Abstract
This study addresses the problem of scheduling magnetic resonance imaging examinations to reduce indirect waiting time—that is, the time that elapses between the patient’s call for an appointment and the scheduled appointment time. Two alternative scheduling approaches are proposed: online and offline characterized, respectively, by the absence or presence of a batch of patients waiting for an appointment. Specifically, with an online approach, patients are scheduled when they call for an appointment, and consequently there is no need to have a batch of patients. On the contrary, the offline approach assumes that patients are given an expected waiting time when they call; they are subsequently called back and assigned an appointment within few days. With an offline approach, patients are thus collected in a batch until a scheduling policy is run; clearly, the batch size or equivalently the frequency according to which patients are scheduled impacts on the performance of the scheduling policy. The offline approach allows a better planning with respect to the online approach where the decision regarding a patient is greatly affected by the schedule of patients who called before him. On the other side, the online approach allows a prompt accommodation of the patient. In an attempt at trade-off these two approaches, the offline one is experimented with three different scheduling frequencies: once per week, two times per week, and three times per week. The paper describes a novel MIP model for implementation of the offline approach and a greedy heuristic for implementation of the online one. Online and offline approaches are then compared in terms of effectiveness, equity, efficiency and discrimination power, using a rolling horizon of 52 weeks and assuming different demand patterns. The comparison includes an evaluation of the impact of two managerial practices—examination overlapping and radiologist cross-training—when using the two forms of scheduling. The key findings are that the offline approach achieves smaller, less variable values for tardiness and enables scheduling of a higher number of patients in situations where capacity is scarce. As to managerial practices, overlapping is found to be relatively more effective than cross-training when an offline approach is adopted, while for online scheduling, cross-training is more effective than overlapping. The results presented are based on an extensive experimental campaign based on real data coming from a leading Italian hospital.
Similar content being viewed by others
References
Ahmadi-Javid A, Jalali Z, Klassen KJ (2017) Outpatient appointment systems in healthcare: a review of optimization studies. Eur J Oper Res 258(1):3–34. https://doi.org/10.1016/j.ejor.2016.06.064
Bailey NTJ (1952) A study of queues and appointment systems in hospital out-patient departments, with special reference to waiting-times. J R Stat Soc Ser B (Methodol) 14(2):185–199
Balasubramanian H et al (2014) Dynamic allocation of same-day requests in multi-physician primary care practices in the presence of prescheduled appointments. Health Care Manag Sci 17(1):31–48
Begen MA, Queyranne M (2011) Appointment scheduling with discrete random durations. Math Oper Res 36(2):240–257
Canadian Association of Radiologists (2013) National maximum wait time access targets for medical lmaging
Carpenter AP et al (2011) Managing magnetic resonance imaging machines: support tools for scheduling and planning. Health Care Manag Sci 14(2):158–173
Cayirli T, Veral E (2003) Outpatient scheduling in health care: a review of literature. Prod Oper Manag 12(4):519–549
Cayirli T, Veral E, Rosen H (2006) Designing appointment scheduling systems for ambulatory care services. Health Care Manag Sci 9(1):47–58
Çinlar E (1975) Introduction to stochastic processes. Prentice-Hall, Englewood Cliffs
Denton B, Gupta D (2003) A sequential bounding approach for optimal appointment scheduling. IIE Trans 35(11):1003–1006
Erdogan SA, Gose A, Denton BT (2015) On-line appointment sequencing and scheduling. IIE Trans. https://doi.org/10.1080/0740817X.2015.1011355
Feldman J, Liu N, Topaloglu H, Ziya S (2014) Appointment scheduling under patient preference and no-show behavior. Oper Res 62(4):794–811
Field A (2009) Discovering statistics using SPSS, 3rd edn. SAGE Publication Inc, Thousand Oaks
Fries BE, Marathe VP (1981) Determination of optimal variables-sized multiple-block appointment systems. Oper Res 29(2):324–345
Gallucci G, Swartz W, Hackerman F (2005) Impact of the wait for an initial appointment on the rate of kept appointments at a mental health center. Psychiatr Serv 56(3):344–346
Geng N, Xie X (2012) Optimizing contracted resource capacity with two advance cancelation modes. Eur J Oper Res 221(3):501–512. https://doi.org/10.1016/j.ejor.2012.04.017
Geng N, Xie X (2016) Optimal dynamic outpatient scheduling for a diagnostic facility with two waiting time targets. IEEE Trans Autom Control 61(12):3725–3739
