3.4 Examine how big data is shaping the health of Australians
About the dot point
Big data in health refers to very large, complex and varied information that can be analysed to identify patterns in disease, service use, and health outcomes across Australia. Because it can bring together data from sources such as Medicare, hospitals, electronic health records, and linked datasets, big data is increasingly influencing how health problems are detected, how care is delivered, and how public health is planned.
How to approach it
The directive verb in this dot point is examine. This means you need to look closely and carefully into the ways big data is shaping the health of Australians, using relevant evidence and examples from the content. As you work through this page, focus on what the data reveals about individual care and system-level decisions, and use the examples and statistics provided to support a purposeful inquiry, rather than simply listing facts.
1. Big data in health
Big data in health refers to extremely large datasets that can be analysed to identify patterns, trends and associations. In Australia, this includes very large amounts of health information collected across clinical care, administration, research, surveillance, and consumer technologies.
What makes big data useful is not simply the amount of information. Its value comes from being able to combine, analyse and interpret information from many sources. This helps health professionals, researchers and governments make better decisions about care, prevention, research and policy.
Big data has significant potential to improve the health of Australians because it supports better planning, earlier intervention, more efficient care, stronger research, and more personalised treatment.
However, these benefits depend on strong protections for privacy, confidentiality, and public trust. If those protections are weak, the risks increase. If they are strong, big data can be used in ways that improve both health outcomes and the overall effectiveness of the healthcare system.
1.1 Where health big data comes from
Health big data can come from many sources, including:
- clinical data, such as hospital records, pathology, imaging, and medicines information
- administrative data, such as Medicare and PBS claims
- population and surveillance data, such as immunisation and communicable disease collections
- research data, including trial data and genomic data
- consumer-generated data, such as information from apps, wearables, and remote monitoring devices
When these sources are analysed together, they provide a much broader picture of the health of Australians than any single dataset can provide on its own.
1.2 Why big data matters
Big data is shaping the health of Australians because it supports evidence-based decision-making. This means decisions are based on strong data rather than guesswork or assumptions.
It helps health professionals understand disease patterns, helps services understand demand and outcomes, helps governments plan programmes and spending, and helps researchers identify better ways to prevent, diagnose and treat disease.
It is especially valuable when datasets are linked. Linked data means information from different sources is connected in a secure way so that health journeys, service use, and outcomes can be understood more clearly over time.
Example: A health service may link hospital admissions data with medicines data and follow-up care data. This can show whether patients are being discharged with the right treatment and whether they are returning to hospital soon afterwards.
2. How is it being used?
Big data is being used at the individual, service, and population levels. At each level, the purpose is slightly different, but the overall goal is the same: to improve decisions and improve health outcomes.
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Area of use |
How big data is used |
|---|---|
|
Individual care |
Supports diagnosis, treatment decisions, medicine safety, ongoing monitoring, and the management of long-term conditions. |
|
Service planning and delivery |
Improves staffing, capacity, patient flow, follow-up care, programme evaluation, and service improvement. |
|
Public health, policy and research |
Tracks disease patterns, identifies groups experiencing poorer outcomes, informs health promotion, guides resource allocation, evaluates policies and programmes, and supports health research. |
2.1 Individual care
At the individual level, big data helps clinicians build a more complete picture of a person’s health. Information from tests, treatment history, medicines, previous admissions, and ongoing monitoring can be brought together to support safer and more informed decisions.
This is especially useful when a person has a long-term condition that needs regular review over time rather than one-off treatment. Instead of relying on a single appointment or result, clinicians can look at patterns and trends.
Example: A person living with type 1 diabetes uses a continuous glucose monitor. The repeated data shows regular overnight drops in blood glucose levels. This helps the clinician adjust treatment before the person has a serious hypoglycaemic episode.
2.2 Service planning and delivery
At the service level, big data is used to improve how healthcare is delivered. Health services can analyse patterns in admissions, waiting times, treatment outcomes, and service use to identify pressure points and improve patient flow.
This helps services:
- plan staffing more effectively
- identify areas of high demand
- reduce delays and bottlenecks
- improve follow-up care
- evaluate whether programmes are working
Big data is therefore important not only for treatment, but also for healthcare administration and service improvement.
