A loss network model with overflow for capacity planning of a neonatal unit

Abstract

The main aim of this paper is to derive a solution to the capacity problem faced by many perinatal networks in the United Kingdom. We propose a queueing model to determine the number of cots at all care units for any desired overflow and rejection probability in a neonatal unit. The model formulation is developed, being motivated by overflow models in telecommunication systems. Exact expressions for the overflow and rejection probabilities are derived. The model is then applied to a neonatal unit of a perinatal network in the UK.

Capacity planning of a perinatal network with generalised loss network model with overflow

“…In this study, exponential (M), two-phase hyper-exponential (H 2 ) and two-phase Erlang (E 2 ) distributions are considered to derive rejection probabilities as these were suggested to be appropriate interarrival and LOS distributions for the five neonatal hospitals we considered (Asaduzzaman et al, 2010a;Asaduzzaman, 2010). Hyper-exponential distributions were also found apprpriate by Griffiths et al (2006) in the context of a classical ICU.…”

“…We have addressed this issue in several papers (Asaduzzaman and Chaussalet, 2008;Asaduzzaman et al, 2010a,b;Asaduzzaman, 2010;Asaduzzaman and Chaussalet, 2011). However, as noted by Asaduzzaman et al (2010a) and Asaduzzaman (2010) the mean is much lower than standard deviation for interarrival times and length of stay (LOS). Clearly this is a violation of the property of the exponential distribution, and hence of the Markovian assumption.…”

An open source software project for obstetrical procedure scheduling and occupancy analysis

“…These models are particularly relevant to this work in that they highlight the utility and tractability of infinite capacity queueing models. Finite capacity queueing network models have been used recently to explicitly model patient flow blocking in mental health facility [31] and overflow for neonatal ICU capacity planning [32].…”

Integrated decision making in healthcare : an operations research and management science perspective

“…Models that consider only a single unit neglect the possibility of admitting patients in a less appropriate care unit and thus the interaction between patient flows and the interrelationship between care units. Next to estimating utilization and the probability of admission rejections or delays, models that do incorporate multiple care units, also focus on the percentage of time that patients are placed in a care unit of a lower level or less appropriate care unit, or in a higher level care unit [11,108,202,217,327,439]. The first situation negatively impacts quality of care as it can lead to increased morbidity and mortality [478] and the second negatively impacts both quality of care, as it may block admission of another patient, and efficient resource use [217,439].…”

“…Methods: computer simulation [7,19,104,108,112,156,157,206,221,222,238,241,242,243,244,259,292,295,347,371,372,373,375,415,439,478,497,517,519,523,524], heuristics [309], Markov processes [7,68,172,192,199,245,246,247,335], mathematical programming [127,202,241,318,327,375,376], queueing theory [11,29,…”