Unit of study descriptions

Master of Health Technology Innovation

Core, Specialist, Project and Foundation units of study for the Master of Health Technology Innovation and Graduate Diploma in Health Technology Innovation are shown in the following tables.
Candidates for the Master of Health Technology Innovation are required to complete 96 credit points from the units of study as follows:
1. 24 credit points of the Core units of study
2. a minimum of 24 credit points of the Specialist units of study
3. a minimum of 12 credit points of the Project units of study
4. a maximum of 24 credit points of Foundation units of study. Candidates will be required to select units which complement their prior background and qualifications (subject to assessment by the Academic Director).
Candidates for the Graduate Diploma in Health Technology Innovation are required to complete 60 credit points from the units of study as follows:
1. a minimum of 12 credit points of the Core units of study
2. a minimum of 12 credit points of the Specialist units of study
3. a maximum of 24 credit points of the Foundation units of study
If a reduction in the volume of learning is given to candidates of the Graduate Diploma or the Master's degree, this is to be applied to Foundation or Specialist units of study related to your prior background and qualifications.

Core Units

Candidates for the Master of Health Technology Innovation must complete a minimum of 24 credit points from the listed Core units of study.
HTIN5001 Nature of Systems

Credit points: 6 Session: Semester 1 Classes: Lectures, E-Learning, Workgroups, Presentation, Laboratories Assumed knowledge: The unit is aimed at graduates and researchers who are interested in developing skills in complex systems analysis. The unit is highly interdisciplinary and as such, there is no assumed prior knowledge or course prerequisites. However, students who will benefit most from the unit will be those who are open-minded and motivated to think outside the box. The unit has been deliberately designed as a new teaching and learning experience. Assessment: Through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
This core unit of study aims to introduce the central concepts of systems approaches to addressing complex, multi-dimensional, multi-scale issues. Systems approaches are increasingly being recognised as essential for unravelling the complex network of influences on human health, from biology and nutrition to economics and society. The Charles Perkins Centre is committed to fostering new ways of thinking with systems approaches, which it sees as the key to identifying innovative solutions to the growing global health problems associated with diet and lifestyle. An understanding of concepts from complex systems thinking will help students develop their own thinking about complex health issues and identify novel approaches and research questions.
HTIN5002 Quality Frameworks for Health Innovation

Credit points: 6 Session: Semester 2 Classes: Lectures Prerequisites: at least 36cp of 3000-level or higher units and a WAM of 70+ Assessment: Through semester assessment (100%) Mode of delivery: Block mode
Introduces students to quality frameworks, and associated regulatory and legal frameworks within which medical research, clinical innovation and medical device development occurs. This unit will focus on explaining quality framework rationale and requirements. Practical research and development case studies will be utilized to identify interplay between frameworks and outcomes. This unit will allow students of a broad range of backgrounds to understand the role of quality frameworks in facilitating medical research, development and management, and the central importance of concise, consistent and coherent documentation of activity within quality frameworks to occur on a regular basis.
HTIN5003 Health Technology Evaluation

Credit points: 6 Session: Semester 2b Classes: Workshops Assessment: Through semester assessment (100%) Mode of delivery: Block mode
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.
HTIN5004 Integrated Approaches to Chronic Disease

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assessment: Through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study aims to introduce the student to the strategy of the Charles Perkins Centre to ease the burden of obesity, diabetes and cardiovascular disease. While other approaches would focus on these diseases as purely medical conditions this unit will challenge the student to focus on an interdisciplinary approach, bringing together medicine, biological science, psychology, economics, law, agriculture and other disciplines to understand how real world solutions for these diseases might be developed. Students will be exposed to the world-renowned researchers based in the Charles Perkins Centre and will gain insight into the research strategy of the Centre. Students will also have the opportunity to develop a new interdisciplinary project node for the Centre in collaboration with one of our research leaders.

Specialist Units

Candidates for the Master of Health Technology Innovation must complete a minimum of 24 credit points from the listed Specialist units of study.
BETH5202 Human and Animal Research Ethics

Credit points: 6 Teacher/Coordinator: TBC Session: Semester 2 Classes: 4x8hr intensive or Distance Education (online). Attendance is compulsory if enrolled in face-to-face mode Prohibitions: BETH5208 Assessment: Continuous assessment (short weekly tasks) (10%); 2x400wd short tasks (10%); 1x1500wd essay (30%); 1x2500wd essay (50%) Mode of delivery: Block mode, Online
Note: If an insufficient number of students opt to attend seminars on campus, the co-ordinator may choose to teach this Unit of Study in online mode only. Students will be contacted if this occurs.
This unit of study critically examines research ethics in its wider context, from how research is structured to its dissemination. It explores the ethical underpinnings of a variety of research methods and their uses in humans and non-human animals including the justifications for engaging in research, key concepts in research ethics and research integrity. The unit also briefly examines the history of research and the impact of research abuse on participants, both human and non-human animal.
Textbooks
All readings are made available via elearning.
BETH5203 Ethics and Public Health

Credit points: 6 Teacher/Coordinator: TBC Session: Semester 2 Classes: 5x7hour intensives; or Distance Education (online). Prohibitions: BETH5206 Assessment: 5xOnline Quiz (50%); 1x2500wd essay (50%) Mode of delivery: Block mode, Online
This unit provides students with an overview of the ethical and political issues that underlie public health and public health research. The unit begins with some fundamentals: the nature of ethics, of public health (and how it might be different to clinical medicine) and of public health ethics. It introduces key concepts in public health ethics including liberty, utility, justice, solidarity and reciprocity, and introduces students to different ways of reasoning about the ethics of public health. A range of practical public health problems and issues will be considered, including ethical dimensions of communicable and non-communicable diseases in populations, and the ethical challenges of public health research. Throughout, the emphasis is on learning to make sound arguments about the ethical aspects of public health policy, practice and research. Most learning occurs in the context of five teaching intensives, which are highly interactive and focus on the development and application of reasoning skills.
Textbooks
Students are provided with a list of readings (in digital format).
BMET5921 Biomedical Engineering Technology 2

Credit points: 6 Session: Semester 2 Classes: lectures Prohibitions: MECH3921 OR BMET3921 OR AMME5921 Assumed knowledge: 1000-level biology, 1000-level materials science and some engineering design Assessment: through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study provides an introduction to the field of biomedical engineering, from the point of view of the engineering and the global biomedical industry itself. After completion of this unit, students will have a clear understanding of what biomedical engineering is, both from the engineering perspective and the commercial/industry perspective.
BMET9961 Biomechanics and Biomaterials

