Real-time estimation of surgical procedure duration

Efficiency in the Operating Room (OR) is a topic of growing interest. Planning of care is a crucial element to ensure optimal use of the ORs. Currently, OR scheduling is considered as a complex task based on predictions of surgery duration. The latter are often based on average times, but turn out to be inaccurate in practice because of various factors (such as complexity, patient's characteristics, unexpected events, etc). The aim of this study is to develop a prediction system that estimates in real-time the remaining duration of a surgical procedure. The prediction system was based on monitoring the progress of a procedure by recording the activation of a single piece of equipment in the OR, the electrosurgical device. Support Vector Machines was then used as a classifier to predict the remaining surgical procedure duration and thereby the optimal timing to start preparing the next patient for surgery. The classifier was trained with data on the activation of the electrosurgical device during 55 laparoscopic cholecystectomies. The performance tests showed a mean error rate about 0.2, which means that about 80% of the procedures were classified correctly. The real-time prediction system is a promising tool to improve OR planning and decrease unnecessary patients' waiting times.

[1]  M Koutantji,et al.  Quantifying distraction and interruption in urological surgery , 2007, Quality and Safety in Health Care.

[2]  E. Steyerberg,et al.  Predicting the Unpredictable: A New Prediction Model for Operating Room Times Using Individual Characteristics and the Surgeon's Estimate , 2010, Anesthesiology.

[3]  Tristan Fletcher,et al.  Support Vector Machines Explained , 2008 .

[4]  H. Oxley,et al.  Health Care Reform Controlling Spending and Increasing Efficiency , 1994 .

[5]  Nassir Navab,et al.  Workflow monitoring based on 3D motion features , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[6]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[7]  Moe Thandar Wynn,et al.  Workflow support for scheduling in surgical care processes , 2011, ECIS.

[8]  Alex Macario,et al.  The drive for operating room efficiency will increase quality of patient care , 2006, Current opinion in anaesthesiology.

[9]  Nassir Navab,et al.  On-line Recognition of Surgical Activity for Monitoring in the Operating Room , 2008, AAAI.

[10]  Brian T. Denton,et al.  Optimal Allocation of Surgery Blocks to Operating Rooms Under Uncertainty , 2010, Oper. Res..

[11]  C. Herfarth.  ‘Lean’ surgery through changes in surgical work flow , 2003, The British journal of surgery.

[12]  Nassir Navab,et al.  Modeling and Online Recognition of Surgical Phases Using Hidden Markov Models , 2008, MICCAI.

[13]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[14]  H. Feußner,et al.  Real-time instrument detection in minimally invasive surgery using radiofrequency identification technology. , 2013, The Journal of surgical research.

[15]  N. Sevdalis,et al.  Measuring intra-operative interference from distraction and interruption observedin the operating theatre , 2006, Ergonomics.

[16]  Andreas Stolcke,et al.  Nonparametric feature normalization for SVM-based speaker verification , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[17]  Nassir Navab,et al.  Recovery of Surgical Workflow Without Explicit Models , 2006, MICCAI.

[18]  Nassir Navab,et al.  A Boosted Segmentation Method for Surgical Workflow Analysis , 2007, MICCAI.

[19]  Loubna Bouarfa,et al.  Recognizing surgical patterns , 2012 .

[20]  Nassir Navab,et al.  Statistical modeling and recognition of surgical workflow , 2012, Medical Image Anal..

[21]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  D. Oleynikov,et al.  Radio frequency identification (RFID) applied to surgical sponges , 2007, Surgical Endoscopy.

[23]  Yan Xiao,et al.  An Algorithm for Processing Vital Sign Monitoring Data to Remotely Identify Operating Room Occupancy in Real-Time , 2005, Anesthesia and analgesia.

[24]  Sander M. Bohte,et al.  Adaptive resource allocation for efficient patient scheduling , 2009, Artif. Intell. Medicine.

[25]  Thomas Neumuth,et al.  Sensor-based surgical activity recognition in unconstrained environments , 2014, Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy.

[26]  Yan Xiao,et al.  Making Management Decisions on the Day of Surgery Based on Operating Room Efficiency and Patient Waiting Times , 2004, Anesthesiology.

[27]  Sylvain Arlot,et al.  A survey of cross-validation procedures for model selection , 2009, 0907.4728.

[28]  Nassir Navab,et al.  Modeling and Segmentation of Surgical Workflow from Laparoscopic Video , 2010, MICCAI.

[29]  Guang-Zhong Yang,et al.  Eye-Gaze Driven Surgical Workflow Segmentation , 2007, MICCAI.

[30]  A C P Guédon,et al.  Tracking surgical day care patients using RFID technology , 2015, BMJ Innovations.

[31]  Heinz Wörn,et al.  Workflow analysis and surgical phase recognition in minimally invasive surgery , 2012, 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[32]  J. Ledolter,et al.  Automatic Updating of Times Remaining in Surgical Cases Using Bayesian Analysis of Historical Case Duration Data and “Instant Messaging” Updates from Anesthesia Providers , 2009, Anesthesia and analgesia.

[33]  Robert D. Howe,et al.  GPU based real-time instrument tracking with three-dimensional ultrasound , 2007, Medical Image Anal..

[34]  David W. Aha,et al.  A Comparative Evaluation of Sequential Feature Selection Algorithms , 1995, AISTATS.

[35]  Nassir Navab,et al.  Workflow mining for visualization and analysis of surgeries , 2008, International Journal of Computer Assisted Radiology and Surgery.

[36]  George T. S. Ho,et al.  The Study on Using Passive RFID Tags for Indoor Positioning , 2011 .

[37]  Isabelle Guyon,et al.  A Scaling Law for the Validation-Set Training-Set Size Ratio , 1997 .

[38]  Jenny Dankelman,et al.  Discovery of high-level tasks in the operating room , 2011, J. Biomed. Informatics.

[39]  Timothy W. Finin,et al.  A Pervasive Computing System for the Operating Room of the Future , 2007, Mob. Networks Appl..

[40]  Erik Demeulemeester,et al.  Operating room planning and scheduling: A literature review , 2010, Eur. J. Oper. Res..