Credit points


Campus offering

No unit offerings are currently available for this unit



Unit rationale, description and aim

The rapidly emerging digital health landscape is requiring fundamental knowledge relating to data, data use and digital ecosystems. In addition, there is a need for professionals working in health to understand how data can be used to improve service provision and person-centredness. Health data represent the greatest asset supporting value-based health care within a digital health ecosystem. Health data supports individual patient health journeys, person-centred care, work and communication flows. Secondary use of health data informs decision making, resource management, analytics, reporting, funding and policy development. Health data fundamentals refer to specialised components of the new interdisciplinary science now known as digital health. These include health data, their attributes, collection, storage, stewardship, governance, semantics, and the ethical use as components of any data supply chain within a digital health ecosystem.

The health data supply chain needs to not only support clinical practice at every point of care, but support all user needs throughout the national health system as whole. There is a need for compliance with legislative requirements, preservation of data privacy and maintenance of security measures throughout. All data use must comply with ethical principles and support human dignity for those served by each health service provider within a digital health ecosystem.

In this unit, students are introduced to the key concepts relating to health data including concept representation, semantics, ontologies, information management and ecosystem networks. The focus is on degrees of data expressivity and formalism required to support automation, advanced data analytics, national operational activities, semantic data processing and communication flows. The aim of this unit is to provide clinicians, health service and ICT professionals with the knowledge and skills needed to address data integrity throughout the data supply chain, enabling them to have confidence that data integrity is maintained. 

Learning outcomes

To successfully complete this unit you will be able to demonstrate you have achieved the learning outcomes (LO) detailed in the below table.

Each outcome is informed by a number of graduate capabilities (GC) to ensure your work in this, and every unit, is part of a larger goal of graduating from ACU with the attributes of insight, empathy, imagination and impact.

Explore the graduate capabilities.

On successful completion of this unit, students should be able to:

Explain the importance of the adoption and use of data standards within a digital health ecosystem  (GA5)

Describe the health data supply chain and data use at every level within a national digital health ecosystem (GA5)

Analyse information and communication flows required to support people’s health journeys and the data supply chain (GA4)

Critically evaluate the link between the adoption of health data standards and health data exchange protocols relative to data integrity and ethical use of data (GA3, GA8)

Graduate attributes

GA3 - apply ethical perspectives in informed decision making

GA4 - think critically and reflectively 

GA5 - demonstrate values, knowledge, skills and attitudes appropriate to the discipline and/or profession 

GA8 - locate, organise, analyse, synthesise and evaluate information 


Topics will include:

·        Health languages and their attributes suitable for a digital era

·        Health data standards, including data specification for the data supply chain, maintaining data integrity, stewardship

·        Coding and classification systems, ownership, use cases and implementation

·        Terminologies, degrees of data expressivity and formalisms

·        Domain ontologies, reference information models

·        Data modelling, templates, presentation formats

·        Data collection, accurate little data, data value sets

·        Data storage methods and data supply chains

·        Registries, data lakes, data hubs and warehouses

·        Managing digital data access, transfers, linkages, querying

·        Big data, ethical data retrieval, primary and secondary use, legislation

·        Data/information governance at all levels

Learning and teaching strategy and rationale

This unit will be delivered in online mode using an active learning approach where students are expected to complete readings, reflect and engage with peers over a twelve-week semester or equivalent study period. Students will have access to self-paced learning modules, readings, webinars, discussion forums and assessment tasks via the ACU Learning Environment Online (LEO). While there are no formal lectures for this unit, students will be required to attend weekly one-hour online forums, which will provide opportunities to analyse and evaluate various health data-related challenges and concepts to meet unit learning outcomes. Online forums and chat rooms will facilitate learning by sharing experiences and findings with peers, which is particularly effective for exploring data standard selection and use. This learning approach is flexible and inclusive, allowing students the opportunity to analyse and critically evaluate the complexity associated with health data, information and knowledge attributes and the adoption of technical and data standards.


Students should anticipate undertaking 150 hours of study for this unit, including readings, participation in online forums and completion of assessment tasks.

Assessment strategy and rationale

The assessment strategy for this unit allows students to demonstrate a critical mindset in evaluating the impact of data and information management strategies associated with the delivery of person-centred health services. In order to develop this level of capability, in the first two assessment tasks students will be required to demonstrate their knowledge on how to identify and evaluate the use of data standards within the context of a data supply chain which is a foundational infrastructure component of any digital health ecosystem. The final assessment task allows students to demonstrate the depth of their knowledge and understanding of work in a digital health enhanced world through a case study assignment. The assessment tasks for this unit are designed for students to demonstrate achievement of each learning outcome.

Overview of assessments

Brief Description of Kind and Purpose of Assessment TasksWeightingLearning OutcomesGraduate Attributes

Assessment Task 1

Assessment Task 1 requires students to apply their critical knowledge of concepts and skills learned throughout this unit of study. The purpose of this assessment task is to evaluate the student’s grasp of the complexities associated with data standards within any health care organisation and setting supporting individual health journeys.

Example: A written reflective journal based on contributions to online discussions throughout the unit.


LO1, LO2


Assessment Task 2

Assessment Task 2 requires students to apply knowledge learned in their exploration of how data attributes and characteristics interact with various health informatics data exchange standards, and reflect on retaining data sharing semantics and information computability.

Individual written report.


LO2, LO3

G4, GA5

Assessment Task 3

Assessment Task 3 requires students to apply their critical knowledge of concepts and skills learned throughout the unit and produce a case study report. The case study can be based on the student’s work situation where applicable, or a defined example case study. The purpose of this assessment task is to examine students’ grasp of both theoretical and practical aspects of the unit through their problem solving and application of theoretical knowledge to real-life health service delivery problems in a given scenario.

Case Study.


LO2, LO3, LO4

GA3, GA4, GA5, GA8

Representative texts and references

Australian Institute of Health and Welfare (2018). Metadata standards. Available from

Celi, L. A., Majumder, M. S., Ordoñez, P., Osorio, J. S., Paik, K. E., & Somai, M. (Eds.). (2020). Leveraging data sciences for global health. Springer (Open Access)

Cimino, J. J. (1998). Desiderata for controlled medical vocabularies in the twenty-first century. Methods of Information in Medicine, 37(4-5), 394-403. doi: 10.1055/S-0038-1634558

Hovenga, E. J. S. & Grain, H. (Eds.). (2013). Health information governance in a digital environmentStudies in Health Technology Informatics 193. IOS Press.

Hovenga, E. J. S, Kidd, M., R., Garde, S. & Hullin (Eds.). (2010). Health informatics: An overview. Studies in Health Technology Informatics. IOS Press

International Organization for Standardization (2019). ISO/TS 21526:2019. Health informatics – Metadata repository requirements (MetaRep). International Organization for Standardization

International Organization for Standardization (2019). ISO/TS 21564:2019. Health informatics – Terminology resource map quality measures (MapQual). International Organization for Standardization

Kubben, P., Dumontier, M., and Dekker, A. (Eds.). (2019). Fundamentals of clinical data science. Springer (Open Access)

Standards Australia (2005). AS 5021-2005: The language of health concept representation. Standards Australia

Standards Australia (2015). IOS/IEC 11179-1:2015. Information technology – Metadata registries (MDR). Standards Australia

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