• 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.

• A is correct: Expanding the National Health Data Hub to include private providers and wearable data would provide broader datasets, allowing more comprehensive analysis.

Other options:

• B is incorrect: Ethical considerations are central to data use in healthcare and cannot be dismissed as irrelevant.
• C is incorrect: Expansion would broaden the scope of big data use beyond government agencies, not restrict it.
• D is incorrect: While de-identification is common in research, the key issue in the question is expansion of datasets, not assurance of data anonymity.

• B is correct: Big data reduces expenditure primarily through early intervention and preventative measures

Other options:

• A is incorrect: The document doesn’t suggest reducing doctors’ roles. Instead, it emphasises how big data helps physicians make better decisions.
• C is incorrect: There’s no mention of replacing medical equipment with computers.
• D is incorrect: The document doesn’t indicate reducing healthcare workers. Rather, it focuses on helping them work more effectively with better data.

• 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.

• B is correct: Big data reduces healthcare spending by using predictive analytics for early disease detection, allowing prevention or early intervention which is less costly than advanced treatment.

Other options:

• A is incorrect: Reducing costly late-stage treatments is a consequence of earlier detection, not the direct mechanism by which big data operates.
• C is incorrect: Expanding medical error reporting improves safety but does not itself reduce spending unless paired with analysis and preventative action.
• D is incorrect: Increasing productivity of emergency staff may improve efficiency but it is primarily workforce management and option B provides a better description.