Modelling activities at a neurological rehabilitation unit

Abstract

DeclarationThis work has not previously been accepted in substance for any degree and is not concurrently being submitted in candidature for any degree. Signed:Date: Statement 1This thesis is the result of my own investigations, except where otherwise stated. Other sources are acknowledged by giving explicit references. A bibliography is appended. Signed:Date: Statement 2I hereby give consent for my thesis, if accepted, to be available for photocopying and interlibrary loan, and for the title and summary to be made available to outside organisations. SummaryA queuing model is developed for the neurological rehabilitation unit at Rookwood Hospital in Cardiff. Arrivals at the queuing system are represented by patient referrals and service is represented by patient length of stay (typically five months). Since there are often delays to discharge, length of stay is partitioned into two parts: admission until date ready for discharge (modelled by Coxian phase-type distribution) and date ready for discharge until ultimate discharge (modelled by exponential distribution). The attributes of patients (such as age, gender, diagnosis etc) are taken into account since they affect these distributions. A computer program has been developed to solve this multi-server (21 bed) queuing system to produce steady-state probabilities and various performance measures.However, early on in the project it became apparent that the intensity of treatment received by patients has an effect on the time, from admission, until they are ready for discharge. That is, the service rates of the Coxian distribution are dependent on the amount of therapy received over time. This directly relates to the amount of treatment allocated in the weekly timetables. For the physiotherapy department, these take about eight hours to produce each week by hand. In order to ask the valuable what-if questions that relate to treatment intensity, it is therefore necessary to produce an automated scheduling program that replicates the manual assignment of therapy. The quality of timetables produced using this program was, in fact, considerably better than its alternative and so replaced the by-hand approach. Other benefits are more clinical time (since less employee input is required) and a convenient output of data and performance measures that are required for audit purposes.Once the model is constructed a number of relevant hypothetical scenarios are considered.Such as, what if delays to discharge are reduced by 50%? Also, through the scheduling program, the effect of changes to the composition of staff or therapy sessions can be evaluated, for example, what if the number of therapists is increased by one third? The effects of such measures are analysed by studying performance measures (such as throughput and occupancy) and the associated costs.

Optimising resource management in neurorehabilitation

“…To this end, we introduce two well-known tools of operational research -personnel scheduling and queue modelling -and exemplify the benefits they can bring to service delivery through a case study at a major unit in the UK. The purpose of this paper is not to provide a detailed account of how these tools are implemented (for this see (Griffiths et al, 2012a(Griffiths et al, , 2012b), but to provide an overview of how and why they are used and the kind of improvements they can bring. It must also be acknowledged that, whilst many units share similar features, the model created here is designed specifically for Rookwood hospital, and that other units will likely require bespoke approaches.…”

“…For the model corresponding to the optimal values, there is only a 0.2% and 0.5% difference in the model and empirical results for these measures. See (Griffiths, Williams, & Wood, 2012a) for a more detailed description of the construction of the queuing system.…”

A dynamic benchmarking system for assessing the recovery of inpatients: Evidence from the neurorehabilitation process

“…Consequently, there has been a significant research effort focused on resolving the mismatch between patient numbers and availability of resources. The majority of these studies attempted to rectify shortages by focusing on one of the following external causes: (1) rescheduling and adjusting the allocation of medical resources including nurses and wards (Blake & Carter, 2002;Geng & Xie, 2012;Kusters & Groot, 1996;Ridge, Jones, Nielsen, & Shahani, 1998;Siferd, 1994;Valouxis, Gogos, Goulas, Alefragis, & Housos, 2012;Verheyen, 1992;Vissers, 1998;Worthington, 1988;Zonderland & Timmer, 2012), (2) reducing patient waiting times (Chien, Tseng, & Chen, 2008;Griffiths, Williams, & Wood, 2013;Iversen, 2000;Saghafian, Hopp, Oyen, Desmond, & Kronick, 2012;Shimshak, Damico, & Burden, 1981;Vuyst, Bruneel, & Fiems, 2014), (3) adopting new patient admission criteria to lighten the load on hospitals (Vissers, Adan, & Dellaert, 2007), and (4) employing new managerial policies (Li & Benton, 2003;Oliveira & Bevan, 2008). The methodologies employed in the above references are able to temporarily alleviate the effects of insufficient medical resources; however, alone they cannot completely eliminate the problem.…”

A preliminary study on the role of simulation models in generating insights

“…When this service was first launched, it failed to cope with patient demand. However, a service manned by low-cost staff with no medical experience is expected to lead to high operating costs (Griffiths, Williams, and Wood 2013).…”