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Methods Inf Med 2012; 51(05): 371-382
DOI: 10.3414/ME11-01-0093
Original Articles
Schattauer GmbH

Surgical Workflow Management Schemata for Cataract Procedures

Process Model-based Design and Validation of Workflow Schemata
T. Neumuth
1   Universität Leipzig, Innovation Center for Computer Assisted Surgery (ICCAS), Leipzig, Germany:
,
P. Liebmann
1   Universität Leipzig, Innovation Center for Computer Assisted Surgery (ICCAS), Leipzig, Germany:
,
P. Wiedemann
2   University Hospital Leipzig, Department of Ophthalmology, Leipzig, Germany
,
J. Meixensberger
3   University Hospital Leipzig, Department of Neurosurgery, Leipzig, Germany, Universität Leipzig, Innovation Center for Computer Assisted Surgery (ICCAS), Leipzig, Germany
› Author Affiliations
Further Information

Publication History

received:18 November 2011

accepted:27 April 2012

Publication Date:
20 January 2018 (online)

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Summary

Objective: Workflow guidance of surgical activities is a challenging task. Because of variations in patient properties and applied surgical techniques, surgical processes have a high variability. The objective of this study was the design and implementation of a surgical workflow management system (SWFMS) that can provide a robust guidance for surgical activities. We investigated how many surgical process models are needed to develop a SWFMS that can guide cataract surgeries robustly.

Methods: We used 100 cases of cataract surgeries and acquired patient-individual surgical process models (iSPMs) from them. Of these, randomized subsets iSPMs were selected as learning sets to create a generic surgical process model (gSPM). These gSPMs were mapped onto workflow nets as work-flow schemata to define the behavior of the SWFMS. Finally, 10 iSPMs from the disjoint set were simulated to validate the workflow schema for the surgical processes. The measurement was the successful guidance of an iSPM.

Results: We demonstrated that a SWFMS with a workflow schema that was generated from a subset of 10 iSPMs is sufficient to guide approximately 65% of all surgical processes in the total set, and that a subset of 50 iSPMs is sufficient to guide approx. 80% of all processes.

Conclusion: We designed a SWFMS that is able to guide surgical activities on a detailed level. The study demonstrated that the high inter-patient variability of surgical processes can be considered by our approach.

Keywords

Operative surgical procedures - workflow - computer-assisted decision making - computer-assisted surgery - surgical process model
 

