatives in healthcare settings.
2. AI Suitability Report: You will be required to assess whether AI is suitable for a real healthcare problem by creating and applying your own contextualized evidence-based framework that considers clinical informatics principles, stakeholder needs, and professional standards.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Applied Clinical Informatics Project
You will take on the role of the clinical informatics manager (s) to develop the data management and communications plan for a quality improvement initiative in a real-world healthcare context. The plan will address a diversity of perspectives that will include the healthcare organisation, clinical stakeholders and patients. You will also need to select an appropriate audience and develop a culturally safe communications plan. You will have the opportunity to receive feedback and discuss any questions in relation to the assessments during the tutorial sessions scheduled throughout the semester.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
Assessment: AI Suitability Report
This assessment is designed to help you evaluate whether artificial intelligence is suitable for addressing a real healthcare problem. Part 1: You will first deliver a three-minute pitch that outlines your chosen problem, a potential AI solution, and the key questions in your suitability framework. You will participate in a structured peer feedback activity during tutorials following the pitch presentation. You are expected to reflect on peer feedback and incorporate relevant improvements into the final written AI suitability report. Part 2: You will then complete a written report where you develop and apply your AI suitability framework, analyse stakeholder and ethical considerations, and provide evidence-based recommendations. Together, these two parts assess your ability to integrate clinical informatics knowledge with practical decision-making about AI in healthcare.
Part 1 (3 minute pitch) assignment is not eligible for the 48-hour late submission period and assignment extensions. Part 2 (Report ) is eligible for the 48-hour late submission period and assignment extensions.
The ethical and responsible use of generative artificial intelligence (GenAI) tools is authorised in this assessment. See the relevant assessment details in Canvas for specific guidelines.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Risk Assessment Statement
There are no out of the ordinary risks in this unit apart from those associated with substantial computer-based work.
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
ACHSM Master Health Service Management Competency Framework 2022
A.a): Action - Communication
Relates to: Applied Clinical Informatics Project
Relates to: Applied Clinical Informatics Project, AI Suitability Report
Relates to: Applied Clinical Informatics Project
A.b): Action - Relationship Management
Relates to: Applied Clinical Informatics Project
Relates to: Applied Clinical Informatics Project
A.g): Action - Digital Management
Relates to: Applied Clinical Informatics Project, AI Suitability Report
Relates to: AI Suitability Report
Relates to: Applied Clinical Informatics Project, AI Suitability Report
Relates to: Applied Clinical Informatics Project, AI Suitability Report
Relates to: Applied Clinical Informatics Project, AI Suitability Report
Relates to: Applied Clinical Informatics Project, AI Suitability Report
E.b): Enabling - Impact and Influence
Relates to: Applied Clinical Informatics Project
Relates to: Applied Clinical Informatics Project
Course Learning Outcomes
This unit is designed to support your development of the following course/study area learning outcomes.PU70 Graduate Certificate in Digital Health Leadership and Management
- Apply a holistic understanding of how digital data, information and knowledge are managed for clinical care, research and health policy and planning. [KNOWLEDGE, SKILLS AND PRACTICE]
Relates to: ULO1 - Take leadership in identifying the challenges, opportunities and potential of disruptive technologies, and evaluate their impact on models of healthcare delivery. [SKILLS, PRACTICE AND KNOWLEDGE]
Relates to: ULO1, ULO2, ULO3 - Critically analyse socio-technical, ethical and political issues associated with the implementation and use of digital information systems in healthcare. [VALUES, DISPOSITIONS]
Relates to: ULO2, ULO3, ULO4, ULO5 - Lead the management of technologies in multi-disciplinary teams across diverse stakeholders at all stages of the health information system life cycle. [SKILLS AND PRACTICE]
Relates to: ULO1, ULO2, ULO4, ULO5
PU87 Master of Health Management and Leadership
- Design innovative and strategic responses to health leadership and management challenges to improve consumer, community, organisational and system level outcomes.
Relates to: Applied Clinical Informatics Project - Apply operational management skills to plan, organise and supervise internal organisational processes required for achieving high performance.
Relates to: Applied Clinical Informatics Project - Formulate strategies for culturally safe and inclusive approaches to optimise health system design and service delivery.
Relates to: Applied Clinical Informatics Project - Critically analyse and manage the implementation, benefits and risks of contemporary and emerging health technologies.
Relates to: Applied Clinical Informatics Project, AI Suitability Report