Linking Six Sigma to simulation a new roadmap to improve the quality of patient care

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Linking Six Sigma to simulation:a new roadmap to improve the

quality of patient careGiovanni Celano, Antonio Costa and Sergio Fichera

Dipartimento di Ingegneria Industriale e Meccanica, University of Catania,Catania, Italy, and

Giuseppe TringaliAzienda Ospedaliero Universitaria Policlinico Vittorio Emanuele,

Catania, Italy

AbstractPurpose Improving the quality of patient care is a challenge that calls for a multidisciplinaryapproach, embedding a broad spectrum of knowledge and involving healthcare professionals fromdiverse backgrounds. The purpose of this paper is to present an innovative approach that implementsdiscrete-event simulation (DES) as a decision-supporting tool in the management of Six Sigma qualityimprovement projects.

Design/methodology/approach A roadmap is designed to assist quality practitioners and healthcare professionals in the design and successful implementation of simulation models within thedefine-measure-analyse-design-verify (DMADV) or define-measure-analyse-improve-control (DMAIC)Six Sigma procedures.

Findings A case regarding the reorganisation of the flow of emergency patients affected by vertigosymptoms was developed in a large town hospital as a preliminary test of the roadmap. The positivefeedback from professionals carrying out the project looks promising and encourages further roadmap

testing in other clinical settings.

Practical implications The roadmap is a structured procedure that people involved in qualityimprovement can implement to manage projects based on the analysis and comparison of alternativescenarios.

Originality/value The role of Six Sigma philosophy in improvement of the quality of healthcareservices is recognised both by researchers and by quality practitioners; discrete-event simulationmodels are commonly used to improve the key performance measures of patient care delivery. The twoapproaches are seldom referenced and implemented together; however, they could be successfullyintegrated to carry out quality improvement programs. This paper proposes an innovative approach tobridge the gap and enrich the Six Sigma toolbox of quality improvement procedures with DES.

Keywords Quality improvement, Six sigma, Modelling, Discrete event simulation, Simulation,Health services

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0952-6862.htm

The authors wish to thank the anonymous referees for valuable suggestions which allowedimprovement of the quality of the final paper. Furthermore, the authors are grateful toDr G. Brown, PhD (Department of Psychology, Royal Holloway, University of London),and Dr F Messineo, PhD (Kent School of Law, University of Kent), who gave valuable supportduring the editing process of this paper.

IJHCQA25,4

254

Received 9 September 2010Revised 15 December 2010Accepted 27 December 2010

International Journal of Health Care

Quality Assurance

Vol. 25 No. 4, 2012

pp. 254-273

q Emerald Group Publishing Limited

0952-6862

DOI 10.1108/09526861211221473


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IntroductionContinuous quality improvement of patients care is recognised as essential forachieving excellence in the healthcare service delivery. In the last 20 years, Six Sigma(6s) has received a lot of attention among the several quality management

philosophies. Six Sigma is a rigorous, focused and highly effective implementation ofproven quality principles and techniques, which aims at virtually error free businessperformance (Pyzdek, 2003). In the 1980s, Six Sigma was initially implemented in theindustrial setting by Motorola; it subsequently attracted much attention among peopleworking in the service sector, including healthcare professionals. In 1998, KentuckysCommonwealth Health Corporation was the first healthcare organisation which fullyembraced the Six Sigma philosophy the US; successively, Six Sigma was implementedwithin several US organisations (Sehwail and DeYong, 2003). A survey of Six Sigmaprograms in 56 US healthcare organisations conducted by means of questionnaires ispresented by (Feng and Manuel, 2008). Six Sigma improvement programmes andpersonnel training have also been employed in European healthcare organisations.Some case studies presenting Six Sigma implementation in the nursing department atthe Red Cross Hospital of Beverwijk (The Netherlands) are discussed in Van denHeuvel et al. (2004); the annual savings obtained in the same hospital by means of theimplementation of quality improvement projects are presented in Van Den Heuvel et al.(2005). A review about the Six Sigma methodology application to healthcareorganisations is proposed in Taner et al. (2007). More recently, Taner and Sezen (2009)have proposed the implementation of the Six Sigma toolbox to study the turnoverproblem of doctors in medical emergency services and paramedic backup. However, allof these case studies show that a strong financial and organisational effort is needed tocarry out a successful Six Sigma project in the healthcare field. This is a barrier to itsdeployment within organisations with a limited budget capacity or which are alreadyimplementing another quality management philosophy like, for example, the ISO 9000

family of standards. The question arises if there are ways to introduce the Six Sigmatechniques within budget-conscious healthcare organisation while retaining aneffective outcome.