  • References

  • 1 Lemke HU, Vannier MW. The operating room and the need for an IT infrastructure and standards. Int J Comput Assisted Radiol Surg 2006; 1: 117-112.
    CrossrefPubMedGoogle Scholar
  • 2 Cleary K, Kinsella A. OR2020: the operating room of the future. J Laparoendosc Adv Surg Tech A 2005; 15 495 497-573.
    PubMedGoogle Scholar
  • 3 Sandberg WS, Ganous TJ, Steiner C. Setting a research agenda for perioperative systems design. Semin Laparosc Surg 2003; 10: 57-70.
    PubMedGoogle Scholar
  • 4 Deinhardt M. Manipulators and integrated OR systems - requirements and solutions. Minim Invasive Ther Allied Technol 2003; 12: 284-292.
    CrossrefPubMedGoogle Scholar
  • 5 Patkin M. What surgeons want in operating rooms. Minim Invasive Ther Allied Technol 2003; 12: 256-262.
    CrossrefPubMedGoogle Scholar
  • 6 Jolesz FA, Shtern F. The operating room of the future. Report of the National Cancer Institute Workshop “Imaging-Guided Stereotactic Tumor Diagnosis and Treatment”. Invest Radiol 1992; 27: 326-328.
    CrossrefPubMedGoogle Scholar
  • 7 Jablonski S, Bussler C. Workflow-Management: Modelling Concepts, Architecture and Implementation. London: Thompson Computer Press; 1996
    Google Scholar
  • 8 van der Aalst WMP, van Hee K. Workflow Management: Models, Methods, and Systems. Mit Press: Cambridge; 2004
    Google Scholar
  • 9 Sutherland JV, van den Heuvel WJ, Ganous T, Burton MM, Kumar A. Towards an intelligent hospital environment: OR of the future. Stud Health Technol Inform 2005; 118: 278-312.
    PubMedGoogle Scholar
  • 10 Archer T, Macario A. The drive for operating room efficiency will increase quality of patient care. Curr Opin Anaesthesiol 2006; 19: 171-176.
    CrossrefPubMedGoogle Scholar
  • 11 Neumuth T, Jannin P, Strauss G, Meixensberger J, Burgert O. Validation of knowledge acquisition for surgical process models. J Am Med Inform Assoc 2009; 16: 72-80.
    CrossrefPubMedGoogle Scholar
  • 12 AHRQ. Agency for Health Care Research and Quality: National Guideline Clearinghouse [Internet]. Available from. http://www.guideline.gov. Access date April 23th 2012
    Google Scholar
  • 13 AWMF. Leitlinien für Diagnostik und Therapie. (German clinical guidelines) [Internet]. Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften e. V. Available from. http:// www.awmf-leitlinien.de/. Access date April 23 2012
    Google Scholar
  • 14 Dexter F, Dexter E, Ledolter J. Influence of procedure classification on process variability and parameter uncertainty of surgical case durations. Anesth Analg 2010; 110: 1155-1163.
    CrossrefPubMedGoogle Scholar
  • 15 Mans RS, Aalst WMP, Russell NC, Bakker PJM, Moleman AJ. Process-Aware Information System Development for the Healthcare Domain - Consistency, Reliability, and Effectiveness. In Rinderle-Ma S, Sadig S, Leymann F. eds Business Process Management Workshops: BPM 2009 International Workshops. 2009: 635-646.
    Google Scholar
  • 16 Quaglini S, Stefanelli M, Lanzola G, Caporusso V, Panzarasa S. Flexible guideline-based patient careflow systems. Artif Intell Med 2001; 22: 65-80.
    CrossrefPubMedGoogle Scholar
  • 17 Quaglini S, Stefanelli M, Cavallini A, Micieli G, Fassino C, Mossa C. Guideline-based careflow systems. Artif Intell Med 2000; 20: 5-22.
    CrossrefPubMedGoogle Scholar
