IDEAS home Printed from https://ideas.repec.org/a/wly/navres/v63y2016i4p287-304.html
   My bibliography  Save this article

Value management of diagnostic equipment with cancelation, no‐show, and emergency patients

Author

Listed:
  • Yongbo Xiao
  • Yan Zhu

Abstract

Magnetic resonance imaging and other multifunctional diagnostic facilities, which are considered as scarce resources of hospitals, typically provide services to patients with different medical needs. This article examines the admission policies during the appointment management of such facilities. We consider two categories of patients: regular patients who are scheduled in advance through an appointment system and emergency patients with randomly generated demands during the workday that must be served as soon as possible. According to the actual medical needs of patients, regular patients are segmented into multiple classes with different cancelation rates, no‐show probabilities, unit value contributions, and average service times. Management makes admission decisions on whether or not to accept a service request from a regular patient during the booking horizon to improve the overall value that could be generated during the workday. The decisions should be made by considering the cancelation and no‐show behavior of booked patients as well as the emergency patients that would have to be served because any overtime service would lead to higher costs. We studied the optimal admission decision using a continuous‐time discrete‐state dynamic programming model. Identifying an optimal policy for this discrete model is analytically intractable and numerically inefficient because the state is multidimensional and infinite. We propose to study a deterministic counterpart of the problem (i.e., the fluid control problem) and to develop a time‐based fluid policy that is shown to be asymptotically optimal for large‐scale problems. Furthermore, we propose to adopt a mixed fluid policy that is developed based on the information obtained from the fluid control problem. Numerical experiments demonstrate that this improved policy works effectively for small‐scale problems. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 287–304, 2016

