Modelling the critical care pathway for cardiothoracic surgery

Nicolas Bahou, Claire Fenwick, Gillian Anderson, Robert van der Meer, Tony Vassalos

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

The west of Scotland heart and lung center based at the Golden Jubilee National Hospital houses all adult cardiothoracic surgery for the region. Increased
demand for scheduled patients and fluctuations in emergency referrals resulted in increasing waiting times and patient cancellations. The main issue was
limited resources, which was aggravated by the stochastic nature of the length of stay (LOS) and arrival of patients. Discrete event simulation (DES) was used to assess if an enhanced schedule was sufficient, or more radical changes, such as capacity or other resource reallocations should be considered in order to solve the problem. Patients were divided into six types depending on their condition and LOS at the different stages of the process. The simulation model portrayed each patient type’s pathway with sufficient detail. Patient LOS figures were analyzed and distributions were formed from historical data, which were then used in the simulation. The model proved successful as it showed figures that were close to actual observations. Acquiring results and knowing exactly when and what caused a cancellation was another strong point of the model. The results demonstrated that the bottleneck in the system was related to the use of High Dependency Unit (HDU) beds, which were the recovery beds used by most patients. Enhancing the schedule by leveling out the daily arrival of patients to HDUs reduced patient cancellations by 20%. However, coupling this technique
with minor capacity reallocations resulted in more than 60% drop in cancellations.
LanguageEnglish
Pages1-12
Number of pages17
JournalHealthcare Management Science
Early online date16 May 2017
DOIs
Publication statusE-pub ahead of print - 16 May 2017

Fingerprint

Critical Pathways
Critical Care
Length of Stay
Appointments and Schedules
Modeling
Pathway
Surgery
Scotland
Emergencies
Referral and Consultation
Lung
Cancellation

Keywords

  • simulation
  • OR in healthcare
  • patient scheduling
  • hospital operations

Cite this

@article{2750ef5d7ad0445d975577d33b790f82,
title = "Modelling the critical care pathway for cardiothoracic surgery",
abstract = "The west of Scotland heart and lung center based at the Golden Jubilee National Hospital houses all adult cardiothoracic surgery for the region. Increaseddemand for scheduled patients and fluctuations in emergency referrals resulted in increasing waiting times and patient cancellations. The main issue waslimited resources, which was aggravated by the stochastic nature of the length of stay (LOS) and arrival of patients. Discrete event simulation (DES) was used to assess if an enhanced schedule was sufficient, or more radical changes, such as capacity or other resource reallocations should be considered in order to solve the problem. Patients were divided into six types depending on their condition and LOS at the different stages of the process. The simulation model portrayed each patient type’s pathway with sufficient detail. Patient LOS figures were analyzed and distributions were formed from historical data, which were then used in the simulation. The model proved successful as it showed figures that were close to actual observations. Acquiring results and knowing exactly when and what caused a cancellation was another strong point of the model. The results demonstrated that the bottleneck in the system was related to the use of High Dependency Unit (HDU) beds, which were the recovery beds used by most patients. Enhancing the schedule by leveling out the daily arrival of patients to HDUs reduced patient cancellations by 20{\%}. However, coupling this techniquewith minor capacity reallocations resulted in more than 60{\%} drop in cancellations.",
keywords = "simulation, OR in healthcare, patient scheduling, hospital operations",
author = "Nicolas Bahou and Claire Fenwick and Gillian Anderson and {van der Meer}, Robert and Tony Vassalos",
year = "2017",
month = "5",
day = "16",
doi = "10.1007/s10729-017-9401-y",
language = "English",
pages = "1--12",
journal = "Health Care Management Science",
issn = "1386-9620",

}

Modelling the critical care pathway for cardiothoracic surgery. / Bahou, Nicolas; Fenwick, Claire; Anderson, Gillian; van der Meer, Robert; Vassalos, Tony.

In: Healthcare Management Science, 16.05.2017, p. 1-12.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Modelling the critical care pathway for cardiothoracic surgery

AU - Bahou, Nicolas

AU - Fenwick, Claire

AU - Anderson, Gillian

AU - van der Meer, Robert

AU - Vassalos, Tony

PY - 2017/5/16

Y1 - 2017/5/16

N2 - The west of Scotland heart and lung center based at the Golden Jubilee National Hospital houses all adult cardiothoracic surgery for the region. Increaseddemand for scheduled patients and fluctuations in emergency referrals resulted in increasing waiting times and patient cancellations. The main issue waslimited resources, which was aggravated by the stochastic nature of the length of stay (LOS) and arrival of patients. Discrete event simulation (DES) was used to assess if an enhanced schedule was sufficient, or more radical changes, such as capacity or other resource reallocations should be considered in order to solve the problem. Patients were divided into six types depending on their condition and LOS at the different stages of the process. The simulation model portrayed each patient type’s pathway with sufficient detail. Patient LOS figures were analyzed and distributions were formed from historical data, which were then used in the simulation. The model proved successful as it showed figures that were close to actual observations. Acquiring results and knowing exactly when and what caused a cancellation was another strong point of the model. The results demonstrated that the bottleneck in the system was related to the use of High Dependency Unit (HDU) beds, which were the recovery beds used by most patients. Enhancing the schedule by leveling out the daily arrival of patients to HDUs reduced patient cancellations by 20%. However, coupling this techniquewith minor capacity reallocations resulted in more than 60% drop in cancellations.

AB - The west of Scotland heart and lung center based at the Golden Jubilee National Hospital houses all adult cardiothoracic surgery for the region. Increaseddemand for scheduled patients and fluctuations in emergency referrals resulted in increasing waiting times and patient cancellations. The main issue waslimited resources, which was aggravated by the stochastic nature of the length of stay (LOS) and arrival of patients. Discrete event simulation (DES) was used to assess if an enhanced schedule was sufficient, or more radical changes, such as capacity or other resource reallocations should be considered in order to solve the problem. Patients were divided into six types depending on their condition and LOS at the different stages of the process. The simulation model portrayed each patient type’s pathway with sufficient detail. Patient LOS figures were analyzed and distributions were formed from historical data, which were then used in the simulation. The model proved successful as it showed figures that were close to actual observations. Acquiring results and knowing exactly when and what caused a cancellation was another strong point of the model. The results demonstrated that the bottleneck in the system was related to the use of High Dependency Unit (HDU) beds, which were the recovery beds used by most patients. Enhancing the schedule by leveling out the daily arrival of patients to HDUs reduced patient cancellations by 20%. However, coupling this techniquewith minor capacity reallocations resulted in more than 60% drop in cancellations.

KW - simulation

KW - OR in healthcare

KW - patient scheduling

KW - hospital operations

UR - http://link.springer.com/journal/10729

U2 - 10.1007/s10729-017-9401-y

DO - 10.1007/s10729-017-9401-y

M3 - Article

SP - 1

EP - 12

JO - Health Care Management Science

T2 - Health Care Management Science

JF - Health Care Management Science

SN - 1386-9620

ER -