Credit points: 6 Session: Semester 2 Classes: lectures Prohibitions: AMME5961 OR AMME9961 OR MECH4961 OR BMET4961 Assumed knowledge: AMME9901 or BMET9901 or 6 credit points of junior biology, 6 credit points of junior chemistry, 6 credit points of junior materials science, 6 credit points of engineering design, Chemistry, biology, materials engineering, and engineering design at least at the Junior level. Assessment: through semester assessment (60%), final exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
This course is divided into two parts: biomechanics and biomaterials: Biomechanics is the study of the body from the point of view of it being an engineering structure. There are many aspects to this since the human body contains soft tissues, hard tissues (skeletal system), and articulating joints. We will begin with a general introduction to biomechanics, modelling the human body from the macroscopic level to the microscopic level. We will then study soft tissue mechanics, with respect to both non-linear and viscoelastic descriptions, with a significant focus on the mathematical methods used in relation to the mechanics of the system. We will then look at specific aspects of biomechanics: muscle mechanics, joint mechanics, kinematics and dynamics of human gait (gait analysis), biomechanics of cells, physiological fluid flow, biomechanics of injury, functional and mechanical response of tissues to mechanical loading. Biomaterials This course will involve the study of biomaterials from two perspectives: firstly, the response of the body towards the biomaterial - an immune response and foreign body reaction; secondly, the response of the biomaterial to the body - corrosion, biodegradation, and mechanical failure. Our study will begin with the response of the body towards the biomaterial. We will begin by looking at the immune system itself and then move on to look at the normal inflammatory response. We will then study in detail the foreign body reaction caused by biomaterials. The final part of this section is the study of protein adsorption onto biomaterials, with a strong focus on the Vroman effect. Then we will move onto the response of the biomaterial to the body. We will begin by a review of biomaterials, their applications, and compositions, and mechanical properties. We will then look at key problems such as corrosion, stress shielding, static fatigue, and mechanical failure. Finally, we will take a practical look at the materials themselves. Beginning with metals, then polymers (thermoplastic, thermosetting, and biodegradable), and finally ceramics (bioinert, biodegradable, and bioactive).
BMET9971 Tissue Engineering

Credit points: 6 Session: Semester 1 Classes: lectures, tutorials Prerequisites: (AMME5921 or BMET5921) Prohibitions: AMME5971 OR AMME9971 OR AMME4971 OR BMET4971 Assumed knowledge: AMME9901 or BMET9901 or [6 credit points of 1000-level biology and 6 credit points of 1000-level chemistry] Assessment: through semester assessment (65%), final exam (35%) Mode of delivery: Normal (lecture/lab/tutorial) day
With the severe worldwide shortage of donor organs and the ubiquitous problem of donor organ rejection, there is a strong need for developing technologies for engineering replacement organs and other body parts. Recent developments in engineering and the life sciences have begun to make this possible, and as a consequence, the very new and multidisciplinary field of tissue engineering has been making dramatic progress in the last few years. This unit will provide an introduction to the principles of tissue engineering, as well as an up to date overview of recent progress and future outlook in the field of tissue engineering. This unit assumes prior knowledge of cell biology and chemistry and builds on that foundation to elaborate on the important aspects of tissue engineering. The objectives are: To gain a basic understanding of the major areas of interest in tissue engineering; To learn to apply basic engineering principles to tissue engineering systems; To understand the promises and limitations of tissue engineering; To understand the advances and challenges of stem cell applications; Enable students to access web-based resources in tissue engineering; Enable students to develop basic skills in tissue engineering research.
CLTR5001 Trial Design and Methods

Credit points: 6 Teacher/Coordinator: Rebecca Asher, Adrienne Kirby Session: Semester 1 Classes: discussion groups and problem based learning Assessment: 2 x short answer, including calculations quizzes (2x10%), 2 x short answer question, calculations, long answer questions and diagrams assignments (2x40%) Mode of delivery: Online
This unit of study will focus on the strengths and weaknesses of different clinical study designs. Designs considered will include cohort (retrospective and prospective), cross-sectional, case-control and randomized controlled designs. The different phases of clinical trial designs in the development of therapies will also be examined including phase I (first in man), phase II/pilot and phase III comparative designs. Extension and adaption of randomized designs will also be covered including cluster and factorial designs and adaptive pilot studies. Students will gain the skills necessary to choose between these designs for best practice. Types of outcomes (continuous, categorical, time-to-event) will be discussed. Methods of allocating participants to interventions (randomization), as well blinding and allocation concealment will be covered together with aspects of protocol development. On completion of this unit, the student will be familiar with the differences between study types and study designs, as well as the principles and practice of randomisation. It is also expected that the candidate will be able to develop stratified randomisation schemes for their own studies.
Textbooks
Recommended reading: many available eg S Piantadosi, Clinical Trials A Methodological Perspective. KJ Rothman and S Greenland Modern Epidemiology. Recommended reading: Interpreting and Reporting of Clinical Trials: a guide to the Consort statement.
COMP5046 Natural Language Processing

Credit points: 6 Session: Semester 1 Classes: Lectures, Laboratory Assumed knowledge: Knowledge of an OO programming language Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
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.
Students 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. Students will annotate data as part of completing a real-world NLP task.
COMP5048 Visual Analytics

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials Assumed knowledge: It is assumed that students will have basic knowledge of data structures, algorithms and programming skills. Assessment: Through semester assessment (60%) and Final Exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
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.
COMP5206 Information Technologies and Systems

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Tutorials Assessment: Through semester assessment (50%) Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit will provide a comprehensive introduction to the field of information systems from organisational and managerial perspectives. The emergence of the digital firm and its implications will be studied. The critical role of information and knowledge management will be emphasised from both conceptual and practical standpoints.
Key topics covered will include: Basic Information Systems Concepts; Systems Approach and Systems Thinking; E-Business and E-Commerce; IT Strategy and Competitive Advantage; Data and Knowledge Management; Information Systems Development and IS Management; Decision support systems, business intelligence and online analytical processing systems (OLAP); Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) systems, Enterprise Content Management and Supply Chain Management (SCM) systems; Ethical, Legal and Social Aspects of Information technologies.
COMP5216 Mobile Computing

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials Assumed knowledge: COMP5214 OR COMP9103. Software Development in JAVA, or similar introductory software development units. Assessment: Through semester assessment (45%) and Final Exam (55%) Mode of delivery: Normal (lecture/lab/tutorial) day
Mobile computing is becoming a main stream for many IT applications, due to the availability of more and more powerful and affordable mobile devices with rich sensors such as cameras and GPS, which have already significantly changed many aspects in business, education, social network, health care, and entertainment in our daily life. Therefore it has been critical for students to be equipped with sufficient knowledge of such new computing platform and necessary skills. The unit aims to provide an in-depth overview of existing and emerging mobile computing techniques and applications, the eco-system of the mobile computing platforms, and its key building components. The unit will also train students with hand-on experiences in developing mobile applications in a broad range of areas.
COMP5318 Machine Learning and Data Mining

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Tutorials Assumed knowledge: INFO2110 OR ISYS2110 OR COMP9120 OR COMP5138 Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
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.
COMP5349 Cloud Computing

Credit points: 6 Session: Semester 1 Classes: Lectures, Practical Labs, Project Work Assumed knowledge: Good programming skills, especially in Java for the practical assignment, as well as proficiency in databases and SQL. The unit is expected to be taken after introductory courses in related units such as COMP5214 or COMP9103 Software Development in JAVA Assessment: Through semester assessment (45%) and Final Exam (55%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit covers topics of active and cutting-edge research within IT in the area of 'Cloud Computing'.
Cloud Computing is an emerging paradigm of utilising large-scale computing services over the Internet that will affect individual and organization's computing needs from small to large. Over the last decade, many cloud computing platforms have been set up by companies like Google, Yahoo!, Amazon, Microsoft, Salesforce, Ebay and Facebook. Some of the platforms are open to public via various pricing models. They operate at different levels and enable business to harness different computing power from the cloud.
In this course, we will describe the important enabling technologies of cloud computing, explore the state-of-the art platforms and the existing services, and examine the challenges and opportunities of adopting cloud computing. The course will be organized as a series of presentations and discussions of seminal and timely research papers and articles. Students are expected to read all papers, to lead discussions on some of the papers and to complete a hands-on cloud-programming project.
COMP5424 Information Technology in Biomedicine

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
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.
COMP5427 Usability Engineering

Credit points: 6 Session: Semester 2 Classes: Lectures, Laboratory Assessment: Through semester assessment (60%) and Final Exam (40%) Mode of delivery: Normal (lecture/lab/tutorial) day
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
DATA5207 Data Analysis in the Social Sciences

Credit points: 6 Session: Intensive December,Semester 1 Classes: lectures, laboratories Assumed knowledge: COMP5310 Assessment: through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
Note: Department permission required for enrolment
Data science is a new, rapidly expanding field. There is an unprecedented demand from technology companies, financial services, government and not-for-profits for graduates who can effectively analyse data. This subject will help students gain a critical understanding of the strengths and weaknesses of quantitative research, and acquire practical skills using different methods and tools to answer relevant social science questions.
This subject will offer a nuanced combination of real-world applications to data science methodology, bringing an awareness of how to solve actual social problems to the Master of Data Science. We cover topics including elections, criminology, economics and the media. You will clean, process, model and make meaningful visualisations using data from these fields, and test hypotheses to draw inferences about the social world.
Techniques covered range from descriptive statistics and linear and logistic regression, the analysis of data from randomised experiments, model selection for prediction and classification tasks, to the analysis of unstructured text as data, multilevel and geospatial modelling, all using the open source program R. In doing this, not only will we build on the skills you have already mastered through this degree, but explore different ways to use them once you graduate.
ELEC5622 Signals, Software and Health

Credit points: 6 Session: Semester 2 Classes: Project Work - in class, Project Work - own time, Presentation, Tutorials, Laboratories Assessment: Through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit aims to introduce students to the main issues involved in producing systems that use sensor data, such as those from physiology and activity tracking, often combined with patients self-reports. As sensing devices become ubiquitous, data processing, storage and visualisation techniques are becoming part of all health systems, both institutionalised and individually driven.
The unit is related to, but distinct, to health informatics- an area that focuses on the the use of computing to deliver cost efficient healthcare and the area of bioinformatics, that explores the role of computing in understanding biology at the cellular level (e. g. genome). This unit focuses on the technical and non-technical problems of developing increasingly ubiquitous devices and systems that can be used for personal and clinical monitoring.
GLOH5201 Global Qualitative Health Research

Credit points: 6 Teacher/Coordinator: Dr Sarah Bernays Session: Semester 2 Classes: ol: 12 weeks/ weekly (module) lecture: lecture+ content reading+ exemplar reading+ case study video+ individual activity bm: 9am-3.30pm x 4 days, 9am-12pm x 1 day. workshops made up of individual modules: f2f lecture + content reading + exemplar reading+ case study video+ f2f individual activity. Prohibitions: PUBH5500 or QUAL5005 or QUAL5006 or PUBH5005 Assessment: 1xinterviewing activity (35%); 1x2000-word essay (35%); 3 x multiple choice quizzes (20%); assessable tutorial discussion(10%) Mode of delivery: Block mode, Online
This unit of study introduces you to qualitative research in a global health setting, providing you with core concepts and skills. It is designed for beginners and people who want an advanced-level introduction. Over the course of the unit we will address: What is qualitative research? How is it different from quantitative research? What is its history? What research problems can it address? How do I design a qualitative study? What are the different (and best) ways to generate data? How do you analyse qualitative data? Is methodology different to method? What are ontology and epistemology? What is reflexivity (and aren't qualitative researchers biased)? What are the ethical issues? What is good quality qualitative research? How can I use qualitative evidence in policy or practice? You will get practical experience and skills through carrying out an observation, participating in a focus group, conducting an interview, analysing data, arguing for qualitative research in health, and appraising the quality of published literature. You will hear from working qualitative researchers about how they use qualitative methods in their work. This unit will give you the skills and confidence to begin conducting and using qualitative research.
HSBH5003 e-Health for Health Professionals

Credit points: 6 Teacher/Coordinator: Professor Tim Shaw, Anna Janssen Session: Semester 1 Classes: online and 3x3-hrs face to face workshops Assessment: eHealth Evaluation (40%), eHealth Innovation Challenge (40%), eHealth reflection task (10%), participation (10%) Mode of delivery: Distance education/intensive on campus
The aim of this unit is to provide future health professionals with a strong foundation in e-Health on which they can make evidence-based decisions. In particular, this unit will provide students with opportunities to examine:
. How technology affects health care in different Australian health contexts
. Ethical issues surrounding e-Health
. Innovations in e-Health
. How emerging technologies affect patient-centred communication between health professionals, and health professionals and their clients/patients
. Strategies for interacting with patients and clients using different technologies
. Strategies for engaging in multi-disciplinary e-Healthcare delivery
. The relationship between technologies, data and the wider information network
Students will develop their skills in a variety of technologies identified as key e-Health skills for clinicians. Students will create an e-Health delivery portfolio to showcase these skills. This unit will also enable students to be lifelong learners by providing them with reflective learning skills. Reflective learning skills are identified as essential for lifelong learning.
INFO5306 Enterprise Healthcare Information Systems

