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New technologies for information retrieval to achieve situational awareness and higher patient safety in the surgical operating room: the MRI institutional approach and review of the literature

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Abstract

Background

Technical progress in the operating room (OR) increases constantly, but advanced techniques for error prevention are lacking. It has been the vision to create intelligent OR systems (“autopilot”) that not only collect intraoperative data but also interpret whether the course of the operation is normal or deviating from the schedule (“situation awareness”), to recommend the adequate next steps of the intervention, and to identify imminent risky situations.

Methods

Recently introduced technologies in health care for real-time data acquisition (bar code, radiofrequency identification [RFID], voice and emotion recognition) may have the potential to meet these demands. This report aims to identify, based on the authors’ institutional experience and a review of the literature (MEDLINE search 2000–2010), which technologies are currently most promising for providing the required data and to describe their fields of application and potential limitations.

Results

Retrieval of information on the functional state of the peripheral devices in the OR is technically feasible by continuous sensor-based data acquisition and online analysis. Using bar code technologies, automatic instrument identification seems conceivable, with information given about the actual part of the procedure and indication of any change in the routine workflow. The dynamics of human activities also comprise key information. A promising technology for continuous personnel tracking is data acquisition with RFID. Emotional data capture and analysis in the OR are difficult. Although technically feasible, nonverbal emotion recognition is difficult to assess. In contrast, emotion recognition by speech seems to be a promising technology for further workflow prediction.

Conclusion

The presented technologies are a first step to achieving an increased situational awareness in the OR. However, workflow definition in surgery is feasible only if the procedure is standardized, the peculiarities of the individual patient are taken into account, the level of the surgeon’s expertise is regarded, and a comprehensive data capture can be obtained.

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References

  1. Sutherland JV, van den Heuvel W, 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 

  2. Bouarfa L, Jonker PP, Dankelman J (2010) Discovery of high-level tasks in the operating room. J Biomed Inform. [Epub ahead of print]

  3. Neumuth T, Strauß G, Meixensberger J, Lemke H, Burgert O (2006) Acquisition of process descriptions from surgical interventions. In: Database and expert systems applications. http://dx.doi.org/10.1007/11827405_59

  4. Mishra A, Catchpole K, Dale T, McCulloch P (2008) The influence of nontechnical performance on technical outcome in laparoscopic cholecystectomy. Surg Endosc 22:68–73

    Article  PubMed  CAS  Google Scholar 

  5. Padoy N, Blum T, Essa I, Feussner H, Berger MO, Navab N (2007) A boosted segmentation method for surgical workflow analysis. Med Image Comput Comput Assist Interv 10:102–109

    PubMed  CAS  Google Scholar 

  6. 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 11:627–635

    PubMed  Google Scholar 

  7. Vankipuram M, Kahol K, Cohen T, Patel VL (2009) Visualization and analysis of activities in critical care environments. AMIA Annu Symp Proc 662–666

  8. James A, Vieira D, Lo B, Darzi A, Yang GZ (2007) Eye-gaze-driven surgical workflow segmentation. Med Image Comput Comput Assist Interv 10:110–117

    PubMed  CAS  Google Scholar 

  9. Aktionsbündnis Patientensicherheit e.V (2010) http://www.aktionsbuendnis-patientensicherheit.de. Retrieved 24 May 2010

  10. Shepherd JP, Brickley MR, Jones ML (1994) Automatic identification of surgical and orthodontic instruments. Ann R Coll Surg Engl 76:59–62

    PubMed  CAS  Google Scholar 

  11. Kumar S, Swanson E, Tran T (2009) RFID in the healthcare supply chain: usage and application. Int J Health Care Qual Assur 22:67–81

    Article  PubMed  Google Scholar 

  12. Iadanza E, Dori F (2009) Custom active RFID solution for children tracking and identifying in a resuscitation ward. Conf Proc IEEE Eng Med Biol Soc 5223–5226

  13. Rogers A, Jones E, Oleynikov D (2007) Radiofrequency identification (RFID) applied to surgical sponges. Surg Endosc 21:1235–1237

    Article  PubMed  CAS  Google Scholar 

  14. Green A, Duthie HL, Young HL, Peters TJ (1990) Stress in surgeons. Br J Surg 77:1154–1158

    Article  PubMed  CAS  Google Scholar 

  15. Sargent MC, Sotile W, Sotile MO, Rubash H, Barrack RL (2004) Stress and coping among orthopaedic surgery residents and faculty. J Bone Joint Surg Am 86:1579–1586

