simulation improves patient flow at a dental clinic

7/27/2019 Simulation Improves Patient Flow at a Dental Clinic

1/5

SIMULATION IMPROVES PATIENT FLOW

AND PRODUCTIVITY AT A DENTAL CLINIC

Matthew CzechMichael Witkowski

Industrial & Manufacturing Systems EngineeringEngineering Complex

University of Michigan Dearborn4901 Evergreen Road

Dearborn, MI 48128 U.S.A.

Edward J. WilliamsPMC

Suite 1006 Parklane Towers WestThree Parklane Boulevard

Dearborn, MI 48126 [email protected]

ABSTRACT

The health care industry in the United States, and in

many other countries as well, is undergoing unremittingpressure s to improve standards of patient care and service,reduce costs, and increase efficiency. These pressuresstem from higher expectations by health-care consumers,increased demands stemming from changingdemographics (particularly the graying of populations),and more rigorous auditing of expenditures by bothprivate insurers and government. In response, health-careindustry practitioners, managers, and administrators areincreasingly availing themselves of the analyticaltechniques, including simulation, provided by thediscipline of industrial engineering. In this paper, wedocument a simulation study undertaken to improve

patient service at a dental clinic. The simulation analysisvalidated innovative ways to improve patient throughputand decrease patient waiting times with zero incrementalcost.

INTRODUCTION

Historically, the first major application area ofdiscrete-event process simulation was the manufacturingsector of the economy . More recently, and currently veryvigorously, the health-care industry is availing itself of theanalytical and predictive powers of simulation to reducecosts, improve efficiency, and enhance service to patients(Lowery 1996). Higher patient expectations, increasedcost pressures from private and government insurers, anddemographically induced increases in overall demand areall compelling reasons for this sector of the economy toimprove its overall performance (McGuire 1998). Recentexamples of simulation application to various aspects ofhealth care are contributions from (Costantino, De Gravio,and Tronci 2005) (improvement of a supply chain forurgently needed provision of medical oxygen), (Martin,Grnhaug, and Haugene 2003) (evaluation of proposals toreduce overcrowding and improve patient care in thegeriatric department of a large hospital), and (Kumar andOzdamar 2004) (simulation in the service of business

process reengineering of hospital operationalcomplexities.

In the application documented here, simulation wasapplied to investigate and improve daily operationalprocedures at a dental clinic. Achievements of the studyincluded both reducing patient waiting times andincreasing the number of patients seen per day, withneither increase of cost nor compromise to quality of care.

OVERVIEW OF ROUTINE PROCEDURES AT THE

DENTAL CLINIC

The dental clinic under study comprises one dentist,two dental hygienists, and a receptionist. A heavymajority of the patients come by appointment

(historically, patients have waited weeks or even monthsfor their appointment day to arrive) to have their teethcleaned (prophylaxis) and checked. Inasmuch as thepatients have made their appointments far in advance, anda dental clinic will never be in contention for a favoriteplace to wait idly, patient impatience and intolerance foreven relatively short waits is high.

The typical patient in good or at least stable dentalhealth comes to the clinic approximately every six monthsfor a dental prophylaxis (cleaning of teeth and assessmentof dental health). On arrival, patients check in with thereceptionist. After doing so, they will have to await theattention of one of the dental hygienists. If the patientneeds dental X-rays, taking them and setting them asidefor development of the film will be the first taskperformed by the hygienist. Except in rare cases (e.g., thepatient complains of disquieting symptoms), X-rays areneeded once a year, i.e. every other visit for prophylaxis .Next, the hygienist cleans (scrapes, scales, flosses, andpolishes) the patients teeth; while doing so, the hygienistwill also document any presumptive problems observedfor the attention of the dentist. Upon completing thesetasks, the hygienist notifies the dentist, and the patientthen may have to await the arrival of the dentist. Uponarrival at the patients chair, the dentist will audit the

Proceedings 21st European Conference on Modelling and SimulationIvan Zelinka, Zuzana Oplatkov, Alessandra Orsoni ECMS 2007ISBN 978-0-9553018-2-7 / ISBN 978-0-9553018-3-4 (CD)


2/5

hygienists work and examine X-rays (those just taken, ifthe patient was X-rayed this visit, and/or X-rays in thepatients file, which may show trends in the patients

dental health). Further, the dentist questions the patientconcerning any symptoms or complaints, and discussesany future needed dental work such as filling of cavities,referrals for root canal therapy (done by a different dentalclinic specializing in endodontic dental work), provisionfor crowns, or extraction of teeth. After this consultationis complete, the patient returns to the receptionist to checkout, arrange for bill payment, and often (even presumably)schedule the next appointment. The patient then leavesthe clinic. The canonical flow process from the patientsperspective appears in the Appendix, Figure 1. Indeed,during later model construction, this figure, having beenvalidated with the client, was the immediate guide formodel construction.