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials, Laboratories Assumed knowledge: The unit is expected to be taken after introductory courses in related units such as COMP5206 Information Technologies and Systems (or COMP5138/COMP9120 Database Management Systems). Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
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.
INFO5992 Understanding IT Innovations

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Tutorials Prohibitions: PMGT5875 Assumed knowledge: INFO5990 Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
An essential skill for an IT manager is the ability to keep up-to-date with emerging technologies, and be able to evaluate the significance of these technologies to their organisation's business activities. This unit of study is based around a study of current technologies and the influence of these technologies on business strategies.
Important trends in innovation in IT are identified and their implications for innovation management explored. Major topics include: drivers of innovation; the trend to open information ("open source") rather than protected intellectual property; and distribution of innovation over many independent but collaborating actors.
On completion of this unit, students will be able to identify and analyse an emerging technology and write a detailed evaluation of the impact of this technology on existing business practices.
ISYS5050 Knowledge Management Systems

Credit points: 6 Session: Semester 1 Classes: Lectures, Tutorials Assumed knowledge: An undergraduate degree in Computer Science or Information Systems. Good grasp of database technologies and the role of information systems in organisations. Assessment: Through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
The need to track and facilitate the sharing of the core knowledge resources in contemporary organisations is widely recognised. This course will provide a comprehensive introduction to the area of Knowledge Management (KM) from both technological and organisational perspectives. We will review and discuss a range of published papers, case studies, and other publications that deal with a range of important KM-related topics. One of the key knowledge management technologies, Business Intelligence Systems, will be covered in detail. It will also include hands-on work using the BI (Online Analytical Processing- OLAP) tool, COGNOS.
Some of the main themes to be covered will include: KM- Conceptual Foundations; Taxonomies of organizational knowledge and KM mechanisms; Case/Field Studies of KM Initiatives; Data Warehousing and OLAP/Business Analytics; Data, text, and web mining; Social media,crowdsourcing, and KM; Big data and actionable knowledge.
MRTY5133 Medical Image Optimisation

Credit points: 6 Teacher/Coordinator: Dr Ernest Ekpo Session: Semester 2 Classes: Distance Education Assessment: Online quiz (20%), online discussion (20%), 1 x essay, 2500 words (60%) Mode of delivery: Online
This UoS will investigate issues pertaining to the optimisation of medical imaging, aiming to ensure that imaging is best suited to answer the diagnostic questions posed. It will include discussion of the choice of imaging modalities, 2D and 3D radiographic imaging systems, as well as optimisation of display processing technologies and of display systems. In addition, issues pertaining to the relationship between dose and image quality will also be discussed. The aim of this UoS is to provide students with a clear understanding of how optimisation can affect diagnostic outcomes.
NURS5070 Creating a Culture of Safety and Quality

Credit points: 6 Session: Semester 2 Classes: four intensive, on-campus study days Assessment: essay (45%), case study part A (10%) and case study part B (45%) Mode of delivery: Block mode
This unit of study pursues a critical analysis of the theoretical constructs and practical applications underpinning good clinical governance in health care organisations. Many studies identify the factors influencing a culture of safety and quality in the clinical environment and most concur with six main domains: the safety climate, teamwork, perceptions of management, working conditions, job satisfaction and stress recognition. These factors and how to influence them positively will be examined in this unit of study utilising a better practice (quality/continuity of care/health outcomes/governance) framework.
PUBH5010 Epidemiology Methods and Uses

Credit points: 6 Teacher/Coordinator: Dr Erin Mathieu, Professor Tim Driscoll Session: Semester 1 Classes: 1x 1hr lecture and 1x 2hr tutorial per week for 13 weeks - face to face or their equivalent online Prohibitions: BSTA5011 or CEPI5100 Assessment: 1x 6 page assignment (25%), 10 weekly quizzes (5% in total) and 1x 2.5hr supervised open-book exam (70%). For distance students, it may be possible to complete the exam externally with the approval of the course coordinator. Mode of delivery: Normal (lecture/lab/tutorial) day, Normal (lecture/lab/tutorial) evening, Online
This unit provides students with core skills in epidemiology, particularly the ability to critically appraise public health and clinical epidemiological research literature regarding public health and clinical issue. This unit covers: study types; measures of frequency and association; measurement bias; confounding/effect modification; randomized trials; systematic reviews; screening and test evaluation; infectious disease outbreaks; measuring public health impact and use and interpretation of population health data. In addition to formal classes or their on-line equivalent,it is expected that students spend an additional 2-3 hours at least each week preparing for their tutorials.
Textbooks
Webb, PW. Bain, CJ. and Pirozzo, SL. Essential Epidemiology: An Introduction for Students and Health Professionals Second Edition: Cambridge University Press 2017.
PUBH5224 Advanced Epidemiology

Credit points: 6 Teacher/Coordinator: Professor Tim Driscoll Session: Semester 2 Classes: Weekly classes (combined lectures and tutorials) for 13 weeks. Prerequisites: (PUBH5010 or CEPI5100) and PUBH5018 Assessment: 1x 1500 word assignment or equivalent class presentation (30%); 1x 4000 word assignment (or equivalent answers to specific methodological questions) (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study is intended for students who have completed Epidemiology Methods and Uses (or an equivalent unit of study) at a credit or higher level. It is designed to extend students' practical and theoretical knowledge of epidemiology beyond basic principles, provide students with an opportunity to consolidate critical appraisal skills and to acquire some of the practical knowledge and skills needed to design epidemiological research.
PUBH5422 Health and Risk Communication

