Dimensioning hospital wards using the Erlang loss model

Bruin, A. M. de; Bekker, René; Zanten, L. van; Koole, Ger

doi:10.1007/s10479-009-0647-8

Cited by 122 publications

(118 citation statements)

References 16 publications

Supporting

Mentioning

101

Contrasting

Unclassified

Order By: Relevance

“…To determine appropriate assumptions regarding the arrival process of patients and the LOS distribution, we considered 24 clinical wards of a university medical center, see de Bruin et al (2008) for details. Based on these data, we make the following assumptions:…”

Section: Model and Data Analysismentioning

confidence: 99%

“…Besides the weekend effect, the arrival rate may also depend on the day of the week, or the time of day. Bruin et al (2008) the ALOS ranges from 1.5 to 7.8 days for the 24 clinical wards.…”

Section: Model and Data Analysismentioning

confidence: 99%

“…This model assumes a Poisson arrival process of patients, exponentially distributed lengths of stay, s operational beds, and an infinitely large waiting room for patients that are waiting until a bed becomes available. Some recent studies (de Bruin et al 2008;McManus et al 2004) show that the M/G/s/s queue (or Erlang loss model) often accurately describes the number of patients present at a ward. In the M/G/s/s model, there is no waiting room for patients.…”

Section: Introductionmentioning

confidence: 99%

“…To do this, we assume that patients arrive according to a time-dependent Poisson process. We employ a loss model based on de Bruin et al (2008) in which patients who find all beds occupied are refused. One of our main interests is in the impact of the weekend on the bed occupancy and proportion of refused admissions across the week.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Time-dependent analysis for refused admissions in clinical wards

Bekker

Bruin

2009

Ann Oper Res

Self Cite

View full text Add to dashboard Cite

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

show abstract

Section: Model and Data Analysismentioning

confidence: 99%

“…Besides the weekend effect, the arrival rate may also depend on the day of the week, or the time of day. Bruin et al (2008) the ALOS ranges from 1.5 to 7.8 days for the 24 clinical wards.…”

Section: Model and Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Time-dependent analysis for refused admissions in clinical wards

Bekker

Bruin

2009

Ann Oper Res

Self Cite

View full text Add to dashboard Cite

show abstract

“…Jagerman, 1975;Jennings and Massey, 1997;Abdalla and Boucherie, 2002;Alnowibet and Perros, 2006) to hospital wards (e.g. Bruin et al, 2010). The majority of these systems have significant variations in their arrival rates.…”

Section: Introductionmentioning

confidence: 99%

Approximate analysis of non-stationary loss queues and networks of loss queues with general service time distributions

Izady¹,

Worthington²

2011

European Journal of Operational Research

View full text Add to dashboard Cite

a b s t r a c tA Fixed Point Approximation (FPA) method has recently been suggested for non-stationary analysis of loss queues and networks of loss queues with Exponential service times. Deriving exact equations relating time-dependent mean numbers of busy servers to blocking probabilities, we generalize the FPA method to loss systems with general service time distributions. These equations are combined with associated formulae for stationary analysis of loss systems in steady state through a carried load to offered load transformation. The accuracy and speed of the generalized methods are illustrated through a wide set of examples.

show abstract

A pragmatic method to compare international critical care beds: Implications to pandemic preparedness and non‐pandemic planning

Jones¹

2022

Health Planning & Management

View full text Add to dashboard Cite

Background: The current method for assessing critical care (CCU) bed numbers between countries is unreliable.Methods: A pragmatic method is presented using a logarithmic relationship between CCU beds per 1000 deaths and deaths per 1000 population, both of which are readily available. The method relies on the importance of the nearness to death effect, and on the effect of population size. Results:The method was tested using CCU bed numbers from 65 countries. A series of logarithmic relationships can be seen. High versus low countries can be distinguished by adjusting all countries to a common crude mortality rate. Hence at 9.5 deaths per 1000 population 'high' CCU bed countries average of around 30 CCU beds per 1000 deaths, while 'very low' countries only average 3 CCU beds per 1000 deaths. The United Kingdom falls among countries with low critical care provision with an average of 8 CCU beds per 1000 deaths, and during the COVID-19 epidemic UK industry intervened to rapidly manufacture various types of ventilators to avoid a catastrophe. CCU bed numbers in India are around 8.1 per 1000 deaths, which places it in the low category. However, such beds are inequitably distributed with the poorest states all in the 'very low' category. In India only around 50% of CCU beds have a ventilator. Conclusion: A feasible region is defined for the optimum number of CCU beds.

show abstract

Dimensioning hospital wards using the Erlang loss model

Cited by 122 publications

References 16 publications

Time-dependent analysis for refused admissions in clinical wards

Time-dependent analysis for refused admissions in clinical wards

Approximate analysis of non-stationary loss queues and networks of loss queues with general service time distributions

A pragmatic method to compare international critical care beds: Implications to pandemic preparedness and non‐pandemic planning

Contact Info

Product

Resources

About