Based on several years of experience about the implementation of Six Sigmaprogrammes, Magnusson et al. (2003) claim that there are three main approaches todeploy Six Sigma within a company. The first approach assumes that Six Sigma can beinitiated as a company-wide strategy at the highest level and deployed throughout theentire organisation under the full senior management commitment. This approachrequires a strong company effort to achieve breakthrough quality-improvementobjectives which should be supported by a well defined organisational structure,(Glickman et al., 2007). Alternatively, the second approach assumes that Six Sigma canbe implemented as an improvement programme by one or more organisational units

and, if successful, extended to the other areas of the company. Finally, accordingly tothe third approach Six Sigma can be adopted as a toolbox into specific projects forimprovement with a particular focus to problem solving: this third approach does notrequire the commitment from senior management or a large company involvement.This third vision and approach to Six Sigma will be adopted in this paper; it fits withthe definition of Six Sigma given in (Linderman et al., 2003), who consider Six Sigma asan organised and systematic problem-solving method for strategic systemimprovement and new product and service development based on statistical methods.

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Hundreds of papers and several research journals show the implementation ofoperations research (OR) tools in the field of healthcare service. Operations Research isapplied to problems that concern how to conduct and coordinate the operations (i.e. theactivities) within an organisation. Among the OR tools, discrete-event simulation

(DES) allows for the complexity of stochastic systems to be modelled which cannot becaught by a pure mathematical model (Hillier and Lieberman, 2005). Healthcaresettings, like those investigated in the case study discussed in this paper, are oftencomplex stochastic systems. These are characterised by random variables describingthe times to complete activities; complex flow of patients to be managed throughout thedepartments, random patient interarrival times; shared resources availability amongseveral departments. Thus, describing all these aspects by means of a puremathematical model is often a prohibitive task; otherwise, they can be efficiently (andquite easily) modelled through DES. A comprehensive review of discrete-eventsimulation models applied to healthcare problems is reported in Jun et al. (1999): thispaper surveys the application of discrete-event simulation modelling to healthcareclinics and systems of clinics, i.e. hospitals, outpatient clinics, emergency departments,and pharmacies. A second more recent review (Fletcher and Worthington, 2009) isfocused on the simulation of emergency patient flows and provides for exhaustivereferences to papers related to the study of accident and emergency (A&E)departments, bed management, surgery, critical care and diagnostics and other specificenvironments.

Often the key performance measures (KPM) optimised by means of a discrete-eventsimulation model can coincide with the critical to quality (CTQ) parameters selected toachieve quality-improvement within a Six Sigma programme. Thus, trying to get asynergy between Six Sigma and DES techniques seems to be appropriate and deservesattention from the researchers and quality practitioners. In light of this, a recent paper(Tang et al., 2007) suggests as a major strengthening for Six Sigma to expand its

toolbox through the integration of the OR techniques in the training courses for SixSigma Black Belts.In this paper, a theoretical framework based on a roadmap facilitating the process

of embedding discrete-event simulation as a decision tool within a Six Sigmaquality-improvement project is discussed. The roadmap is built as a structured andhierarchical procedure to achieve quality improvement in a general setting. A casestudy showing the first in-hospital implementation of the roadmap is presented to thereaders.

In the next section, the proposed roadmap is discussed in detail. Then, thedevelopment of a DES based quality-improvement Six Sigma programme is presentedto show the implementation of the roadmap in practice. This was a programme aimedat reorganising the flow of emergency patients affected by vertigo symptoms.

Successively, the lessons learned from the case study and the potential benefits fromthe proposed approach are discussed. Finally, conclusions and future researchopportunities complete the paper.

Linking Six Sigma to simulation: definition of a roadmapSix Sigma quality practitioners select, develop and validate a quality-improvementproject by selecting and implementing a define-measure-analyse-improve-control(DMAIC) or, alternatively, a define-measure-analyse-design-verify (DMADV) five step

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procedure. Whenever process improvement should be investigated by comparing anexisting configuration to alternative scenarios, the DMAIC methodology is adopted.When new processes, products or services should be designed, the DMADVmethodology is adopted instead; for further details, readers can refer to (Pyzdek, 2003).

Often, the comparison among different scenarios or testing new product or processconfigurations can only be performed by means of simulation because of the complexmanagement issues and associated costs required in the performance of real trial runs.

The design of a simulation model is a structured five-step procedure working asfollows. First, the process requires mapping and a clear definition of its boundaries: areview and application of several visual techniques to map processes in the healthcarefield can be found in (Guglielmino et al., 2009). Then, the stochastic process data arecollected to define the base of knowledge needed to design the model. The third stepconsists of analysing these data to get a good statistical fit of their variability. Then,the simulation model logic can be designed and developed. Finally, the model isvalidated through a set of trial runs. After the simulation model has been validated, itcan be used to compare the current configuration, if it exists, against hypothesised newscenarios in order to find the optimum with respect to one or more pre-selected keyperformance measures. This five-step procedure has been adapted in Seppanen et al.(2005) to fit it with the DMAIC procedure.