DATA COLLECTION AND ANALYSIS

Since the client had very little physical data archived,an early step in this project was on-site data collection atthe dental clinic. First, it was noted that patients typicallymake their next appointment (usually six months henceunless problems found during the prophylaxis demandattention sooner) while checking out and settling accountswith the receptionist. At that time, patients may eitherspecify which hygienist they prefer, or specify a preferredappointment hour; in the latter case, they will be assignedan appointment hour.

The simulation analysts then gathered data from 9amto 2pm on several Mondays and Wednesdays (skipping aone-hour scheduled lunch break). The receptionist agreedthat data collected on these days at these times would betypical and representative, even across a calendar year (atoothache can come on anytime!). Gathering the dataused typical time-study methods (Mundel and Danner1994); at the conclusion of the study, the clientspecifically commended the data collection work,deeming it quiet, unobtrusive, and hence unlikely toprovoke the Hawthorne Effect (Martin-Vega 2001). Mostdata observations were taken in the small office anteroom

where patients sit while waiting for their hygienist tobecome available. Numerical data collected included:1. The arrival times of patients2. How long patients waited before going to the

room for prophylaxis3. Whether X-rays were required, and the time

required to take them if so4. The prophylaxis process time5. The length of waiting time for the dentist to arrive

subsequent to the prophylaxis6. The time the dentist spent with the patient after

the prophylaxis7. The time required to book the next appointment.

Item #2 was particularly useful for later modelvalidation; the others were all directly used to constructthe model.

CONSTRUCTION, VERIFICATION, AND

VALIDATION OF THE SIMULATION MODEL

Owing to ready availability within both academic andindustrial contexts, and ample software power to bothsimulate and animate the health-care service system inquestion (although the animation was two-dimensionalonly, an issue of trifling consequence), the Arenasimulation modeling software (Kelton, Sadowski, andSturrock 2007) was used. This software provides directaccess to concepts of process flow logic, queuingdisciplines (e.g., FIFO), modeling of processes which maybe automated, manual, or semi-automated, use ofResources (here, the receptionist, the dental hygienists,and the dentist), definition of shift schedules, constant orvariable transit times between various parts of the model(e.g., patient walking time from the anteroom to theprophylaxis room), extensibility (in the ProfessionalEdition) via user-defined modules (Bapat and Sturrock2003), and an Input Analyzer (used as discussed in theprevious section to choose between empirical and closed-form distributions). Observed data for the X-ray,prophylaxis, dentists consultation, and next -appointmentbooking times were fitted using this Input Analyzer; ineach of these cases, a triangular distribution fitted theobserved data well.

Verification and validation techniques used included avariety of methods such as tracking one entity through themodel, initially removing all randomness from the modelfor easier desk-checking, structured walkthroughs amongthe team members, step-by-step examination of theanimation, and confirming reasonableness of thepreliminary results of the model with the client managerby use of Turing tests (Sargent 2004). For example,relative to the base case model, the observed wait timeaverage and maximum before being taken to anexamination room for prophylaxis were 1.6 minutes and9.5 minutes respectively for the first hygienist, and 2.7

minutes and 11.1 minutes for the second hygienist.Likewise, the observed wait time average and maximumfor the dentists consultation after prophylaxis were 4.0and 7.0 minutes. The base case model matched all six ofthese numbers to within 5%.

RESULTS AND INDICATED FURTHER WORK

The simulation model was specified to be terminating,not steady-state, because this service process, like most,empties itself each night and over weekends (Altiok andMelamed 2001). Therefore, warm-up time was alwayszero. Results and comparisons between the current and


3/5

proposed systems were all based on five replications eachof length five working days (one typical work week, sincethe clinic opens on Saturdays only for emergencies and

hence on an ad-hoc basis ), thereby making comparisonsconsistent despite slight and perhaps unobservedworkload-trend patterns among the days from Monday toFriday inclusive (recall that the actual data collection hadbeen taken only on Mondays and Wednesdays). Againcorresponding to actual clinic practice, each work daylasted ten hours, including lunch times and very shortbreak times.

In the current process (both in practice and in the basesimulation model), patients have chosen, or are assigned,a hygienist prior to arrival, and must await the availabilityof that hygienist. In the proposed process as simulated, apatient, upon arrival, wil l be assigned to whicheverhygienist first becomes available; i.e., the hygienists willbe treated as a resource pool rather than as twodistinguishable resources. The model simulating thisproposed revision to the process predicted a maximumwaiting time fora hygienist of less than 0.5 minute. Thisprediction (a delightful surprise to the client) is in keepingwith classical closed-form results for M/M/n queueswhich demonstrate the benefit of pooling resources andusing one common queue (Anderson, Sweeny, andWilliams 2005) Furthermore, this proposed processmodel simultaneously predicted a 5% patient throughputincrease, implying a proportionate revenue increase forthe clinic.

