DSS (Decision Support System) for Allocating Appointment Times to Calling Patients at a Medical Facility

  • Adel AlaeddiniEmail author
  • Katta G. Murty
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 212)


A variety of terms are used to describe medical facilities offering treatment to general patients—hospitals, clinics, etc. We will use the generic term hospital for such a facility. In this chapter, we will discuss one common decision making problem encountered in daily operations at such a facility. When a patient has a health condition that needs treatment, she or he calls her/his hospital for an appointment. The receptionist who receives that call asks the caller a few basic questions, like who her/his doctor is, and the reason for the call, etc. The receptionist then looks up the patient records on the desktop computer in front of her, and based on the data stored in that record, and information obtained from that phone call, she faces the decision making problem of selecting the date and time for the patient’s appointment with the required caregiver in the hospital subject to various operational conditions and requirements to optimize important objectives described in more detail later. At most hospitals, receptionists have to deal with hundreds of such calls every day, and the solution for the decision making problem in each call has to be found during the short duration of the call. The name used for procedures for solving such decision making problems occurring sequentially over the time with the requirement that the solution of each must be determined within a very short time of the problems occurring is real-time decision making algorithms. Clearly, receptionists at hospitals need a decision support system (DSS) installed on the desktop in front of them, which they can use to determine the appointment time with medical practitioners in the hospital to calling patients. This chapter deals with the problem of developing such a DSS.


Primary Care Physician Decision Support System Appointment Date Evil Spirit Patient Panel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Fig. 5.1a–c are from the articles “Castor oil plant”, and “Piptoporus betulinus” at AMPPURLStart, and AMPPURLStart, Fig. 5.2a–c is from the articles “Ancient Egyptian medicine,” “Pyramid,” and “Ancient Egyptian Medicine History” at AMPPURLStart, http://en., and Fig. 5.3a, b is from the articles “The ‘Tribal Medicine Project’ (Part 1)” and “Palm-leaf manuscript” at the-tribal-medicine-project-part-1.html and Please see these articles for detailed information on them.

Supplementary material

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  1. 1.
    Alaeddini, A., Yang, K., Reddy, C., & Yu, S. (2011). A probabilistic model for predicting the probability of no-show in hospital appointments. Health Care Management Science, 14(2), 146.CrossRefGoogle Scholar
  2. 2.
    Barron, W. M. (1980). Failed appointments: Who misses them, why they are missed, and what can be done. Primary Care, 7(4), 563.Google Scholar
  3. 3.
    Bech, M. (2005). The economics of non-attendance and the expected effect of charging a fine on non-attendees. Health Policy, 74(2), 181.CrossRefGoogle Scholar
  4. 4.
    Cayirli, T., & Veral, E. (2003). Outpatient scheduling in health care: A review of the literature. Production Operations Management, 12(4), 519.CrossRefGoogle Scholar
  5. 5.
    Dixon, S., Sampson, F., O’Cathain, A., & Pickin, D. M. (2006). Advanced access: More than just GP waiting times? Family practice, 23(2), 233.CrossRefGoogle Scholar
  6. 6.
    Dockery, F., Rajkumar, C., Chapman, C., Bulpitt, C., & Nicholl, C. (2001). The effect of reminder calls in reducing non-attendance rates at care of the elderly clinics. Postgraduate Medical Journal, 77, 37.CrossRefGoogle Scholar
  7. 7.
    Gariti, P., Alterman, A., Holub-Beyer, E., Volpicelli, J. R., Prentice, N., & O’Brien, C. (1995). Effects of an appointment reminder call on patient show rates. Journal of Substance Abuse Treatment, 12(3), 207.CrossRefGoogle Scholar
  8. 8.
    Green, L. (2007). Providing timely access to care: What is the right patient panel size? The Joint Commission Journal on Quality and Patient Safety, 33(4), 8.CrossRefGoogle Scholar
  9. 9.
    Grumbach, K. (2004). Can health care teams improve primary care practice? American Medical Association, 291(10), 6.CrossRefGoogle Scholar
  10. 10.
    Hixon, A. L., Chapman, R. W., & Nuovo, J. (1999). Failure to keep clinic appointments: Implications for residency education and productivity. Family Medicine, 31(9), 627.Google Scholar
  11. 11.
    Moore, C. G., Wilson-Witherspoon, P., & Probst, J. C. (2001). Time and money: Effects of no-shows at a family practice residency clinic. Family Medicine, 33(7), 522.Google Scholar
  12. 12.
    Murray, M. (2003). Improving timely access to primary care case studies of the advanced access model. American Medical Association, 289(8), 5.Google Scholar
  13. 13.
    Murray, M., & Davies, M. (2007). Panel size—How many patients can one doctor manage? Archives of Internal Medicine, 171(13), 10.Google Scholar
  14. 14.
    Murty, K. G. (2005). Chapter 2 in Junior level optimization models for decision making, Vol. 1.
  15. 15.
    Rust, C. T., Gallups, N. H., Clark, S., Jones, D. S., Wilcox, W. D., & Adolesc, A. (1995). Patient appointment failures in pediatric resident continuity clinics.Pediatric and Adolescent Medicine, 149(6), 693.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.University of Texas at San AntonioSan AntonioUSA
  2. 2.University of MichiganAnn ArborUSA

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