Echocardiography appointment scheduling through better utilization of resources

Document Type: Research Paper


1 Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran 1411713116, Iran.

2 Department of Cardiology Tehran Heart Centre Tehran University of medical sciences Tehran Iran


Background and Objectives: Appointment scheduling systems are applied in a broad variety of healthcare environments to reduce costs, increase resource utilization, and facilitate patients’ access to care. This study strives to present efficient scheduling models for the Echocardiography Department of Tehran Heart Center (THC). These models seek to optimize both patient and hospital utility by maximizing the weighted number of performed echos and minimizing overtime.
Methods: There are two major problems in developing such models: shift scheduling problem and capacity allocation problem.In this paper, two mixed integer linear programming models are presented based on two different sets of assumptions. The first model is developed according to the current routines of the hospital.In this model, it is assumed that the assignment of specialists to echocardiography laboratories in different shifts is predetermined. Thus this model merely allocates the available capacity of specialists and labs to different types of patients. However, the second model is more comprehensive, as it schedules the shifts of the specialists and allocates the capacity to the patients simultaneously.
Results: The efficiency of the proposed models is evaluated using the real data of the Echocardiography Department of THC. The results showed that both models increased the utility (12.35% and 19.14%, respectively) in comparison with the current status of the department. The first model improved the performance of the department significantly through better utilization of resources; however, the second model improved the performance much more than the first one through creating more capacity and utilizing the capacity efficiently.
Conclusion: Although both models showed significant improvements, the second model was found to be more efficient. The reason is that the first model assumes the specialists' shift assignment to be predetermined, while the second model finds the best shift assignment itself.


1.   Trang A, Kampangkaew J, Fernandes R, Tiwana J, Misra A, Hamzeh I, et al. Understanding by General Providers of the Echocardiogram Report. The American Journal of Cardiology. 2019;124(2):296-302.

2.   Lancellotti P, Price S, Edvardsen T, Cosyns B, Neskovic AN, Dulgheru R, et al. The use of echocardiography in acute cardiovascular care: recommendations of the European Association of Cardiovascular Imaging and the Acute Cardiovascular Care Association. European Heart Journal-Cardiovascular Imaging. 2014;16(2):119-46.

3.   Gandhi R. Increasing the Daily Throughput of Echocardiogram Patients using Discrete Event Simulation [Master's thesis]: University of Toronto; 2013.

4.   Munt B, O’Neill B, Koilpillai C, Gin K, Jue J, Honos G, et al. Treating the right patient at the right time: Access to echocardiography in Canada. Canadian Journal of Cardiology. 2006;22(12):1029-33.

5.   Castro E, Petrovic S. Combined mathematical programming and heuristics for a radiotherapy pre-treatment scheduling problem. Journal of Scheduling. 2012;15(3):333-46.

6.   Saure A, Patrick J, Tyldesley S, Puterman ML. Dynamic multi-appointment patient scheduling for radiation therapy. European Journal of Operational Research. 2012;223(2):573-84.

7.   Pena SM, Lawrence N. Analysis of wait times and impact of real-time surveys on patient satisfaction. Dermatologic Surgery. 2017;43(10):1288-91.

8.   Murray M, Berwick DM. Advanced access: reducing waiting and delays in primary care. JAMA. 2003;289(8):1035-40.

9.   Gupta D, Denton B. Appointment scheduling in health care: Challenges and opportunities. IIE Transactions. 2008;40(9):800-19.

10. Cayirli T, Veral E. Outpatient scheduling in health care: a review of literature. Production and Operations Management. 2003;12(4):519-49.

11. about Tehran Heart Center. Available at: Accessed July 17, 2019.

12. Batun S, Begen MA. Optimization in healthcare delivery modeling: Methods and applications.  Handbook of Healthcare Operations Management: Springer; 2013: 75-119.

13. Ahmadi-Javid A, Jalali Z, Klassen KJ. Outpatient appointment systems in healthcare: A review of optimization studies. European Journal of Operational Research. 2017;258(1):3-34.