  • 18 Greiner U, Mueller R, Rahm E, Ramsch J, Heller B, Loeffler M. AdaptFlow: protocol-based medical treatment using adaptive workflows. Methods Inf Med 2005; 44: 80-88.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 19 Haux R, Seggewies C, Baldauf-Sobez W, Kullmann P, Reichert H, Luedecke L, Seibold H. Soarian - workflow management applied for health care. Methods Inf Med 2003; 42: 25-36.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 20 Latoszek-Berendsen A, Tange H, van den Herik HJ. Hasman A. From clinical practice guidelinesto computer-interpretable guidelines. A litera-ture overview. Methods Inf Med 2010; 49: 550-570.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 21 Kyriacou E, Fakas G, Pavlaki V. A completely decentralized workflow management system for the support of emergency telemedicine and patient monitoring. Conf Proc IEEE Eng Med Biol Soc 2006; 1: 5663-5666.
    PubMedGoogle Scholar
  • 22 Sutherland J, van den Heuvel W. Towards an Intelligent Hospital Environment: Adaptive Workflow in the OR of the Future. In: Systems Sciences. 2006: 100b
    Google Scholar
  • 23 Mans RS, Russell NC, van der Aalst WMP, Bakker PJM, Molemann AJ. Simulation to Analyze the Impact of a Schedule-aware Workflow Management System. Simulation 2010; 86: 519-541.
    CrossrefPubMedGoogle Scholar
  • 24 Poulymenopoulou M, Malamateniou F, Vassilacopoulos G. Emergency healthcare process automation using workflow technology and web services. Med Inform Internet Med 2003; 28: 195-207.
    CrossrefPubMedGoogle Scholar
  • 25 Zhang J, Lu X, Nie H, Huang Z, van der Aalst WMP. Radiology information system: a workflow-based approach. Int J Comput Assist Radiol Surg 2009; 4: 509-516.
    CrossrefPubMedGoogle Scholar
  • 26 Halsted MJ, Froehle CM. Design, implementation, and assessment of a radiology workflow management system. Am J Roentgenol 2008; 191: 321-327.
    CrossrefPubMedGoogle Scholar
  • 27 Reichert M, Dadam P, Mangold R, Kreienberg R. [Computer support of workflow in the hospital: concepts, technology and application]. Zentralbl Gynaekol 2000; 122: 53-67.
    PubMedGoogle Scholar
  • 28 Panzarasa S, Quaglini S, Cavallini A, Micieli G, Pernice C, Passina M, Stefanelli M. Workflow technology to enrich a computerized clinical chart with decision support facilities. AMIA Annu Symp Proc. 2006: 619-623.
    Google Scholar
  • 29 Jung W-R, Youn C-H, Kim H, Kim D, Hazemi F, Shim EB. Policy-based hybrid workflow management system for heart disease identification. In: Proceedings of the 15th Asia-Pacific conference on Communications. 2009: 826-829.
    Google Scholar
  • 30 Zai AH, Grant RW, Estey G, Lester WT, Andrews CT, Yee R, Mort E, Chueh HC. Lessons from implementing a combined workflow-informatics system for diabetes management. J Am Med Inform Assoc 2008; 15: 524-533.
    CrossrefPubMedGoogle Scholar
  • 31 Hansen TR, Bardram JE. Applying mobile and pervasive computer technology to enhance coordination of work in a surgical ward. Stud Health Technol Inform 2007; 129: 107-111.
    PubMedGoogle Scholar
  • 32 Agarwal S, Joshi A, Finin T, Yesha Y, Ganous T. A pervasive computing system for the operating room of the future. Mob Netw Appl 2007; 12: 215-228.
    CrossrefPubMedGoogle Scholar
  • 33 Prinyapol N, Lau SK, Fan JP-O. A Dynamic Nursing Workflow Management System: A Thailand Hospital Scenario. In. Huang X, Ao S-I, Castillo O. (eds) Intell Automation Comput Eng. 2009: 489-501.
    Google Scholar
  • 34 Fissell K. Workflow-based approaches to neuroimaging analysis. Methods Mol Biol 2007; 401: 235-266.
    PubMedGoogle Scholar
  • 35 Krefting D, Vossberg M, Hoheisel A, Tolxdorf T. Simplified implementation of medical image processing algorithms into a grid using a workflow management system. Future Generation Comput Syst 2010; 26: 681-684.
    CrossrefPubMedGoogle Scholar
