The transformation of medicine and health by big data and artificial intelligence is already underway, with ever more routine data collection and its linkage through electronic means. Herein lies the potential to supply real-time personalised healthcare, deep clinical phenotyping and diagnostic capabilities, and prognostic predictions of disease and intervention outcomes. Data science techniques underpin these approaches. This unit will provide a deep dive into understanding the entire end-to-end data cycle / pipeline of healthcare data: from its acquisition (e.g., health records, imaging, sensors etc), to its processing (e.g., cleaning, feature extraction, data linkage etc), to analysing the data (e.g., decision support / computer aided diagnosis) and finally to use the data for prediction (e.g., prognosis and modelling). We will also study the importance of using the data to its stakeholders (patients, clinicians, society etc.) by taking into account of the ethics, privacy, security and measurable benefits from the use of the data. On completion of this unit, students will have a solid understanding of how the healthcare data is now being exploited, through data science principles and tools, to provide improved healthcare delivery. Students will also learn practical skills in healthcare data analysis using Python programming language.

The current health data revolution promises transformative advancements in healthcare services and delivery. However, the data generated are vast and complex. Extracting actionable understanding requires cross-disciplinary engagement between data science with healthcare. This unit explores the computational technologies involved in integrating and making sense of the breath of health data, and their use in better understanding the patient. Students will understand the data challenges presented by the various assays in which patients are quantified, spanning genetic testing to organ imaging. Students will explore how computational and machine learning models can span health data to derive integrated understanding of the links and patterns across them. They will employ such models in performing diagnosis and forecasting disease progression and intervention outcomes, thus enabling personalised medicine and supporting clinical decision making. This unit will develop students' understanding of current healthcare challenges, how these can be framed as data science questions, and how they can engage and apply their knowledge in cross-disciplinary ventures to improve healthcare.

The aim of this unit is to provide future health professionals with a strong foundation in digital health on which they can make evidence-based decisions. In particular, this unit will provide students with opportunities to examine: How technology can affect health and healthcare delivery in different contexts, ethical issues surrounding digital health, innovations in digital health, how emerging technologies affect communication between health professionals, and health professionals and their clients or patients, strategies for interacting with patients and clients using different technologies, and the relationship between users, technologies, data and the wider information network. Students will develop their skills in critically thinking about digital health and its potential to support healthcare. Students will use and evaluate a digital health tool and generate an idea for a new digital intervention to showcase these skills. This unit will also enable students to be lifelong learners by providing them with reflective learning skills.

This unit will offer you the opportunity to develop your understanding of opportunities, theoretical and practical issues in digital health transformation projects. Students will have the chance to consider their own approaches in relation to project design and implementation. Drawing on recent research digital health theory and practice, students will design, develop and evaluate their digital health transformation and implementation proposal relevant to their chosen project context.

The transformation of medicine and health by big data and artificial intelligence is already underway, with ever more routine data collection and its linkage through electronic means. Herein lies the potential to supply real-time personalised healthcare, deep clinical phenotyping and diagnostic capabilities, and prognostic predictions of disease and intervention outcomes. Data science techniques underpin these approaches. This unit will provide a deep dive into understanding the entire end-to-end data cycle / pipeline of healthcare data: from its acquisition (e.g., health records, imaging, sensors etc), to its processing (e.g., cleaning, feature extraction, data linkage etc), to analysing the data (e.g., decision support / computer aided diagnosis) and finally to use the data for prediction (e.g., prognosis and modelling). We will also study the importance of using the data to its stakeholders (patients, clinicians, society etc.) by taking into account of the ethics, privacy, security and measurable benefits from the use of the data. On completion of this unit, students will have a solid understanding of how the healthcare data is now being exploited, through data science principles and tools, to provide improved healthcare delivery. Students will also learn practical skills in healthcare data analysis using Python programming language.

