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Annual scheduling for anesthesiology medicine residents in task-related programs with a focus on continuity of care

  • Sebastian KraulEmail author
Article

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.

Keywords

OR in health services Resident scheduling Task-related Real-world data Genetic algorithm 

Notes

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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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Chair of Health Care Operations/Health Information Management, Faculty of Business and EconomicsUniversity of Augsburg, University Center for Health Sciences at the Augsburg Clinic – UNIKA-TAugsburgGermany

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