case study weight loss clinic

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Improving patient waiting times:

a simulation study of an obesity

care service

Antuela A Tako,1 Kathy Kotiadis,2 Christos Vasilakis,3 Alexander Miras,4

Carel W le Roux4,5

1School of Business andEconomics, LoughboroughUniversity, Loughborough, UK2Warwick Business School,University of Warwick, Coventry,UK3UCL Clinical OperationalResearch Unit, University College

London, London, UK4Imperial Weight Centre,Imperial College London,London, UK5Experimental Pathology, UCDConway Institute, School ofMedicine and Medical Science,University College Dublin,Dublin, Ireland

Correspondence toDr Antuela A Tako, School ofBusiness and Economics,Loughborough University,Richard Morris Building,Ashby Road, Loughborough

LE11 3TU, UK;[email protected]

Received 28 April 2013Revised 5 August 2013Accepted 27 August 2013

To cite:Tako AA, Kotiadis K,Vasilakis C,et al.BMJ Qual

SafPublished Online First:[please include Day MonthYear] doi:10.1136/bmjqs-2013-002107

ABSTRACTBackground Obesity care services are often

faced with the need to adapt their resources to

rising levels of demand. The main focus of this

study was to help prioritise planned investments

in new capacity allowing the service to improve

patient experience and meet future anticipated

demand.

Methods We developed computer models of

patient flows in an obesity service in an

Academic Health Science Centre that provides

lifestyle, pharmacotherapy and surgery treatment

options for the UKs National Health Service.

Using these models we experiment with different

scenarios to investigate the likely impact of

alternative resource configurations on patient

waiting times.

Results Simulation results show that the timing

and combination of adding extra resources (eg,

surgeons and physicians) to the service areimportant. For example, increasing the capacity

of the pharmacotherapy clinics equivalent to

adding one physician reduced the relevant

waiting list size and waiting times, but it then led

to increased waiting times for surgical patients.

Better service levels were achieved when the

service operates with the resource capacity of

two physicians and three surgeons. The results

obtained from this study had an impact on the

planning and organisation of the obesity service.

Conclusions Resource configuration combined

with demand management (reduction in referralrates) along the care service can help improve

patient waiting time targets for obesity services,

such as the 18 week target of UKs National

Health Service. The use of simulation models can

help stakeholders understand the

interconnectedness of the multiple microsystems

(eg, clinics) comprising a complex clinical service

for the same patient population, therefore,

making stakeholders aware of the likely impact

of resourcing decisions on the different

microsystems.

INTRODUCTIONObesity is a major concern in a numberof countries worldwide.1 Thirty per centand 24% of adults in the USA and UK,respectively are currently classified asobese, and these figures are expected todouble in the future.2 3 The clinical treat-ment of obesity has become part ofobesity care services delivered by dedi-cated care providers.4

So far, mathematical and computermodelling efforts with regards to obesityhave concentrated on developing epi-demiological models that estimateexpected obesity trends and healthcareexpenditure due to related diseases in theUSA and the UK.4 5 Given that multidis-ciplinary obesity centres are a relativelynew service, at least to the National

Health Service (NHS) in England, studieslooking at their performance from anoperational perspective have not beenreported in the literature. An obesity careservice typically forms a complex inter-connected clinical service comprisingmultiple microsystems that provide arange of non-surgical and surgical ser-vices to the same patient population.6

Obesity care service performance is typic-ally judged using targets aimed at ensuringpatients right to accessing services within a

maximum waiting time. In the UK, the18-week target from when a patient isreferred to when treatment is provided7 8

has received significant attention in the last1015 years, and has been used to evaluatethe performance of NHS institutions. Thistarget is also used to evaluate the perform-ance of obesity centres, however, for thepurposes of obesity treatment, this targetneeds to be measured differently to accom-modate for the time the patient needs toprepare to receive some specific treatments

ORIGINAL RESEARCH

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such as obesity surgery. Further detail about how thesetargets are measured is provided in the Methods section.

This paper reports on a simulation study under-taken to help prioritise planned investments in newcapacity that will allow the service to improve patientexperience and meet future anticipated demand. Wechose to use discrete event simulation (DES) as it isgenerally considered appropriate for modelling and

evaluating the allocation of resources in the healthcontext.9 10 Its overall aim is to evaluate the likelyeffect that the capacity of different resource configura-tions has on patient waiting times in a UK-basedobesity service situated in an Academic Health ScienceCentre. The novelty of the study is that multiplemicrosystems have been included in the model ratherthan the single microsystem (eg, an outpatient clinic)usually modelled in simulation studies.1116 Thisin turn has enabled us to explore targets such as the18 weeks target that involve a longer part of thepatient journey through a number of clinics withinthe obesity system. A number of computer models

were used to explore the impact of alternative config-urations of resources on the emerging waiting lists.These models explored the following options: Increasing capacity to meet demand, that is, employing

additional clinical staff (surgeons and physicians).

