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A robust and non-obtrusive automatic event tracking system for operating room management to improve patient care

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Abstract

Background

Optimization of OR management is a complex problem as each OR has different procedures throughout the day inevitably resulting in scheduling delays, variations in time durations and overall suboptimal performance. There exists a need for a system that automatically tracks procedural progress in real time in the OR. This would allow for efficient monitoring of operating room states and target sources of inefficiency and points of improvement.

Study design

We placed three wireless sensors (floor-mounted pressure sensor, ventilator-mounted bellows motion sensor and ambient light detector, and a general room motion detector) in two ORs at our institution and tracked cases 24 h a day for over 4 months.

Results

We collected data on 238 total cases (107 laparoscopic cases). A total of 176 turnover times were also captured, and we found that the average turnover time between cases was 35 min while the institutional goal was 30 min. Deeper examination showed that 38 % of laparoscopic cases had some aspect of suboptimal activity with the time between extubation and patient exiting the OR being the biggest contributor (16 %).

Conclusion

Our automated system allows for robust, wireless real-time OR monitoring as well as data collection and retrospective data analyses. We plan to continue expanding our system and to project the data in real time for all OR personnel to see. At the same time, we plan on adding key pieces of technology such as RFID and other radio-frequency systems to track patients and physicians to further increase efficiency and patient safety.

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Acknowledgments

We acknowledge Linda Moore from Houston Methodist for an interesting discussion on the ACS-NSQIP and potential correlation analysis with our SmartOR data. We also thank the Dunn OR team for their cooperation on the clinical study, and we express our gratitude to Michael Garcia for his interest, support, and feedback on our system.

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Authors and Affiliations

  1. University of Houston, Houston, TX, USA

    Remi Salmon & Marc Garbey

  2. Department of Surgery, Houston Methodist Hospital, 6550 Fannin St, Suite 1661, Houston, TX, 77030, USA

    Albert Y. Huang, Brian Dunkin, Vadim Sherman & Barbara L. Bass

  3. Methodist Institute of Technology Innovation and Education, Houston, TX, USA

    Albert Y. Huang, Brian Dunkin & Marc Garbey

  4. LASIE UMR CNRS, Univ., La Rochelle, France

    Guillaume Joerger & Marc Garbey

Authors
  1. Albert Y. Huang
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  2. Guillaume Joerger
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  3. Remi Salmon
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  4. Brian Dunkin
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  5. Vadim Sherman
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  6. Barbara L. Bass
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  7. Marc Garbey
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Corresponding author

Correspondence to Albert Y. Huang.

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Disclosures

Albert Y. Huang, Guillaume Joerger, Remi Salmon, Vadim Sherman, Brian J. Dunkin, Barbara L. Bass, and Marc Garbey have no disclosures and no conflicts of interest.

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Huang, A.Y., Joerger, G., Salmon, R. et al. A robust and non-obtrusive automatic event tracking system for operating room management to improve patient care. Surg Endosc 30, 3638–3645 (2016). https://doi.org/10.1007/s00464-015-4610-2

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  • DOI: https://doi.org/10.1007/s00464-015-4610-2

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