Estimating misclassification error in a binary performance indicator: case study of low value care in Australian hospitals
Tim Badgery-Parker, Sallie-Anne Pearson, Adam G Elshaug
BMJ Quality & Safety | BMJ PUBLISHING GROUP | Published : 2020
OBJECTIVE: Indicators based on hospital administrative data have potential for misclassification error, especially if they rely on clinical detail that may not be well recorded in the data. We applied an approach using modified logistic regression models to assess the misclassification (false-positive and false-negative) rates of low-value care indicators. DESIGN AND SETTING: We applied indicators involving 19 procedures to an extract from the New South Wales Admitted Patient Data Collection (1 January 2012 to 30 June 2015) to label episodes as low value. We fit four models (no misclassification, false-positive only, false-negative only, both false-positive and false-negative) for each indic..View full abstract
Awarded by National Health and Medical Research Council
This study was funded by National Health and Medical Research Council (1109626), NSW Ministry of Health, HCF Research Foundation, The University of Sydney.