Analysis of surgical intervention populations using generic surgical process models

  • Thomas NeumuthEmail author
  • Pierre Jannin
  • Juliane Schlomberg
  • Jürgen Meixensberger
  • Peter Wiedemann
  • Oliver Burgert
Original Article



According to differences in patient characteristics, surgical performance, or used surgical technological resources, surgical interventions have high variability. No methods for the generation and comparison of statistical ‘mean’ surgical procedures are available. The convenience of these models is to provide increased evidence for clinical, technical, and administrative decision-making.


Based on several measurements of patient individual surgical treatments, we present a method of how to calculate a statistical ‘mean’ intervention model, called generic Surgical Process Model (gSPM), from a number of interventions. In a proof-of-concept study, we show how statistical ‘mean’ procedure courses can be computed and how differences between several of these models can be quantified. Patient individual surgical treatments of 102 cataract interventions from eye surgery were allocated to an ambulatory or inpatient sample, and the gSPMs for each of the samples were computed. Both treatment strategies are exemplary compared for the interventional phase Capsulorhexis.


Statistical differences between the gSPMs of ambulatory and inpatient procedures of performance times for surgical activities and activity sequences were identified. Furthermore, the work flow that corresponds to the general recommended clinical treatment was recovered out of the individual Surgical Process Models.


The computation of gSPMs is a new approach in medical engineering and medical informatics. It supports increased evidence, e.g. for the application of alternative surgical strategies, investments for surgical technology, optimization protocols, or surgical education. Furthermore, this may be applicable in more technical research fields, as well, such as the development of surgical workflow management systems for the operating room of the future.


Surgical workflow Surgical Process Model Health care evaluation mechanisms Cataract surgery 


