Collections
Biomarkers
This collection of data groups represents the measurements of common biomarker analytes used to manage or monitor chronic diseases and to drive decision support.
These data groups are designed to align with common practice, where clinical systems either extract this data from pathology test results or clinicians enter it manually. Each data group is designed separately, representing only the analyte measurements for each biomarker, serving as a temporary measure until a more formal ‘Pathology test result’ data group is established in future AUCDI updates.
As new use cases are identified in scope for AUCDI, the range of the Biomarker collection is expected to expand with additional data group concepts.
Interventions
This collection of data groups represents the Interventions used to prevent, diagnose, treat, or manage a health condition, support mental or physical well-being, or address social and environmental factors that influence health outcomes.
The term ‘intervention’ is widely used within the clinical care domain, although often with an equally broad interpretation. Clinicians typically understand each other’s intent during verbal communications, however, for documentation purposes in electronic health records, a more precise definition is necessary. This precision ensures that each type of intervention can be appropriately recorded within appropriate data groups. Accurate semantics underlying these data groups will be required to safely support data-driven tools like clinical decision support systems or AI applications.
In a series of workshops held in late 2024 and early 2025, the Sparked CDG explored and discussed how the concept of an ‘intervention’ was defined and used by a broad range of healthcare professionals. Outcomes from these workshops identified:
- The term ‘intervention’ referred to a range of activities carried out by clinicians, rather than representing a single generic activity.
- Different clinicians used a range of different types of interventions.
- The universal need to be able to track the progress of interventions from initiation through to completion, especially within the context of a distributed care management team and a shared, team-based care plan.
As new use cases are identified in scope for AUCDI, the range of the Interventions collection is expected to expand with additional data group concepts.
Definition of Intervention:
Based on the Sparked CDG’s feedback, the working definition of an intervention for AUCDI is:
A single therapeutic activity, or a series of activities, intended to prevent, diagnose, treat, or manage a health condition, support mental or physical well-being, or address social and environmental factors that influence health outcomes.
Interventions approach
Measurements & Vital signs
This collection of data groups represents common anthropometric measurements and critical physiological parameters.
Each measurement or vital sign is designed as a separate data group. In future releases of AUCDI, it is anticipated that each data group will be extended, by including extra attributes that provide additional context such as the state of the patient at the time of measurement and the method of measurement needed for the accurate interpretation. These additional attributes will vary depending on the measurement or vital sign, and the range of variation has been represented in the mind map found in the ‘For future consideration’ section for each data group.
As new use cases are identified in scope for AUCDI, the range of the Measurements and Vital signs collection is expected to expand with additional data group concepts.
Social Determinants of Health (SDOH)
This collection of data groups represents common social determinants of health.
The World Health Organization[1] describes Social Determinants of Health (SDOH) as “the non-medical factors that influence health outcomes. They are the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life. These forces and systems include economic policies and systems, development agendas, social norms, social policies and political systems.”
It has been noted that there is a lack of a standardisation about SDOH data collection and reporting[2]. While questionnaire-based assessments are the most commonly used methods for collecting SDOH data, primarily to identify problems or risks, there is also no consensus on which key SDOH domains need to be collected[3]&[4] nor how it should be represented.
In Australia there are significant gaps in data on SDOH and health inequities that needed to be addressed[5]. Recognising the importance of this issue, the Sparked CDG has prioritised the inclusion of SDOH data in AUCDI.
There appears to be little prior research on how to standardise the recording of SDOH data in a neutral manner within electronic health records. Consequently, the initial data groups outlined in this section are designed for comprehensive documentation of health and care information within clinical systems, building on previous AUCDI data group design and philosophy. This includes incorporating specific data elements that are particularly significant and relevant for SDOH initiatives, and which can be reused in risk assessments or for reporting purposes, as needed.
As new use cases are identified in scope for AUCDI, the range of the SDOH collection is expected to expand with additional data group concepts.
[1] https://www.who.int/health-topics/social-determinants-of-health#tab=tab_1
[2] Social Determinants of Health Data: Survey results on the collection, integration and use https://www.ahima.org/media/03dbonub/ahima_sdoh-data-report.pdf
[3] Social Determinants of Health Data: Survey results on the collection, integration and use https://www.ahima.org/media/03dbonub/ahima_sdoh-data-report.pdf
[4] https://thegravityproject.net/advancing-sdoh-and-health-equity-data-interoperability/
[5] The need for improved Australian data on social determinants of health inequities https://onlinelibrary.wiley.com/doi/pdf/10.5694/mja2.51495