Definition of Predictive Analytics:

Predictive analytics uses historical data, machine learning, and statistical algorithms to forecast future outcomes.

In the healthcare sector, it is used to predict patient readmission rates and identify those at high risk of needing additional treatment.

How Predictive Analytics Applies to Hospitals:

Hospitals analyze patient histories, symptoms, treatments, and recovery rates to determine the likelihood of readmission.

Predictive models help healthcare providers take proactive measures, such as tailored post-discharge care plans, to reduce readmission risks.

This leads to better patient outcomes and cost savings.

Why Other Options Are Incorrect:

B. Prescriptive analytics:

Prescriptive analytics goes beyond prediction and provides recommendations for action. In this case, the hospital is only determining which patients are likely to require additional treatment, not recommending treatments.

C. Descriptive analytics:

Descriptive analytics focuses on summarizing past data without making predictions. It would be used to report on past patient admissions but not to predict future readmissions.

D. Diagnostic analytics:

Diagnostic analytics analyzes the causes of past events but does not forecast future patient readmissions.

IIA’s Perspective on Data Analytics in Decision-Making:

IIA GTAG (Global Technology Audit Guide) on Data Analytics emphasizes the role of predictive analytics in risk assessment and operational efficiency.

COSO ERM Framework supports predictive modeling as part of strategic risk management.

IIA References:

IIA GTAG – Data Analytics in Risk Management

COSO Enterprise Risk Management (ERM) Framework

NIST Big Data Framework for Predictive Analytics