Managing demand through a reduction in patient refer-

ral rates into the service.

Although the results obtained are specific to theparticular service, the methods and findings could beuseful to other similar centres within and outside theUK.

METHODSThe care setting

The obesity care service studied was designated as anInternational Centre of Excellence for bariatricsurgery by the Surgical Review Corporation and one ofthe preferred providers of bariatric surgery services forLondon and Northern Ireland. The service providerswanted to understand how the 18 week target could beconsistently met in the foreseeable future, without addingunnecessary capacity, by employing new resources suchas surgeons and physicians. The service was experiencing,at the time of the study (2009), increasing numbers ofreferrals and an increased pressure to meet the demandfor consultation and treatment. The pressure was mostlyexperienced in the parts of the system treating patientswith pharmacotherapy (medication) and surgery. Theservice referrals were increasing each year at an exponen-tial rate which made planning difficult. In addition stake-holders found it difficult to consider the effect of addingresources on the entire system. A visual interactive simula-tion tool was considered appropriate in bridging the sta-keholdersviews.

The treatment of obesity focuses on the reduction ofbody weight using three different options depending onpatient choice: a change in lifestyle, pharmacotherapy

and bariatric surgery (also known as obesity surgery).The first option involves diet, exercise and behaviouralchange. The second option involves the administrationand management of weight loss medications over along period. The third and final option is surgery.17 Inthe NHS the three most common types of surgicalinterventions are gastric band, sleeve gastrectomy andgastric bypass.18 The choice of treatment is made based

on patient preferences and health indicators such as thebody mass index and specific comorbidities.Patients were referred to the obesity care service

either from primary care services (general practi-tioners) or from other secondary care services (as aresult of serious comorbidities related to obesity). Agroup induction session was organised once a weekfor newly referred patients, where members of theteam (physician, surgeon and nurses) explained treat-ment options. Patients completed a questionnaire withdetails of their health conditions and treatment prefer-ences. Next, a specialist nurse screened the question-naires and referred patients to one of the three

medical outpatient clinics which focused on preparingpatients for the lifestyle, pharmacotherapy or surgerytreatment options.

The lifestyle clinic led by dieticians operated twice aweek. Patients attended in total six consultation visits,on average 1 month apart. After that patients weredischarged and advised to continue the dietary regimefor life, while also having the option of attending spe-cific lifestyle group support sessions.

The pharmacotherapy clinic led by a physician oper-ated weekly. Two types of medication therapies weretypically prescribed, reviewed initially after a 3 months

period and 9 months thereafter. Patients successfullytreated were discharged to the care of the general prac-titioner to continue on a lifelong treatment. If one typeof drug did not work for the patient, the second typeof drug was prescribed. If none of the drug typesworked, patients were either referred to the lifestyle orsurgical clinic or discharged.

The surgical care component of the obesity serviceinvolves a range of outpatient appointments and a sur-gical procedure. Patients were first seen in an out-patient clinic, called the Eligibility clinic, led by aphysician and psychiatrist, who reviewed the patientshistory and assessed whether surgery would be appro-priate. Patients, with psychological comorbidities thatneed further optimisation were sent for a 3-monthpsychiatric review and if a satisfactory improvementwas achieved, they were then referred on for a surgicalopinion. In the next clinic called the Decisionclinic, a surgeon and dietician assessed the patient toestablish if he/she can safely have an operation.Following the decision to operate, patients were regis-tered on the surgery waiting list. Before their sched-uled operation, they were reviewed and educated in aPre-assessment clinic led by an anaesthetist and aspecialist nurse. If the patient passed the necessary

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health checks he/she was scheduled for one of thethree types of surgical interventions (gastric band,sleeve gastrectomy or gastric bypass). The number ofoperations scheduled on an operating list would varydepending on the type of surgery. Patients were admit-ted on the day of surgery. After operation postoperativecare was provided depending on the type of surgery.Patients were then discharged from hospital and seen

as follow-up cases at the medical or surgical outpatientclinics under a shared care arrangement.