  • 36 Bardram JE, N'rskov N. A context-aware patient safety system for the operating room. In: UbiComp ’08 Proceedings of the 10th international conference on Ubiquitous computing. 2008: 272-281.
    Google Scholar
  • 37 Riedl S. Modern operating room management in the workflow of surgery. Spectrum of tasks and challenges of the future. Anaesthesist 2003; 52: 957-963.
    CrossrefPubMedGoogle Scholar
  • 38 Gebhard E, Hartwig E, Isenmann R, Triebsch K, Gerstner H, Bailer M, Brinkmann A. OR-manager: surgeon or anesthesiologist? An interdisciplinary study. Anaesthesist 2003; 52: 1062-1067.
    CrossrefPubMedGoogle Scholar
  • 39 Münchenberg JE, Brief J, Raczkowsky J, Wörn H, Hassfeld S, Mühling J. Operation planning of robot supported surgical Interventions. In: Proceedings of IEEE/RSJ international conference on intelligent robots and system. 2001: 547-552.
    Google Scholar
  • 40 Dickhaus CF, Burghart C, Tempany C, D’Amico A, Haker S, Kikinis R, Woern H. Workflow modeling and analysis of computer guided prostate brachytherapy under MR imaging control. Stud Health Technol Inform 2004; 98: 72-74.
    PubMedGoogle Scholar
  • 41 Qi J, Jiang Z, Zhang G, Miao R, Su Q. A Surgical Management Information System Driven by Workflow. In: IEEE International Conference on Service Operations and Logistics, and Informatics. 2006: 1014-1018.
    Google Scholar
  • 42 Maruster L, van der Aalst WMP, Weijters T, van der Bosch A, Daelemans W. Automated Discovery of Workflow Models from Hospital Data. In: Proceedings of the 13th BelgiumNetherlands Conference on Artificial Intelligence BNAIC. 2001: 183-190.
    Google Scholar
  • 43 Barkaoui K, Dechambre P, Hachicha R. Verification and Optimisation of an Operating Room Workflow. In: 35th Annual Hawaii International Conference on System Sciences. 2002: 210
    Google Scholar
  • 44 Ceglowski A, Churilov L, Wassertheil J. Knowledge Discovery through Mining Emergency Department Data. In: 38th Annual Hawaii International Conference on System Sciences. 2005: 142c
    Google Scholar
  • 45 Mans R, Schonenberg H, Leonardi G, Panzarasa S, Cavallini A, Quaglini S, van der Aalst WMP. Process mining techniques: an application to stroke care. Stud Health Technol Inform 2008; 136: 573-578.
    PubMedGoogle Scholar
  • 46 Mans RS, Schonenberg MH, Song M, van der Aalst WMP, Bakker PJM. Application of Process Mining in Healthcare - A Case Study in a Dutch Hospital. In. Fred A, Filipe J, Gamboa H. (eds) Biomedical Engineering Systems and Technologies. 2009: 425-438.
    Google Scholar
  • 47 Lang M, Bürkle T, Laumann S, Prokosch HU. Process mining for clinical workflows: challenges and current limitations. Stud Health Technol Inform 2008; 136: 229-234.
    PubMedGoogle Scholar
  • 48 Zhou W, Piramuthu S. Healthcare Process Mining with RFID. In Rinderle-Ma S, Sadiq S, Leymann F. (eds) Business Process Management Workshops: BPM 2009 International Workshops. 2010: 405-411.
    Google Scholar
  • 49 Fernandez-Llatas C, Meneu T, Benedi JM, Traver V. Activity-based process mining for clinical pathways computer aided design. Conf Proc IEEE Eng Med Biol Soc 2010; 6178-6181.
    PubMedGoogle Scholar
  • 50 Workflow Management Coalition. Terminology and Glossary — Document Number WFMC-TC-1011 [Internet], 1999. Available from. Access date April http://www.wfmc.org/standards/docs/TC-1011_term_glossary_v3. pdf. 23 2012
    Google Scholar
  • 51 Neumuth D, Loebe F, Herre H, Neumuth T. Modeling Surgical Processes: A Four-Level Translational Approach. Artif Intell Med 2011; 51: 147-161.
    CrossrefPubMedGoogle Scholar