Geng N, Xie X, Augusto V et al (2011a) A monte carlo optimization and dynamic programming approach for managing MRI examinations of stroke patients. IEEE Trans Autom Control 56(11):2515–2529
Geng N, Xie X, Jiang Z (2011b) Capacity reservation and cancellation of critical resources. IEEE Trans Autom Sci Eng 8(3):470–481
Geng N, Xie X, Jiang Z (2013) Implementation strategies of a contract-based MRI examination reservation process for stroke patients. Eur J Oper Res 231(2):371–380
Granja C et al (2014a) An optimization based on simulation approach to the patient admission scheduling problem: diagnostic imaging department case study. J Digit Imaging 27(1):33–40
Granja C et al (2014b) An optimization based on simulation approach to the patient admission scheduling problem using a linear programing algorithm. J Biomed Inform 52:427–437. https://doi.org/10.1016/j.jbi.2014.08.007
Green LV, Savin S (2008) Reducing delays for medical appointments: a queueing approach. Oper Res 56(6):1526–1538
Green LV, Savin S, Wang B (2006) Managing patient service in a diagnostic medical facility. Oper Res 54(1):11–25
Gupta D, Denton B (2008) Appointment scheduling in health care: challenges and opportunities. IIE Trans 40(9):800–819
Gupta D, Wang L (2008) Revenue management for a primary-care clinic in the presence of patient choice. Oper Res 56(3):576–592
Hendel RC (2008) Utilization management of cardiovascular imaging. Pre-certification and appropriateness. JACC Cardiovasc Imaging 1(2):241–248
Ho CJ, Lau HS (1992) Minimizing total cost in scheduling outpatient appointments. Manag Sci 38(12):1750–1764
Huh WT, Liu N, Truong V (2013) Multiresource allocation scheduling in dynamic environments. Manuf Serv Oper Manag 15(2):280–291
Isken MW, Ward TJ, Mckee TC (1999) Simulating outpatient obstetrical clinics. In: Proceedings of the 1999 winter simulation conference, pp 1557–1563
Jansson B (1966) Choosing a good appointment system-a study of queues of the type (D, M, 1). Oper Res 14(2):292–312
Kaandorp GC, Koole G (2007) Optimal outpatient appointment scheduling. Health Care Manag Sci 10(3):217–229
Kemper B, Klaassen CAJ, Mandjes M (2014) Optimized appointment scheduling. Eur J Oper Res 239(1):243–255. https://doi.org/10.1016/j.ejor.2014.05.027
Klassen KJ, Rohleder TR (1996) Scheduling outpatient appointments in a dynamic environment. J Oper Manag 14(2):83–101
Klassen KJ, Rohleder TR (2004) Outpatient appointment scheduling with urgent clients in a dynamic, multi-period environment. Int J Serv Ind Manag 15(2):167–186
Klassen KJ, Yoogalingam R (2009) Improving performance in outpatient appointment services with a simulation optimization approach. Prod Oper Manag 18(4):447–458
Klassen KJ, Yoogalingam R (2014) Strategies for appointment policy design with patient unpunctuality. Decis Sci 45(5):881–911
Kolisch R, Sickinger S (2008) Providing radiology health care services to stochastic demand of different customer classes. OR Spectr 30(2):375–395
Krishnamoorthy A, Pramod PK, Chakravarthy SR (2014) Queues with interruptions: a survey. Top 22(1):290–320
Kuiper A, Kemper B, Mandjes M (2015) A Computational approach to optimized appointment scheduling. Queueing Syst 79(1):5–36
Kutner MH et al (2005) Applied linear statistical models, 5th edn. McGraw-Hill, Inc, New York, NY, US. http://books.google.fr/books?id=0xqCAAAACAAJ&dq=intitle:Applied+linear+statistical+models+djvu&hl=&cd=1&source=gbs_api
Law AM, Kelton WD (2000) Simulation modeling and analysis. McGraw-Hill, Inc., New York,NY,US. http://www.amazon.ca/exec/obidos/redirect?tag=citeulike09-20&path=ASIN/0071165371
Leemis LM (1991) Nonparametric estimation of the cumulative intensity function for a nonhomogeneous poisson process. Manag Sci 37(7):886–900
Leemis LM, Park SK (2006) Discrete-event simulation: a first course. Pearson Prentice Hall, Upper Saddle River
Li L, Benton WC (2003) Hospital capacity management decisions: emphasis on cost control and quality enhancement. Eur J Oper Res 146(3):596–614. https://doi.org/10.1016/S0377-2217(02)00225-4
Liu N, Ziya S, Kulkarni VG (2010) Dynamic scheduling of outpatient appointments under patient no-shows and cancellations. Manuf Serv Oper Manag 12(2):347–364
Luo J, Kulkarni VG, Ziya S (2012) Appointment scheduling under patient no-shows and service interruptions. Manuf Serv Oper Manag 14(4):670–684
Lysdahl KB, Børretzen I (2007) Geographical variation in radiological services: a nationwide survey. BMC Health Serv Res 7:21
Lysdahl KB, Hofmann BM (2009) What causes increasing and unnecessary use of radiological investigations? A survey of radiologists’ perceptions. BMC Health Serv Res 9:155
Mahar S, Bretthauer KM, Salzarulo PA (2011) Locating specialized service capacity in a multi-hospital network. Eur J Oper Res 212(3):596–605. https://doi.org/10.1016/j.ejor.2011.03.008
