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Online time and resource management based on surgical workflow time series analysis

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Hospitals’ effectiveness and efficiency can be enhanced by automating the resource and time management of the most cost-intensive unit in the hospital: the operating room (OR). The key elements required for the ideal organization of hospital staff and technical resources (such as instruments in the OR) are an exact online forecast of both the surgeon’s resource usage and the remaining intervention time.

Methods

This paper presents a novel online approach relying on time series analysis and the application of a linear time-variant system. We calculated the power spectral density and the spectrogram of surgical perspectives (e.g., used instrument) of interest to compare several surgical workflows.

Results

Considering only the use of the surgeon’s right hand during an intervention, we were able to predict the remaining intervention time online with an error of 21 min 45 s ±9 min 59 s for lumbar discectomy. Furthermore, the performance of forecasting of technical resource usage in the next 20 min was calculated for a combination of spectral analysis and the application of a linear time-variant system (sensitivity: 74 %; specificity: 75 %) focusing on just the use of surgeon’s instrument in question.

Conclusion

The outstanding benefit of these methods is that the automated recording of surgical workflows has minimal impact during interventions since the whole set of surgical perspectives need not be recorded. The resulting predictions can help various stakeholders such as OR staff and hospital technicians. Moreover, reducing resource conflicts could well improve patient care.

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References

  1. Stahl JE, Egan MT, Goldman JM, Tenney D, Wiklund RA, Sandberg WS, Gazelle S, Rattner DW (2005) Introducing new technology into the operating room: measuring the impact on job performance and satisfaction. Surgery 137(5):518–526

    Article  PubMed  Google Scholar 

  2. Baumgart A, Schüpfer G, Welker A, Bender H-J, Schleppers A (2010) Status quo and current trends of operating room management in Germany. Curr Opin Anaesthesiol 23(2):193–200

    Article  PubMed  Google Scholar 

  3. Arora S, Sevdalis N, Nestel D, Tierney T, Woloshynowych M, Kneebone R (2009) Managing intraoperative stress: what do surgeons want from a crisis training program? Am J Surg 197(4):537–543

    Article  PubMed  Google Scholar 

  4. Cleary K, Kinsella A (2005) OR 2020: the operating room of the future. J Laparoendosc Adv Surg Tech A 15(5):495–573

    Article  PubMed  Google Scholar 

  5. Lemke HU, Ratib OM, Horii SC (2005) Workflow in the operating room: review of Arrowhead 2004 seminar on imaging and informatics. In: Medical imaging: SPIE, pp 83–96

  6. Sutherland JV, van den Heuvel Willem-Jan, Ganous T, Burton MM, Kumar A (2005) Towards an intelligent hospital environment: OR of the future. Stud Health Technol Inform 118:278–312

    PubMed  Google Scholar 

  7. Lemke HU, Vannier MW (2006) The operating room and the need for an IT infrastructure and standards. Int J Comput Assist Radiol Surg 1(3):117–121

    Article  Google Scholar 

  8. Dexter F, Abouleish AE, Epstein RH, Whitten CW, Lubarsky DA (2003) Use of operating room information system data to predict the impact of reducing turnover times on staffing costs. Anesth Analg 97(4):1119–1126 table of contents

    Article  PubMed  Google Scholar 

  9. Wright JG, Roche A, Khoury AE (2010) Improving on-time surgical starts in an operating room. Can J Surg 53(3):167–170

    PubMed  PubMed Central  Google Scholar 

  10. Lalys F, Jannin P (2014) Surgical process modelling: a review. Int J Comput Assist Radiol Surg 9(3):495–511

    Article  PubMed  Google Scholar 

  11. Neumuth T, Jannin P, Schlomberg J, Meixensberger J, Wiedemann P, Burgert O (2011) Analysis of surgical intervention populations using generic surgical process models. Int J Comput Assist Radiol Surg 6(1):59–71

    Article  PubMed  Google Scholar 

  12. Neumuth T, Loebe F, Jannin P (2012) Similarity metrics for surgical process models. Artif Intell Med 54(1):15–27

    Article  PubMed  Google Scholar 

  13. Blum T, Padoy N, Feussner H, Navab N (2008) Modeling and online recognition of surgical phases using Hidden Markov Models. Med Image Comput Comput Assist Interv 11(Pt 2):627–635

    PubMed  Google Scholar 

  14. Franke S, Meixensberger J, Neumuth T (2013) Intervention time prediction from surgical low-level tasks. J Biomed Inform 46(1):152–159

    Article  PubMed  Google Scholar 

  15. Katić D, Wekerle A-L, Gärtner F, Kenngott H, Müller-Stich BP, Dillmann R, and Speidel S (2013) Ontology-based prediction of surgical events in laparoscopic surgery. In: SPIE medical imaging: SPIE, pp 86711A

  16. Forestier G, Petitjean F, Riffaud L, Jannin P (2015) Optimal sub-sequence matching for the automatic prediction of surgical tasks. In: Holmes JH, Bellazzi R, Sacchi L, Peek N (eds) Lecture notes in computer science, artificial intelligence in medicine. Springer International Publishing, Cham, pp 123–132

  17. Franke S, Schreiber E, Neumuth T (2012) A time and resource management support system for the digital operating room based on surgical process models. Int J Comput Assist Radiol Surg 7(S1):507–508

