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An Intelligent and Data-Driven Decision Support Solution for the Online Surgery Scheduling Problem

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Enterprise Information Systems (ICEIS 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 363))

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Abstract

In operational business situations it is necessary to be aware of and to understand what happens around you and what probably will happen in the near future to make optimal decisions. For example, Online Surgery Scheduling is the planning and control task of Operating Room Management and includes decisions that are difficult to deal with due to high cognitive and communicational efforts to gather the needed information. In addition, several uncertainties like complications, cancellations and emergencies as well as the need to monitor and control the interventions during execution distinguish the operational decision tasks in surgery scheduling from the tactical and strategical planning decisions. However, the emerging trend of connecting devices and intelligent methods in analytics, facilitate innovative approaches for decision support in this area. With the utilization of these concepts, we propose a data-driven approach for a Decisions Support System including components for monitoring, prediction and optimization in Online Surgery Scheduling.

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References

  1. Macario, A.: What does one minute of operating room time cost? J. Clin. Anesth. 22, 233–236 (2010)

    Article  Google Scholar 

  2. Katić, D., et al.: Bridging the gap between formal and experience-based knowledge for context-aware laparoscopy. Int. J. Comput. Assist. Radiol. Surg. 11, 881–888 (2016)

    Article  Google Scholar 

  3. Twinanda, A.P., Shehata, S., Mutter, D., Marescaux, J., de Mathelin, M., Padoy, N.: EndoNet: a deep architecture for recognition tasks on laparoscopic videos. IEEE Trans. Med. Imaging 36, 86–97 (2017)

    Article  Google Scholar 

  4. May, J.H., Spangler, W.E., Strum, D.P., Vargas, L.G.: The surgical scheduling problem: current research and future opportunities. Prod. Oper. Manag. 20, 392–405 (2011)

    Article  Google Scholar 

  5. Demeulemeester, E., Belién, J., Cardoen, B., Samudra, M.: Operating room planning & scheduling. In: Handbook of Healthcare Operations Management: Methods and Applications, pp. 121–152 (2013)

    Google Scholar 

  6. Hans, E.W., Vanberkel, P.T.: Operating theatre planning and scheduling. In: Hall, R. (ed.) Handbook of Healthcare System Scheduling. International Series in Operations Research & Management Science, vol. 168, pp. 105–130. Springer, Boston (2012). https://doi.org/10.1007/978-1-4614-1734-7_5

    Chapter  Google Scholar 

  7. Dexter, F., Epstein, R.H., Traub, R.D., Xiao, Y.: Making management decisions on the day of surgery based on operating room efficiency and patient waiting times. Anesthesiology 101, 1444–1453 (2004)

    Article  Google Scholar 

  8. Guerriero, F., Guido, R.: Operational research in the management of the operating theatre: a survey. Health Care Manag. Sci. 14, 89–114 (2011)

    Article  Google Scholar 

  9. Riise, A., Mannino, C., Burke, E.K.: Modelling and solving generalised operational surgery scheduling problems. Comput. Oper. Res. 66, 1–11 (2016)

    Article  MathSciNet  Google Scholar 

  10. Guido, R., Conforti, D.: A hybrid genetic approach for solving an integrated multi-objective operating room planning and scheduling problem. Comput. Oper. Res. 87, 270–282 (2017)

    Article  MathSciNet  Google Scholar 

  11. Samudra, M., Demeulemeester, E., Cardoen, B., Vansteenkiste, N., Rademakers, F.E.: Due time driven surgery scheduling. Health Care Manag. Sci. 20, 326–352 (2017)

    Article  Google Scholar 

  12. Eijkemans, M.J.C., van Houdenhoven, M., Nguyen, T., Boersma, E., Steyerberg, E.W., Kazemier, G.: Predicting the unpredictablea new prediction model for operating room times using individual characteristics and the surgeon’s estimate. J. Am. Soc. Anesth. 112, 41–49 (2010)

    Google Scholar 

  13. Guédon, A., et al.: ‘It is time to prepare the next patient’ real-time prediction of procedure duration in laparoscopic cholecystectomies. J. Med. Syst. 40, 271–277 (2016)

    Article  Google Scholar 

  14. Samudra, M., van Riet, C., Demeulemeester, E., Cardoen, B., Vansteenkiste, N., Rademakers, F.E.: Scheduling operating rooms: achievements, challenges and pitfalls. J. Sched. 19, 493–525 (2016)

    Article  MathSciNet  Google Scholar 

  15. Vieira, G., Herrmann, J., Lin, E.: Rescheduling manufacturing systems: a framework of strategies, policies, and methods. J. Sched. 6, 39–62 (2003)

    Article  MathSciNet  Google Scholar 

  16. Aytug, H., Lawley, M.A., McKay, K., Mohan, S., Uzsoy, R.: Executing production schedules in the face of uncertainties: a review and some future directions. Eur. J. Oper. Res. 161, 86–110 (2005)