Accordingly, the client (the dentist running the clinic,in conjunction with the office manager) has decided toimplement a change from the current process to therevised and recommended process. The improvementspredicted in important process metrics (average andmaximum wait times, and total throughput of patientsserved) have been deemed sufficiently significant tojustify gently weaning the minority of patients having apreferred hygienist away from a guarantee of receivingservice from that hygienist. Indeed, the process shift hasalready begun, and improvements have already beennoticed. Specifically, the process shift has been

undertaken by asking the patients What appointmenttime would you like? without any reference to Whichhygienist? unless and until the patient explicitly opts inby volunteering a strong preference for one hygienist. Inparticular, the office manager has remarked that theunfortunate and exasperating frequent tendency of theoffice to run further and further behind its appointmentschedule as the day progresses is diminishing mostgratifyingly. Since scheduling has a six-month lead time,both client and analysts expect that process performancewill gradually approach and reach the predictedimprovements over that span of time this approach hasalready begun.

OVERALL CONCLUSIONS AND IMPLICATIONS

More broadly speaking, the benefits of this study

extend beyond the improvement of service achieved inone dental clinic. Publicity accorded to the study by theuniversity (as is routinely done for many senior projectsor capstone projects) has drawn beneficial localattention to the ability of simulation (and by implication,other analytical methods within the discipline of industrialengineering) to help the beleaguered health-care industryrise to the simultaneous challenges of cost containmentand increased quality-of-service expectations.Additionally, the success of this study has increased thewillingness of local business and management leaders towelcome and provide project opportunities for advancedundergraduate students. This willingness stems partlyfrom the short-term attraction of having useful industrial-engineering work done, and partly from the long-termattraction of making an investment in the experience levelof students who will shortly be entering the labor marketas industrial engineers (Black and Chick 1996).

ACKNOWLEDGMENTS

The authors gratefully acknowledge the support of Dr.Onur lgen (president of PMC and full professor in theUniversity of Michigan Dearborn Department ofIndustrial Engineering), not only for technical adviceinformally provided to this project, but for his ongoingsupport of simulation education (both inside and outside

the traditional classroom milieu), projects of satisfyingobvious practical value, and student research at theUniversity. Additionally, the criticisms of twoanonymous referees have been most helpful in improvingthe clarity and presentation of this paper.

REFERENCES

Altiok, Tayfur, and Benjamin Melamed. 2001. SimulationModeling and Analysis with Arena. Piscataway, NewJersey: Cyber Research, Incorporated, and EnterpriseTechnology Solutions, Incorporated.

Anderson, David R., Dennis J. Sweeney, and Thomas A.Williams. 2005. Quantitative Methods for Business, 10th

ed. Mason, Ohio: South-Western, a division of ThomsonLearning.Bapat, Vivek, and David T. Sturrock. 2003. The Arena Product

Family: Enterprise Modeling Solutions. In Proceedings ofthe 2003 Winter Simulation Conference, Volume 1, eds.Stephen E. Chick, Paul J. Snchez, David Ferrin, andDouglas J. Morrice, 210-217.

Black, John J., and Stephen E. Chick. 1996. MichiganSimulation User Group Panel Discussion: How Do WeEducate Future Discrete Event Simulationists.

International Journal of Industrial Engineering

Applications and Practice 3(4):223-232.Costantino, Francesco, Guilio Di Gravio, and Massimo Tronci.

2005. Simulation Model of the Logistic Distribution in a


4/5

Medical Oxygen Supply Chain. In Proceedings of the 19th

European Conference on Modelling and Simulation, eds.Yuri Merkuryev, Richard Zobel, and Eugne Kerckhoffs,175-183.

Kelton, W. David, Randall P. Sadowski, and David T. Sturrock.2007. Simulation with Arena , 4th edition. New York, NewYork: The McGraw-Hill Companies, Incorporated.

Jumar, Arun, and Linet Ozdamar. 2004. Business ProcessReengineering at the Hospitals: A Case Study at SingaporeHospital. In Proceedings of the 18 th European SimulationMulticonference , ed. Graham Horton, 308-317.

Law, Averill M. 2007. Simulation Modeling and Analysis , 4th

edition. New York, New York: The McGraw-HillCompanies, Incorporated.

Lowery, Julie C. 1996. Introduction to Simulation in HealthCare. In Proceedings of the 1996 Winter SimulationConference, eds. John M. Charnes, Douglas M. Morrice,Daniel T. Brunner, and James J. Swain, 78-84.