A roadmap extending this approach is proposed here as a theoretical framework tomanage Six Sigma projects aimed at improving the quality of a service: it merges boththe DMADV and DMAIC methodologies to achieve a hierarchical procedure able tomanage an ongoing Six Sigma quality-improvement project of an existing servicebased on a discrete-event simulation model. We denote it as the DMAIC-DMADVroadmap. Similarly, if the Six Sigma improvement project is focused on new serviceconfigurations to be investigated, the double DMADV2 roadmap should be considered.Figure 1 shows the flow chart of the DMAIC-DMADV roadmap.

The roadmap description intentionally refers to a generic process, thus assuring ahigh degree of generality and a wide applicability of the framework either to a serviceor a manufacturing environment. In the proposed hierarchical approach, the DMAICmethodology defines the five steps for the quality-improvement project and containsthe DMADV methodology related to the DES model design and development. Thus, inFigure 1, the upper (lower) case notation and plain (dashed) line boxes denote a phaseand the activities belonging to the outer DMAIC (inner DMADV) methodology,respectively. Of course, given the parental relationship between the two methodologies,each activity belonging to the inner DMADV methodology is also part of the outerDMAIC methodology. In the next Sub-sections a brief description of each phase of theroadmap is presented.

DefineThe first phase of the outer DMAIC methodology consists of the sequence of stepsaimed at deciding if a quality-improvement project is suitable to be carried out byfollowing the proposed roadmap. First, the quality-improvement project should beselected: the expected quality improvement of patient care and, possibly, cost savingsare drivers to the selection; then, the work team should be formed by involving, ifneeded, external consultants and people from the quality assurance and the otherdepartments affected by the project. To implement the proposed approach, the

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prerequisites are: the need to test one specific process scenario or comparing differentprocess (service) configurations; the need to implement the simulation tool to model theprocess because of the massive presence of random variables and uncertainty, multipleflows of information and activities, several interactions between resources and the

Figure 1.The roadmap linking 6sand the discrete-eventsimulation modeldevelopment

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difficulty of performing trial runs. Once the presence of these prerequisites has beenconfirmed, the project continues by starting the inner define phase aimed atmodelling the process. The clear understanding about which department is involvedand, for each department, what the resources and the areas to be modelled are allows

for the boundaries of the process to be defined. This represents the starting milestoneto define the cross-functional process maps and the value added stream of activities.Software allowing the visualisation and analysis of complex information, systems andprocesses and common spreadsheets are useful during this phase. For each activitydefined within a cross-functional process map the following information should berecorded: upstream and downstream activities, required resources, time to completionand associated costs. The outcomes of this phase are one or more detailed processflowcharts and a partially complete database of records each containing an activity tobe performed and its related information. Some empty fields of these records, like timeand costs to perform the activities, cannot be filled at this step of the roadmap. Thus,the measurement of the available historical data or the on-line collection of new data isrequired: to accomplish this information, the measure phase should be started.

MeasureAssuming that a measurement system is already present and active, during this phasethe project development proceeds with the model building. Data retrieved from theorganisations databases and/or hardcopy registries should be checked and filtered toremove errors. The data should also be formatted for the subsequent roadmap phases.Sometimes, it is not possible to retrieve information about some categories of data. Inthose cases, the only ways to gather knowledge about them are direct sampling orreliable estimates based on interviews with those directly involved in the servicedelivery. Usually, the data to be collected to simulate a healthcare service includestandard costs and time schedules related to the involved resources, i.e. nurses,technicians and physicians; costs and times needed to perform exams and/or surgicalinterventions; transportation times between departments, and so on. Basic informationabout patients includes volumes, arrival rates at each time of day (TOD), type andseverity of illness, length of stay. The outcomes of this phase are spreadsheetscollecting the data to be analysed.

AnalyseOnce collected and pre-processed, the data should be classified and analysed. Standardcosts can be retrieved from the organisations accounting system: usually, they have adeterministic value. Conversely, a statistical distribution fitting is required for all therandom data: for example, times to perform a consultation, to complete an exam, or to

perform a surgical intervention are stochastic and should be statistically modelled.Usually lognormal, normal or triangular distributions are adopted. The patientsarrival times are modelled as a non-stationary Poisson process with exponentialinterarrival times. The final step of this phase assigns costs and times to a specificactivity area of the process coinciding with each department included within themodel. The outcome of this phase is a set of deterministic data and statisticaldistributions completing the information contained within the database of theactivities. Then, the project can proceed with the DES model building.