    Article  Google Scholar 

  18. Franke S, Meixensberger J, Neumuth T (2015) Multi-perspective workflow modeling for online surgical situation models. J Biomed Inform 54(1):158–166

    Article  PubMed  Google Scholar 

  19. Eijkemans MJC, van Houdenhoven M, Nguyen T, Boersma E, Steyerberg EW, Kazemier G (2010) Predicting the unpredictable: a new prediction model for operating room times using individual characteristics and the surgeon’s estimate. Anesthesiology 112(1):41–49

    Article  PubMed  Google Scholar 

  20. Ahmadi S-A, Sielhorst T, Stauder R, Horn M, Feussner H, Navab N (2006) Recovery of surgical workflow without explicit models. Med Image Comput Comput Assist Interv 9(Pt 1):420–428

  21. Dexter F, Epstein RH, Lee JD, Ledolter J (2009) Automatic updating of times remaining in surgical cases using bayesian analysis of historical case duration data and \(\backslash \)”instant messaging\(\backslash \)” updates from anesthesia providers. Anesthesia Analgesia 108(3):929–940

    Article  PubMed  Google Scholar 

  22. Stepaniak PS, Heij C, de Vries G (2010) Modeling and prediction of surgical procedure times. Stat Neerl 64(1):1–18

    Article  Google Scholar 

  23. Padoy N, Blum T, Ahmadi S-A, Feussner H, Berger M-O, Navab N (2012) Statistical modeling and recognition of surgical workflow. Med Image Anal 16(3):632–641

    Article  PubMed  Google Scholar 

  24. Neumuth T, Meissner C (2012) Online recognition of surgical instruments by information fusion. Int J Comput Assist Radiol Surg 7(2):297–304

    Article  PubMed  Google Scholar 

  25. Unger M, Chalopin C, Neumuth T (2014) Vision-based online recognition of surgical activities. Int J Comput Assist Radiol Surg 9(6):979–986

    Article  PubMed  Google Scholar 

  26. Bohn S, Lindner D, Franke S, Neumuth T, (2013) “An interoperability architecture for networked medical devices and its application to neurosurgery,” In: The 15th International Conference on Biomedical Engineering (ICBME 2013), Singapore,

  27. Forestier G, Lalys F, Riffaud L, Trelhu B, Jannin P (2012) Classification of surgical processes using dynamic time warping. J Biomed Inform 45(2):255–264

    Article  PubMed  Google Scholar 

  28. Riffaud L, Neumuth T, Morandi X, Trantakis C, Meixensberger J, Burgert O, Trelhu B, Jannin P (2010) Recording of surgical processes: a study comparing senior and junior neurosurgeons during lumbar disc herniation surgery. Neurosurgery 67(2):325–332 Suppl Operative

    PubMed  Google Scholar 

  29. Oppenheim AV, Schafer RW, Buck JR (1999) Discrete-time signal processing, 2nd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  30. Welch P (1967) The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans Audio Electroacoust 15(2):70–73

    Article  Google Scholar 

  31. Allen J Applications of the short time Fourier transform to speech processing and spectral analysis. In: IEEE international conference on acoustics, speech, and signal Processing, pp 1012–1015

  32. Hlawatsch F, Boudreaux-Bartels GF (1992) Linear and quadratic time-frequency signal representations. IEEE Signal Process Mag 9(2):21–67

    Article  Google Scholar 

  33. Orfanidis SJ (1996) Introduction to Signal Processing. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  34. Phillips CL, Parr JM, Riskin EA (2003) Signals, systems, and transforms, 3rd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  35. Mitra SK, Kaiser JF (1993) Handbook for digital signal processing. Wiley, New York

    Google Scholar 

  36. Neumuth T, Durstewitz N, Fischer M, Strauss G, Dietz A, Meixensberger J, Jannin P, Cleary K, Lemke HU, Burgert O, Horii SC, Ratib OM (2006) Structured recording of intraoperative surgical workflows. In: Medical imaging: SPIE, pp 61450A-61450A-12

  37. Hollander M, Wolfe DA, Chicken E (2013) Nonparametric statistical methods. Wiley, New Jersey

    Google Scholar 

  38. McNemar Q (1947) Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2):153–157

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

This study was kindly funded by the German Federal Ministry of Education and Research (BMBF) and the Saxon Ministry of Science and Fine Arts (SMWK) under Unternehmen Region (Grant Number 03Z1LN12) as well as by the European Regional Development Fund (ERDF) and the State of Saxony in connection with measures to support the technology sector. For this type of study, formal consent is not required. Informed consent was obtained from each participant included in the study.

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Authors and Affiliations

  1. Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Semmelweisstr. 14, 04103, Leipzig, Germany

    M. Maktabi & T. Neumuth

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  1. M. Maktabi
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  2. T. Neumuth
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Correspondence to M. Maktabi.

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The authors declare that they have no conflict of interest.

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Maktabi, M., Neumuth, T. Online time and resource management based on surgical workflow time series analysis. Int J CARS 12, 325–338 (2017). https://doi.org/10.1007/s11548-016-1474-4

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  • DOI: https://doi.org/10.1007/s11548-016-1474-4

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