  • 52 Neumuth T, Jannin P, Schlomberg J, Meixensberger J, Wiedemann P. Analysis of Surgical Intervention Populations Using Generic Surgical Process Models. Int J Comput Assisted Radiol Surg 2011; 6: 59-71.
    CrossrefPubMedGoogle Scholar
  • 53 Neumuth T, Trantakis C, Riffaud L, Strauss G, Meixensberger J, Burgert O. Assessment of technical needs for surgical equipment by Surgical Process Models. Minim Invasive Ther Allied Technol 2009; 18: 841-849.
    PubMedGoogle Scholar
  • 54 Krauss A, Münsterer OJ, Neumuth T, Wachowiak R, Donaubauer B, Korb W, Burgert O. Workflow analysis of laparoscopic Nissen fundoplication in infant pigs - a model for surgical feedback and training. J Laparoendosc Adv Surg Tech 2009; 19: 117-122.
    CrossrefPubMedGoogle Scholar
  • 55 Seeburger J, Leontjev S, Neumuth T, Noack T, Höbartner M, Misfeld M, Borger MA, Mohr FW. Trans-apical beating-heart implantation of neo-chordae to mitral valve leaflets: results of an acute animal study. Eur J Cardiothorac Surg 2011. Eur J Cardiothorac Surg 2012; 41 (01) 173-176.
    CrossrefPubMedGoogle Scholar
  • 56 Neumuth T, Krauss A, Meixensberger J, Muensterer OP. Impact quantification of the DaVinci Telemanipulator system on the surgical workflow using resource impact profiles. Int J Med Robot 2011; 7: 156-164.
    CrossrefPubMedGoogle Scholar
  • 57 Padoy N, Blum T, Ahmadi SA, Feussner H, Berger MO, Navab N. Statistical modeling and recognition of surgical workflow. Med Image Anal 2012; 16 (03) 632-641.
    CrossrefPubMedGoogle Scholar
  • 58 Lalys F, Riffaud L, Morandi X, Jannin P. Automatic phases recognition in pituitary surgeries by microscope images classification. In: Proceedings of the 1st international conference on Information processing in computer-assisted interventions. 2010: 34-44.
    Google Scholar
  • 59 Neumuth T, Strauss G, Meixensberger J, Lemke HU, Burger O. Acquisition of process descriptions from surgical interventions, In. Bressan S, Küng J, Wagner R. (eds) Database and Expert Systems Applications. Springer: 2006: 602-611.
    Google Scholar
  • 60 German Federal Statistical Office. International Classification of Diseases 10: National health statistics for 2007, Statistisches Bundesamt. 2008
    Google Scholar
  • 61 Neumuth T, Jannin P, Schlomberg J, Meixensberger J, Wiedemann P. Analysis of Surgical Intervention Populations Using Generic Surgical Process Models. Int J Comput Assisted Radiol Surg 2011; 6: 59-71.
    CrossrefPubMedGoogle Scholar
  • 62 van der Aalst WMP. Verification of workflow nets, In. Azema P, Balbo G. (eds) Proceedings of the 18th International Conference on Application and Theory of Petri Nets. 1997: 407-426.
    Google Scholar
  • 63 van der Aalst WMP, ter Hofstede AHM. YAWL: yet another workflow language. Information Systems 2005; 30: 245-275.
    CrossrefPubMedGoogle Scholar
  • 64 PostgreSQL Global Development Group . PostgreSQL: The world’s most advanced open source database [Internet], 2010. Available from. http:// www.postgresql.org/, Access date April 23 2012
    PubMedGoogle Scholar
  • 65 Workflow Management Coalition. The Workflow Reference Model. Document Number WFMC-TC-1003, [Internet] 1995. Available from. www.wfmc.org/standards/docs/tc003v11.pdf Access date April 23, 2012.
    PubMedGoogle Scholar
  • 66 Foundation R. The R Project for Statistical Computing [Internet] 2010, Available from. http://www.r-project.org/. Access date April 23 2012
    PubMedGoogle Scholar
  • 67 SPSS Inc. SPSS, Data Mining, Statistical Analysis Software, Predictive Analysis, Decision Support Systems [Internet], 2012. Available from. http:// www.spss.com/de/, Access date April 23 2012
