Time-dependent analysis for refused admissions in clinical wards

Abstract

For capacity planning issues in health care, such as the allocation of hospital beds, the admissions rate of patients is commonly assumed to be constant over time. In addition to the purely random fluctuations, there is also typically a predictable pattern in the number of arriving patients. For example, roughly 2/3 of the admitted patients at an Intensive Care Unit arrives during office hours. Also, most of the scheduled admissions occur during weekdays instead of during the weekend.Using approximations based on the infinite-server queue, we analyze an M t /H /s/s model to determine the impact of the time-dependent arrival pattern on the required number of operational beds and fraction of refused admissions for clinical wards. In particular, the results show that the effect of the daily pattern is rather limited for clinical wards in contrast to the week-weekend pattern, for which the difference in the fraction of refused admissions across the week is considerable. We also show that an increased variability in length of stay distribution has a stabilizing effect on the time-dependent required number of beds. Finally, we demonstrate a method to determine the required number of beds across the week.Keywords Hospital capacity planning · Daily and weekly admission patterns · Time-dependent arrivals · Refused admissions · Infinite-server queues · Modified-offered-load approximation

Dimensioning hospital wards using the Erlang loss model

“…The impact of a time-varying arrival rate on refused admissions, specifically for in-patient flow, is also described in literature (Bekker and de Bruin 2009).…”

Integral resource capacity planning for inpatient care services based on bed census predictions by hour

“…Previous analytical studies have addressed partial resource capacity planning issues within the inpatient care chain, for example by dimensioning care units in isolation (eg Green and Nguyen, 2001;Gorunescu et al, 2002;Bekker and De Bruin, 2010), balancing bed utilization across multiple units (eg Akcali et al, 2006;Cochran and Bharti, 2006;Li et al, 2009), or focussing on improving the MSS to balance inpatient care demand (eg Van Oostrum et al, 2008;Adan et al, 2009;Beliën et al, 2009;Vanberkel et al, 2010b;Bekker and Koeleman, 2011). More integral approaches can be found in simulation studies (eg Harper and Shahani, 2002;Harper, 2002;Vanberkel and Blake, 2007;Troy and Rosenberg, 2009).…”

Staffing of time-varying queues using a geometric discrete time modelling approach

“…The importance of time-dependent as opposed to steady-state analysis for many real queueing systems is well established. Call centres (see for example Gans et al 2003), communication networks (see for example Abdalla and Boucherie 2002), healthcare (see for example Izady and Worthington 2012;Bekker and de Bruin 2010) and traffic flows (see for example Griffiths et al 1991) are all areas where time-dependent analysis can be essential.…”