Modeling the critical care pathway for cardiothoracic surgery
Nicolas Bahou (),
Claire Fenwick,
Gillian Anderson,
Robert Meer and
Tony Vassalos
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Nicolas Bahou: University of Strathclyde
Claire Fenwick: Golden Jubilee National Hospital
Gillian Anderson: University of Strathclyde
Robert Meer: University of Strathclyde
Tony Vassalos: Golden Jubilee National Hospital
Health Care Management Science, 2018, vol. 21, issue 2, No 4, 192-203
Abstract:
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.
Keywords: Simulation; OR in healthcare; Patient scheduling; Hospital operations (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10729-017-9401-y
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