    PubMedGoogle Scholar
  • 68 van der Aalst WMP, van Dongen BF, Herbst J, Maruster L, Schimm G, Weijters AJMM. Workflow mining: A survey of issues and approaches. Data Knowl Eng 2003; 47: 237-267.
    CrossrefPubMedGoogle Scholar
  • 69 Cook JE, Wolf AL. Automating process discovery through event-data analysis. In: ICSE ’95: Proceedings of the 17th international conference on software engineering. New York: 1995: 73-82.
    Google Scholar
  • 70 Agrawal R, Gunopulos D, Leymann F. Mining processmodels from workflow logs. In. Ramos I, Alonso G, Schek H, Saltor F. (eds) Advances in database technology - EDBT’98. 1998: 469-483.
    Google Scholar
  • 71 Schimm G. Mining exact models of concurrent workflows. Comput Industry 2004; 53: 265-281.
    CrossrefPubMedGoogle Scholar
  • 72 de Medeiros AKA, Weijters AJMM, van der Aalst WMP. Genetic process mining: a basic approach and its challenges. In: BPM Workshops. 2005: 203-215.
    Google Scholar
  • 73 Lalys F, Riffaud L, Bouget D, Jannin P. An application-dependent framework for the recognition of high-level surgical tasks in the OR. In: 14th Inter-national Conference on Medical Image Computing and Computer Assisted Intervention. 2011
    PubMedGoogle Scholar
  • 74 Neumuth T, Meißner C. Online recognition of surgical instruments by information fusion. Int J Comput Assist Radiol Surg 2012; 7 (02) 297-304.
    CrossrefPubMedGoogle Scholar
  • 75 Cheung CYL, Li H, Lamoureux EL, Mitchell P, Wang JJ, Tan AG, Johari LK, Liu J, Lim JH, Aung T, Wong TY. Validity of a new computer-aided diagnosis imaging program to quantify nuclear cataract from slit-lamp photographs. Invest Ophthalmol Vis Sci 2011; 52: 1314-1319.
    CrossrefPubMedGoogle Scholar
  • 76 Ligabue EA, Giordano C. Interpretation of aberrometry measurements in cataract surgery. J Refract Surg 2007; 23: S996-1004.
    CrossrefPubMedGoogle Scholar
  • 77 Maeda N. Clinical applications of wavefront aberrometry - a review. Graefes Arch Clin Exp Ophthalmol 2009; 37: 118-129.
    CrossrefPubMedGoogle Scholar
  • 78 Tanabe N, Go K, Sakurada Y, Imasawa M, Mabuchi F, Chiba T, Abe K, Kashiwagi K. A remote operating slit lamp microscope system. Development and its utility in ophthalmologic examinations. Methods Inf Med 2011; 50: 427-434.
    Article in Thieme ConnectPubMedGoogle Scholar
  • 79 Jasman AA, Shaharuddin B, Noor RAM, Ismail S, Ghani ZA, Embong Z. Prediction error and accuracy of intraocular lens power calculation in pediatric patient comparing SRK II and Pediatric IOL Calculator. BMC Ophthalmol 2010; 10: 20.
    CrossrefPubMedGoogle Scholar
  • 80 Carvalho LA, Stefani M, Romà AC, Carvalho LA, Stefani M, Romoa AC, de Castro JC, Tonissi S, Schor P, Charmon W. Videokeratoscopes for dioptric power measurement during surgery. J Cataract Refract Surg 2002; 28: 2006-2016.
    CrossrefPubMedGoogle Scholar
  • 81 Kreutzer TC, Al Saeidi R, Kampik A, Grueterich M. Real-time intraocular pressure measurement in standard and microcoaxial phacoemulsification. J Cataract Refract Surg 2010; 36: 53-57.
    CrossrefPubMedGoogle Scholar
  • 82 Leng T, Lujan BJ, Yoo SH, Wang J. Three-dimensional spectral domain optical coherence tomography of a clear corneal cataract incision. Ophthalmic Surg Lasers Imaging 2008; 39: S132-134.
    PubMedGoogle Scholar
  • 83 Schallhorn JM, Tang M, Li Y, Song JC, Huang D. Optical coherence tomography of clear cornealincisions for cataract surgery. J Cataract Refract Surg 2008; 34: 1561-1565.
    CrossrefPubMedGoogle Scholar
