Towards automatic quantification of operating table interaction in operating rooms

International Journal of Computer Assisted Radiology and Surgery, vol. 20, pp. 1999–2010

Abstract

PURPOSE: Perioperative staff shortages are a problem in hospitals worldwide. Keeping the staff content and motivated is a challenge in the busy hospital setting of today. New operating room technologies aim to increase safety and efficiency. This causes a shift from interaction with patients to interaction with technology. Objectively measuring this shift could aid the design of supportive technological products, or optimal planning for high-tech procedures. METHODS: 35 Gynaecological procedures of three different technology levels are recorded: open- (OS), minimally invasive- (MIS) and robot-assisted (RAS) surgery. We annotate interaction between staff and the patient. An algorithm is proposed that detects interaction with the operating table from staff posture and movement. Interaction is expressed as a percentage of total working time. RESULTS: The proposed algorithm measures operating table interactions of 70.4%, 70.3% and 30.1% during OS, MIS and RAS. Annotations yield patient interaction percentages of 37.6%, 38.3% and 24.6%. Algorithm measurements over time show operating table and patient interaction peaks at anomalous events or workflow phase transitions. CONCLUSIONS: The annotations show less operating table and patient interactions during RAS than OS and MIS. Annotated patient interaction and measured operating table interaction show similar differences between procedures and workflow phases. The visual complexity of operating rooms complicates pose tracking, deteriorating the algorithm input quality. The proposed algorithm shows promise as a component in context-aware event- or workflow phase detection.

Authors 6

1. Rick M. Bütler corresponding

   Delft University of Technology

   Affiliation as printed

   Delft University of Technology, Delft, the Netherlands. r.m.butler@tudelft.nl

2. Anne M Schouten Aachen

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft University of Technology, Delft, the Netherlands

   Leiden University Medical Center, Leiden, the Netherlands

3. Anne Catherine van der Eijk Aachen

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

   Delft University of Technology, Delft, the Netherlands

   Leiden University Medical Center, Leiden, the Netherlands

4. Maarten van der Elst

   Reinier de Graaf Hospital · Delft University of Technology

   Affiliation as printed

   Delft University of Technology, Delft, the Netherlands

   Reinier de Graaf Gasthuis, Delft, the Netherlands

5. Benno H. W. Hendriks

   Philips (Netherlands) · Delft University of Technology

   Affiliation as printed

   Delft University of Technology, Delft, the Netherlands

   Philips Healthcare, Best, the Netherlands

6. John J. van den Dobbelsteen

   Delft University of Technology

   Affiliation as printed

   Delft University of Technology, Delft, the Netherlands