Suggested Citation

  • Yongbo Xiao & Yan Zhu, 2016. "Value management of diagnostic equipment with cancelation, no‐show, and emergency patients," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(4), pages 287-304, June.
    • Handle: RePEc:wly:navres:v:63:y:2016:i:4:p:287-304
    DOI: 10.1002/nav.21691
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/nav.21691
    Download Restriction: no
    File URL: https://libkey.io/10.1002/nav.21691?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. William L. Cooper, 2002. "Asymptotic Behavior of an Allocation Policy for Revenue Management," Operations Research, INFORMS, vol. 50(4), pages 720-727, August.
    2. Youyi Feng & Guillermo Gallego, 1995. "Optimal Starting Times for End-of-Season Sales and Optimal Stopping Times for Promotional Fares," Management Science, INFORMS, vol. 41(8), pages 1371-1391, August.
    3. Chrwan-Jyh Ho & Hon-Shiang Lau, 1992. "Minimizing Total Cost in Scheduling Outpatient Appointments," Management Science, INFORMS, vol. 38(12), pages 1750-1764, December.
    4. Guillermo Gallego & Garrett van Ryzin, 1997. "A Multiproduct Dynamic Pricing Problem and Its Applications to Network Yield Management," Operations Research, INFORMS, vol. 45(1), pages 24-41, February.
    5. Jonathan Patrick & Martin L. Puterman & Maurice Queyranne, 2008. "Dynamic Multipriority Patient Scheduling for a Diagnostic Resource," Operations Research, INFORMS, vol. 56(6), pages 1507-1525, December.
    6. Lamiri, Mehdi & Xie, Xiaolan & Dolgui, Alexandre & Grimaud, Frederic, 2008. "A stochastic model for operating room planning with elective and emergency demand for surgery," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1026-1037, March.
    7. Qian Liu & Garrett van Ryzin, 2008. "On the Choice-Based Linear Programming Model for Network Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 10(2), pages 288-310, October.
    8. Yigal Gerchak & Diwakar Gupta & Mordechai Henig, 1996. "Reservation Planning for Elective Surgery Under Uncertain Demand for Emergency Surgery," Management Science, INFORMS, vol. 42(3), pages 321-334, March.
    9. Bo Zeng & Ayten Turkcan & Ji Lin & Mark Lawley, 2010. "Clinic scheduling models with overbooking for patients with heterogeneous no-show probabilities," Annals of Operations Research, Springer, vol. 178(1), pages 121-144, July.
    10. Martin I. Reiman & Qiong Wang, 2008. "An Asymptotically Optimal Policy for a Quantity-Based Network Revenue Management Problem," Mathematics of Operations Research, INFORMS, vol. 33(2), pages 257-282, May.
    11. Richard Van Slyke & Yi Young, 2000. "Finite Horizon Stochastic Knapsacks with Applications to Yield Management," Operations Research, INFORMS, vol. 48(1), pages 155-172, February.
    12. Linda V. Green & Sergei Savin & Ben Wang, 2006. "Managing Patient Service in a Diagnostic Medical Facility," Operations Research, INFORMS, vol. 54(1), pages 11-25, February.
    13. Diwakar Gupta & Lei Wang, 2008. "Revenue Management for a Primary-Care Clinic in the Presence of Patient Choice," Operations Research, INFORMS, vol. 56(3), pages 576-592, June.
    14. Nan Liu & Serhan Ziya & Vidyadhar G. Kulkarni, 2010. "Dynamic Scheduling of Outpatient Appointments Under Patient No-Shows and Cancellations," Manufacturing & Service Operations Management, INFORMS, vol. 12(2), pages 347-364, September.
    15. Constantinos Maglaras & Joern Meissner, 2006. "Dynamic Pricing Strategies for Multiproduct Revenue Management Problems," Manufacturing & Service Operations Management, INFORMS, vol. 8(2), pages 136-148, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jacob Feldman & Nan Liu & Huseyin Topaloglu & Serhan Ziya, 2014. "Appointment Scheduling Under Patient Preference and No-Show Behavior," Operations Research, INFORMS, vol. 62(4), pages 794-811, August.
    2. Van-Anh Truong, 2015. "Optimal Advance Scheduling," Management Science, INFORMS, vol. 61(7), pages 1584-1597, July.
    3. Hans-Jörg Schütz & Rainer Kolisch, 2013. "Capacity allocation for demand of different customer-product-combinations with cancellations, no-shows, and overbooking when there is a sequential delivery of service," Annals of Operations Research, Springer, vol. 206(1), pages 401-423, July.
    4. Paola Cappanera & Filippo Visintin & Carlo Banditori & Daniele Feo, 2019. "Evaluating the long-term effects of appointment scheduling policies in a magnetic resonance imaging setting," Flexible Services and Manufacturing Journal, Springer, vol. 31(1), pages 212-254, March.
    5. Ahmadi-Javid, Amir & Jalali, Zahra & Klassen, Kenneth J, 2017. "Outpatient appointment systems in healthcare: A review of optimization studies," European Journal of Operational Research, Elsevier, vol. 258(1), pages 3-34.
    6. Pornpawee Bumpensanti & He Wang, 2020. "A Re-Solving Heuristic with Uniformly Bounded Loss for Network Revenue Management," Management Science, INFORMS, vol. 66(7), pages 2993-3009, July.
    7. Dan Zhang & Larry Weatherford, 2017. "Dynamic Pricing for Network Revenue Management: A New Approach and Application in the Hotel Industry," INFORMS Journal on Computing, INFORMS, vol. 29(1), pages 18-35, February.
    8. Miao Bai & Bjorn Berg & Esra Sisikoglu Sir & Mustafa Y. Sir, 2023. "Partially partitioned templating strategies for outpatient specialty practices," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 301-318, January.
    9. Schütz, Hans-Jörg & Kolisch, Rainer, 2012. "Approximate dynamic programming for capacity allocation in the service industry," European Journal of Operational Research, Elsevier, vol. 218(1), pages 239-250.
    10. Yongbo Xiao, 2018. "Dynamic pricing and replenishment: Optimality, bounds, and asymptotics," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(1), pages 3-25, February.
    11. Stefanus Jasin & Amitabh Sinha, 2015. "An LP-Based Correlated Rounding Scheme for Multi-Item Ecommerce Order Fulfillment," Operations Research, INFORMS, vol. 63(6), pages 1336-1351, December.
    12. Katsumi Morikawa & Katsuhiko Takahashi & Daisuke Hirotani, 2018. "Performance evaluation of candidate appointment schedules using clearing functions," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 509-518, March.
    13. Qu, Xiuli & Peng, Yidong & Shi, Jing & LaGanga, Linda, 2015. "An MDP model for walk-in patient admission management in primary care clinics," International Journal of Production Economics, Elsevier, vol. 168(C), pages 303-320.
    14. Christos Zacharias & Tallys Yunes, 2020. "Multimodularity in the Stochastic Appointment Scheduling Problem with Discrete Arrival Epochs," Management Science, INFORMS, vol. 66(2), pages 744-763, February.
    15. Qian Liu & Garrett van Ryzin, 2008. "On the Choice-Based Linear Programming Model for Network Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 10(2), pages 288-310, October.
    16. Stefanus Jasin & Sunil Kumar, 2012. "A Re-Solving Heuristic with Bounded Revenue Loss for Network Revenue Management with Customer Choice," Mathematics of Operations Research, INFORMS, vol. 37(2), pages 313-345, May.
    17. Liping Zhou & Na Geng & Zhibin Jiang & Shan Jiang, 2022. "Integrated Multiresource Capacity Planning and Multitype Patient Scheduling," INFORMS Journal on Computing, INFORMS, vol. 34(1), pages 129-149, January.
    18. Jingui Xie & Weifen Zhuang & Marcus Ang & Mabel C. Chou & Li Luo & David D. Yao, 2021. "Analytics for Hospital Resource Planning—Two Case Studies," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1863-1885, June.
    19. Tsai, Pei-Fang Jennifer & Teng, Guei-Yu, 2014. "A stochastic appointment scheduling system on multiple resources with dynamic call-in sequence and patient no-shows for an outpatient clinic," European Journal of Operational Research, Elsevier, vol. 239(2), pages 427-436.
    20. Yasin Gocgun & Martin Puterman, 2014. "Dynamic scheduling with due dates and time windows: an application to chemotherapy patient appointment booking," Health Care Management Science, Springer, vol. 17(1), pages 60-76, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:navres:v:63:y:2016:i:4:p:287-304. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1520-6750 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.