This unit of study is not available in 2019

Credit points: 6 Teacher/Coordinator: Dr Claire Hooker, Associate Professor Julie Leask, Professor Phyllis Butow Session: Semester 2 Classes: Block/intensive 2 blocks of 2 x 9-5 full days; please check with the coordinator for scheduling Assessment: Assignment 1: 1 x 2500 word (35%), Assignment 2: 1 x 2500 words or equivalent (35%), online activities (30%). Attendance at intensives is compulsory and 80% attendance is required to pass the unit of study. Mode of delivery: Block mode
In this unit, students learn how to communicate effectively with respect to health risks, both to individuals with health concerns, and with respect to risks to the public. The first half covers individual health risk communication in clinical settings, including: theories of health communication, patient centred care and shared decision making; evidence-based communication skills; research paradigms including interaction analysis; cross-cultural communication in health care; discussing prognosis; and informed consent. The second half explores risk communication for public health, including: how to effectively manage outbreak or other crisis situations; how to communicate about issues where the risk is low but ublic concern is high (such as with respect to the fluoridation of water); and how to best manage controversies. We teach theories of risk perception and communication with particular application to public health incident responses. We give practical guides to media messages, risk message framing, public engagement, traditional and social media, and the ethical aspects of public communication. The unit offers students the opportunity to learn from outstanding guest lecturers who work in these areas and interactive opportunities for students to try their skills in risk communication and decision making.
Textbooks
Students are provided with a list of readings (in digital format). Most supplementary readings can be accessed through the library or online.
PUBH5500 Advanced Qualitative Health Research

This unit of study is not available in 2019

Credit points: 6 Teacher/Coordinator: Dr Julie Mooney-Somers (Semester 1); Andrea Smith (Semester 2) Session: Semester 1,Semester 2 Classes: 2x3 full day workshop in March/April (semester 1); 2x3 full day workshops in August/September (semester 2) Prohibitions: QUAL5005 or QUAL5006 Assessment: interviewing activity with reflection (25%); 2000wd essay (25%); 2x group presentations (20%); multiple choice quizzes (20%); in-class participation (10%) Mode of delivery: Block mode
This unit of study provides a comprehensive introduction to qualitative inquiry in health. It is designed for beginners and people who want an advanced-level introduction. Over the course of the unit we will address: What is qualitative research? How is it different from quantitative research? What is its history? What research problems can it address? How do I design a qualitative study? What are the different (and best) ways to generate data? How do you analyse qualitative data? Is methodology different to method? What are ontology and epistemology? What is reflexivity (and aren't qualitative researchers biased)? What are the ethical issues? What is good quality qualitative research? Can I generalise qualitative findings? You will get practical experience and skills through carrying out an observation, participating in a focus group, conducting an interview, analysing data, arguing for qualitative research in health, and appraising the quality of published literature. In both workshops you will meet working qualitative researchers and hear about their projects. This advanced unit will show you a new way of thinking critically about research and researching, and give you the skills and confidence to begin evaluating and doing qualitative research for yourself.
STAT5003 Computational Statistical Methods

Credit points: 6 Teacher/Coordinator: A/Prof Shelton Peiris Session: Semester 1,Semester 2 Classes: 2x1-hr lectures; 1x1-hr tutorial/wk Prerequisites: STAT5002 Assessment: Assignments (40%), quizzes (20%); 2-hour final examination (40%) Mode of delivery: Normal (lecture/lab/tutorial) evening
Note: Department permission required for enrolment
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.
Textbooks
(1) An Introduction to Statistical Learning (with Applications in R), Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, (2014), Springer;

Foundation Units

Students may complete a maximum of 24 credit points of the Foundation units of study. Candidates will be required to select units which complement their prior background and qualifications (subject to assessment by the Academic Director).
BETH5102 Philosophy of Medicine

Credit points: 6 Teacher/Coordinator: A/Prof Christopher Jordens Session: Semester 1 Classes: discussions and formal content (lectures) are all online. Assessment: 1x1200wd short written exercise (20%); 1x3000-4000wd major essay (60%); online quizzes and participation online discussions (20%) Mode of delivery: Online
Note: Online only.
This unit of study introduces some philosophical questions and debates concerning medicine and the biomedical sciences. It is divided into three sections. The first explores basic concepts and distinctions such as health, disease, mental illness and disability. The second section deals with topics that lie at the heart of a scientific approach to medicine, namely, causation, experimentation, evidence and clinical reasoning. The final section of the course invites students to reflect critically on the preceding section by exploring the rationality claims of non-orthodox approaches, by inquiring closely into the meaning of medical terms, and by taking a broad view of the notion of risk. All assessments must be completed to pass this Unit.
Textbooks
Required readings are available through the unit of study website
BMET5951 Fundamentals of Neuromodulation

Credit points: 6 Session: Semester 1 Classes: lectures Prohibitions: AMME5951 Assumed knowledge: ELEC1103 or equivalent, (MECH2901 OR BMET2901 OR AMME9901 OR BMET9901), and (MECH3921 or BMET3921 or AMME5921 OR BMET5921) Assessment: through semester assessment (100%) Mode of delivery: Normal (lecture/lab/tutorial) day
Implantable microelectronic devices functioning either as nerve stimulators or nerve blockers comprise one of the largest markets in the global medical device industry. The aim of this unit of study is to give students a complete overview of the underlying technology (microelectronics, encapsulation biomaterials, electrode biomaterials, electrode-neural interactions, inductive power systems and data links, signal processing) and an expert review of the major technological applications on the market, which include Cochlear implants, pacemakers and implantable defibrillators, deep brain stimulators, pain control nerve blockers, bionic eye implants, functional electrical stimulation systems. The unit will also review emerging applications such as gastrointestinal disorders, obesity; vagal nerve stimulation - epilepsy, depression, carotid artery stimulation - hypertension, spinal cord stimulation - ischemic disorders, angina, peripheral vascular disease, incontinence, erectile dysfunction. The unit will conclude with a snapshot of the future: 'brain on a chip' progress, nerve regrowth, neurotropins, drug/device combinations. This is a Master of Professional Engineering Unit of Study intended for biomedical engineering students with an interest in working in the medical device industry in the large market sector area of implantable electronic devices.
COMP5310 Principles of Data Science

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Laboratory Prohibitions: INFO3406 Assessment: Through semester assessment (50%) Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
The focus of this unit is on understanding and applying relevant concepts, techniques, algorithms, and tools for the analysis, management and visualisation of data- with the goal of enabling discovery of information and knowledge to guide effective decision making and to gain new insights from large data sets.
To this end, this unit of study provides a broad introduction to data management, analysis, modelling and visualisation using the Python programming language. Development of custom software using the powerful, general-purpose Python scripting language; Data collection, cleaning, pre-processing, and storage using various databases; Exploratory data analysis to understand and profile complex data sets; Mining unlabelled data to identify relationships, patterns, and trends; Machine learning from labelled data to predict into the future; Communicate findings to varied audiences, including effective data visualisations.
Core data science content will be taught in normal lecture + tutorial delivery mode. Python programming will be taught through an online learning platform in addition to the weekly face-to-face lecture/tutorials. The unit of study will include hands-on exercises covering the range of data science skills above.
COMP9007 Algorithms