Example: A hospital analyses admission data and finds that emergency presentations rise sharply on weekends in one region. It responds by adjusting staffing and expanding weekend support services.
2.3 Public health, policy and research
At the population level, big data is used to monitor disease, identify groups experiencing poorer outcomes, inform health promotion, guide resource allocation, and evaluate whether programmes and policies are working.
This allows governments and health organisations to respond more strategically. Instead of applying the same strategy everywhere, they can target support where the data shows the greatest need.
Example: Screening data may show that one area has lower participation in bowel cancer screening than similar communities. Public health agencies can then target awareness campaigns and local outreach to that area.
3. How is it reducing healthcare spending?
Big data can reduce healthcare spending when it helps the system deliver higher-value healthcare. Higher-value healthcare means achieving better health outcomes through more effective use of resources.
The saving does not come from simply collecting more data. It comes from using data to reduce waste, improve efficiency, and prevent avoidable illness or treatment.
|
Supporting prevention |
Big data can reduce healthcare spending by supporting prevention. When large datasets reveal patterns in disease, risk factors, and service use, health organisations can identify where preventable illness is most likely to occur. This allows prevention strategies to be targeted more effectively rather than applying the same approach to every population. This can reduce long-term spending because prevention is often less expensive than treating advanced disease. For instance, if health data shows rising obesity, low physical activity, and poor diet in a particular region, targeted prevention programmes can be introduced before rates of type 2 diabetes and cardiovascular disease rise further. |
|
Enabling earlier intervention |
Big data also reduces spending by enabling earlier intervention. When data identifies risk patterns early, health services can respond before conditions become more severe and more expensive to treat. This matters because treatment is usually less costly and more effective when problems are addressed early rather than after complications develop. Earlier intervention may include:
For instance, if linked data shows increasing numbers of people with pre-diabetes markers, services can provide screening, nutrition support, and behaviour change programmes before many of those individuals develop type 2 diabetes. |
|
Reducing duplication |
Another way big data reduces spending is by reducing duplication. If health professionals can access previous test results, treatment history, or service use information more easily, there is less need to repeat the same tests, appointments, or procedures. Reducing duplication can lower spending by:
For instance, if a specialist can access recent imaging already completed through another provider, the patient may not need to repeat the same investigation. |
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Identifying low-value care |
Big data can help identify low-value care. Low-value care is care that adds cost without meaningfully improving health outcomes. By analysing large datasets across hospitals, clinics, and regions, the healthcare system can identify patterns of care that are not producing strong results. This is important because some services may be widely used without clear benefit. If one region is performing much higher rates of a particular investigation or procedure without better patient outcomes, the data may suggest overuse rather than improved care. This allows resources to be redirected towards care that is more effective. |
|
Improving efficiency |
Big data can also reduce healthcare spending by improving efficiency. When health services understand patterns in admissions, waiting times, staffing needs, treatment flow, and discharge timing, they can run services more effectively. Improved efficiency can include:
For instance, a hospital that uses historical admissions data to predict peak demand can roster staff more effectively and reduce overcrowding in emergency care. |
|
Strengthening resource allocation |
Big data strengthens resource allocation by helping governments and health organisations decide where funding and services are needed most. If the data shows that some groups or locations are experiencing higher disease burden, poorer outcomes, or reduced access to care, resources can be directed more strategically. This improves both sustainability and equity because funding is based more clearly on need. For instance, if one rural area shows much higher rates of avoidable hospital admissions for asthma, more funding can be directed towards local respiratory education, primary care support, and earlier follow-up. |
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Reducing avoidable hospital admissions and readmissions |
Big data can reduce costs by reducing avoidable hospital admissions and readmissions. If data identifies people at greater risk of deterioration or repeat admission, services can respond earlier with follow-up care, medicines review, or community-based support. This can reduce spending because hospital care is expensive, especially when admissions could have been prevented through earlier intervention. For instance, a person discharged after treatment for heart failure may be flagged as high risk for readmission, allowing early monitoring and follow-up support that reduces the chance of returning to hospital soon after discharge. |
4. How is it being used to cure and manage diseases?
Big data does not cure disease by itself. Instead, it helps health professionals and researchers understand disease better, identify risks earlier, improve treatment choices, and manage conditions more effectively over time.