The simulation model

The obesity care pathway described above was repre-sented in a simulation model built using Simul8, aDES software.19 The main objective was to identifythe impact of capacity changes in resources (namelysurgeons and physicians) and patient referral rates onpatient waiting times. Capacity is modelled as theavailable patient appointments (slots). Stakeholdersdetermine this in practice by translating the availableresources (eg, physicians, surgeons, nurses, dieticians,

beds, rooms, etc) of each microsystem into availablepatient capacity. The model runs for one simulatedyear with a time unit of 1 day. Patient movement isbest represented in days for two main reasons. Firstthe overall system in simulation terms is slow movingas it takes several weeks for a typical patient to movethrough the system. Second, the individual microsys-tems (eg, clinics) are modelled as patient slots (cap-acity) which do not require a time unit lower than aday as it would be unlikely to attend two clinicswithin a day. Results on patient waiting times arerepresented in multiples of days, which are automatic-

ally converted into weeks to fit with the targets ofinterest (eg, 18 week target). Patients are shown toarrive into the model in appropriate intervals and canfollow various routes within the obesity system. Theseroutes are determined by probability distributions.Movement of patients to the next process (eg, clinic)is determined by the available capacity. If there is noavailable capacity, patients are held in the model inqueues, which conceptually fit waiting lists. Patientsare assigned a range of attributes such as time stampsat different points within the model in order for thecalculations of waiting times to be performed.

We chose DES because it can depict the concept ofindividual patients competing for different types ofresources (eg, a slot in the operating theatre list or apostoperative bed) and thus making it possible tomodel the impact of different levels of availableresources and referrals on waiting times. Furthermore,the software can graphically display on screen themovement of patients, which members of the obesityteam found useful to gain an overall understanding ofthe obesity care pathway. Figure 1 shows a flowchartof the flow of patients within the obesity carepathway and the simulation model. Table 1 provides asummary of the objects, model parameters and

distributions used in the model, which further explainthe simulation model developed.

As it is the case in modelling studies, some aspectsof the real life service which were not relevant to theobjectives of the study were not modelled and otherswere modified for simplification purposes. Theassumptions and simplifications made are listed

below: The capacity relevant to the study was mostly related to

the number of physicians and surgeons. This was due to

the difficulties when it comes to increasing these resources

because of costs and limited availability in expertise.

Other staff specialties (ie, nurses, dietician, etc.) are indir-

ectly modelled as capacity in some parts of the system

(eg, lifestyle clinics) but in this system their availability

exceeded demand. Infrastructure components (eg, equip-

ment and operating theatres) were a secondary concern

when determining the capacity.

Repeat outpatient appointments for patients returning for

regular check-ups after treatment were not included. In

fact, follow-up clinics were being established in primary

care which followed the agenda of the NHS plan, but also

allowed an almost unlimited capacity in principle as GPs

would take up the follow-up care for uncomplicated

patients.

While placement of patients on the surgery waiting list is

in reality more complex, taking into consideration

patient preferences and fulfilment of requirements for

surgery, in the model they are allocated an operation

using a simplified first in first out rule.

It is standard practice in DES to improve the accur-acy of the results by removing the initial transient

effects (due to the simulated service starting empty)and by running the simulation several times toaccount for the variability in the input parameters.20 21

Statistical estimations informed our choice of using awarm-up period of 1 year and execute 30 simulationruns for each model configuration.22 The computermodel then continues to run for a further yearwhereby the results show the level of the differentperformance measures calculated at that point intime.

A number of models were developed, including thebaseline model, representing the obesity servicerunning a year into the future as it was resourced atthe time of the study, with the equivalent capacityresulting from operating with one surgeon and onephysician. The remaining models represented otherfuture scenarios of different options, which were pre-viously agreed with the obesity care team. Thesemodels forecast the future performance of the serviceunder variations of the following parameters: Number of surgeons: additional surgeons are repre-

sented as multiples of the capacity of services requiring a

surgeon in Decision clinic and Operation theatre slots

Number of physicians: additional physicians are repre-

sented as multiples of the capacity of services requiring a

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physician in Group Induction, Pharmacotherapy clinic

and Eligibility clinic

Managing demand (referrals): reducing patient referrals

to half of the baseline figures

Six scenarios (models) representing the feasible solu-tion space were selected by the stakeholders. Thesescenarios look 1 year into the future (table 2).

