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

Maryam Gholinejad; Bjørn Edwin; Ole Jakob Elle; Jenny Dankelman; Arjo J Loeve

doi:10.1007/s00464-023-10166-y

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

Surg Endosc. 2023 Sep;37(9):7083-7099. doi: 10.1007/s00464-023-10166-y. Epub 2023 Jun 29.

Authors

Maryam Gholinejad¹, Bjørn Edwin^{2

3

4}, Ole Jakob Elle^{2

5}, Jenny Dankelman⁶, Arjo J Loeve⁶

Affiliations

¹ Department of Biomechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The Netherlands. m.gholinejad-1@tudelft.nl.
² The Intervention Centre, Oslo University Hospital, Oslo, Norway.
³ Medical Faculty, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
⁴ Department of HPB Surgery, Oslo University Hospital, Oslo, Norway.
⁵ Department of Informatics, University of Oslo, Oslo, Norway.
⁶ Department of Biomechanical Engineering, Faculty of Mechanical, Maritime and Materials Engineering, Delft University of Technology, Delft, The Netherlands.

Abstract

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.

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

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Hepatectomy / methods
Humans
Laparoscopy* / methods
Length of Stay
Liver Neoplasms* / surgery