Mak H-Y, Rong Y, Zhang J (2014) Sequencing appointments for service systems using sequencing appointments for service systems using inventory approximations. Manuf Serv Oper Manag 16(2):251–262. https://doi.org/10.1287/msom.2013.0470
Ministero della Salute (2010) Piano Nazionale di Governo delle Liste di Attesa (PNGLA 2010-2012)
Nuti S, Vainieri M (2012) Managing waiting times in diagnostic medical imaging. Br Med J (open) 2(6):1–10
Nuti S, Seghieri C, Vainieri M (2013) Assessing the effectiveness of a performance evaluation system in the public health care sector: some novel evidence from the Tuscany region experience. J Manag Gov 17(1):59–69
OECD (2014) Magnetic resonance imaging (MRI) exams, total 2014/1
Patrick J, Puterman ML (2007) Improving resource utilization for diagnostic services through flexible inpatient scheduling: a method for improving resource utilization. J Oper Res Soc 58:235–245
Patrick J, Puterman ML (2008) Reducing wait times through operations research: optimizing the use of surge capacity. Healthcare policy = Politiques de sante 3(3):75–88
Patrick J, Puterman ML, Queyranne M (2008) Dynamic multipriority patient scheduling for a diagnostic resource. Oper Res 56(6):1507–1525
Qu X, Shi J (2009) Effect of two-level provider capacities on the performance of open access clinics. Health Care Manag Sci 12(1):99–114
R Core Team (2016) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
Rauner MS, Vissers JMH (2003) OR applied to health services: planning for the future with scarce resources. Eur J Oper Res 150(1):1–2. https://doi.org/10.1016/S0377-2217(02)00775-0
Reiner B, Siegel E, Carrino JA (2002) Workflow optimization: current trends and future directions. J Digit Imaging 15(3):141–152
Robinson LW, Chen RR (2003) Scheduling doctors’ appointments: optimal and empirically-based heuristic policies. IIE Trans 35(3):295–307
Salemi Parizi M, Ghate A (2016) Multi-class, multi-resource advance scheduling with no-shows, cancellations and overbooking. Comput Oper Res 67:90–101
Samorani M, Ganguly S (2016) Optimal sequencing of unpunctual patients in high-service-level clinics. Prod Oper Manag 25(2):330–346
Samorani M, Laganga LR (2015) Outpatient appointment scheduling given individual day-dependent no-show predictions. Eur J Oper Res 240(1):245–257. https://doi.org/10.1016/j.ejor.2014.06.034
Sickinger S, Kolisch R (2009) The performance of a generalized Bailey–Welch rule for outpatient appointment scheduling under inpatient and emergency demand. Health Care Manag Sci 12(4):408–419
Smith-Bindman R, Miglioretti DL, Larson EB (2008) Rising use of diagnostic medical imaging in a large integrated health system. Health Aff (Project Hope) 27(6):1491–1502. https://doi.org/10.1377/hlthaff.27.6.1491
Soriano A (1966) Comparison of two scheduling systems. Oper Res 14(3):388–397
Testi A, Tànfani E (2009) Tactical and operational decisions for operating room planning: efficiency and welfare implications. Health Care Manag Sci 12(4):363–373
Truong V-A (2015) Optimal advance scheduling. Manag Sci 61(7):1584–1597. https://doi.org/10.1287/mnsc.2014.2067
Van Lent WAM et al (2012) Reducing the throughput time of the diagnostic track involving CT scanning with computer simulation. Eur J Radiol 81(11):3131–3140. https://doi.org/10.1016/j.ejrad.2012.03.012
van Sambeek JRC et al (2011) Reducing MRI access times by tackling the appointment-scheduling strategy. BMJ Qual Saf 20(12):1075–1080
Vermeulen IB et al (2009) Adaptive resource allocation for efficient patient scheduling. Artif Intell Med 46(1):67–80
Wang PP (1997) Optimally scheduling N customer arrival times for a single-server system. Comput Oper Res 24(8):703–716. https://doi.org/10.1016/S0305-0548(96)00093-7
Wang J et al (2012) Modeling and analysis of work flow and staffing level in a computed tomography division of University of Wisconsin Medical Foundation. Health Care Manag Sci 15(2):108–120
Welch J, Bailey NT (1952) Appointment systems in hospital outpatient departments. Lancet 1(6718):1105–1108
White M, Pike M (1964) Appointment systems in out-patients’ clinics and the effect of patients’ unpunctuality. Med Care 2(3):133–145
Acknowledgements
The authors are grateful to Meyer University Children’s Hospital, and in particular to Dr. Alberto Zanobini, Dr. Francesca Bellini, Dr. Claudio De Filippi and Dr. Giuseppe Brancato, for supporting the research project that inspired this study. The authors also thank Dr. Niccolò Montigiani for the preliminary results obtained during his Master’s Thesis. Finally, we warmly thank the Editor and the anonymous reviewers for their insightful critical comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Cappanera, P., Visintin, F., Banditori, C. et al. Evaluating the long-term effects of appointment scheduling policies in a magnetic resonance imaging setting. Flex Serv Manuf J 31, 212–254 (2019). https://doi.org/10.1007/s10696-018-9306-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10696-018-9306-1