The current health data revolution promises transformative advancements in healthcare services and delivery. However, the data generated are vast and complex. Extracting actionable understanding requires cross-disciplinary engagement between data science with healthcare. This unit explores the computational technologies involved in integrating and making sense of the breath of health data, and their use in better understanding the patient. Students will understand the data challenges presented by the various assays in which patients are quantified, spanning genetic testing to organ imaging. Students will explore how computational and machine learning models can span health data to derive integrated understanding of the links and patterns across them. They will employ such models in performing diagnosis and forecasting disease progression and intervention outcomes, thus enabling personalised medicine and supporting clinical decision making. This unit will develop students' understanding of current healthcare challenges, how these can be framed as data science questions, and how they can engage and apply their knowledge in cross-disciplinary ventures to improve healthcare.

The aim of this unit is to provide future health professionals with a strong foundation in digital health on which they can make evidence-based decisions. In particular, this unit will provide students with opportunities to examine: How technology can affect health and healthcare delivery in different contexts, ethical issues surrounding digital health, innovations in digital health, how emerging technologies affect communication between health professionals, and health professionals and their clients or patients, strategies for interacting with patients and clients using different technologies, and the relationship between users, technologies, data and the wider information network. Students will develop their skills in critically thinking about digital health and its potential to support healthcare. Students will use and evaluate a digital health tool and generate an idea for a new digital intervention to showcase these skills. This unit will also enable students to be lifelong learners by providing them with reflective learning skills.

This unit will offer you the opportunity to develop your understanding of opportunities, theoretical and practical issues in digital health transformation projects. Students will have the chance to consider their own approaches in relation to project design and implementation. Drawing on recent research digital health theory and practice, students will design, develop and evaluate their digital health transformation and implementation proposal relevant to their chosen project context.

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.

Many issues have been identified that are of potential relevance for planning, implementation and execution of an evaluation study in the health and technology innovations. This unit aims to address issues covering all phases of an evaluation study: Preliminary outline, study design, operationalization of methods, planning, execution and completion of the evaluation study. Students completing this unit will have better insights leading to a higher quality of evaluation studies for health technology solutions.
This unit is an important component towards building stronger evidence and thus to progress towards evidence-based health solutions and technology innovations.
Graduates of this unit of study will have a strong interdisciplinary knowledge base, covering diverse areas such as health, economics, health technologies, health informatics, social science and information systems.
Topics areas covered: 1. Economic Aspects of Health Technology Evaluation; 2. The Development of Health Technologies and Health Informatics Evaluation; 3. The Role of Evaluation in the Use and Diffusion of Health Technology.

This unit is an essential starting point for software developers, IT consultants, and computer scientists to build their understanding of principle computer operation. Students will obtain knowledge and skills with procedural programming. Crucial concepts include defining data types, control flow, iteration, functions, recursion, the model of addressable memory. Students will be able to reinterpret a general problem into a computer problem, and use their understanding of the computer model to develop source code. This unit trains students with software development process, including skills of testing and debugging. It is a prerequisite for more advanced programming languages, systems programming, computer security and high performance computing.

Object-oriented (OO) programming is a technique that arranges code into classes, each encapsulating in one place related data and the operations on that data. Inheritance is used to reuse code from a more general class, in specialised situations. Most modern programming languages provide OO features. Understanding and using these are an essential skill to software developers in industry. This unit provides the student with the concepts and individual programming skills in OO programming, starting from their previous mastery of procedural programming.

This unit introduces computational linguistics and the statistical techniques and algorithms used to automatically process natural languages (such as English or Chinese). It will review the core statistics and information theory, and the basic linguistics, required to understand statistical natural language processing (NLP). Statistical NLP is used in a wide range of applications, including information retrieval and extraction; question answering; machine translation; and classifying and clustering of documents. This unit will explore the key challenges of natural language to computational modelling, and the state of the art approaches to the key NLP sub-tasks, including tokenisation, morphological analysis, word sense representation, part-of-speech tagging, named entity recognition and other information extraction, text categorisation, phrase structure parsing and dependency parsing. You will implement many of these sub-tasks in labs and assignments. The unit will also investigate the annotation process that is central to creating training data for statistical NLP systems. You will annotate data as part of completing a real-world NLP task.

Visual Analytics aims to facilitate the data analytics process through Information Visualisation. Information Visualisation aims to make good pictures of abstract information, such as stock prices, family trees, and software design diagrams. Well designed pictures can convey this information rapidly and effectively. The challenge for Visual Analytics is to design and implement effective Visualisation methods that produce pictorial representation of complex data so that data analysts from various fields (bioinformatics, social network, software visualisation and network) can visually inspect complex data and carry out critical decision making. This unit will provide basic HCI concepts, visualisation techniques and fundamental algorithms to achieve good visualisation of abstract information. Further, it will also provide opportunities for academic research and developing new methods for Visual Analytic methods.