    Article  MathSciNet  Google Scholar 

  17. Spangenberg, N., Wilke, M., Augenstein, C., Franczyk, B.: Online surgery rescheduling - a data-driven approach for real-time decision support. In: Proceedings of the 20th International Conference on Enterprise Information Systems, ICEIS 2018, Funchal, Madeira, Portugal, 21–24 March 2018, vol. 1, pp. 336–343 (2018)

    Google Scholar 

  18. Niederlag, W., Lemke, H.U., Strauß, G., Feußner, H. (eds.): Der digitale Operationssaal. Health Academy, vol. 2. De Gruyter, Berlin (2014)

    Google Scholar 

  19. Franke, S., Meixensberger, J., Neumuth, T.: Intervention time prediction from surgical low-level tasks. J. Bio. Inf. 46, 152–159 (2013)

    Article  Google Scholar 

  20. Ahmadi, S.-A., Sielhorst, T., Stauder, R., Horn, M., Feussner, H., Navab, N.: Recovery of surgical workflow without explicit models. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006, Part I. LNCS, vol. 4190, pp. 420–428. Springer, Heidelberg (2006). https://doi.org/10.1007/11866565_52

    Chapter  Google Scholar 

  21. Lalys, F., Bouget, D., Riffaud, L., Jannin, P.: Automatic knowledge-based recognition of low-level tasks in ophthalmological procedures. Int. J. Comput. Assist. Radiol. Surg. 8, 39–49 (2013)

    Article  Google Scholar 

  22. Padoy, N., Blum, T., Ahmadi, S.A., Feussner, H., Berger, M.O., Navab, N.: Statistical modeling and recognition of surgical workflow. Med. Image Anal. 16, 632–641 (2012)

    Article  Google Scholar 

  23. Malpani, A., Lea, C., Chen, C.C.G., Hager, G.D.: System events: readily accessible features for surgical phase detection. Int. J. Comput. Assist. Radiol. Surg. 11, 1201–1209 (2016)

    Article  Google Scholar 

  24. Meissner, C., Meixensberger, J., Pretschner, A., Neumuth, T.: Sensor-based surgical activity recognition in unconstrained environments. Minim. Invasive Ther. Allied Technol. MITAT Off. J. Soc. Minim. Invasive Ther. 23, 198–205 (2014)

    Article  Google Scholar 

  25. Dergachyova, O., Bouget, D., Huaulme, A., Morandi, X., Jannin, P.: Automatic data-driven real-time segmentation and recognition of surgical workflow. Int. J. Comput. Assist. Radiol. Surg. 11, 1081–1089 (2016)

    Article  Google Scholar 

  26. Maktabi, M., Neumuth, T.: Online time and resource management based on surgical workflow time series analysis. Int. J. Comput. Assist. Radiol. Surg. 12, 325–338 (2017)

    Article  Google Scholar 

  27. Erdogan, S.A., et al.: Surgery planning and scheduling. In: Wiley Encyclopedia of Operations Research and Management Science. Wiley Online Library (2010)

    Google Scholar 

  28. Atkin, J.A.D., Burke, E.K., Greenwood, J.S., Reeson, D.: On-line decision support for take-off runway scheduling with uncertain taxi times at London heathrow airport. J. Sched. 11, 323–346 (2008)

    Article  MathSciNet  Google Scholar 

  29. Ngai, E., Leung, T., Wong, Y.H., Lee, M., Chai, P., Choi, Y.S.: Design and development of a context-aware decision support system for real-time accident handling in logistics. Decis. Support Syst. 52, 816–827 (2012)

    Article  Google Scholar 

  30. Guo, Z.X., Ngai, E., Yang, C., Liang, X.: An RFID-based intelligent decision support system architecture for production monitoring and scheduling in a distributed manufacturing environment. Int. J. Prod. Econ. 159, 16–28 (2015)

    Article  Google Scholar 

  31. Dios, M., Molina-Pariente, J.M., Fernandez-Viagas, V., Andrade-Pineda, J.L., Framinan, J.M.: A decision support system for operating room scheduling. Comput. Ind. Eng. 88, 430–443 (2015)

    Article  Google Scholar 

  32. Erdogan, S.A., Gose, A., Denton, B.T.: Online appointment sequencing and scheduling. IIE Trans. 47, 1267–1286 (2015)

    Article  Google Scholar 

  33. van Essen, J.T., Hurink, J.L., Hartholt, W., van den Akker, B.J.: Decision support system for the operating room rescheduling problem. Health Care Manag. Sci. 15, 355–372 (2012)

    Article  Google Scholar 

  34. Spangenberg, N., Augenstein, C., Franczyk, B., Wagner, M., Apitz, M., Kenngott, H.: Method for intra-surgical phase detection by using real-time medical device data. In: 30th IEEE International Symposium on Computer-Based Medical Systems, pp. 1–8 (2017)