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Tutorials Prohibitions: COMP5211 Assumed knowledge: This unit of study assumes that students have general knowledge of mathematics (especially Discrete Math) and problem solving. Having moderate knowledge about Data structure can also help students to better understand the concepts of Algorithms will be taught in this course. Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
The study of algorithms is a fundamental aspect of computing. This unit of study covers data structures, algorithms, and gives an overview of the main ways of computational thinking from simple list manipulation and data format conversion, up to shortest paths and cycle detection in graphs. Students will gain essential knowledge in computer science, including basic concepts in data structures, algorithms, and intractability, using paradigms such as dynamic programming, divide and conquer, greed, local search, and randomisation, as well NP-hardness.
COMP9103 Software Development in Java

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lecture, Laboratory Prohibitions: COMP5214 Assessment: Through semester assessment (40%) and Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
Programming in a legible, maintainable, reusable way is essential to solve complex problems in the pervasive computing environments. This unit will equip students with foundation of programming concepts that are common to widely used programming languages. Students will be progressively guided in this introductory unit from necessary and important building blocks of programming to the object-oriented approach. Java, one of the most popular programming languages, is used in this unit. It provides interdisciplinary approaches, applications and examples to support students from broad backgrounds such as science, engineering, and mathematics.
COMP9110 System Analysis and Modelling

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Tutorials Prohibitions: ELEC3610 OR ELEC5743 OR INFO2110 OR INFO5001 OR ISYS2110 Assumed knowledge: Experience with a data model as in COMP9129 or COMP9103 or COMP9220 or COMP9120 or COMP5212 or COMP5214 or COMP5028 or COMP5138 Assessment: Through semester assessment (30%) and Final Exam (70%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit provides a comprehensive introduction to the analysis of complex systems. Key topics are the determination and expression of system requirements (both functional and non-functional), and the representation of structural and behavioural models of the system in UML notations. Students will be expected to evaluate requirements documents and models as well as producing them. This unit covers essential topics from the ACM/IEEE SE2004 curriculum, especially from MAA Software Modelling and Analysis. Note: The lectures of this unit are co-taught with ISYS2110.
COMP9120 Database Management Systems

Credit points: 6 Session: Semester 1,Semester 2 Classes: Lectures, Tutorials, Project work Prohibitions: INFO2120 OR INFO2820 OR INFO2005 OR INFO2905 OR COMP5138 OR ISYS2120. Students who have previously studied an introductory database subject as part of their undergraduate degree should not enrol in this foundational unit, as it covers the same foundational content. Assumed knowledge: Some exposure to programming and some familiarity with data model concepts Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Normal (lecture/lab/tutorial) day
This unit of study provides a conceptual and practical introduction to the use of common platforms that manage large relational databases. Students will understand the foundations of database management and enhance their theoretical and practical knowledge of the widespread relational database systems, as these are used for both operational (OLTP) and decision-support (OLAP) purposes. The unit covers the main aspects of SQL, the industry-standard database query language. Students will further develop the ability to create robust relational database designs by studying conceptual modelling, relational design and normalization theory. This unit also covers aspects of relational database management systems which are important for database administration. Topics covered include storage structures, indexing and its impact on query plans, transaction management and data warehousing.
In this unit students will develop the ability to: Understand the foundations of database management; Strengthen their theoretical knowledge of database systems in general and relational data model and systems in particular; Create robust relational database designs; Understand the theory and applications of relational query processing and optimisation; Study the critical issues in data and database administration; Explore the key emerging topics in database management.
COMP9121 Design of Networks and Distributed Systems

Credit points: 6 Session: Semester 2 Classes: Lectures, Tutorials Prohibitions: COMP5116 Assessment: Through semester assessment (40%) Final Exam (60%) Mode of delivery: Normal (lecture/lab/tutorial) day
The unit covers general foundations of communication systems and a detailed walk through of the implementation of the TCP/IP protocol stack, which forms the basis of the Internet. The unit also covers the basic knowledge of how to analyse, design and implement simple communication protocols.
On completion of this unit students will have developed an understanding of the principles and practice of the layered model of communications architecture, the TCP/IP protocol stack and its component protocols, and various common techniques and tools for protocol analysis and design.
GLOH5101 Foundations of Global Health

Credit points: 6 Teacher/Coordinator: Dr Seye Abimbol, Dr Giselle Manalo Session: Semester 1 Classes: 1x3hr seminar per week for 12 weeks, 1x1day group presentations Prohibitions: MIPH5131 or MIPH5132 Assessment: 1x1500 word assignment (25%), 1x asynchronized group presentation for online and f2f students (25%), 1x2500 word assignment (40%), assessable tutorial discussion (10%) Mode of delivery: Online, Normal (lecture/lab/tutorial) day
This core unit for the Master of Global Health will give students insight into historical and contemporary contexts of global health and global health interventions, as well as looking at global health as a system. Students will engage critically with the essential issues of global health, including cultural relativism and ethics, equity, human rights, measuring global health, global responsibility and securitisation of health, the role of non-health entities impact on global health, and foreign aid.
Textbooks
Readings are available on the unit's eLearning site
INFO9003 IT for Health Professionals

Credit points: 6 Session: Semester 2 Classes: Lectures, Laboratories, Project Work - own time Prohibitions: INFO5003 Assessment: Through semester assessment (50%) and Final Exam (50%) Mode of delivery: Block mode
Information technologies (IT) and systems have emerged as the primary platform to support communication, collaboration, research, decision making, and problem solving in contemporary health organisations. The essential necessity for students to acquire the fundamental knowledge and skills for applying IT effectively for a wide range of tasks is widely recognised. This is an introductory unit of study which prepares students in the Health discipline to develop the necessary knowledge, skills and abilities to be competent in the use of information technology for solving a variety of problems. The main focus of this unit is on modelling and problem solving through the effective use of using IT. Students will learn how to navigate independently to solve their problems on their own, and to be capable of fully applying the power of IT tools in the service of their goals in their own health domains while not losing sight of the fundamental concepts of computing.
Students are taught core skills related to general purpose computing involving a range of software tools such as spreadsheets, database management systems, internet search engine. Students will undertake practical tasks including scripting languages and building a small scale application for managing information. In addition, the course will address the issues arising from the wide-spread use of information technology in a variety of Health area.
MRTY5132 Medical Image Perception