    PubMed  Google Scholar 

  16. Granholm E, Steinhauer SR (2004) Pupillometric measures of cognitive and emotional processes. Int J Psychophysiol 52:1–6

    Article  PubMed  Google Scholar 

  17. Schuller B (2008) Sprachkommunikation (ITG-FB 211). VDE Verlag, Berlin

    Google Scholar 

  18. Schneider A (2006) Intraoperative workflow analyse bei minimal invasiven Eingriffen. Master’s thesis. TU München

  19. Kranzfelder M, Schneider A, Blahusch G, Schaaf H, Feussner H (2009) Feasibility of opto-electronic surgical instrument identification. Minim Invasive Ther Allied Technol 18:253–258

    Article  PubMed  Google Scholar 

  20. Gillen S, Wilhelm D, Meining A, Fiolka A, Doundoulakis E, Schneider A, von Delius S, Friess H, Feussner H (2009) The “ELITE” model: construct validation of a new training system for natural orifice transluminal endoscopic surgery (NOTES). Endoscopy 41:395–399

    Article  PubMed  CAS  Google Scholar 

  21. Schuller B, Rigoll G, Can S, Feussner H (2008) Emotion-sensitive speech control for human-robot interaction in minimal invasive surgery. www.ieeexplore.ieee.org. Retrieved 15 May 2010

  22. Leotta DF (2004) An efficient calibration method for freehand 3-D ultrasound imaging systems. Ultrasound Med Biol 30:999–1008

    Article  PubMed  Google Scholar 

  23. Lindseth F, Tangen GA, Langø T, Bang J (2003) Probe calibration for freehand 3-D ultrasound. Ultrasound Med Biol 29:1607–1623

    Article  PubMed  Google Scholar 

  24. Draper KJ, Blake CC, Gowman L, Downey DB, Fenster A (2000) An algorithm for automatic needle localization in ultrasound-guided breast biopsies. Med Phys 27:1971–1979

    Article  PubMed  CAS  Google Scholar 

  25. Ortmaier T, Gröger M, Boehm DH, Falk V, Hirzinger G (2005) Motion estimation in beating heart surgery. IEEE Trans Biomed Eng 52:1729–1740

    Article  PubMed  Google Scholar 

  26. Novotny PM, Stoll JA, Vasilyev NV, del Nido PJ, Dupont PE, Zickler TE, Howe RD (2007) GPU-based real-time instrument tracking with three-dimensional ultrasound. Med Image Anal 11:458–464

    Article  PubMed  Google Scholar 

  27. Smith A, Offodile F (2002) Information management of automatic data capture: an overview of technical developments. http://www.emeraldinsight.com/Insight/ViewContentServlet?Filename=/published/emeraldfulltextarticle/pdf/2760560502_ref.html. Retrieved 15 May 2010

  28. Macario A, Morris D, Morris S (2006) Initial clinical evaluation of a handheld device for detecting retained surgical gauze sponges using radiofrequency identification technology. Arch Surg 141:659–662

    Article  PubMed  Google Scholar 

  29. Kreysa U (2006) Bar coding of medical devices. J Med Dev Reg. http://scholar.google.de/scholar?q=Bar+coding+of+medical+devices.+J+Med+Dev+Reg+2006%3BFeb.&hl=de&as_sdt=0&as_vis=1&oi=scholart. Retrieved 15 May 2010

  30. Neuenschwander M, Cohen MR, Vaida AJ, Patchett JA et al (2005) The 2D data matrix bar code. Comput Control Eng 16:39

    Article  Google Scholar 

  31. Greenberg CC, Diaz-Flores R, Lipsitz SR, Regenbogen SE, Mulholland L, Mearn F, Rao S, Toidze T, Gawande AA (2008) Bar coding surgical sponges to improve safety: a randomized controlled trial. Ann Surg 247:612–616

    Article  PubMed  Google Scholar 

  32. Jones ML, Shepherd JP, Brickley MR (1995) A technique for the computerized identification of orthodontic instruments. Br J Orthod 22:269–271

    PubMed  CAS  Google Scholar 

  33. Neuenschwander M, Cohen MR, Vaida AJ, Patchett JA, Kelly J, Trohimovich B (2003) Practical guide to bar coding for patient medication safety. Am J Health Syst Pharm 60:768–779