Machine learning is the process of automatically building mathematical models that explain and generalise datasets. It integrates elements of statistics and algorithm development into the same discipline. Data mining is a discipline within knowledge discovery that seeks to facilitate the exploration and analysis of large quantities for data, by automatic and semiautomatic means. This subject provides a practical and technical introduction to machine learning and data mining.
Topics to be covered include problems of discovering patterns in the data, classification, regression, feature extraction and data visualisation. Also covered are analysis, comparison and usage of various types of machine learning techniques and statistical techniques.

Information technology (IT) has significantly contributed to the research and practice of medicine, biology and health care. The IT field is growing enormously in scope with biomedicine taking a lead role in utilising the evolving applications to its best advantage. The goal of this unit of study is to provide students with the necessary knowledge to understand the information technology in biomedicine. The major emphasis will be on the principles associated with biomedical digital imaging systems and related biomedicine data processing, analysis, visualisation, registration, modelling, retrieval and management. A broad range of practical integrated clinical applications will be also elaborated.

The aim of the unit is to introduce students to basic statistical concepts and methods for further studies. Particular attention will be paid to the development of methodologies related to statistical data analysis and Data Mining. A number of useful statistical models will be discussed and computer oriented estimation procedures will be developed. Smoothing and nonparametric concepts for the analysis of large data sets will also be discussed. Students will be exposed to the R computing language to handle all relevant computational aspects in the course.

The objectives of this unit of study are to develop an understanding of modern computationally intensive methods for statistical learning, inference, exploratory data analysis and data mining. Advanced computational methods for statistical learning will be introduced, including clustering, density estimation, smoothing, predictive models, model selection, combinatorial optimisation methods, sampling methods, the Bootstrap and Monte Carlo approach. In addition, the unit will demonstrate how to apply the above techniques effectively for use on large data sets in practice.

Unprecedented growth in computing power, the advent of artificial intelligence (AI)/machine learning technologies, and global data platforms are changing the way in which we approach real-world healthcare challenges. This interdisciplinary unit will introduce students from different backgrounds to the fundamental concepts of data analytics and AI, and their practical applications in healthcare. Throughout the unit, students will learn about the key concepts in data analytics and AI techniques, and obtain hands-on experience in applying these techniques to a broad range of healthcare problems. At the same time, they will develop an understanding of the ethical considerations in health data analytics and AI, and how their use impacts society: from the patient, to the doctor, to the broader community. A key element of the learning process will be a team-based Datathon project where students will deploy their knowledge to address an open-ended healthcare problem, in particular developing a practical solution and analysing how it's use may change things in the healthcare domain. Upon completion of this unit, students will understand and be able to enlist data analytics and AI tools to design solutions to healthcare problems.

Biomedical imaging technology is a fundamental element of both clinical practice and biomedical research, enabling the visualisation of biological characteristics and function often in a non-invasive fashion. The advancement of digital scanning technologies alongside the development of computational tools has driven significant progress in medical image analysis tools that support clinical decisions and the analysis of data from biological experiments. The focus of this unit will be the development of fundamental computational skills and knowledge in biomedical imaging, including data acquisition, formats, visualisation, segmentation, feature extraction, and machine learning based image analysis. On completion of this unit, students will be able to engineer and develop solutions for different biomedical imaging tasks encountered across a variety of use cases: clinical practice (e.g., computerised disease detection and diagnosis), research (e.g., cell video analysis), and industry (e.g., fabrication of customised implants from patient image data).

Supply of medical devices, diagnostics and related therapeutic products is regulated in most jurisdictions, with sophisticated and complex regulatory regimes in all large economies. These regulations are applied both to manufacturers and designers and to biomedical engineers undertaking device custom manufacture or maintenance in clinical environments. This UoS will explore the different regulatory frameworks in the 'Global Harmonisation Task Force' group of jurisdictions (US, EU, Canada, Japan, Australia), as well as emerging regulatory practices in Asia and South America. Emphasis will be on the commonality of the underlying technical standards and the importance of sophisticated risk management approaches to compliance.