Martin, Eleazer, Roar Grnhaug, and Kristin Haugene. 2003.Proposals to Reduce Over-Crowding, Lengthy Stays andImprove Patient Care: Study of the Geriatric Department in

Norways Largest Hospital. In Proceedings of the 2003Winter Simulation Conference, Volume 2, eds. Stephen E.Chick, Paul J. Snchez, David Ferrin, and Douglas J.Morrice, 1876-1881.

Martin-Vega, Louis A. 2001. The Purpose and Evolution ofIndustrial Engineering. In Maynards Industrial

Engineering Handbook, 5th edition, ed. Kjell B. Zandin,1.3-1.19. New York, New York: The McGraw-HillCompanies, Incorporated.

McGuire, Frank. 1998. Simulation in Healthcare. InHandbookof Simulation, ed. Jerry Banks, 605-627. New York, NewYork: John Wiley & Sons, Incorporated.

Mundel, Marvin E., and David L. Danner. 1994. Motion andTime Study: Improving Productivity, 7th ed. EnglewoodCliffs, New Jersey: Prentice-Hall, Incorporated.

Sargent, Robert G. 2004. Validation and Verification ofSimulation Models. In Proceedings of the 2004 WinterSimulation Conference, Volume 1, eds. Ricki G. Ingalls,Manuel D. Rossetti, Jeffrey S. Smith, and Brett A. Peters,17-28.

AUTHOR BIOGRAPHIES

MATTHEW CZECH is a senior at the University ofMichigan Dearborn majoring in Industrial & SystemsEngineering. He is serving as President of theInstitute of

Industrial Engineers student chapter for the 2006-2007academic year. He is also an active member in Society ofManufacturing Engineers and the Institute ofTransportation Engineers. He attended the Regional andNational Conferences for IIE in 2006. He alsoparticipated in two Lean/Six Sigma symposia atBeaumont hospital in Royal Oak, MI. His relevant workexperience includes two rotations as a co-operativestudent at the National Aeronautic and SpaceAdministration [NASA] Kennedy Space Center inFlorida, U.S.A. He was assigned to the OperationsManagement Branch of the International Space StationProcessing Directorate. His tasks included scheduling and

oversight of government contractors as well as integrationwith other divisions. After he obtains his Bachelorsdegree in April 2008, he will seek employment in the

aerospace industry using his industrial engineeringexperience and skills.

MICHAEL WITKOWSKI, a December 2006 graduateof the University of Michigan - Dearborn (UM-D),received a B.S.E. in Industrial and Systems Engineering.As Vice President of the UM-D chapter of the Institute ofIndustrial Engineers during his senior year, he attendedseveral IIE events including the 2006 Regional andNational Conferences, plus a Lean/Six Sigma symposiu mat William Beaumont Hospital. Through his work at theuniversity, Michael has participated in two separate co-operative education work placements, one in fall 2004with Visteon, a firs t-tier automotive supplier, where heimplemented quality controls to reduce scrap, andimplemented sound-proofing methods. His second co-opexperience took place at DTE Energy, where hestandardized work practices and managed several capitalbudget projects; began in May 2005, and continuedthrough graduation. Michael is now works at DTE as anAssociate Engineer, working in Distribution Operations.

EDWARD J. WILLIAMS holdsbachelors and masters degrees inmathematics (Michigan State University,1967; University of Wisconsin, 1968).From 1969 to 1971, he did statisticalprogramming and analysis of biomedicaldata at Walter Reed Army Hospital,Washington, D.C. He joined Ford Motor

Company in 1972, where he worked until retirement inDecember 2001 as a computer software analyst supportingstatistical and simulation softwa re. After retirement fromFord, he joined PMC, Dearborn, Michigan, as a seniorsimulation analyst. Also, since 1980, he has taughtevening classes at the University of Michigan, includingboth undergraduate and graduate simulation classes usingGPSS/H, SLAM II, SIMAN, ProModel ,

SIMUL8, or Arena. He is a member of the Institute ofIndustrial Engineers [IIE], the Society for Computer

Simulation International [SCS], and the MichiganSimulation Users' Group [MSUG]. He serves on theeditorial board of the International Journal of Industrial

Engineering Applications and Practice . During the lastseveral years, he has given invited plenary addresses onsimulation and statistics at conferences in Monterrey,Mxico; Istanbul, Turkey; Genova, Italy; and Riga,Latvia. He has served as Program Chair of the 2004,2005, and 2006 Summer Computer SimulationConferences, and also for the 2005 IIE SimulationConference. His university web page is :http://www-personal.umd.umich.edu/~williame .


5/5

APPENDIX

Figure 1. Patient Process Flow Chart

PatientArrives

Check-in

Wait

PatientDeparts

DentistConsult

NeedX-Ray?

WaitCleaning

ScheduleNext

Appointment

Take X-RayYes

No

simulation improves patient flow at a dental clinic

Documents