Data collection

In order to populate the models input parameters weobtained real life data from members of the obesitycare service. The data used are detailed in table 1,where the data sources are also explained. The obesityservice was regularly audited which meant that somedata such as number of patients in the differentwaiting lists, number of patients discharged, etc, wasavailable in reasonable quantity. These are detailed intable 1 as existing data. In most cases, data referringto the proportion of patients that choose a specifictreatment were collected based on a randomly chosensample of 60 patients from a database containing 600patients (every 1 patient in 10 was chosen from theexisting patient database). Some of the data, such asthe waiting time between clinics, were not known

with accuracy and it was not possible to estimateempirically, hence informed guesses were made andapproximate distributions were used in the model suchas the triangular distribution (see table 1). Data onpatient referrals were also estimated based on existingnumbers accounting for the increased future demandfor obesity services, hence slightly higher than the exist-ing numbers. The source is detailed as expert opinion.

Performance indicators

We were mainly interested in patient waiting list sizeand times as these indicators are used by senior man-agement to assess the performance of the obesity careservice. Waiting list size for Group Induction: the number of first

time patients on the waiting list for group induction

session.

Waiting list size for Pharmacotherapy clinic: the number

of patients waiting to be seen in the Pharmacotherapy

clinic.

Waiting list size for Operations: the total number of sur-

gical patients (who opted for surgical intervention at

group induction), waiting for surgery at any point in the

surgical pathway. This includes the waiting list for the

Figure 1 Flow chart showing the process flow within the obesity service. WL, waiting list.

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Eligibility clinic, Decision clinic, surgery ( patients who

are allocated a date) and the weekly operating list (ie,

patients scheduled for operation).

Waiting time to operation: the total time (in weeks)

patients spent in the system from first referral to the day

of operation.

18 week targets: the proportion of patients waiting more

than 18 weeks from referral to receiving treatment. Two

separate 18-week targets were calculated, specific to the

obesity pathway as opposed to an overall 18 week target

used in other clinical areas,7 where patients require

shorter timescales to first treatment. The first target

counts the combined waiting time from first time referral

until a first treatment is provided (in Eligibility or

Pharmacotherapy clinic). The second target is counted

only for surgical patients, where the clock starts counting

from Eligibility clinic until surgery. Effectively the clock

stopped when the patient was seen for the first time and

Table 1 Input parameters to simulation model in baseline scenario

Parameter ResourcesValue in baselinescenario

Distributiontype

Datasources

Referral rate 100 patients/month Poisson Expert opinion

Group induction (one group session per week) NursePhysician Surgeon

Up to 20 patients/week Existing data

Following group induction

Patient assessment 93% Bernoulli Existing

Do not continue 7% Bernoulli DataPatient assessment (once a week) Nurse Up to 20 patients/week

Following patient assessment

Lifestyle clinic 5% Bernoulli Existing

Pharmacotherapy clinic 16% Bernoulli Data

Eligibility for surgery clinic 79% Bernoulli

Lifestyle clinic (two group sessions per week) Dietician Up to 8 patients/week Existing data

Six separate appointments/patient

Time period between appointments 20 working days (1 month) Triangular Expert opinion

Pharmacotherapy clinic (once a week) Physician Up to 14 patients/ week Existing data

Following Pharmacotherapy Expert

Receive drugs (drug A) 84% success rate Bernoulli opinion

Receive drug B (if drug A fails) 80% success rate Bernoulli

Time period for second appointment 90 days (3 months) Triangular

Time period for third appointment 120 days (6 months) Triangular

If drugs A and B fail, Expert

Referral to surgery 15% Bernoulli Opinion

Referral to lifestyle clinic 10% Bernoulli

Discharged 75% Bernoulli

Eligibility clinic (outpatients) PhysicianPsychiatrist

Up to 10 patients/week Existing data

Following Eligibility clinic Existing

Decision clinic (surgery) 60% Data

Psychiatric review 30%

DNA surgery 10%Decision (for surgery) clinic Surgeon Dietician 8 patients/week Existing data

Preassessment clinic (2 weeks before the scheduledoperation)

Anaesthetist Nurse 8 patients/week Existing data

Operations (three types of surgical procedures) SurgeonAnaesthetist

3 half day theatrelists/week

Existing data

Gastric band (1 h procedure) 19% Bernoulli

Sleeve gastrectomy (1.5 h) 22% Bernoulli

Gastric bypass (2 h) 59% Bernoulli

Postoperative length of stay following Beds Depending on type of surgery

Expert opinion

Gastric band 1 day

Sleeve gastrectomy 2 days

Gastric bypass 2 days

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proportion of patients waiting for longer than 18 weekswas reduced to 9% (95% CI 9% to 12%) with the add-ition of one surgeon (scenario 1), whereas the additionof two surgeons (scenarios 3 and 5) provided a furtherslight improvement, reducing this proportion to 8%(95% CI 7% to 10%).

