The Graduate Certificate Programme offers four courses:
Course 1: Implementation Science (4 units)
This course will introduce health care professionals to the principles of translating evidence for better services into a clinical setting. Participants will learn processes and factors associated with successful integration of evidence-based interventions within a particular setting, assess whether the core components of the original intervention were faithfully transported to the real-world setting and gain new knowledge about the adaptation of the implemented intervention to the local context.
On successful completion of this course, students will be able to:
- Design interventions based on community, patient, clinician and organizational inputs to translate findings into clinical practice, policy and public health
- Design evaluations of interventions that translate evidence into practice
- Develop better proposals
- Develop a design for a research implementation and/or a dissemination and evaluation project
Course 2: Research Methods for Health Services Research (4 units)
This course is about the basic principles of research and how they apply to health services research. It will include a discussion of hypothesis testing, the different types of data used for research and a formal treatment of Fisherian inference will be provided. The theory will be shown in practice with worked examples and there will be a general discussion of the reproducibility crisis. Different approaches to assess causality and associations are taught. An introduction to qualitative research methods is provided and participants are taught how to design good surveys.
Upon completion of the course, students should be able to:
- Articulate the research process and formally state a hypothesis
- Understand Type I and II error
- Be able to critique published research that uses a range of study designs
- Understand the different approaches to assessing causality used in health services research
- Apply the methods of qualitative research
- Understand the requirements for good survey design
Course 3: Health Technology Assessment, Economic Evaluation and Decision Making (4 units)
You will learn about Heath Technology Assessment and Cost-Effectiveness Analysis, and how they relate to decision making for health services. Health Technology Assessment is a well-established tool used when decisions need to be made quickly. Economic evaluation and cost-effectiveness, emerge from welfare economics and offer a theory based approach to informing choice and trade-offs given scarcity of resources. Often, new data are required or a more formal research approach is used. Various modelling approaches are used to complete economic evaluations.
Upon completion of the course, students should be able to:
- Complete their own health technology assessment project and understand the rationale and methods for doing one.
- Understand the principles of welfare economics, market failures and how they apply to the supply of health care services.
- Be able to build and evaluate a cost-effectiveness model
- Be able to read and interpret a published cost-effectiveness study, able to collaborate with a health economist for a new study.
- Understand the scientific paradigms of decision making and how they come into conflict with traditional scientific approaches.
Course 4: Healthcare + Data Science (4 units)
This course exposes students to the foundation concepts, case studies and applications, some mathematics behind data science models and algorithms. There will also be practical sessions for model development, training, validation and tests. Students will acquire new knowledge of data science techniques. The new knowledge from this course will enable predictions to be made about likely diagnoses, prognoses of health conditions and risks of adverse events.
There will also be a mini-project and healthcare case studies to demonstrate the applicability of data science as a key enabler for improving the delivery of health services.
Upon completion, students should be able to:
- Understand the value and application of data science for the future of health services
- Develop the ability to independently conduct health data science projects