The impact of missing sensor information on surgical workflow management.

Objective: Sensor systems in the operating room may encounter intermittent data losses that reduce the performance of surgical workflow management systems (SWFMS). Sensor data loss could impact SWFMS-based decision support, device parameterization, and information presentation. The purpose of this study was to understand the robustness of surgical process models when sensor information is partially missing. SWFMS changes caused by wrong or no data from the sensor system which tracks the progress of a surgical intervention were tested.

Materials And Methods: The individual surgical process models (iSPMs) from 100 different cataract procedures of 3 ophthalmologic surgeons were used to select a randomized subset and create a generalized surgical process model (gSPM). A disjoint subset was selected from the iSPMs and used to simulate the surgical process against the gSPM. The loss of sensor data was simulated by removing some information from one task in the iSPM. The effect of missing sensor data was measured using several metrics: (a) successful relocation of the path in the gSPM, (b) the number of steps to find the converging point, and (c) the perspective with the highest occurrence of unsuccessful path findings.

Results: A gSPM built using 30% of the iSPMs successfully found the correct path in 90% of the cases. The most critical sensor data were the information regarding the instrument used by the surgeon.

Conclusion: We found that use of a gSPM to provide input data for a SWFMS is robust and can be accurate despite missing sensor data. A surgical workflow management system can provide the surgeon with workflow guidance in the OR for most cases. Sensor systems for surgical process tracking can be evaluated based on the stability and accuracy of functional and spatial operative results.