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HMS, HAG EQ-Bank 054

Explain how predictive analytics using big data can reduce healthcare spending in Australia, providing two specific examples.   (5 marks)

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  • Predictive analytics reduces healthcare spending because it enables early disease detection through statistical analysis of patient data. This works by analysing patient records, genetic information and lifestyle factors to identify high-risk individuals before symptoms appear.
  • As a result, healthcare providers can implement preventative measures that cost significantly less than treating advanced diseases. This occurs because early-stage treatments are simpler and require fewer resources than late-stage interventions.
  • For instance, when predictive models identify patients at risk of developing diabetes, doctors can prescribe lifestyle changes and monitoring. This leads to reduced spending as these preventative measures cost far less than managing diabetes complications like kidney failure.
  • Another example is using predictive analytics to forecast hospital readmissions. This happens when data identifies patients likely to relapse after discharge. As a result, follow-up care can be arranged earlier, which prevents expensive further hospital stays.
  • In these ways, predictive analytics creates a shift from expensive reactive care to cost-effective preventative care.
Show Worked Solution
  • Predictive analytics reduces healthcare spending because it enables early disease detection through statistical analysis of patient data. This works by analysing patient records, genetic information and lifestyle factors to identify high-risk individuals before symptoms appear.
  • As a result, healthcare providers can implement preventative measures that cost significantly less than treating advanced diseases. This occurs because early-stage treatments are simpler and require fewer resources than late-stage interventions.
  • For instance, when predictive models identify patients at risk of developing diabetes, doctors can prescribe lifestyle changes and monitoring. This leads to reduced spending as these preventative measures cost far less than managing diabetes complications like kidney failure.
  • Another example is using predictive analytics to forecast hospital readmissions. This happens when data identifies patients likely to relapse after discharge. As a result, follow-up care can be arranged earlier, which prevents expensive further hospital stays.
  • In these ways, predictive analytics creates a shift from expensive reactive care to cost-effective preventative care.

HMS, HAG EQ-Bank 048

Outline three ways in which big data can reduce healthcare spending in Australia.   (3 marks)

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  • Predictive analytics: Analysing patient records, genetics and lifestyle data helps identify individuals at high risk of diseases such as diabetes. Early detection allows for timely interventions which are less costly than treating advanced illness.
  • Preventing medical errors: Big data supports more accurate diagnoses and treatment planning. This reduces complications and extended hospital stays that increase costs.
  • Targeted resource allocation: Insights from big data ensure funding is directed to high-impact areas. This helps increase efficiency across the healthcare system and reduce overall spending.
Show Worked Solution
  • Predictive analytics: Analysing patient records, genetics and lifestyle data helps identify individuals at high risk of diseases such as diabetes. Early detection allows for timely interventions which are less costly than treating advanced illness.
  • Preventing medical errors: Big data supports more accurate diagnoses and treatment planning. This reduces complications and extended hospital stays that increase costs.
  • Targeted resource allocation: Insights from big data ensure funding is directed to high-impact areas. This helps increase efficiency across the healthcare system and reduce overall spending.

HMS, HAG EQ-Bank 058 MC

A regional hospital is reviewing patient records, genetic information and lifestyle data. Analysts use this information to identify patients at high risk of diabetes and implement early intervention programs.

Which of the following best explains how this use of big data reduces healthcare spending?

  1. It lowers costs by avoiding expensive late-stage complications of disease
  2. It reduces the number of health records stored in hospital systems
  3. It increases the ability of emergency departments to facilitate early intervention
  4. It prevents the need for training staff in chronic disease management
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\(A\)

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  • A is correct: By identifying at-risk patients and enabling early intervention, big data reduces the likelihood of costly late-stage complications such as dialysis or amputations, lowering overall healthcare spending.

Other options:

  • B is incorrect: Reducing the number of stored health records does not significantly impact spending and is not the purpose of big data analysis.
  • C is incorrect: While emergency departments may respond quickly, their primary role is for acute care in emergencies, not preventive care.
  • D is incorrect: Training staff remains essential. Big data supports decision-making but does not replace professional education.

HMS, HAG EQ-Bank 057 MC

A rural local health district analyses five years of de-identified hospital admission data. The analysis shows that the district has a high rate of preventable hospitalisations for chronic obstructive pulmonary disease (COPD).

In response, the district redirects funding into community-based respiratory clinics and home-monitoring programs.

Which of the following best explains how this use of big data reduces healthcare spending?

  1. It allows GPs to identify individual patients at high risk of COPD and intervene early.
  2. It directs funding to services that prevent costly hospital admissions where need is greatest.
  3. It reduces spending by shifting the cost of COPD care from hospitals to patients.
  4. It replaces hospital-based treatment with digital health services for all COPD patients.
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\(B\)

Show Worked Solution
  • B is correct: Analysing population-level admission data shows where preventable hospitalisations are concentrated. Funding can then be redirected.

Other options:

  • A is incorrect: The data is de-identified, so it cannot be used to identify individual patients. 
  • C is incorrect: Community clinics and home monitoring are publicly funded services. Costs are reduced overall, not transferred to patients.
  • D is incorrect: Home monitoring supports hospital care but does not replace it, and “all COPD patients” overstates the response.

HMS, HAG NSE-Bank 15 MC

Which of the following identifies examples of how big data could be used to reduce hospital expenditure?

  1. Personalised online medical appointments and reduced staff numbers.
  2. Improved pharmaceutical research and enhanced management performance.
  3. Improved population health measures and patient access to digital health records.
  4. Personalised, targeted healthcare and early diagnosis resulting in specialist referrals.
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\(B\)

Show Worked Solution
  • B is correct. Big data enables faster identification of effective treatments, reducing drug development costs. It also optimises hospital resource allocation and operational efficiency.

Other options:

  • A is incorrect. Reducing staff numbers could compromise patient care. Big data optimises healthcare delivery, not simply cuts costs through staff reductions.
  • C is incorrect. While beneficial, these don’t directly reduce hospital expenditure. Digital health records may initially increase costs through implementation.
  • D is incorrect. Early diagnosis leading to specialist referrals typically increases immediate healthcare costs, though may reduce long-term expenses.