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ImproveThis phase of the project coincides with the formalisation of different processconfigurations to be compared. In the proposed roadmap, Improve is the core phasebecause it involves the DES model design and validation: that is, it includes the design

and verify phases belonging to the inner DMADV procedure, see Figure 1. For eachscenario, the model logic is built within a specific simulation software environment byreferring to the cross-functional process maps drawn during the define phase. Eachblock of the simulation model is defined by loading the quantitative data modelledduring the Analyse phase. Finally, the critical to quality parameters, which weredefined so as to compare the different process configurations, are embedded within theDES model as variables or expressions to be triggered during the simulation. This stepends the design phase for the DES model. Then, during the verify phase the DES modellogic is tested and validated through a set of pilot simulation runs to demonstrate itsconsistency with real process data. Once the DES model has been validated, anexperimental plan can be designed by setting at different levels the factors influencingthe quality of service delivery: usually, these factors may include processconfigurations, amount of available resources capacity and costs. For eachexperimental run, the CTQs are the response variables to be optimised. Each run ofthe DES model performed with a specific combination of factors represents areplication of the experimental study. A statistical analysis allows the best processconfiguration to be selected. The outcomes of this phase are the simulation models ofthe different process configurations and a report presenting the results of the statisticalanalysis. Then, the last phase of the quality-improvement project can be initiated.

ControlOnce the best scenario is selected, all the cost and time savings are computed anddocumented for each activity area. Finally, the quality-improvement project requires

the documentation and dissemination of the results throughout the involved areas ofthe organisation and, if required, outside the organisation. Brainstorming meetingsshould be scheduled to share opinions about the results and to organise theimplementation the best process configuration.

An in-hospital implementation of the roadmapA first in-hospital test of the roadmap has been performed in the emergencydepartment of a large town public hospital in southern Italy to compare two differentprocedures designed to manage the flow of emergency patients affected by vertigosymptoms illness (later denoted as VSI patients). This quality-improvement projectdevelopment has been carried out by academic staff from the University of Catania andby staff from the emergency department and the other departments involved in the

vertigo patients flow reorganisation. The hospital has an ISO 9000 standardcertification, and it has never implemented a Six Sigma project in the past. The SixSigma approach was not adopted to carry out quality improvement programs withinthe hospital organisation for two main reasons. First, a cultural issue arising from theapproach to quality based on the ISO 9000 standard. This puts more emphasis oncorrect management of documentation, procedures description and bookkeeping,rather than to promote efforts reducing variability and improving processes by meansof sharply focused projects. Second, an operational issue: the lack of internal resources

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having a strong background on the Six Sigma techniques rendered theirimplementation difficult.

Getting a diagnosis for a walk-in emergency patient affected by vertigo symptomsrequires the execution of several exams and consultations from different departments.

Thus, the time to diagnosis can be extremely long and high rates of light symptomspatients leave the service. The objective of the quality-improvement projectimplementation in the ED has been to eliminate unnecessary exams and to reducethe time and cost needed to get the VSI patients diagnosis leading to ward admission ortheir discharge. To achieve the Six Sigma improvement project goals, a new procedure(scenario B) for the management of VSI patients in the emergency department has beendesigned and compared to the current procedure, denoted here as scenario A. Bothprocedures are described below and graphically detailed in Figures 2 and 3.

The combined DMAIC-DMADV roadmap served as the framework problem solvingtool. In Table I, each step of the roadmap is presented with reference to the project. Thedefine phase started by deciding to compare the time to diagnosis and the associatedcosts by means of two DES models describing the currently adopted procedure(scenario A) and the alternative procedure (scenario B). Implementing DES modelsallowed to cope with the VSI patient flow complexity and to avoid the raising of projectcosts associated to real trial runs. Historical data about VSI patients were availablefrom the emergency department registries. At the beginning of the inner define phasethe process boundaries have been defined: the team developing the project decided thatall but the ED activities required for VSI patients should be included within the models.This strong simplification avoids to simulate all the ED activities, i.e. an overwhelmingtask far from the project objectives. As a consequence, the results from the simulationruns have been considered as the lower bound values corresponding to a limitingcondition where all resources are unlimited, i.e. not shared with other emergencypatients. After a cycle of meetings and interviews with the chairs from the departments

involved in the project, two cross-functional process maps related to scenario A(current procedure) and scenario B (alternative procedure) have been generated, seeFigures 2 and 3, respectively.

In the two figures, a rectangular box corresponds to a value-added activity to beconsidered in the value stream analysis as well as in the database recordinginformation about resources needed to perform each activity. The term value-addedactivity means an operation (a physician consultation or an exam specificallyperformed for that patient) which adds information to the patient diagnosis profile. Theset of value-added activities for the VSI patients has been defined by means ofinterviews with people from the departments involved in the project.

Both scenarios start with the triage nursing assessment and colour code assignmentto a patient, which ranges through the following three levels: white, yellow and green

colour from the less to the most severe assessment. The two scenarios significantlydiffer in the route whereby yellow and green patients are examined across the hospitaldepartments.

Accordingly to the current procedure (scenario A), see Figure 2, only white codepatients (those having apparently light symptoms) immediately undergo anotoneurologic examination, whose outcome decides for their admission or discharge.Yellow and green code patients immediately receive a complete medical assessmentand undergo a full blood count. If no internal pathologies are diagnosed, then the

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patient path continues with a cardiological EGC examination and the study of bloodcardiac markers. If abnormal cardiac conditions are diagnosed, the patient is admittedto the cardiology ward, otherwise further exams are started to diagnose suspectneurologic pathologies: a computer assisted tomography (CAT) exam is performed.The presence of a pathological condition immediately calls for the ward admission to astroke unit or a neurologic/neurosurgery department If the CAT exam does not revealany pathology, then the patient is sent to the audiology department to undergo an

Figure 2.Cross-functional processmap of the current VSIpatient path (scenario A)

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otoneurologic exam. The outcome of this exam decides for his/her admission ordischarge.