14. Bailey NT. A study of queues and appointment systems in hospital out‐patient departments, with special reference to waiting‐times. Journal of the Royal Statistical Society: Series B (Methodological). 1952;14(2):185-99.

15. Hong Y-C, Cohn A, Epelman MA, Alpert A. Creating resident shift schedules under multiple objectives by generating and evaluating the Pareto frontier. Operations Research for Health Care,. 2018,

16. Volland J, Fügener A, Brunner JO. A column generation approach for the integrated shift and task scheduling problem of logistics assistants in hospitals. European Journal of Operational Research. 2017;260(1):316-34.

17. Brunner JO, Bard JF, Kolisch R. Flexible shift scheduling of physicians. Health Care Management Science. 2009;12(3):285-305.

18. Nguyen TBT, Sivakumar AI, Graves SC. A network flow approach for tactical resource planning in outpatient clinics. Health care management science. 2015;18(2):124-36.

19. Choi S, Wilhelm WE. On capacity allocation for operating rooms. Computers & Operations Research. 2014;44:174-84.

20. LaGanga LR, Lawrence SR. Appointment overbooking in health care clinics to improve patient service and clinic performance. Production and Operations Management. 2012;21(5):874-88.

21. Aringhieri R, Landa P, Soriano P, Tànfani E, Testi A. A two-level metaheuristic for the operating room scheduling and assignment problem. Computers & Operations Research. 2015;54:21-34.

22. Marchesi JF, Pacheco MAC, editors. A genetic algorithm approach for the master surgical schedule problem. Evolving and Adaptive Intelligent Systems (EAIS), 2016 IEEE Conference on; 2016: IEEE.

23. Fairley M, Scheinker D, Brandeau ML. Improving the efficiency of the operating room environment with an optimization and machine learning model. Health care management science. 2018,

24. Guido R, Ielpa G, Conforti D. Scheduling outpatient day service operations for rheumatology diseases. Flexible Services and Manufacturing Journal. 2019,

25. M'Hallah R, Visintin F. A stochastic model for scheduling elective surgeries in a cyclic Master Surgical Schedule. Computers & Industrial Engineering. 2019;129:156-68.

26. Hamid M, Nasiri MM, Werner F, Sheikhahmadi F, Zhalechian M. Operating room scheduling by considering the decision-making styles of surgical team members: a comprehensive approach. Computers & Operations Research. 2019;108:166-81.

27. Atighehchian A, Sepehri MM, Shadpour P. Operating room scheduling in teaching hospitals: a novel stochastic optimization model. International Journal of Hospital Research. 2015;4(4):171-6.

28. Sadeghzadeh H, Sadat S. Increasing Operating Room Profits and Decreasing Wait Lists by Use of a Data-Driven Overbooking Model. International Journal of Hospital Research. 2018;7(2):1-20.

29. Holm LB, Bjornenak T, Kjaeserud GG, Noddeland H, editors. Using discrete event simulation and soft systems methodology for optimizing patient flow and resource utilization at the surgical unit of radiumhospitalet in Oslo, NORWAY. 2017 Winter Simulation Conference (WSC); 2017: IEEE.

30. Durán G, Rey PA, Wolff P. Solving the operating room scheduling problem with prioritized lists of patients. Annals of Operations Research. 2017;258(2):395-414.

31. Najjarbashi A, Lim GJ. A variability reduction method for the operating room scheduling problem under uncertainty using CVaR. Operations Research for Health Care. 2019;20:25-32.

32. Katsi VK, Vrachatis DA, Politi A, Papageorgiou M, Koumoulidis A, Vlasseros I, et al. Cardiac echo-lab productivity in times of economic austerity. SpringerPlus. 2014;3(1):703.

33. Bakshi S. Business process re-engineering a cardiology department. World Hospitals and Health Services: The Official Journal of the International Hospital Federation. 2013;50(2):40-5.

34. Geronimo R. Improving Stress Echocardiogram Access for Patients with Low-Risk Chest Pain in the Emergency Department Clinical Decision Unit [Master's thesis]: University of San Francisco; 2017.