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  1. 1.
    Neumuth T, Jannin P, Strauss G, Meixensberger J, Burgert O (2009) Validation of knowledge acquisition for surgical process models. J Am Med Inform Assoc 16(1): 72–80CrossRefPubMedGoogle Scholar
  2. 2.
    Jannin P, Raimbault M, Morandi X, Riffaud L, Gibaud B (2003) Model of surgical procedures for multimodal image-guided neurosurgery. Comput Aided Surg 8(2): 98–106CrossRefPubMedGoogle Scholar
  3. 3.
    Jannin P, Morandi X (2007) Surgical models for computer-assisted neurosurgery. Neuroimage 37(3): 783–791CrossRefPubMedGoogle Scholar
  4. 4.
    Ahmadi S, 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(1): 420–428PubMedGoogle Scholar
  5. 5.
    James A, Vieira D, Lo B, Darzi A, Yang GZ (2007) Eye-gaze driven surgical workflow segmentation. Med Image Comput Comput Assist Interv 10(2): 110–117PubMedGoogle Scholar
  6. 6.
    Münchenberg JE, Brief J, Raczkowsky J, Wörn H, Hassfeld S, Mühling J (2001) Operation planning of robot supported surgical Interventions. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems. IEEE Computer Society, pp 547–552Google Scholar
  7. 7.
    Mehta NY, Haluck RS, Frecker MI, Snyder AJ (2002) Sequence and task analysis of instrument use in common laparoscopic procedures. Surg Endosc 16(2): 280–285CrossRefPubMedGoogle Scholar
  8. 8.
    Casaletto JA, Rajaratnam V (2004) Surgical process re-engineering: carpal tunnel decompression—a model. Hand Surg 9(1): 19–27CrossRefPubMedGoogle Scholar
  9. 9.
    Malik R, White PS, Macewen CJ (2003) Using human reliability analysis to detect surgical error in endoscopic DCR surgery. Clin Otolaryngol Allied Sci 28(5): 456–460CrossRefPubMedGoogle Scholar
  10. 10.
    den Boer KT, Straatsburg IH, Schellinger AV, de Wit LT, Dankelman J, Gouma DJ (1999) Quantitative analysis of the functionality and efficiency of three surgical dissection techniques: a time-motion analysis. J Laparoendosc Adv Surg Tech A 9(5): 389–395CrossRefPubMedGoogle Scholar
  11. 11.
    Strauss G, Fischer M, Meixensberger J, Falk V, Trantakis C, Winkler D et al (2006) Workflow analysis to assess the efficiency of intraoperative technology using the example of functional endoscopic sinus surgery. HNO 54(7): 528–535CrossRefPubMedGoogle Scholar
  12. 12.
    MacKenzie CL, Ibbotson A, Cao CGL, Lomax A (2001) Hierarchical decomposition of laparoscopic surgery: a human factors approach to investigating the operating room environment 10(3):121–128Google Scholar
  13. 13.
    Meng F, D’Avolio LW, Chen AA, Taira RK, Kangarloo H (2005) Generating models of surgical procedures using UMLS concepts and multiple sequence alignment. AMIA Annu Symp Proc 520–524Google Scholar
  14. 14.
    Blum T, Padoy N, Feußner H, Navab N (2008) Workflow mining for visualization and analysis of surgeries. Int J Comput Assist Radiol Surg 3(5): 379–386CrossRefGoogle Scholar
  15. 15.
    Westbrook JI, Ampt A (2009) Design, application and testing of the Work Observation Method by Activity Timing (WOMBAT) to measure clinicians’ patterns of work and communication. Int J Med Inform 78(Suppl 1): 25–33CrossRefGoogle Scholar
  16. 16.
    Cook JE, Wolf AL (1995) Automating process discovery through event-data analysis. In: ICSE’95: Proceedings of the 17th international conference on software engineering, New York, pp 73–82Google Scholar
  17. 17.
    Agrawal R, Gunopulos D, Leymann F (1998) Mining process models from workflow logs. In: Ramos I, Alonso G, Schek H, Saltor F (eds) Advances in database technology—EDBT’98, pp 469–483Google Scholar
  18. 18.
    Schimm G (2004) Mining exact models of concurrent workflows 53(3):265-281Google Scholar
  19. 19.
    de Medeiros AKA, Weijters AJMM, van der Aalst WMP (2005) Genetic process mining: a basic approach and its challenges. In: Workshop on Business Process Intelligence (BPI), NancyGoogle Scholar
  20. 20.
    Aalst WMP, Dongena BFV, Herbst J, Marustera L, Schimm G, Weijters AJMM (2003) Workflow mining: a survey of issues and approaches 47(2):237–267Google Scholar
  21. 21.
    AWMF (2008) Leitlinien für Diagnostik und Therapie. (German clinical guidelines) [Internet]. Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften e. V. Available from:
  22. 22.
    AHRQ (2008) Agency for Health Care Research and Quality: National Guideline Clearinghouse [Internet]. Available from:
  23. 23.
    Neumuth T, Durstewitz N, Fischer M, Strauss G, Dietz A, Meixensberger J et al (2006) Structured recording of intraoperative surgical workflows. In: Horii SC, Ratib OM (eds) SPIE medical imaging 2006—PACS and imaging informatics: progress in biomedical optics and imaging. Bellingham, WA CID 61450AGoogle Scholar
  24. 24.
    Cleary K, Kinsella A, Mun SK (2005) OR2020 Workshop report: operating room of the future. In: Lemke HU, Inamura K, Doi K, Vannier MW, Farman AG (eds) Proceedings of the 19th Computer Assisted Radiology and Surgery CARS, pp 832–838Google Scholar
  25. 25.
    Archer T, Macario A (2006) The drive for operating room efficiency will increase quality of patient care. Curr Opin Anaesthesiol 19(2): 171–176CrossRefPubMedGoogle Scholar
  26. 26.
    Schuster M, Wicha LL, Fiege M, Goetz AE (2007) Utilization rates and turnover times as indicators of OR workflow efficiency. Anaesthesist 56(10): 1060–1066CrossRefPubMedGoogle Scholar

Copyright information

© CARS 2010

Authors and Affiliations

  • Thomas Neumuth
    • 1
    Email author
  • Pierre Jannin
    • 2
    • 3
    • 4
  • Juliane Schlomberg
    • 5
  • Jürgen Meixensberger
    • 1
    • 6
  • Peter Wiedemann
    • 5
  • Oliver Burgert
    • 1
  1. 1.Innovation Center Computer Assisted Surgery (ICCAS)Universität LeipzigLeipzigGermany
  2. 2.Faculty of MedicineINSERM, U746RennesFrance
  3. 3.VisAGeS Unit/ProjectINRIARennesFrance
  4. 4.CNRS, UMR 6074, IRISAUniversity of Rennes IRennesFrance
  5. 5.Department of OphthalmologyUniversity Hospital LeipzigLeipzigGermany
  6. 6.Department of NeurosurgeryUniveristy Hospital LeipzigLeipzigGermany

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