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A Modeling Framework for Control of Preventive Services

Author

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  • E. Lerzan Örmeci

    (Department of Industrial Engineering, Koç University, 34450 Istanbul, Turkey)

  • Evrim Didem Güneş

    (College of Administrative Sciences and Economics, Koç University, 34450 Istanbul, Turkey)

  • Derya Kunduzcu

    (Credit Analytics, Akbank, 34774 Istanbul, Turkey)

Abstract

We present a modeling framework for facilities that provide both screening (preventive) and diagnostic (repair) services. The facility operates in a random environment that represents the condition of the population that needs screening and diagnostic services, such as the disease prevalence level. We model the environment as a partially endogenous process: the population’s health can be improved by providing screening services, which reduces future demand for diagnostic services. We use event-based dynamic programming to build a framework for modeling different kinds of these facilities. This framework contains a number of service priority policies that are concerned with prioritizing screening versus diagnostic services. The main trade-off is between serving urgent diagnostic needs and providing screening services that may decrease future diagnostic needs. Under certain conditions, this trade-off reverses the famous cμ rule; i.e., the patients with lower waiting cost are given priority over the others. We define appropriate event operators and specify the properties preserved by these operators. These characterize the structure of optimal policies for all models that can be built within this framework. A numerical study on colonoscopy services illustrates how the framework can be used to gain insights on developing good screening policies.

Suggested Citation

  • E. Lerzan Örmeci & Evrim Didem Güneş & Derya Kunduzcu, 2016. "A Modeling Framework for Control of Preventive Services," Manufacturing & Service Operations Management, INFORMS, vol. 18(2), pages 227-244, May.
    • Handle: RePEc:inm:ormsom:v:18:y:2016:i:2:p:227-244
    DOI: 10.1287/msom.2015.0556
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    References listed on IDEAS

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    1. Özkan, Can & Karaesmen, Fikri & Özekici, Süleyman, 2013. "Structural properties of Markov modulated revenue management problems," European Journal of Operational Research, Elsevier, vol. 225(2), pages 324-331.
    2. Evrim Didem Güneş & Egemen Lerzan Örmeci & Derya Kunduzcu, 2015. "Preventing and Diagnosing Colorectal Cancer with a Limited Colonoscopy Resource," Production and Operations Management, Production and Operations Management Society, vol. 24(1), pages 1-20, January.
    3. 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.
    4. Oguzhan Alagoz & Lisa M. Maillart & Andrew J. Schaefer & Mark S. Roberts, 2007. "Determining the Acceptance of Cadaveric Livers Using an Implicit Model of the Waiting List," Operations Research, INFORMS, vol. 55(1), pages 24-36, February.
    5. Chaitra Gopalappa & Selen Aydogan-Cremaschi & Tapas Das & Seza Orcun, 2011. "Probability model for estimating colorectal polyp progression rates," Health Care Management Science, Springer, vol. 14(1), pages 1-21, March.
    6. Fatih Safa Erenay & Oguzhan Alagoz & Adnan Said, 2014. "Optimizing Colonoscopy Screening for Colorectal Cancer Prevention and Surveillance," Manufacturing & Service Operations Management, INFORMS, vol. 16(3), pages 381-400, July.
    7. Dimitri frosinin & L. Breuer, 2006. "Threshold policies for controlled retrial queues with heterogeneous servers," Annals of Operations Research, Springer, vol. 141(1), pages 139-162, January.
    8. Hongzhou Wang & Hoang Pham, 2006. "Reliability and Optimal Maintenance," Springer Series in Reliability Engineering, Springer, number 978-1-84628-325-3, January.
    9. S. Özekici & R. Soyer, 2003. "Network reliability assessment in a random environment," Naval Research Logistics (NRL), John Wiley & Sons, vol. 50(6), pages 574-591, September.
    10. Steven M. Shechter & Matthew D. Bailey & Andrew J. Schaefer & Mark S. Roberts, 2008. "The Optimal Time to Initiate HIV Therapy Under Ordered Health States," Operations Research, INFORMS, vol. 56(1), pages 20-33, February.
    11. Nilay Tan{i}k Argon & Serhan Ziya, 2009. "Priority Assignment Under Imperfect Information on Customer Type Identities," Manufacturing & Service Operations Management, INFORMS, vol. 11(4), pages 674-693, June.
    12. 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.
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    4. Chen, Yi & Ding, Shuai & Zheng, Handong & Zhang, Youtao & Yang, Shanlin, 2018. "Exploring diffusion strategies for mHealth promotion using evolutionary game model," Applied Mathematics and Computation, Elsevier, vol. 336(C), pages 148-161.

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