The new procedure (scenario B), see Figure 3, maintains the same patient flow forthe white code patients as scenario A. However, yellow and green patients immediatelyreceive a clinical assessment with questionnaires to detect an abnormal brain diffuse

Figure 3.Cross-functional process

map of the alternative VSI

patient path (scenario B)

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DMAIC

DMADV

Step

Tool

Outcome

Define

***

Selectthequality-improvementproject

Brainstormingmeetings

Managingtheflowofemergency

patientsaffectedbyvertigo

symptoms

illness(VSIpatients)

***

Shouldoneormorescenariosbe

considered?


Twoscenarios:thecurrent

andone

alternativeproceduremana

gingVSI

patients

***

Istheareaofimprovementacomplex

process?


Yes.Involvementofseveral

departments.Complexpatientpathway

***

Checkavailabilityofexistinghistorical

dataorpossibilityofestimation


Yes.Existenceofpatientsr

ecordsand

unitarycosts

Define

Definetheprocessboundaries

Brainstorming,meetings

Studyrestrictedtotheflow

ofpatients

affectedbyVSIbetweenED

entrance

andadmission/discharge

Define

Observe,documentandmapprocess

stepsandflow

BrainstorminginterviewsMicro

soft

Visio

Cross-functionalprocessmapsforthe

twoscenarios

Define

Definethevaluestream

BrainstormingMicrosoftExcel

Databaseofactivitiesforeachinvolved

Departmenttobecompleted

Measure

Measure

Collecthistoricalprocessdata

DataminingtechniquesMicrosoftExcelRawDatabasesofhistoricaldata

Measure

Pre-processhistoricalprocessdata

DataminingtechniquesMicrosoftExcelCleanedDatabasesofhistoricaldata

Measure

Gatherunbiasedestimatesoffurther

data

InterviewssamplingMicrosoftExcel

Estimatesandconfidenceintervalsof

furtherdata

Analyse

Analyse

Stratificationoftheavailabledata

DataminingtechniquesMicrosoftExcelFortheVSIPatients:intera

rrivaltimes

perdayandmonthRatesofpatients

leavingtheserviceProbabilityforeach

colourcodeassignedatthe

triageVSI

PatientsIllnessdiagnosisoccurrencies

Analyse

Distributionfittingofthegrouped

samplesofdata

Statisticalinferenceminitab

Triangulardistributionsfittingtimesto

performeachactivity

Analyse

Classificationofthevalueadded

operationsintoactivityareas

Resourceallocationtools

Completedatabaseofactivitiesforeach

involvedDepartment

(continued)

Table I.The proposed roadmapfor the VSI patient flowquality-improvementproject

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DMAIC

DMADV

Step

Tool

Outcome

Improve

Design

DefinetheDESmodellogicfromthe

processmap

RockwellARENA

Logicdefinitionforthesimulation

modelsofthetwoscenarios

Design

Addtheinformationabouttheavailable

datatotheDESmodel

RockwellARENA

Characterisationofeachobjectmodelfor

thesimulationofthetwoscenarios

Design

DefinetheproperCTQparametersin

the

DESmodel

BrainstormingRockwellARENA

CTQs:Expectedoverallcos

tperyear

ExpectedDepartmentalcos

tsperyear

FlowtimesforeachcategoryofVSI

patient

Verify

ChecktheDESModelLogic

TestingproceduresRockwellARENA

Correctmodellogicforeachscenario

Verify

ValidatetheDESmodel

TestingproceduresinterviewsR

ockwell

ARENA

DESmodelsforthetwoscenarios

validatedandreadytoberunfor

comparisonpurposes

Improve

***

Designtheexperimentalplan

Minitab/designexpertRockwell

ARENA

CTQsassumedasresponse

variables

Factorspossiblyaffectinge

achscenario

configurationDesignofthe

experiment

***

RuntheDESmodelreplications

RockwellARENA

ValuesoftheCTQsforeachrun

***

PerformastatisticalanalysisonCTQ

sto

comparethescenarios

Minitab/designexpertRockwell

ARENA

Empiricaldistributionsand

parameters

fortheCTQs

***

Selectthebestperformingscenario

BrainstormingRockwellARENA

Bestscenarioandoptimalv

aluesforthe

CTQs

Control

***

Estimatecost/timesavingsandresource

utilisation

RockwellARENA

Reportsaboutcostandtimedataand

resourceutilisation

***

Document,communicateandvisualise

results

Meetings

Presentationanddissemina

tionofthe

results

Table I.

