2010Management DecisionRequires access

Queuing analysis for outpatient and inpatient services: a case study

K. M. Mital

Open publisher page 34 citations

Abstract

Purpose The purpose of this paper is to carry out queuing analysis to analyse patient load in outpatient and inpatient services to facilitate more realistic resource planning. Design/methodology/approach The paper adopts an analytical approach based on real life data (e.g. not a priori or an academic one where data are mingled to fit a theoretical stance) in accordance with the service level prescribed by the hospital administration. A service level is usually specified in terms of admissible range of queuing characteristics, such as mean patient waiting time, reduction of inordinate delays, incidences of minimum delays, average queue length, etc. which the management of a health organisation may decide to aim and control. Findings Queuing analysis reported in this case study provides a basis for estimating medical staff size and number of beds, which are two very important resources for outpatient and inpatient services in a large hospital, and all other hospital resources in one way or another depend on them. Research limitations/implications The study challenges and aims to replace thumb‐rule approaches, which can be very conveniently carried out with efficient computer aids available at present for any hospital. Queuing analysis provides valuable insights into a hospital system, though it may not be the best approach as several underlying assumptions may not always hold true. In hospitals, for example, there can be several interacting queues, many of which could be cyclic with interaction among them. Accordingly, treatment of each queue individually, independent of others may not be a valid assumption. Practical implications Medical staff (doctors) and beds are very basic hospital resources, which largely depend on the hospital load in terms of arrival rates of patients in outpatient and inpatient services. When hospitals are adequately staffed and equipped in terms of beds and other key resources, it is unlikely that patients will turn away to other hospitals for treatment and there will be all round satisfaction with the hospital performance. Originality/value The authors do not claim the findings to be novel or unique but rather more well‐documented and comprehensive in coverage than available in existing literature. The practice‐based themes such as this well‐documented case study may evoke global interest as a multiplier effect for using such methodologies for resource planning in hospitals.

About this research paper

What this paper is about

Purpose The purpose of this paper is to carry out queuing analysis to analyse patient load in outpatient and inpatient services to facilitate more realistic resource planning. Design/methodology/approach The paper adopts an analytical approach based on real life data (e.g. not a priori or an academic one where data are mingled to fit a theoretical stance) in accordance with the service level prescribed by the hospital administration. A service level is usually specified in terms of admissible range of queuing characteristics, such as mean patient waiting time, reduction of inordinate delays, incidences of minimum delays, average queue length, etc. which the management of a health organisation may decide to aim and control. Findings Queuing analysis reported in this case study provides a basis for estimating medical staff size and number of beds, which are two very important resources for outpatient and inpatient services in a large hospital, and all other hospital resources in one way or another depend on them. Research limitations/implications The study challenges and aims to replace thumb‐rule approaches, which can be very conveniently carried out with efficient computer aids available at present for any hospital. Queuing analysis provides valuable insights into a hospital system, though it may not be the best approach as several underlying assumptions may not always hold true. In hospitals, for example, there can be several interacting queues, many of which could be cyclic with interaction among them. Accordingly, treatment of each queue individually, independent of others may not be a valid assumption. Practical implications Medical staff (doctors) and beds are very basic hospital resources, which largely depend on the hospital load in terms of arrival rates of patients in outpatient and inpatient services. When hospitals are adequately staffed and equipped in terms of beds and other key resources, it is unlikely that patients will turn away to other hospitals for treatment and there will be all round satisfaction with the hospital performance. Originality/value The authors do not claim the findings to be novel or unique but rather more well‐documented and comprehensive in coverage than available in existing literature. The practice‐based themes such as this well‐documented case study may evoke global interest as a multiplier effect for using such methodologies for resource planning in hospitals.

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Available abstract

Purpose The purpose of this paper is to carry out queuing analysis to analyse patient load in outpatient and inpatient services to facilitate more realistic resource planning. Design/methodology/approach The paper adopts an analytical approach based on real life data (e.g. not a priori or an academic one where data are mingled to fit a theoretical stance) in accordance with the service level prescribed by the hospital administration. A service level is usually specified in terms of admissible range of queuing characteristics, such as mean patient waiting time, reduction of inordinate delays, incidences of minimum delays, average queue length, etc. which the management of a health organisation may decide to aim and control. Findings Queuing analysis reported in this case study provides a basis for estimating medical staff size and number of beds, which are two very important resources for outpatient and inpatient services in a large hospital, and all other hospital resources in one way or another depend on them. Research limitations/implications The study challenges and aims to replace thumb‐rule approaches, which can be very conveniently carried out with efficient computer aids available at present for any hospital. Queuing analysis provides valuable insights into a hospital system, though it may not be the best approach as several underlying assumptions may not always hold true. In hospitals, for example, there can be several interacting queues, many of which could be cyclic with interaction among them. Accordingly, treatment of each queue individually, independent of others may not be a valid assumption. Practical implications Medical staff (doctors) and beds are very basic hospital resources, which largely depend on the hospital load in terms of arrival rates of patients in outpatient and inpatient services. When hospitals are adequately staffed and equipped in terms of beds and other key resources, it is unlikely that patients will turn away to other hospitals for treatment and there will be all round satisfaction with the hospital performance. Originality/value The authors do not claim the findings to be novel or unique but rather more well‐documented and comprehensive in coverage than available in existing literature. The practice‐based themes such as this well‐documented case study may evoke global interest as a multiplier effect for using such methodologies for resource planning in hospitals.

Key concepts: Queueing theory, Rule of thumb, Queue, Computer science, Service (business), Operations research, Queue management system, Operations management

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