Towards automatic quantification of operating table interaction in operating rooms
Rick Butler (TU Delft - Medical Instruments & Bio-Inspired Technology)
A.M. Schouten (TU Delft - Medical Instruments & Bio-Inspired Technology, Leiden University Medical Center)
A.C. van der Eijk (TU Delft - Medical Instruments & Bio-Inspired Technology, Leiden University Medical Center)
Maarten Van der Elst (Reinier de Graaf Gasthuis, TU Delft - Medical Instruments & Bio-Inspired Technology)
Benno Hendriks (Philips Healthcare, TU Delft - Medical Instruments & Bio-Inspired Technology)
J. van Den Dobbelsteen (TU Delft - Medical Instruments & Bio-Inspired Technology)
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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.
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