Development and validation of a tool to select patients for admission to medical short stay units

Abstract

Medical short stay units help to increase patient fl ow and decrease length of stay, but selecting appropriate patients for admission to such units is diffi cult. The selection tool used in our unit was effective but cumbersome to apply. We collected prospective data on 297 unselected emergency medical admissions and developed a new scoring system based on four key variables using regression analysis. The model predicted a length of stay of <72 h with an area under the receiver operating characteristic curve of 0.68. The model was then used to select patients for admission to the short stay unit in our trust. Length of stay on the short stay unit had decreased by an average of 2.73 days with our original selection tool, but remained unchanged at an average of 3.02 days using the new simpler tool (p>0.05). This model could now be adopted by other units.

Extending the Sydney Triage to Admission Risk Tool (START+) to predict discharges and short stay admissions

“…Previous studies have investigated predicting admission to medical short stay units with promising results 8 10 12 13. Powter  et al 10 used a model that predicted length of stay <72 hours with an area under the curve (AUC) of 0.68. Variables included age >80 years, cognitive impairment and multiple medications on admission 10.…”

“…Powter  et al 10 used a model that predicted length of stay <72 hours with an area under the curve (AUC) of 0.68. Variables included age >80 years, cognitive impairment and multiple medications on admission 10. In another single-centre study of 704 patients, admission disposition was correctly predicted by ED clinicians 71%–85% of the time while the accuracy of predicting inpatient length of stay >3 days varied between 50% and 56% 12…”

Development and Internal Validation of a Model Predicting Safe, Early Discharge Among Patients Presenting to the Emergency Department with an Infection.

“…Several studies have focused on predicting early discharge among speci c patient populations [35,36,[53][54][55][56][57][58] and multiple have proposed prediction models. [36,[55][56][57][58] Only two, however, based their prediction model on ED patients. [57,58] The rst included 894 general ED patients and showed an AUC of 0.84 for the prediction of discharge within 48…”

“…[36,[55][56][57][58] Only two, however, based their prediction model on ED patients. [57,58] The rst included 894 general ED patients and showed an AUC of 0.84 for the prediction of discharge within 48…”

“…[57] The second included 297 general ED patients and demonstrated an AUC of 0.68 for the prediction of discharge within 72 hours. [58] Neither, however, analyzed the safety of these early discharges. One large prospective observational study did analyze safe, early discharges using a score called Halm's criteria.…”