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Open Access Open Badges Research article

Global quantitative indices reflecting provider process-of-care: data-base derivation

John L Moran1*, Patricia J Solomon2 and the Adult Database Management Committee (ADMC) of the Australian and New Zealand Intensive Care Society (ANZICS)3

Author Affiliations

1 Department of Intensive Care Medicine, The Queen Elizabeth Hospital, Woodville SA 5011, Australia

2 School of Mathematical Sciences, University of Adelaide, Adelaide SA 5000, Australia

3 Australian and New Zealand Intensive Care Society, Carlton Victoria 3053, Australia

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BMC Medical Research Methodology 2010, 10:32  doi:10.1186/1471-2288-10-32

Published: 19 April 2010



Controversy has attended the relationship between risk-adjusted mortality and process-of-care. There would be advantage in the establishment, at the data-base level, of global quantitative indices subsuming the diversity of process-of-care.


A retrospective, cohort study of patients identified in the Australian and New Zealand Intensive Care Society Adult Patient Database, 1993-2003, at the level of geographic and ICU-level descriptors (n = 35), for both hospital survivors and non-survivors. Process-of-care indices were established by analysis of: (i) the smoothed time-hazard curve of individual patient discharge and determined by pharmaco-kinetic methods as area under the hazard-curve (AUC), reflecting the integrated experience of the discharge process, and time-to-peak-hazard (TMAX, in days), reflecting the time to maximum rate of hospital discharge; and (ii) individual patient ability to optimize output (as length-of-stay) for recorded data-base physiological inputs; estimated as a technical production-efficiency (TE, scaled [0,(maximum)1]), via the econometric technique of stochastic frontier analysis. For each descriptor, multivariate correlation-relationships between indices and summed mortality probability were determined.


The data-set consisted of 223129 patients from 99 ICUs with mean (SD) age and APACHE III score of 59.2(18.9) years and 52.7(30.6) respectively; 41.7% were female and 45.7% were mechanically ventilated within the first 24 hours post-admission. For survivors, AUC was maximal in rural and for-profit ICUs, whereas TMAX (≥ 7.8 days) and TE (≥ 0.74) were maximal in tertiary-ICUs. For non-survivors, AUC was maximal in tertiary-ICUs, but TMAX (≥ 4.2 days) and TE (≥ 0.69) were maximal in for-profit ICUs. Across descriptors, significant differences in indices were demonstrated (analysis-of-variance, P ≤ 0.0001). Total explained variance, for survivors (0.89) and non-survivors (0.89), was maximized by combinations of indices demonstrating a low correlation with mortality probability.


Global indices reflecting process of care may be formally established at the level of national patient data-bases. These indices appear orthogonal to mortality outcome.