How much do operational processes affect hospital inpatient discharge rates?
ABSTRACT The objective of this study is to determine the effect of day of the week, holiday, team admission and rotation schedules, individual attending physicians and their length of coverage on daily team discharge rates.
We conducted a retrospective analysis of the General Internal Medicine (GIM) inpatient service at our institution for years 2005 and 2006, which included 5088 patients under GIM care.
Weekend discharge rate was more than 50% lower compared with reference rates whereas Friday rates were 24% higher. Holiday Monday discharge rates were 65% lower than regular Mondays, with an increase in pre-holiday discharge rates. Teams that were on-call or that were on call the next day had 15% higher discharge rates compared with reference whereas teams that were post-call had 20% lower rates. Individual attending physicians and length of attending coverage contributed small variations in discharge rates. Resident scheduling was not a significant predictor of discharge rates.
Day of the week and holidays followed by team organization and scheduling are significant predictors of daily variation in discharge rates. Introducing greater holiday and weekend capacity as well as reorganizing internal processes such as admitting and attending schedules may potentially optimize discharge rates.
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ABSTRACT: This paper examines the long-standing operational issue of patients boarding in the emergency department (ED), who have been admitted to hospital (inpatient ‘boarders’). From this analysis we design a conceptual model that provides a roadmap to create sustainable improvements in ED waiting times. The conceptual model is built using system dynamics methodology, and illustrates the use of system archetypes, a set of common causal feedback loops that illustrate how well-intended decisions have unintentional side effects. This paper outlines the journey taken by one large academic health centre to address these issues, and highlights the larger implications and recommendations that are applicable to other publicly funded hospitals.Journal of the Operational Research Society 01/2012; 63(1):79-88. DOI:10.1057/jors.2010.164 · 0.91 Impact Factor
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ABSTRACT: Adverse events in hospitals cause significant morbidity and mortality, and considerable effort has been invested into analysing their incidence and preventability. An unresolved issue in models of medical adverse events is potential endogeneity of length of stay (LOS): whilst the probability of suffering a medical adverse event during the episode is likely to increase as a patient stays longer, there are a range of unobservable patient and hospital factors affecting both the occurrence of adverse events and LOS, such as unobserved patient complexity and hospital management. Therefore, statistical models of adverse events which do not account for the potential endogeneity of LOS may generate inconsistent and biased estimates.Our objective is to examine the factors impacting on the incidence of adverse events, accounting for endogeneity of LOS by estimating structural equation models. We estimate separate models for three of the most common and serious types of medical adverse events: Adverse drug reactions, hospital acquired infections, and pressure ulcers. We use episode level administrative hospital data from public hospitals in the state of Victoria, Australia, for the years 2004/05 and 2005/06. These data contain detailed information on patients, in particular medical complexity and adverse events suffered during admission. We use instrumental variable probit and conditional mixed process methods, with days and months of discharge as instruments for LOS. Results show that LOS is endogenous in models of adverse events, and that LOS increases the probability of adverse events at comparable magnitudes to other risk factors such as age, being an emergency patient, or suffering of significant comorbidities. In contrast to patient risk factors, LOS is a hospital-level risk factors which is directly amenable to the actions of hospital management; patients can be discharged earlier, and part or all of the stay in hospital can be substituted by stays at alternative care providers, or at home. This may be beneficial if it significantly lowers risk of adverse events. Our econometric model of adverse events can inform on the expected cost of days spent in hospital, of which the expected cost of AEs is one component. Although it is more satisfactory to address hospital-level causal reasons for adverse events, such as poor safety procedures, LOS may be the only factor which can be influenced in the short run and under relatively low costs. Our results provide hospital managers with the quantitative evidence to take a pragmatic approach towards the reduction of adverse events, and make informed discharge and care decisions. Considering the large costs of adverse events to patients and the health care system, it seems timely that they are factored into discharge and treatment decisions in a quantitative way.
Article: In reply.Annals of emergency medicine 08/2010; 56(2):203-4. DOI:10.1016/j.annemergmed.2010.02.016 · 4.33 Impact Factor