Interpreting Health Information and Low Value Care using Data Science Techniques
Flagship Program: Transparency of Data to Optimise Clinical Practice and Referral
Risk adjustment is a common issue among medical providers, who are cautious in supporting any initiative regarding transparency and information sharing. Risk adjustment refers to the use of patient and provider level information to explain variation in healthcare spending, resource utilisation and health outcomes over a fixed interval of time.
Medical providers argue that factors influencing the complexity of a procedure – for example, the age and obesity of a patient – must be considered when analysing such variation in order to form an accurate basis for comparison. Patient factors are a significant driver of cost and resource utilisation.
This project will review existing public models of risk adjustment and recommend techniques, approaches and communication frameworks suitable for public and semi-public (ie. via intermediaries) transparency and information sharing purposes.
It will identify the most robust statistical and data science methods for addressing challenges of comparing clinicians and interpreting healthcare information with respect of quality, cost and outcomes.
Identify the most robust statistical and data science methods for addressing challenges of comparing clinicians and interpreting healthcare information with respect of quality, cost and outcomes.