The National Emergency Access Target (NEAT) and the 4-hour rule: time to review the target.

OBJECTIVE: We explored the relationship between the National Emergency Access Target (NEAT) compliance rate, defined as the proportion of patients admitted or discharged from emergency departments (EDs) within 4 hours of presentation, and the risk-adjusted in-hospital mortality of patients admitted to hospital acutely from EDs. DESIGN, SETTING AND PARTICIPANTS: Retrospective observational study of all de-identified episodes of care involving patients who presented acutely to the EDs of 59 Australian hospitals between 1 July 2010 and 30 June 2014. MAIN OUTCOME MEASURE: The relationship between the risk-adjusted mortality of inpatients admitted acutely from EDs (the emergency hospital standardised mortality ratio [eHSMR]: the ratio of the numbers of observed to expected deaths) and NEAT compliance rates for all presenting patients (total NEAT) and admitted patients (admitted NEAT). RESULTS: ED and inpatient data were aggregated for 12.5 million ED episodes of care and 11.6 million inpatient episodes of care. A highly significant (P < 0.001) linear, inverse relationship between eHSMR and each of total and admitted NEAT compliance rates was found; eHSMR declined to a nadir of 73 as total and admitted NEAT compliance rates rose to about 83% and 65% respectively. Sensitivity analyses found no confounding by the inclusion of palliative care and/or short-stay patients. CONCLUSION: As NEAT compliance rates increased, in-hospital mortality of emergency admissions declined, although this direct inverse relationship is lost once total and admitted NEAT compliance rates exceed certain levels. This inverse association between NEAT compliance rates and in-hospital mortality should be considered when formulating targets for access to emergency care.

Model Selection and Variable Aggregation of Australian Hospital Data.

BACKGROUND: Hospital administrative data commonly consist of hundreds of variables with many consisting of hundreds, if not thousands, of distinct categories, especially for disease groups. Conventional approaches to develop regression models for prediction either fail completely due to multicollinearity or sparsity issues or take too long and consume too many computer resources. METHODS: We demonstrate how regularisation and variable aggregation techniques such as Elastic Net can overcome some of these problems. Parameter estimates from univariate generalised linear models (GLM) and Elastic Net models were used to aggregate disease groups into a more manageable number and predict the probability of mortality for a given patient. RESULTS: When employed for variable aggregation and variable selection, Elastic Net models ran at least four times faster than GLMs, though producing a less discriminative model. When applied to final models for predicting hospital mortality, though, both Elastic Net and GLM models demonstrated similar predictive power and efficiently solved an otherwise complex problem. CONCLUSION: Elastic Net regularisation and variable aggregation provide an efficient mechanism for solving healthcare modelling problems.