  • 84 Cuadros J, Bresnick G. EyePACS: an adaptable telemedicine system for diabetic retinopathy screening. J Diabetes Sci Technol 2009; 3: 509-516.
    CrossrefPubMedGoogle Scholar
  • 85 Becker KA, Auffarth GU, Völcker HE. Measurement method for the determination of rotation and decentration of intraocular lenses. Ophthalmologe 2004; 101: 600-603.
    PubMedGoogle Scholar
  • 86 Becker KA, Holzer MP, Reuland AJ, Auffarth GU. Accuracy of lens power calculation and centration of an aspheric intraocular lens. Ophthalmologe 2006; 103: 873-876.
    CrossrefPubMedGoogle Scholar
  • 87 Acharya RU, Yu W, Zhu K, Nayak J, Lim TC, Chan JY. Identification of cataract and post-cataract surgery optical images using artificial intelligence techniques. J Med Syst 2010; 34: 619-628.
    CrossrefPubMedGoogle Scholar
  • 88 Händel A, Martus P, Küchle M, Schönherr U. Microsurgical quality assurance exemplified by cataract surgery. Ophthalmologe 2002; 99: 352-357.
    CrossrefPubMedGoogle Scholar
  • 89 Webster R, Sassani J, Shenk R, Harris M, Gerber J, Benson A, Blumenstock J, Billman C, Haluck R. Simulating the continuous curvilinear capsulorhexis procedure during cataract surgery on the EYESI system. Stud Health Technol Inform 2005; 111: 592-595.
    PubMedGoogle Scholar
  • 90 Devi SP, Rao KS, Sangeetha SS. Prediction of Surgery Times and Scheduling of Operation Theaters in Optholmology Department. J Med Syst 2012; 36 (02) 415-430.
    CrossrefPubMedGoogle Scholar
  • 91 Kubitz J, Epple J, Lützelberger U, Schmidt H, Motsch J, Bach A. Computer simulation and pharmacoeconomics. Computer simulation as an aid for the analysis of operating room efficiency:an example. Anaesthesist 2001; 50: 122-127.
    CrossrefPubMedGoogle Scholar
  • 92 Choi K-S. Soo S, Chung FL. A virtual training simulator for learning cataract surgery with phacoemulsification. Comput Biol Med 2009; 39: 1020-1031.
    CrossrefPubMedGoogle Scholar
  • 93 Doyle L, Gauthier N, Ramanathan S, Okamura A. A simulator to explore the role of haptic feedback in cataract surgery training. Stud Health Technol Inform 2008; 132: 106-111.
    PubMedGoogle Scholar
  • 94 Henderson BA, Kim JY, Golnik KC, Oetting TA, Lee AG, Volpe NJ, Aaron M, Uhler TA, Arnold A, Dunn JP, Prajna NV, Lane AM, Loewenstein JI. Evaluation of the virtual mentor cataract training program. Ophthalmology 2010; 117: 253-258.
    CrossrefPubMedGoogle Scholar
  • 95 Khalifa YM, Bogorad D, Gibson V, Peifer J, Nussbaum J. Virtual reality in ophthalmology training. Surv Ophthalmol 2006; 51: 259-273.
    CrossrefPubMedGoogle Scholar
  • 96 Oetting TA. Surgical competency in residents. Curr Opin Ophthalmol 2009; 20: 56-60.
    CrossrefPubMedGoogle Scholar
  • 97 Privett B, Greenlee E, Rogers G, Oetting TA. Construct validity of a surgical simulator as a valid model for capsulorhexis training. J Cataract Refract Surg 2010; 36: 1835-1838.
    CrossrefPubMedGoogle Scholar
  • 98 Santerre N, Blondel F, Racoussot F, Laverdure G, Karpf S, Dubois P, Rouland JF. A teaching medical simulator: phacoemulsification in virtual reality. J Fr Ophtalmol 2007; 30: 621-626.
    CrossrefPubMedGoogle Scholar
  • 99 Waqar S, Park J, Kersey TL, Modi N, Ong C, Sleep TJ. Assessment of fatigue in intraocular surgery: analysis using a virtual reality simulator. Graefes Arch Clin Exp Ophthalmol 2011; 249: 77-81.
    CrossrefPubMedGoogle Scholar
  • 100 Cinquin P, Troccaz J. Model driven therapy - the instance of computer assisted medical interventions. Methods Inf Med 2003; 42: 169-176.
    Article in Thieme ConnectPubMedGoogle Scholar