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perfusion: if a perfusion is suspected, then cardiological exams are immediatelystarted. The outcome of these exams supports decisions about the ward admissiondestination of a patient. If the abnormal brain diffuse perfusion is not suspected for ayellow/green patient, then the otoneurologic examination is immediately conducted.

The patient flow continues with the admission within the audiology ward or therequest of a neurologic visit including a CAT exam.

When the suspicion of neurological pathologies is excluded, a complete medicalassessment with blood count is started to detect the presence of internal organpathologies. Scenario B significantly increases the audiology department workloadbecause the otoneurologic examination is scheduled at the beginning of the patientflow, whatever the patient colour code. It calls for a 24 hours presence or availability ofa specialised physician. However, at the moment of designing scenario B professionalsinvolved in the project believed this would reduce the overall number of examsrequired to get a diagnosis for a patient and consequently the associated times andcosts.

The VSI patients completing the path to diagnosis and needing to be hospitalisedhave been categorised into one of the following four groups, depending on thedepartment wherein the ward admission is decided: audiology (AD), cardiology (CD),medicine (MD), neurology/neurosurgery/stroke unit (ND). Based on the information inthe cross-functional process maps, the development of a spreadsheet containing thedatabase of activities completed the define phase.

During the measure phase, historical data was collected from the emergencydepartment. Information about every VSI patient arriving at the ED in 2007 wasretrieved and coded into spreadsheets. When a service characterised by multiple flowsof patients is to be modelled, the transportation time estimation and the relativeresources availability should be carefully investigated by practitioners becausedifferent hospital configurations can lead to sensibly different patient flow times. Thus,

further information has been gathered by means of time study and interviews with EDnurses and professionals about the times required to perform exams and to directpatients from one department to another. With the exception of the audiologydepartment, all the other departments and wards involved in the case study are locatedat different floors in the same building. This location of the departments significantlyreduces the transportation times to a few walking minutes. Finally, unit costs ofresources and exams have been retrieved from the hospital IT system.

During the analyse phase, a statistical distribution has been fitted to each family ofsampled data by means of the Minitabw commercial software: patients inter-arrivaltimes have been modelled through exponential distributions; the times to completemedical exams and to transport patients between departments have been modelledthrough triangular and normal distributions. The explicit values of times and costs for

each exam and patient transfer are not reported here for the sake of brevity. Table IIpresents the stratified historical data about the VSI patients.

The improve phase of the roadmap has been started by designing the DES modelscorresponding to the two scenarios within the Rockwell ARENAw simulationenvironment: the cross functional process maps presented in Figures 2 and 3 helpedconsultants in designing the models. Several activity areas each coinciding with adepartment have been introduced in the two models to get explicit cost estimation foreach involved department, (Kelton et al., 2007). Model logic was animated by means of

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suitable pictures to facilitate comprehension during the results dissemination. Then, the

CTQs to be collected by the DES models have been selected. According to the project

objectives, a set of departmental yearly costs have been identified to quantify the service

effectiveness for each scenario. Similarly, the service efficiency for each scenario has been

quantified through the computation of the mean flow time for each category of VSI

patient, from the emergency department entrance to the ward admission or discharge.

Overall, ten response variables have been considered (see Table III).

Model logic checking and validation was achieved through several trial runs. A

direct comparison with the available historical data did not show any anomaly. The

two scenarios have been assumed as potential factors affecting the service CTQs. Foreach scenario model, 1,000 replications have been run. Table IV shows the replication

statistics of the CTQs for both scenarios. An empirical distribution fitting of the yearly

costs based on the Anderson Darling test led to model them as Gamma distributed

random variables. Thus, in Table IV, for each cost the expected value, the scale

parameter uand the shape parameter k of the fitting Gamma distribution are reported.

Interarrival rates of VSI patients showing at the ED(exponential distrib.)2.25 patients/day 8 a.m.-3 p.m.

3 p.m.-8p.m.8 p.m.-8a.m.

0.966 patients/day0.495 patients/day0.788 patients/day

Incidence of colour codes for VSI patientsWhite (%) 9.4Yellow (%) 10.3Green (%) 80.3

VSI patients completing the path to diagnosis (%) 68

VSI patients needing admission (%) 31

Incidence of each in-patient groupAudiology (%) 33Cardiology (%) 15Medicine (%) 43

Neurol./neurosurg./stroke unit (%) 9

Table II.Historical data observed

for the VSI patients

during year 2007

CTQs

CTOT [e/year] Total cost to deliver the service to VSI patientsCED [e/year] Cost at the emergency department

CAD [e/year] Cost at the audiology departmentCLD [e/year] Cost at the laboratory test departmentCRD [e/year] Cost at the radiology department

FTAD [h ] Mean flow time of the patients admitted within the audiology department wardFTCD [h ] Mean flow time of the patients admitted within the cardiology department wardFTMD [h ] Mean flow time of the patients admitted within the medicine department wardFTND [h ] Mean flow time of the pat. adm. within the neurology/neurosurg./stroke unit dept.

wardFTDP [h ] Mean flow time of the discharged patients

Table III.The critical to quality

parameters (CTQs)selected for the case

study

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CTOT

CED

CAD

CLD

CRD

FTAD[h]

Scen.

