Risk models using simple routine clinical data, including age, sex, and blood tests, can predict in-hospital mortality to measure clinical performance without placing extra burden on staff.
Observational
Can routinely collected clinical data be used to predict in-hospital mortality and measure clinical performance?
Routine clinical data can be leveraged to create risk models for predicting in-hospital mortality, providing a feasible metric for clinical performance management.
Following the well-publicized problems with paediatric cardiac surgery at the Bristol Royal Infirmary, there is wide public interest in measures of hospital performance. The Kennedy report on the BRI events suggested that such measures should be meaningful to the public, case-mix-adjusted, and based on data collected as part of routine clinical care. We have found that it is possible to predict in-hospital mortality (a measure readily understood by the public) using simple routine data-age, mode of admission, sex, and routine blood test results. The clinical data items can be obtained at a single venesection, are commonly collected in the routine care of patients, are already stored on hospital core IT systems, and so place no extra burden on the clinical staff providing care. Such risk models could provide a metric for use in evidence-based clinical performance management. National application is logistically feasible.
Prytherch et al. (Sat,) conducted a observational in In-hospital mortality. Risk models using routine clinical data was evaluated on In-hospital mortality prediction. Risk models using simple routine clinical data, including age, sex, and blood tests, can predict in-hospital mortality to measure clinical performance without placing extra burden on staff.