How are technology, digital health and big data reshaping Australian health care, and who benefits?
Investigate how technology, digital health and big data influence health outcomes, access and equity in Australia
A focused HSC Health and Movement Science answer on technology, digital health and big data. Covers My Health Record, Medicare-funded telehealth, wearables and continuous glucose monitors, AI in diagnostics, the Atlas of Healthcare Variation, plus the data-privacy and digital-divide equity questions these technologies raise.
Reviewed by: AI editorial process; not yet individually human-reviewed
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What this sub-topic is asking
NESA wants you to describe the main forms of digital health technology in use in Australia (records, telehealth, wearables, big-data analytics, AI), explain how each affects access and outcomes, and assess the equity and privacy implications, especially the digital divide.
The answer
The main technologies
- My Health Record
- Australia's national personal health record, run by the Australian Digital Health Agency. Allows GPs, specialists, pharmacists and hospitals to view a patient summary (medications, allergies, recent diagnostic results, hospital discharge summaries). Opt-out, so most Australians have a record, though active engagement is much lower.
- Telehealth
- Phone and video consultations funded through the Medicare Benefits Schedule (MBS). Permanent MBS telehealth items were expanded substantially during the COVID-19 pandemic from 2020 and have remained as ongoing items, particularly for GP and mental-health consultations. Removes travel barriers for rural and remote patients and reduces work-time loss for urban patients.
- Wearables and consumer devices
- Fitness trackers and smart watches that measure steps, heart rate, sleep and (on newer devices) ECG or blood oxygen. Continuous glucose monitors (CGMs) are now subsidised through the National Diabetes Services Scheme for many people with Type 1 diabetes; they replace finger-prick testing with near-real-time glucose data and have transformed self-management.
- Big data and population analytics
- Aggregated data sets that let researchers and policymakers see patterns across the whole system. Examples:
- The Australian Atlas of Healthcare Variation (Australian Commission on Safety and Quality in Health Care) maps how rates of procedures, prescribing and admissions vary by geography, surfacing inequity.
- The AIHW's Australia's Health reporting series.
- Linked-data projects that connect MBS, PBS, hospital and registry data.
Artificial intelligence (AI) in diagnostics. AI image-analysis tools are increasingly deployed in radiology (mammography, CT) and dermatology, typically as a "second reader" assisting a clinician rather than replacing them. Australian roll-out is regulated by the Therapeutic Goods Administration; uptake is uneven and still under formal evaluation.
Effects on access and outcomes
- Improving access
- Telehealth removes travel; wearables push prevention into daily life; My Health Record reduces duplicate testing when a patient moves between providers; CGMs improve diabetes self-management.
- Improving outcomes
- Better information at the point of care reduces medication error, missed allergy notes and unnecessary repeats. Atlas-style data drive system-level change by showing geographic variation that cannot be defended clinically.
- Reshaping the workforce
- Clinicians spend more time interacting with screens and less with patients in some settings; rural generalists can access metro specialist input via telehealth; pharmacists use My Health Record to reconcile medications.
Data privacy and digital divide
Privacy. Centralised records create centralised risk. Concerns include consent (especially for sensitive items like mental-health and sexual-health records), secondary use of data for research, and the risk of breaches. Regulation sits with the Office of the Australian Information Commissioner and the Australian Digital Health Agency.
The digital divide. Benefits cluster with those who already have devices, data, English-language confidence and digital health literacy. People who are older, on lower incomes, in remote areas, or who speak a language other than English at home are systematically less likely to use a patient portal, install a wearable, or sustain a video consult on mobile data. Telehealth without translated materials, captioning, or a digital-navigator service can widen the inequity it was meant to reduce.
This is the central equity tension: digital health raises the average but can widen the gap unless explicitly designed for priority populations.
Examples in context
Example 1. The Australian Atlas of Healthcare Variation. Published by the Australian Commission on Safety and Quality in Health Care, the Atlas maps how rates of common procedures (knee arthroscopy, antibiotic prescribing, caesarean section, ADHD medication dispensing) vary across local areas. Variation that is not explained by clinical need flags inequity or low-value care. This is the canonical Australian example of big data being used to drive system change: the data itself becomes an intervention by creating public accountability and informing local clinical-governance work.
Example 2. Continuous Glucose Monitors (CGMs) and Type 1 diabetes. The federal government's expansion of CGM subsidy through the National Diabetes Services Scheme has put near-real-time glucose data into the hands of most Australians with Type 1 diabetes, replacing many finger-prick checks. Self-management, hypoglycaemia awareness and time-in-range have all benefited in published Australian data. The example illustrates how a consumer-style device, when paired with a public subsidy, can shift outcomes for a defined clinical population, while still leaving an equity question about who has the digital literacy and supports to use it well.
Try this
Q1. Outline three digital health technologies in current Australian use and explain how each affects access to care. [6 marks]
- Cue. My Health Record (shared summary across providers), MBS-funded telehealth (removes travel, especially rural/remote), wearables/CGMs (push prevention and self-management into daily life). One sentence on access per item.
Q2. Explain how big data is used to identify and address health inequities in Australia. [6 marks]
- Cue. Australian Atlas of Healthcare Variation (geographic variation, low-value care), AIHW reporting, linked MBS-PBS-hospital data. Big data identifies the inequity, informs targeted policy and creates accountability.
Q3. Assess the impact of telehealth on equity of access to health care in Australia since the 2020 MBS expansion. [8 marks]
- Cue. Improved access (rural/remote, working patients, mental-health continuity). Equity caveats (broadband, digital literacy, language). Use the proportionate-universalism framing: telehealth as a universal expansion plus targeted supports (digital navigators, translated materials) for priority populations. Reach a clear judgement.
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