[e/year]

[e/year]

[e/year]

[e/year]

[e/year]

White

Y/G

FTC

D[h]

FTMD[h]

FTND[h]

FTDP[h]

A

98285

63318

8507

16368

10092

3.25(0.87)

4.57(0.66)

1.7

3(0.07)

1.14(0.02)

2.22(0.15)

2.51(0.14)

u

36014

23178

3216

5990

3790

k

2.729

2.732

2.645

2.732

2.663

B

65209

42261

20279

835

1834

1.50(0.56)

1.71(0.51)

1.2

9(0.02)

1.29(0.01)

2.23(0.62)

1.67(0.3)

u

24136

15634

7533

324.5

672

k

2.702

2.703

2.692

2.574

2.729

DC%

2

33.7

233.3

138.4

2

94.9

2

81.8

DT%

2

53.8

2

62.6

2

25.4

13.2

0.4

2

33.5

Table IV.Comparison of scenario Bvs scenario A. Resultsfrom the simulation runs

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The control phase of the project started with the evaluation of the cost and flow timesavings obtained by simulating the B scenario. Cost comparisons presented in Table IVimmediately show that the effectiveness of scenario B clearly overcomes that ofscenario A:

. The yearly expected cost saving to deliver the service to VSI patients equals 33.7per cent.

. The yearly expected cost saving in the emergency department equals 33.3 percent.

. As expected by the project development team, the number of seized exams in theaudiology department is significantly increased: thus, the yearly expected costincreases of 138.4 per cent.

. The largest cost savings are achieved in the laboratory test department, (DC percent 294.7 per cent), and the radiology department, (DC per cent 281.8 percent); this means that the currently adopted procedure calls for a significantlylarger number of blood and CAT exams than required.

Finally, the availability of empirical distributions for each of the investigated costsallows decision makers to take decisions about provisional budgets for the servicedelivery based on reliable data estimation.

Table IV also presents within parentheses the mean and the standard deviation ofthe flow times for each category of patients and the flow time percentage reduction DTper cent obtained by implementing scenario B as opposed to scenario A. Theperformance of scenario B in terms of efficiency is better than that of scenario A. Inparticular, it has been found that:

. The expected flow times for patients who need to be admitted in the audiologydepartment ward reduce of more than 50 per cent whereas those admitted in the

cardiology department reduce their flow time of more than 25 per cent.. The expected flow time increases for those to be admitted in the medicine

department (13.2 per cent), and remains quite unchanged for patients having apathological condition to be treated within the neurology/neurosurgery/strokeunit departments.

Thus, the project demonstrates that scenario B looks promising both in terms ofservice efficiency and in terms of efficacy and that it is worth choosing it for furtherinvestigation and the implementation of real trial runs. Dissemination of results toprofessionals working in the hospital and a cycle of informative meetings with peoplefrom other hospitals completed the project.

Lessons learned and benefits from the proposed approachThe implementation of the roadmap to the in-hospital case study allowed somestrength points of the proposed theoretical framework to be experienced. First,getting a precise definition of the cross-functional process maps and the value-addedstream analysis at the early steps of the project i.e. during the design phase was demonstrated to be a key factor in stating the problem correctly andsuccessfully developing the project. During the brainstorming meetings, doctors,nurses and technicians were stimulated to re-think every step of the service in

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which they operate as resources; a shared view about the way service is deliveredand the awareness about eliminating unnecessary activities can be progressivelyachieved through this problem solving approach. Some bugs have been detected inthe hospitals information system during the measure and analyse phase aimed at

collecting and fitting the historical data: these have been progressively corrected oreliminated. The improve phase required the design and validation of the twosimulation models. The trial tests on the models were performed at the presence ofthe chairs of the involved departments: the graphical representation of the patientflows during the simulation runs helped professionals from the hospital to visualisethe entire flows and detect the presence of bottlenecks increasing flow times;statistical distribution fitting for the expected costs allowed budgeting forecasts tobe prepared; the evaluation of percentage costs savings and flow times reductionprovided with a quantitative basis of knowledge to promote further investigation ofscenario B. Finally, dissemination of the results inside and outside the hospitalduring meetings and seminars received a positive feedback from operators,

especially in terms of interest to the roadmap implementation to other healthcaresettings. In particular, during seminars the interest was originated by the

opportunity that such a procedure gives to people in achieving self-awareness aboutthe activities they are requested to do in their organisation. In those seminars wheredeveloping part of the design procedure was proposed as a classroom exercise forsmall groups, great emphasis was put by the attendees in the process of visualisingthe procedures they usually follow in their organisations through process maps. Thecommon feeling was that getting precise process maps allowed for a re-thinking andquestioning of apparently well-structured practices and finding useless activities.Sharing different points of view further enriched this process. Showing thegraphical interface of the DES models during the simulation running increasedinterest in the classroom. Costs and time estimates based on simulated data gavepeople more confidence to figure out workload and budgeting for each involveddepartment.