    Google Scholar 

  35. Marz, N., Warren, J.: Big Data: Principles and Best Practices of Scalable Realtime Data Systems. Manning Publications Co., Greenwich (2015)

    Google Scholar 

  36. Spangenberg, N., Wilke, M., Franczyk, B.: A big data architecture for intra-surgical remaining time predictions. Procedia Comput. Sci. 113, 310–317 (2017)

    Article  Google Scholar 

  37. Graubner, B.: OPS Systematisches Verzeichnis 2014: Operationen-und Prozedurenschlüssel-Internationale Klassifikation der Prozeduren in der Medizin Version 2014. Deutscher Ärzteverlag (2013)

    Google Scholar 

  38. Master, N., Scheinker, D., Bambos, N.: Predicting pediatric surgical durations (2016). arXiv preprint: arXiv:1605.04574

  39. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)

    Article  MathSciNet  Google Scholar 

  40. Ceschia, S., Schaerf, A.: Dynamic patient admission scheduling with operating room constraints, flexible horizons, and patient delays. J. Sched. 19, 377–389 (2016)

    Article  MathSciNet  Google Scholar 

  41. Li, X., et al.: Progress estimation and phase detection for sequential processes. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 1–20 (2017)

    Google Scholar 

  42. Twinanda, A.P., Yengera, G., Mutter, D., Marescaux, J., Padoy, N.: RSDNet: learning to predict remaining surgery duration from laparoscopic videos without manual annotations (2018). arXiv preprint: arXiv:1802.03243

  43. Venable, J., Pries-Heje, J., Baskerville, R.: FEDS: a framework for evaluation in design science research. Eur. J. Inf. Syst. 25, 77–89 (2016)

    Article  Google Scholar 

  44. EsperTech Inc.: Esper (2018)

    Google Scholar 

  45. Apache Software Foundation: Apache spark - lightning-fast cluster computing (2018)

    Google Scholar 

  46. Apache Software Foundation: Kafka streams - the easiest way to write mission-critical real-time applications & microservices (2018)

    Google Scholar 

  47. Spangenberg, N., Augenstein, C., Franczyk, B., Wilke, M.: Implementation of a situation aware and real-time approach for decision support in online surgery scheduling. In: 31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018, Karlstad, Sweden, 18–21 June 2018, pp. 417–421 (2018)

    Google Scholar 

  48. Red Hat Inc.: Optaplanner - constraint satisfaction solver (2018)

    Google Scholar 

  49. TimeTable.js: A javascript plugin for beautiful responsive timetables (2018)

    Google Scholar 

  50. Glaser, B., Dänzer, S., Neumuth, T.: Intra-operative surgical instrument usage detection on a multi-sensor table. Int. J. Comput. Assist. Radiol. Surg. 10, 351–362 (2015)

    Article  Google Scholar 

  51. Nara, A., Allen, C., Izumi, K.: Surgical phase recognition using movement data from video imagery and location sensor data. In: Griffith, D.A., Chun, Y., Dean, D.J. (eds.) Advances in Geocomputation. AGIS, pp. 229–237. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-22786-3_21

    Chapter  Google Scholar 

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Acknowledgments

This paper was funded by the German Federal Ministry of Education and Research under the project Competence Center for Scalable Data Services and Solutions Dresden/Leipzig (BMBF 01IS14014B) and by the German Federal Ministry of Economic Affairs and Energy under the project InnOPlan (BMWI 01MD15002E).

Author information

Authors and Affiliations

  1. University of Leipzig, Information Systems Institute, Leipzig, Germany

    Norman Spangenberg, Christoph Augenstein & Moritz Wilke

  2. Wroclaw University of Economics, Wroclaw, Poland

    Bogdan Franczyk

Authors
  1. Norman Spangenberg
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  2. Christoph Augenstein
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  3. Moritz Wilke
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  4. Bogdan Franczyk
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Corresponding author

Correspondence to Norman Spangenberg .

Editor information

Editors and Affiliations

  1. MODESTE/ESEO, Angers, France

    Slimane Hammoudi

  2. Warsaw University of Technology, Warsaw, Poland

    Michał Śmiałek

  3. MODESTE/ESEO, Angers, France

    Olivier Camp

  4. INSTICC and Polytechnic Institute of Setúbal, Setúbal, Portugal

    Joaquim Filipe

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Spangenberg, N., Augenstein, C., Wilke, M., Franczyk, B. (2019). An Intelligent and Data-Driven Decision Support Solution for the Online Surgery Scheduling Problem. In: Hammoudi, S., Śmiałek, M., Camp, O., Filipe, J. (eds) Enterprise Information Systems. ICEIS 2018. Lecture Notes in Business Information Processing, vol 363. Springer, Cham. https://doi.org/10.1007/978-3-030-26169-6_5

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  • DOI: https://doi.org/10.1007/978-3-030-26169-6_5

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