This unit of study provides an overview of a human-centred approach to the design of products and systems. It introduces students to design thinking and how it can be productively applied to different design situations. The theoretical concepts, methods and tools for the key stages of interaction design are covered including user research, ideation, prototyping and user evaluation. It provides students with the principles, processes and tools for working collaboratively on design projects in studio. Students learn to build empathy with users, identify and reframe the problem space, develop value-driven design concepts and persuasively communicate design proposals with an emphasis on the user experience through visual storytelling. This unit is a foundational core unit in the Master of Interaction Design and Electronic Arts program.

This online unit that can be undertaken either face-to-face, fully online, or in intensive block mode, introduces the concept of clinical epidemiology and provides students with core skills in clinical epidemiology at an introductory level. The unit is aimed at clinician learners and as such some clinical experience is required. Topics covered include asking and answering clinical questions; basic and accessible literature searching techniques; study designs used in clinical epidemiological research; confounding and effect modification; sources of bias; interpretation of results including odds ratios, relative risks, confidence intervals and p values; applicability of results to individual patients; critical appraisal of clinical epidemiological research literature used to answer questions of therapy (RCTs and systematic reviews), harm, prognosis, diagnosis and screening; applicability of results to individual patients; and evidence-based use of health resources.

This unit will facilitate students to critically review the ethical issues that underlie the delivery of healthcare. Students will explore: dominant theoretical approaches relevant to ethical reasoning in the clinical context; key ethical concepts in the clinical encounter (such as autonomy, professionalism and confidentiality); major contexts in which ethical issues arise in clinical practice (such as the start and end of life); and the role of clinical ethics consultation. The unit will also consider specific issues and populations within clinical practice, such as healthcare in underserved populations. This Unit is taught predominantly online. Depending on student interest, periodic interactive workshops will also be offered. These can be attended in person, or via Zoom (synchronously or asynchronously).

There is a need to improve the efficient and cost-effective use of health care technologies and services at all levels of the health system. This unit covers all aspects of the policy, assessment, monitoring and re-assessment of technologies and services, and techniques to support investment and disinvestment decision-making by public payers and funders. Students will work through key concepts in health technology assessment as well as the key institutions and processes for regulating and managing the use of health technologies. Students will work through real world scenarios as case examples.

Many who come to assume leadership roles in health care often come to this responsibility without any exposure to leadership theory. Given this, HPOL5012 focuses on combining the development of an understanding of leadership theory with the personal development of students as health care leaders because ultimately leadership is about what you do, not what you know. Initially this is done by exploring the history of leadership theory and then taking this learning and applying it to the health care environment through a hierarchy that moves through ‘leading self’ then onto teams, organisations and ultimately society. The aim of this unit is to increase students' knowledge of leadership theory and their understanding of the connections between this theory and practice so as to assist their personal development as leaders in health care.

Usability engineering is the systematic process of designing and evaluating user interfaces so that they are usable. This means that people can readily learn to use them efficiently, can later remember how to use them and find it pleasant to use them. The wide use of computers in many aspects of people's lives means that usability engineering is of the utmost importance.
There is a substantial body of knowledge about how to elicit usability requirements, identify the tasks that a system needs to support, design interfaces and then evaluate them. This makes for systematic ways to go about the creation and evaluation of interfaces to be usable for the target users, where this may include people with special needs. The field is extremely dynamic with the fast emergence of new ways to interact, ranging from conventional WIMP interfaces, to touch and gesture interaction, and involving mobile, portable, embedded and desktop computers.
This unit will enable students to learn the fundamental concepts, methods and techniques of usability engineering. Students will practice these in small classroom activities. They will then draw them together to complete a major usability evaluation assignment in which they will design the usability testing process, recruit participants, conduct the evaluation study, analyse these and report the results.

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/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 criterion 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/ seminar suitable for academic and/ professional audiences. Implementation science and modern project management techniques should be used where appropriate in projects.

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/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 criterion 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/ seminar suitable for academic and/ professional audiences. Implementation science and modern project management techniques should be used where appropriate in projects.