Credit points: 6 Teacher/Coordinator: Dr Ernest Ekpo Session: Semester 1 Classes: Distance Education Assessment: Online quiz (20%), online discussion activity (20%), literature review 2,500 wd (60%) Mode of delivery: Online
This unit investigates the interaction of the human reader with a medical image. It will start with an examination of the human visual system, including its characteristics and limitations. This will be followed by in-depth studies of the perceptual and cognitive factors that affect the reading of medical images including reader experience, task instructions, and satisfaction of search (as well as other heuristics and biases). The overall aim of this unit is to ensure that students obtain an understanding of the intricacies of image interpretation, and to highlight the components of the process that are technology-independent.
Textbooks
The Handbook of Medical Image Perception and Techniques, E Samei, EA Krupinski (Editors). Cambridge University Press 2014
PUBH5018 Introductory Biostatistics

Credit points: 6 Teacher/Coordinator: Dr Kevin McGeechan, Dr Erin Cvejic Session: Semester 1 Classes: 2 x 2hr lecture, 10 x 1hr lectures, 11 x 2hr tutorials, 2 x 1hr and 8 x 0.5hr statistical computing self directed learning tasks over 12 weeks - lectures and tutorials may be completed online Assessment: Weekly quizzes (10%), 1x4 page assignment (20%), 1 x 1hr online test (20%) and 1x1.5hr open-book exam (50%). For distance students it may be possible to complete the exam externally with the approval of the course coordinator. Mode of delivery: Normal (lecture/lab/tutorial) day, Normal (lecture/lab/tutorial) evening, Online
This unit aims to provide students with an introduction to statistical concepts, their use and relevance in public health. This unit covers descriptive analyses to summarise and display data; concepts underlying statistical inference; basic statistical methods for the analysis of continuous and binary data; and statistical aspects of study design. Specific topics include: sampling; probability distributions; sampling distribution of the mean; confidence interval and significance tests for one-sample, two paired samples and two independent samples for continuous data and also binary data; correlation and simple linear regression; distribution-free methods for two paired samples, two independent samples and correlation; power and sample size estimation for simple studies; statistical aspects of study design and analysis. Students will be required to perform analyses using a calculator and will also be required to conduct analyses using statistical software (SPSS). It is expected that students spend an additional 2 hours per week preparing for their tutorials. Computing tasks are self-directed.
Textbooks
Course notes are provided.
STAT5002 Introduction to Statistics

Credit points: 6 Teacher/Coordinator: A/Prof Shelton Peiris Session: Semester 1,Semester 2 Classes: 2x1-hr lectures; 1x1-hr tutorial/wk Assumed knowledge: HSC Mathematics Assessment: 2 hour examination (60%), assignments (20%), quizzes (20%) Mode of delivery: Normal (lecture/lab/tutorial) evening
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.
Textbooks
All of Statistics, Larry Wasserman, Springer (2004)

Project Units

Candidates for the Master of Health Technology Innovation must complete a minimum of 12 credit points of Project units of study.

Health Technology Innovation Capstone Project

A candidate for the Master of Health Technology Innovation must complete 24 credit points from Core, Specialist or Foundation units of study before taking the Health Technology Innovation Capstone Project. Candidates who do not achieve a credit average may have their eligibility for the Capstone Project subject to review by the Academic Director.
The minimum requirement for the Health Technology Innovation Capstone Project is 12 credit points. Candidates can complete either HTIN6011 and HTIN6012 (6 + 6 credit points over two semesters) or HTIN6020 (12 credit points in one semester).
HTIN6011 Health Technology Innovation Capstone A

Credit points: 6 Session: Semester 1,Semester 2 Classes: Research, Meetings Prohibitions: HTIN6030 or HTIN6032 or HTIN6020 Assessment: Through semester assessment (100%) Mode of delivery: Supervision
Note: Department permission required for enrolment
Note: A candidate for the Master of Health Technology Innovation who has completed 24 credit points from Core, Specialist or Foundation units of study may take this unit. Eligible students for the Health Technology Innovation Capstone project will be required to complete both HTIN6011 (6CPS) and HTIN6012 (6 CPS), totalling 12 CPS. Eligible students of the Master of Health Technology Innovation with a Distinction average or above (after completion of 24 credit points) may choose HTIN6020, HTIN6011/HTIN6012 or HTIN6030/HTIN6032.
The Health Technology Innovation Capstone Project provides an opportunity to utilise technology to model and characterise a complex health challenge, preparing the way for future innovation and/or delivery of a novel technology solution to address an already well-defined health challenge.
It is not expected that the project outcomes of this unit will represent a significant contribution to new knowledge. The unit aims to provide students with the opportunity to carry out a defined piece of independent investigative research or design work in a setting and in a manner that fosters the development of skills in research or design.
The student will be required to demonstrate the desired learning outcome of combining cross-disciplinary contexts of health and technology and management of these in this task. This learning requirement will provide a strong base for future research within the CPC network or work opportunities in the health industry upon completion of the MHTI program.
Projects can be directly tied to a candidate's vocational objectives or interests. Some projects will be experimental in nature, others may involve computer-based simulation, feasibility studies or the design, construction and testing of a software system or equipment. Candidates with experience and expertise from outside the health sector may be invited to partner with relevant team projects. Access to a registry of project opportunities, resources, consultants, co-supervisors will be provided.
Students will generally work individually (or have an individual contribution to group project outcomes) for the semester.
HTIN6012 Health Technology Innovation Capstone B

Credit points: 6 Session: Semester 1,Semester 2 Classes: Research, Meetings Corequisites: HTIN6011 Prohibitions: HTIN6030 or HTIN6032 or HTIN6020 Assessment: Through semester assessment (100%) Mode of delivery: Supervision
Note: Department permission required for enrolment
Note: A candidate for the Master of Health Technology Innovation who has completed 24 credit points from Core, Specialist or Foundation units of study may take this unit. Eligible students for the Health Technology Innovation Capstone project will be required to complete both HTIN6011 (6CPS) and HTIN6012 (6 CPS), totalling 12 CPS. Eligible students of the Master of Health Technology Innovation with a Distinction average or above (after completion of 24 credit points) may choose HTIN6020, HTIN6011/HTIN6012 or HTIN6030/HTIN6032.
The Health Technology Innovation Capstone Project provides an opportunity to utilise technology to model and characterise a complex health challenge, preparing the way for future innovation and/or delivery of a novel technology solution to address an already well-defined health challenge.
It is not expected that the project outcomes of this unit will represent a significant contribution to new knowledge. The unit aims to provide students with the opportunity to carry out a defined piece of independent investigative research or design work in a setting and in a manner that fosters the development of skills in research or design.
The student will be required to demonstrate the desired learning outcome of combining cross-disciplinary contexts of health and technology and management of these in this task. This learning requirement will provide a strong base for future research within the CPC network or work opportunities in the health industry upon completion of the MHTI program.
Projects can be directly tied to a candidate's vocational objectives or interests. Some projects will be experimental in nature, others may involve computer-based simulation, feasibility studies or the design, construction and testing of a software system or equipment. Candidates with experience and expertise from outside the health sector may be invited to partner with relevant team projects. Access to a registry of project opportunities, resources, consultants, co-supervisors will be provided.
Students will generally work individually (or have an individual contribution to group project outcomes) for the semester.
HTIN6020 Health Technology Innovation Capstone