In this way, it contributes to both disease management and the development of better treatments.
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Earlier diagnosis and risk identification |
By analysing very large datasets, health systems can identify patterns that may not be obvious from one person’s case alone. This improves the ability to detect disease earlier, identify people at higher risk, and act sooner. Earlier diagnosis matters because treatment is often more effective when a condition is found before it has progressed. For instance, linked screening and diagnostic data may show that a group has lower rates of early cancer detection. This can lead to earlier testing strategies and improved access to diagnostic services. |
|
Personalised treatment and genomics |
Big data is used to support precision medicine and genomic medicine by comparing a person’s health information with very large datasets from other patients. This helps health professionals identify which treatments are most likely to work for people with similar genes, disease features, symptoms, test results or treatment histories. This supports the management of disease because treatment can be chosen more accurately, monitored more closely and changed sooner if it is not working. It can also support progress towards cures because researchers can use large genomic and treatment datasets to understand why diseases develop, why some treatments work better than others, and where new treatments could be targeted. Genomics is the study of a person’s genes. Genomic data can help identify disease risk, improve diagnosis, classify some cancers more accurately, and guide more personalised treatment. For instance, genomic testing may show that a person’s tumour has a specific mutation. Doctors can compare this information with large cancer datasets to select a treatment that is more likely to work for that cancer type, rather than using a general treatment approach. |
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Managing chronic disease |
Big data is especially useful in the management of chronic disease because long-term conditions generate repeated information over time. Trends in medicines use, monitoring devices, hospital presentations, and other records can help clinicians identify deterioration earlier and adjust care before a crisis develops. This improves continuity of care and can reduce complications. Conditions where this is especially useful include:
For instance, a person with chronic heart failure records daily weight and symptoms through remote monitoring. A sudden change can alert clinicians to fluid retention before the person requires emergency treatment. |
|
Public health surveillance and treatment research |
At the population level, big data supports disease surveillance and treatment research. Surveillance means the ongoing collection and analysis of data to track disease patterns and detect changes over time. This helps health systems:
For instance, during an infectious disease outbreak, rapidly updated case and hospital data can help health authorities identify hotspots and direct public health responses more quickly. |
5. What measures need to be taken to ensure privacy and confidentiality of personal information?
Because health information is highly sensitive, strong protections are needed to ensure both:
- Privacy: a person’s right to have control over their personal information and how it is collected, used and shared.
- Confidentiality: keeping that information secure and only allowing access to authorised people for legitimate purposes.
These protections are essential if big data is to be used responsibly and if the public is to trust the healthcare system. Health data can include information about a person’s:
- medical history
- medicines
- test results
- mental health
- genetics
- treatment history
- personal circumstances
These protections are essential if big data is to be used responsibly and if the public is to trust the healthcare system. Health data can include information about a person’s medical history, medicines, test results, mental health, genetics, treatment history and personal circumstances. If this information is used inappropriately, accessed without permission, or disclosed to the wrong people, it can cause significant harm. It can also reduce trust in health services and make people less willing to share important information.
In Australia, legal protections help manage this risk. The Privacy Act 1988, the Australian Privacy Principles, and the My Health Records Act 2012 set rules about how personal information should be collected, stored, used, shared and protected. These laws are important because they create expectations for responsible data handling across healthcare, research and digital health systems.
