University of Cambridge > Talks.cam > Engineering Design Centre > Measuring Healthcare Quality: A Robust Healthcare Quality Index (HQI) Based on the Generalized Maximum Entropy Formulation

Measuring Healthcare Quality: A Robust Healthcare Quality Index (HQI) Based on the Generalized Maximum Entropy Formulation

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Saba Hinrichs.

This talk will discuss the development of a robust index for the measurement, monitoring and improvement of healthcare quality. The Generalized Maximum Entropy (GME) methodology was employed for the formulation and estimation of the linear system of equations used to develop the index. The index was formulated based on n Key Quality Indicators (KQIs) and m quality factors under each indicator. The Healthcare Quality Index (HQI) was tested with Monte Carlo experiments and empirical data obtained from the 2006 survey of adult in-patients by the Healthcare Commission in England. The empirical study was performed with n=3 and m=5. The results show that GME methodology is a more robust estimator of the index than the popular Least Square Regression (LSR) method and also that service quality values predicted by the HQI , correspond significantly to patients’ perception of the quality of the care they had received as indicated by survey responses.

This talk is part of the Engineering Design Centre series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2019 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity