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.
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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.