Date of Award
9-12-2024
Document Type
Thesis
Degree Name
Engineering Management, MSEM
First Advisor
Niamat Ullah Ibne Hossain
Committee Members
Alexandr M Sokolov, Jonathan Stubblefield
Call Number
ISBN 9798384080398
Abstract
Healthcare professionals must be experts at carrying out complex surgical procedures to fulfill their responsibilities. The aim of the medical treatments is fewer complications, shorter hospital stays, and a better patient experience. They often report on problems with surgical processes, skipped procedures, and lengthy transition times. The event log data allows process mining methods to deliver professionals with understandable findings using Petri Net. This study identifies the parallels and discrepancies between the pre-and post-stages and their respective frequency on each typical Central Venous Catheter (CVC) installation activity, where pre-stage they were given a briefing, and in post-stage they had hands-on training before the operation. The Process Mining for Python (PM4Py) frameworks used major mining algorithms to view the event log (i.e., alpha miner, directly-follows graph (DFG), heuristic miner, inductive miner, and a few more). This study’s findings indicate that residents are more susceptible to error during pre-operative procedures.
Rights Management
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Rabbi, Fazla, "Application Of Process Mining Algorithms to Surgical Training: A Study of Healthcare Settings" (2024). Student Theses and Dissertations. 34.
https://arch.astate.edu/all-etd/34
