Process model analysis of parenchyma sparing laparoscopic liver surgery to recognize surgical steps and predict impact of new technologies

Process model analysis of parenchyma sparing laparoscopic liver surgery to recognize surgical steps and predict impact of new technologies

Gholinejad, M. (TU Delft Medical Instruments & Bio-Inspired Technology)
Edwin, Bjørn (Oslo University Hospital)
Elle, Ole Jakob (Oslo University Hospital; Universitetet i Oslo)
Dankelman, J. (TU Delft Medical Instruments & Bio-Inspired Technology)
Loeve, A.J. (TU Delft Medical Instruments & Bio-Inspired Technology)

2023

Background: Surgical process model (SPM) analysis is a great means to predict the surgical steps in a procedure as well as to predict the potential impact of new technologies. Especially in complicated and high-volume treatments, such as parenchyma sparing laparoscopic liver resection (LLR), profound process knowledge is essential for enabling improving surgical quality and efficiency. Methods: Videos of thirteen parenchyma sparing LLR were analyzed to extract the duration and sequence of surgical steps according to the process model. The videos were categorized into three groups, based on the tumor locations. Next, a detailed discrete events simulation model (DESM) of LLR was built, based on the process model and the process data obtained from the endoscopic videos. Furthermore, the impact of using a navigation platform on the total duration of the LLR was studied with the simulation model by assessing three different scenarios: (i) no navigation platform, (ii) conservative positive effect, and (iii) optimistic positive effect. Results: The possible variations of sequences of surgical steps in performing parenchyma sparing depending on the tumor locations were established. The statistically most probable chain of surgical steps was predicted, which could be used to improve parenchyma sparing surgeries. In all three categories (i–iii) the treatment phase covered the major part (~ 40%) of the total procedure duration (bottleneck). The simulation results predict that a navigation platform could decrease the total surgery duration by up to 30%. Conclusion: This study showed a DESM based on the analysis of steps during surgical procedures can be used to predict the impact of new technology. SPMs can be used to detect, e.g., the most probable workflow paths which enables predicting next surgical steps, improving surgical training systems, and analyzing surgical performance. Moreover, it provides insight into the points for improvement and bottlenecks in the surgical process.

Discrete event simulation
Parenchyma sparing
Surgical data analysis
Surgical navigation platform
Surgical process modeling
Surgical step prediction
Surgical task recognition

http://resolver.tudelft.nl/uuid:5f4eab1e-d7c3-4f59-abbb-8d615ba5e478

https://doi.org/10.1007/s00464-023-10166-y

0930-2794

Surgical Endoscopy: surgical and interventional techniques (online), 37 (9), 7083-7099

Institutional Repository

journal article

© 2023 M. Gholinejad, Bjørn Edwin, Ole Jakob Elle, J. Dankelman, A.J. Loeve