Developing Models for Patient Flow and Daily Surge Capacity Research

Abstract

Between 1993 and 2003, visits to U.S. emergency departments (EDs) increased by 26%, to a total of 114 million visits annually. At the same time, the number of U.S. EDs decreased by more than 400, and almost 200,000 inpatient hospital beds were taken out of service. In this context, the adequacy of daily surge capacity within the system is clearly an important issue. However, the research agenda on surge capacity thus far has focused primarily on large-scale disasters, such as pandemic influenza or a serious bioterrorism event. The concept of daily surge capacity and its relationship to the broader research agenda on patient flow is a relatively new area of investigation. In this article, the authors begin by describing the overlap between the research agendas on daily surge capacity and patient flow. Next, they propose two models that have potential applications for both daily surge capacity and hospitalwide patient-flow research. Finally, they identify potential research questions that are based on applications of the proposed research models.

Development and Validation of a Risk Score for Predicting Hospitalization in Children With Influenza Virus Infection

“…21,22 Between 1993 and 2003, the number of EDs in the US dropped by 425, whereas ED visits increased by 26% to 113.9 million per year. 21 The use of a clinical prediction rule in clinical settings is most useful when the rule has clinical sensibility and suggests a course of action.…”

Scheduled Surgery Admissions and Occupancy at a Children's Hospital

“…[1][2][3][4][5][6][7][8]37,38 This inflow management would ideally consider both admissions and associated LOS.…”

“…Optimizing patient flow improves health care quality (whether measured as safety, efficiency, timeliness, equity, effectiveness, or patient centeredness), and staff satisfaction, training, and value. [1][2][3][4][5][6][7][8] Central to understanding hospital patient flow is occupancy, defined as census (number of patients) divided by bed capacity. Occupancy that is too high is associated with lower quality and access, [9][10][11][12][13] whereas occupancy that is too low may underutilize resources and be costly.…”

Three critical challenges for modeling and simulation in healthcare

“…There is a great deal in the literature about A&E throughput (e.g. Cooke, Wilson and Pearson 2002;Asplin, Flottemesch and Gordon 2006;Cronin and Wright 2006;Kumar and Shim 2007;Hoot et al 2008) but relatively few address the impact of special measures to meet a deadline or the way in which it distorts the discharge profile (Locker and Mason 2005;Mayhew and Smith 2008) and we are unaware of any attempts to model this profile.…”