The introduction of an additional physician resultedin significantly lower waiting lists for group induction,

Pharmacotherapy and Eligibility clinic (scenario 2).However, more patients progressed to the surgicalpart of the system, creating a high backlog of referralswaiting for surgery. This is obvious when comparingscenarios 1 (1 physician) and 2 (2 physicians), where ahigher proportion of patients waited for more than18 weeks (9% compared with 38%). The best per-forming scenarios were those with more surgeonsthan physicians. This demonstrates the dynamicbehaviour of resources and the bottlenecks created inthe system.

Scenarios 4 and 5 operate under a reduced patientreferrals mechanism, where patients who did not have

sleep apnoea, high cardiovascular risk, diabetes orinfertility would be seen by general practitioners inprimary care centres. The reduced referral rate appearsto allow physicians to clear the backlog of patientswaiting for group induction. As a result the proportionof patients waiting for more than 18 weeks to be seenat the Eligibility clinic is reduced from 63% (95% CI62% to 65%) in scenario 2 to 59% (95% CI 58% to60%) in scenarios 4 and 5. In the last month(December year 1) this proportion reaches 0% (scen-arios 4 and 5). Scenario 5 however provided a betterperformance because beyond December of year 1, all

patients wait less than 18 weeks to be seen at theEligibility clinic or to receive an operation.

DISCUSSIONMembers of the obesity care team were involvedthroughout the study and took a keen interest in itsresults. The simulation models built provided a visualrepresentation of the obesity care service, which inturn helped those involved in gaining a wider under-standing of the service. The study confirmed theinitial concerns of the team that the pre-existing cap-acity in place in 2009 (at the start of study), wouldnot be sufficient to cope with the increases in patientreferrals to the service. In real life this would resultinto continuously rising numbers of patients on thewaiting lists with considerably longer waits. Hencechanges needed to be considered. As expected, simu-lation results showed that the addition of surgicalresources and their associate capacity bring aboutimprovements in patient waiting times in the surgicalpart of the service. However, the results also showedthat adding at the same time the capacity of one phys-ician, may lead to deterioration in the waiting timesassociated with the surgical part of the service. Thisoccurs because a bottleneck is developed betweenTa

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WLGroupInduction

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WLforoperation

Waitingtim

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Wait>18weeks*

(targetfor

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combined)

Wait>18weeks

(targetforsurgery

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Scenario|

Meannumber

ofpatients

95%

CI

Meannumber

ofpatients

95%

CI

Meannumber

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patients

95%

CI

Meannumber

ofweeks

95%

CI

Percent

95%CI

Percent

95%

CI

Baseline

523

507to540

141

130to153

79

1

781to800

39

.6

39

.2to39

.9

64

63to65

47

46to47

1

526

510to541

140

128to151

54

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816to851

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63

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38

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in a safe environment and the results can be madequickly available to those concerned.

Contributors AAT built the models and analysed the results.AAT and KK drafted the paper. CV helped with the editing ofthe paper. AM and CWR helped in organising the study,contributed data for the development of the models and editingof the paper. All authors have reviewed the paper and haveapproved the final version.

Funding This study was supported by the UK Engineering andPhysical Sciences Research Council (EPSRC) grant EP/E045871/1.

Competing interests None.

Provenance and peer review Not commissioned; externallypeer reviewed.

Open Access This is an Open Access article distributed inaccordance with the Creative Commons Attribution NonCommercial (CC BY-NC 3.0) license, which permits others todistribute, remix, adapt, build upon this work non-commercially,and license their derivative works on different terms, providedthe original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/3.0/

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doi: 10.1136/bmjqs-2013-002107published online September 19, 2013BMJ Qual Saf

Antuela A Tako, Kathy Kotiadis, Christos Vasilakis, et al.simulation study of an obesity care serviceImproving patient waiting times: a

http://qualitysafety.bmj.com/content/early/2014/03/12/bmjqs-2013-002107.full.htmlUpdated information and services can be found at:

These include:

Referenceshttp://qualitysafety.bmj.com/content/early/2014/03/12/bmjqs-2013-002107.full.html#ref-list-1

This article cites 14 articles, 1 of which can be accessed free at:

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