Credit points: 12 Session: Semester 1,Semester 2 Classes: Research, Meetings Prerequisites: A candidate for the Master of Health Technology Innovation who has completed 24 credit points from Core, Specialist or Foundation units of study may take this unit. Prohibitions: HTIN6030 OR HTIN6032 OR HTIN6011 OR HTIN6012. Students of the Master of Health Technology Innovation with Distinction average marks or above (after completion of 24 credit points) may choose either this unit or HTIN6011/HTIN6012 or HTIN6030/HTIN6032. Assessment: through semester assessment (100%) Mode of delivery: Supervision
Note: Department permission required for enrolment
The Health Technology Innovation Capstone Project provides an opportunity to utilise technology to model and characterise a complex health challenge, preparing the way for future innovation and/or delivery of a novel technology solution to address an already well-defined health challenge. It is not expected that the project outcomes of this unit will represent a significant contribution to new knowledge. The unit aims to provide students with the opportunity to carry out a defined piece of independent investigative research or design work in a setting and in a manner that fosters the development of skills in research or design. The student will be required to demonstrate the desired learning outcome of combining cross-disciplinary contexts of health and technology and management of these in this task. This learning requirement will provide a strong base for future research within the CPC network or work opportunities in the health industry upon completion of the MHTI program. Projects can be directly tied to a candidate's vocational objectives or interests. Some projects will be experimental in nature, others may involve computer-based simulation, feasibility studies or the design, construction and testing of a software system or equipment. Candidates with experience and expertise from outside the health sector may be invited to partner with relevant team projects. Access to a registry of project opportunities, resources, consultants, co-supervisors will be provided. Students will generally work individually (or have an individual contribution to group project outcomes) for the semester.

Health Technology Innovation Dissertation Project

With permission from the Dean or nominee, a candidate for the Master of Health Technology Innovation who has completed 24 credit points from Core, Specialist or Foundation units of study with at least Distinction average marks may take Health Technology Innovation Dissertation Project units.
The minimum requirement for the Health Technology Dissertation Project is 24 credit points (HTIN6030 and HTIN6032).
HTIN6030 Health Technology Innovation Thesis A

Credit points: 12 Session: Semester 1,Semester 2 Classes: Research, Meetings Prohibitions: HTIN6011 or HTIN6012 or HTIN6020 Assessment: Through semester assessment (100%) Mode of delivery: Supervision
Note: Department permission required for enrolment
Note: A candidate for the Master of Health Technology Innovation who has completed 24 credit points from Core, Specialist or Foundation units of study may take this unit. Eligible students for the Health Technology Innovation Thesis project will be required to complete both HTIN6030 (12 CPS) and HTIN6032 (12 CPS), totaling 24 CPS. Eligible students of the Master of Health Technology Innovation with a Distinction average or above (after completion of 24 credit points) may choose HTIN6020, HTIN6011/HTIN6012 or HTIN6030/HTIN6032.
The Health Technology Innovation Thesis Project provides an opportunity to utilise technology to model and characterise a complex health challenge, preparing the way for future innovation and/or delivery of a novel technology solution to address an already well-defined health challenge.
It is not expected that the project outcomes of this unit will represent a significant contribution to new knowledge. The unit aims to provide students with the opportunity to carry out a defined piece of independent research or design work in a setting and in a manner that fosters the development of skills in original research or design.
The student will be required to demonstrate the desired learning outcome of combining cross-disciplinary contexts of health and technology and management of these in this task. This learning requirement will provide a strong base for future research within the CPC network or work opportunities in the health industry upon completion of the MHTI program.
Projects can be directly tied to a candidate's vocational objectives or interests. Some projects will be experimental in nature, others may involve computer-based simulation, feasibility studies or the design, construction and testing of a software system or equipment. Candidates with experience and expertise from outside the health sector may be invited to partner with relevant team projects. Access to a registry of project opportunities, resources, consultants, co-supervisors will be provided.
Students will generally work individually (or have an individual contribution to group project outcomes) for the semester.
HTIN6032 Health Technology Innovation Thesis B

Credit points: 12 Session: Semester 1,Semester 2 Classes: Research, Meetings Corequisites: HTIN6030 Prohibitions: HTIN6011 or HTIN6012 or HTIN6020 Assessment: Through semester assessment (100%) Mode of delivery: Supervision
Note: Department permission required for enrolment
Note: A candidate for the Master of Health Technology Innovation who has completed 24 credit points from Core, Specialist or Foundation units of study may take this unit. Eligible students for the Health Technology Innovation Thesis project will be required to complete both HTIN6030 (12 CPS) and HTIN6032 (12 CPS), totaling 24 CPS. Eligible students of the Master of Health Technology Innovation with a Distinction average or above (after completion of 24 credit points) may choose HTIN6020, HTIN6011/HTIN6012 or HTIN6030/HTIN6032.
The Health Technology Innovation Thesis provides an opportunity to utilise technology to model and characterise a complex health challenge, preparing the way for future innovation and/or delivery of a novel technology solution to address an already well-defined health challenge.
It is not expected that the project outcomes of this unit will represent a significant contribution to new knowledge. The unit aims to provide students with the opportunity to carry out a defined piece of independent research or design work in a setting and in a manner that fosters the development of skills in original research or design.
The student will be required to demonstrate the desired learning outcome of combining cross-disciplinary contexts of health and technology and management of these in this task. This learning requirement will provide a strong base for future research within the CPC network or work opportunities in the health industry upon completion of the MHTI program.
Projects can be directly tied to a candidate's vocational objectives or interests. Some projects will be experimental in nature, others may involve computer-based simulation, feasibility studies or the design, construction and testing of a software system or equipment. Candidates with experience and expertise from outside the health sector may be invited to partner with relevant team projects. Access to a registry of project opportunities, resources, consultants, co-supervisors will be provided.
Students will generally work individually (or have an individual contribution to group project outcomes) for the semester.

For more information on units of study visit CUSP https://cusp.sydney.edu.au