Interpreting health information and low value care using data science techniques

Opportunity 

Australian healthcare consumers, clinicians and funders have limited visibility of provider performance, costs and outcomes. This project aimed to improve transparency through identifying robust statistical and data science methods for addressing challenges of comparing clinicians and interpreting healthcare information with respect of quality, cost and outcomes, while also identifying opportunities to reduce low-value care. 

Project Objectives  

This project set out to: 

• Review existing public models of risk adjustment methods for private health insurance data. 
• Recommend techniques, approaches and communication frameworks for the interpretation of healthcare quality, cost and outcome information. 
• Support development of practical tools that improve transparency and information sharing for healthcare decision-making.