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De-identification and data minimisation |
One key protection is de-identification, which means removing details that could identify a person, such as their name, date of birth, Medicare number or exact address. This allows data to be used for research, planning or service improvement without directly exposing the identity of the individual. Another important measure is data minimisation, which means collecting, storing and sharing only the information that is actually needed for the intended purpose. This reduces risk because less personal information is exposed if a system is misused, hacked or shared incorrectly. Example: A research team studying hospital readmissions may need information about age group, diagnosis, treatment type and readmission rates. They may not need patient names or exact street addresses. Removing unnecessary identifying details lowers privacy risk while still allowing the research to be completed. |
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Secure systems and controlled access |
Health data also needs to be protected through strong digital and organisational safeguards. This includes encryption, secure storage, access controls, audit trails, cybersecurity protections and regular reviews of who can access particular information. Access controls limit sensitive information to authorised people who need it for a legitimate reason, such as providing care, managing a service or conducting approved research. Audit trails record who accessed the data and when, which makes it easier to detect inappropriate access and hold people accountable. These safeguards are especially important because big data often involves large linked datasets. If security is weak, one breach can expose a large amount of sensitive information. |
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Transparency, access and correction rights |
Privacy protection is stronger when people understand how their information is being used and what rights they have. Individuals should be informed about why data is being collected, how it may be used, who may access it and what protections are in place. People should also be able to access their own health information, request corrections if information is wrong, and understand the privacy settings or consent options that apply to them. This improves transparency because individuals are not left uncertain about how their information is being handled. Clear communication also helps build trust. When people believe their information is being handled responsibly, they are more likely to share accurate information with health professionals. |
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Privacy by design |
A strong approach to health data protection is privacy by design. This means privacy safeguards are built into health systems from the beginning, rather than being added after a problem occurs. This matters in big data because large, linked datasets can increase the risk of inappropriate access, accidental disclosure or re-identification. Re-identification occurs when de-identified data is combined with other information in a way that makes a person identifiable again. Building privacy into the design of systems reduces these risks and supports safer, more responsible use of health data. |
Brief Summary
About the dot point and how to approach it
- Big data in health refers to very large, complex and varied information that can be analysed to identify patterns in disease, service use, and health outcomes across Australia.
- Big data can bring together data from sources such as Medicare, hospitals, electronic health records, and linked datasets.
- Big data is increasingly influencing how health problems are detected, how care is delivered, and how public health is planned.
- Examine: look closely and carefully into the ways big data is shaping the health of Australians and focus on what the data reveals about individual care and system-level decisions.
1. Big data in health
- Health big data can come from clinical data, administrative data such as Medicare and PBS claims, population and surveillance data, research data (including genomic data), and consumer-generated data.
- Big data supports evidence-based decision-making and is especially valuable when datasets are linked.
2. How is it being used?
- Big data helps clinicians build a more complete picture of a persons health and supports safer, more informed decisions for long-term conditions.
- Health services analyse patterns in admissions, waiting times, treatment outcomes, and service use to improve staffing, patient flow, follow-up care, and programme evaluation.
- Big data is used to monitor disease, identify groups experiencing poorer outcomes, inform health promotion, guide resource allocation, and evaluate programmes and policies.
3. How is it reducing healthcare spending?
- Big data supports prevention by revealing patterns in disease, risk factors, and service use so strategies can be targeted more effectively.
- Big data enables earlier intervention by identifying risk patterns early so services can respond before conditions become more severe and more expensive to treat.
- Big data reduces duplication by improving access to previous test results, treatment history, and service use information.
- Big data can help identify low-value care so resources can be redirected towards care that is more effective.
- Big data improves efficiency by helping services understand patterns in admissions, waiting times, staffing needs, treatment flow, and discharge timing.
- Big data strengthens resource allocation by showing where disease burden, poorer outcomes, or reduced access to care is greatest.
- Big data reduces avoidable hospital admissions and readmissions by identifying people at greater risk and supporting earlier follow-up and community-based support.
4. How is it being used to cure and manage diseases?
- Big data supports earlier diagnosis and risk identification by finding patterns that may not be obvious from one persons case alone.
- Big data supports precision medicine and genomic medicine by helping identify which treatments are most likely to work for people with similar genes and disease features.
- Big data supports the management of chronic disease by using trends over time to identify deterioration earlier and adjust care before a crisis develops.
- Big data supports disease surveillance and treatment research by monitoring outbreaks, tracking immunisation coverage, and evaluating whether treatments and programmes are effective.
5. What measures need to be taken to ensure privacy and confidentiality of personal information?
- De-identification and data minimisation reduce risk by removing identifying details and limiting information to what is needed for the intended purpose.
- Secure systems and controlled access include encryption, secure storage, access controls, audit trails, cybersecurity protections and regular reviews of who can access information.
- Transparency, access and correction rights mean individuals are informed about data use and can access and correct their health information.
- Privacy by design builds safeguards into systems from the beginning to reduce the risk of inappropriate access, accidental disclosure or re-identification.