It is worth noting that by linking the Six Sigma quality perspective to theoperational research toolbox, the proposed roadmap can serve as an operationalframework to create opportunities for starting innovative Lean Six Sigma experiencesin the healthcare organisation (George, 2003; De Koning et al., 2006): its intrinsicstructure allows both activities control and the identification and elimination of wasteand non-value added activities, i.e. the muda, as commonly known by the lean thinkingpractitioners. Further investigation is suggested in this direction.

Lastly, it is also important to speak about some shortfalls of the proposedframework. First, there are many situations where some prerequisites required to start

the synergy are not respected. Second, as stated in (Jackson, 2003), problem contextswithin an organisation can be different depending on the combination of the followingtwo dimensions: the nature of the investigated system/organisation, i.e. simple orcomplex, and the attitude of participants: unitary or pluralist or coercive. Qualitymanagement in the service sector, and in particular healthcare organisations, can coverall of these problem contexts depending on the level of the organisation it impacts.Given this perspective, the proposed roadmap supporting sharply-focusedquality-improvement projects is particularly suited to support system thinking and

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problem solving when a simple unitary combination is present. This is the field ofapplication of what is called hard system thinking.

ConclusionsThis paper has presented a theoretical framework based on a roadmap defining anapproach integrating Six Sigma and discrete-event simulation during the developmentof a quality-improvement project. The roadmap merges DMAIC/DMADV orDMADV/DMADV Six Sigma procedures in a hierarchical approach aimed atdeveloping the Six Sigma project and the simulation model. The roadmap has beentested to carry out a project focused on the re-organisation of the flow of emergencypatients affected by vertigo symptoms. The implementation of the proposed roadmapallowed the following potential benefits to be identified:

. The design of a DES model aimed at simulating and comparing differentscenarios is significantly enhanced by the adoption of the roadmap, whichsimplifies the formalisation and execution of the successive steps to get a correctmodel.

. Conversely, the Six Sigma quality improvement project cost can be significantlyreduced if DES simulation can be adopted: in fact, real trial runs are not neededto get results about different process configurations.

. Discussion and dissemination of results inside and outside an organisation isfacilitated by the roadmap because it can serve as a structured approach to befollowed during presentations. Visualisation of the different scenarios throughDES models stimulates the interest of people and makes the comprehension andmotivation of results easier.

The positive outcome from the practice has demonstrated the effectiveness of the

proposed roadmap in the healthcare field and calls for further investigation in otherclinical settings. Thus, research will be continued in this direction to enhance theproposed framework and to create potential opportunities for Lean Six Sigmaimplementation in healthcare.

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About the authorsGiovanni Celano received his PhD in 2003 from the University of Palermo defending a thesis on

the sequencing of mixed model assembly lines. He is currently Assistant Professor at theUniversity of Catania (Italy). His research is focused on statistical quality control, productionscheduling and operations management applied to both industrial and service sector. He iscurrently a member of the European Network of Business and Industry Statistics (ENBIS), andthe International Institute for Innovation, Industrial Engineering and Entrepreneurship (I4e2). Hehas authored/co-authored about 85 papers in international journals and in the proceedings ofnational and international conferences. Dr Celano is Associate Editor ofQuality Technology andQuantitative Management. Giovanni Celano is the corresponding author and can be contacted at:[email protected]

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Antonio Costa holds a PhD in Structural Mechanics from the University of Catania. He iscurrently Assistant Professor in Technology and Manufacturing Systems at the University ofCatania (Italy) and a Senior Member of the Associazione Italiana di Tecnologia Meccanica(AITeM). His research involves supply chain network management, mixed model assembly line

sequencing and heuristic optimisation applied to manufacturing systems. He is a co-author ofabout 45 papers published in international journals and in the proceedings of national andinternational conferences.

Sergio Fichera is an Associate Professor in Technology and Manufacturing System at theDipartimento di Ingegneria Industriale e Meccanica of the University of Catania, (Italy). He holdsa MS degree from the University of Catania and MBA degree from the Schools of Management atthe University of Turin. His research interests are in production scheduling, statistical qualitycontrol, optimisation of machining processes. He is a member of the Associazione Italiana diTecnologia Meccanica (AITeM). He has co-authored about 80 papers in international journalsand in the proceedings of national and international conferences.

Giuseppe Tringali is the head of the Audio-Vestibolo-Fonologia Unit at the Vittorio Emanuelehospital, Catania (Italy). He frequently collaborates with the University of Catania to developquality improvement and service re-engineering projects in the healthcare field.

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Linking Six Sigma to simulation a new roadmap to improve the quality of patient care