Abstract
This article presents a new model for constructing annual schedules for medical residents based on the regulations of a German teaching hospital as well as the program restrictions of the German Medical Association. Since resident programs of physicians do not only vary between disciplines but also between countries, it is essential to evaluate the main characteristics of the program. The main difference between the already well-studied resident programs in the US and the one of this article is the task-related structure. Residents need to perform different interventions several times to become specialists. This study will focus on Germany since there was a judgement in 2015 that hospital management needs training schedules guaranteeing the success of the resident program in time. Therefore, a new formulation of a tactical resident scheduling problem is presented. The problem is formulated in two stages considering the total number of interventions, equal progress in training as well as continuity of care. As the second stage of our formulation is a quadratic program and even by linearization standard solvers are not able to generate high-quality solutions within 24 h, a genetic algorithm using standard crossovers is developed for the second stage constructing annual schedules for an existing stock of residents. We evaluate our algorithm by comparing the solutions of the genetic algorithm and standard software with a real-world situation of a German training hospital from 2016.
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References
Accreditation Council for Graduate Medical Education (2017) Common program requirements. http://www.acgme.org/. Accessed 13 Nov 2017
Bard JF, Shu Z, Morrice DJ, Leykum LK (2016a) Annual block scheduling for internal medicine residents with 4+1 templates. J Oper Res Soc 67(7):911–927. https://doi.org/10.1057/jors.2015.109
Bard JF, Shu Z, Morrice DJ, Leykum LK (2016b) Constructing block schedules for internal medicine residents. IISE Trans Healthc Syst Eng 164(2):1–14. https://doi.org/10.1080/19488300.2016.1255284
Bard JF, Shu Z, Morrice DJ, Leykum LK, Poursani R (2016c) Annual block scheduling for family medicine residency programs with continuity clinic considerations. IIE Trans 48(9):797–811. https://doi.org/10.1080/0740817X.2015.1133942
Beliën J, Demeulemeester E (2006) Scheduling trainees at a hospital department using a branch-and-price approach. Eur J Oper Res 175(1):258–278. https://doi.org/10.1016/j.ejor.2005.04.028
Brunner JO, Edenharter GM (2011) Long term staff scheduling of physicians with different experience levels in hospitals using column generation. Health Care Manag Sci 14(2):189–202. https://doi.org/10.1007/s10729-011-9155-x
Burke EK, de Causmaecker P, Berghe GV, van Landeghem H (2004) The state of the art of nurse rostering. J Sched 7(6):441–499. https://doi.org/10.1023/B:JOSH.0000046076.75950.0b
Cohn A, Root S, Kymissis C, Esses J, Westmoreland N (2009) Scheduling Medical Residents at Boston University School of Medicine. Interfaces 39(3):186–195. https://doi.org/10.1287/inte.1080.0369
Davis L (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, New York
Denson JL, McCarty M, Fang Y, Uppal A, Evans L (2015) Increased mortality rates during resident handoff periods and the effect of ACGME duty hour regulations. Am J Med 128(9):994–1000. https://doi.org/10.1016/j.amjmed.2015.03.023
Eiben AE, Smit SK (2011) Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm Evol Comput 1(1):19–31. https://doi.org/10.1016/j.swevo.2011.02.001
Eiben AE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evol Comput 3(2):124–141. https://doi.org/10.1109/4235.771166
Erhard M, Schoenfelder J, Fügener A, Brunner JO (2018) State of the art in physician scheduling. Eur J Oper Res 265(1):1–18. https://doi.org/10.1016/j.ejor.2017.06.037
Franz LS, Miller JL (1993) Scheduling medical residents to rotations: solving the large-scale multiperiod staff assignment problem. Oper Res 41(2):269–279
German College of General Practitioners and Family Physicians (2009) Speciality training for general practice in Germany. A report by a panel of invited international experts. http://www.degam.de/. Accessed 1 Dec 2015
German Medical Association (2003) Speciality training regulations. http://www.bundesaerztekammer.de/. Accessed 19 Sept 2016
Goldberg DE, Deb K (1991) A comparative analysis of selection schemes used in genetic algorithms. In: Rawlins GJE (ed) Foundations of genetic algorithms 1991 (FOGA 1), vol 1. Elsevier Science, Burlington, pp 69–93
Hof S, Fügener A, Schoenfelder J, Brunner JO (2017) Case mix planning in hospitals: a review and future agenda. Health Care Manag Sci 20(2):207–220. https://doi.org/10.1007/s10729-015-9342-2
Holland JH (1975) Adaptation in natural and artificial systems. An introductory analysis with application to biology, control, and artificial intelligence. University of Michigan Press, Ann Arbor
Katzenbach JR, Smith DK (1993) The wisdom of teams: creating the high-performance organization. Harvard Business School Press, Boston
Kraul S, Fügener A, Brunner JO, Blobner M (2018) A robust framework for task-related resident scheduling. Eur H Oper Res 276(2):656–675. https://doi.org/10.1016/j.ejor.2019.01.034
LAG Baden-Württemberg (2015) Judge in 11.09.2015 (1 Sa 5/15), openJur 2015, 19265
Miani C, Hinrichs S, Pitchforth E, Bienkowska-Gibbs T, Disbeschl S, Roland M, Nolte E (2015) Best practice: Medizinische Aus- und Weiterbildung aus internationaler Perspektive. RAND Corporation. http://www.rand.org/content/dam/rand/pubs/research_reports/RR600/RR622z1/RAND_RR622z1.pdf. Accessed 8 Feb 2016
Ozkarahan I (1994) A scheduling model for hospital residents. J Med Syst 18(5):251–265. https://doi.org/10.1007/BF00996605
Proano RA, Agarwal A (2017) Scheduling internal medicine resident rotations to ensure fairness and facilitate continuity of care. Health Care Manag Sci. https://doi.org/10.1007/s10729-017-9403-9
Sherali HD, Ramahi MH, Saifee QJ (2002) Hospital resident scheduling problem. Prod Plan Control 13(2):220–233. https://doi.org/10.1080/09537280110069667
Smalley HK, Keskinocak P (2014) Automated medical resident rotation and shift scheduling to ensure quality resident education and patient care. Health Care Manag Sci. https://doi.org/10.1007/s10729-014-9289-8
Topaloglu S (2006) A multi-objective programming model for scheduling emergency medicine residents. Comput Ind Eng 51(3):375–388. https://doi.org/10.1016/j.cie.2006.08.003
van den Bergh J, Beliën J, de Bruecker P, Demeulemeester E, de Boeck L (2013) Personnel scheduling: a literature review. Eur J Oper Res 226(3):367–385. https://doi.org/10.1016/j.ejor.2012.11.029
Wang CW, Sun LM, Jin MH, Fu CJ, Liu L, Chan CH, Kao CY (2007) A genetic algorithm for resident physician scheduling problem. In: Lipson H (ed) Proceedings of the 9th annual conference on genetic and evolutionary computation, ACM, New York, p 2203. https://doi.org/10.1145/1276958.1277380
Acknowledgements
Special thanks to Prof. Blobner from the Clinic of Anaesthesiology, Technical University of Munich for providing the real-world data and for participating as a clinical expert in discussions and interviews. This research project is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) and Grant no. 405488489.
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Kraul, S. Annual scheduling for anesthesiology medicine residents in task-related programs with a focus on continuity of care. Flex Serv Manuf J 32, 181–212 (2020). https://doi.org/10.1007/s10696-019-09365-4
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DOI: https://doi.org/10.1007/s10696-019-09365-4