    PubMed  Google Scholar 

  34. Houni K, Sawaya W, Delignon Y (2008) One-dimensional bar code reading: an information theoretic approach. Appl Opt 47:1025–1036

    Article  PubMed  Google Scholar 

  35. Schwaitzberg SD (2006) The emergence of radiofrequency identification tags: applications in surgery. Surg Endosc 20:1315–1319

    Article  PubMed  CAS  Google Scholar 

  36. Kranzfelder M, Schneider A, Feussner H (2208) Corpora aliena: can RFID solve the problem? http://www5.informatik.uni-erlangen.de/our-team/hoeller-kurt/publications. Retrieved 15 May 2010

  37. Allaf ME, Jackman SV, Schulam PG, Cadeddu JA, Lee BR, Moore RG, Kavoussi LR (1998) Laparoscopic visual field: voice vs foot pedal interfaces for control of the AESOP robot. Surg Endosc 12:1415–1418

    Article  PubMed  CAS  Google Scholar 

  38. Wichert A, Marcos-Suarez P, Vereczkei A, Seitz T, Bubb H, Feussner H (2004) Improvement of the ergonomic situation in the integrated operating room for laparoscopic operations. http://www.sciencedirect.com. Retrieved 15 May 2010

  39. Eyben F, Wöllmer M, Schuller B (2009) openEAR: introducing the Munich open-source emotion and affect recognition toolkit. In: 4th International HUMAINE association conference on affective computing and intelligent interaction (ACII 2009), Amsterdam, The Netherlands

  40. Schuller B, Can S, Feussner H, Woellmer M, Arsiæ D, Hörnler B (2009) Speech control in surgery: a field analysis and strategies. In: International conference on multimedia and expo (ICME 2009), New York, NY

  41. De Lemos J, Sadeghnia G, Ólafsdóttir I, Jensen O (2008) Measuring emotions using eye tracking. In: Spink A (ed) 6th International conference on methods and techniques in behavioral research. Maastricht, The Netherlands

  42. Eckmann C, Olbrich G, Shekarriz H, Bruch H (2003) The empty OR: process analysis and a new concept for flexible and modular use in minimal invasive surgery. Surg Technol Int 11:45–49

    PubMed  Google Scholar 

  43. Reijnen MMPJ, Zeebregts CJ, Meijerink WJHJ (2005) Future of operating rooms. Surg Technol Int 14:21–27

    PubMed  Google Scholar 

  44. Marohn MR, Hanly EJ (2004) Twenty-first-century surgery using twenty-first century technology: surgical robotics. Curr Surg 61:466–473

    Article  PubMed  Google Scholar 

  45. Feussner H (2003) The operating room of the future: a view from Europe. Semin Laparosc Surg 10:149–156

    PubMed  CAS  Google Scholar 

  46. Yule S, Flin R, Paterson-Brown S, Maran N (2006) Nontechnical skills for surgeons in the operating room: a review of the literature. Surgery 139:140–149

    Article  PubMed  CAS  Google Scholar 

  47. Gawande AA, Zinner MJ, Studdert DM, Brennan TA (2003) Analysis of errors reported by surgeons at three teaching hospitals. Surgery 133:614–621

    Article  PubMed  Google Scholar 

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Disclosures

Michael Kranzfelder, Armin Schneider, Sonja Gillen, and Hubertus Feussner have no conflicts of interest or financial ties to disclose.

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Authors and Affiliations

  1. Department of Surgery, Technical University of Munich, 81675, Munich, Germany

    Michael Kranzfelder, Sonja Gillen & Hubertus Feussner

  2. Workgroup MITI (Minimally Invasive Therapy and Intervention), Klinikum rechts der Isar (MRI), Technical University of Munich, 81675, Munich, Germany

    Michael Kranzfelder, Armin Schneider, Sonja Gillen & Hubertus Feussner

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  1. Michael Kranzfelder
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  2. Armin Schneider
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  3. Sonja Gillen
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  4. Hubertus Feussner
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Correspondence to Hubertus Feussner.

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Kranzfelder, M., Schneider, A., Gillen, S. et al. New technologies for information retrieval to achieve situational awareness and higher patient safety in the surgical operating room: the MRI institutional approach and review of the literature. Surg Endosc 25, 696–705 (2011). https://doi.org/10.1007/s00464-010-1239-z

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