Stochastic Workflow Modeling in a Surgical Ward: Towards Simulating and Predicting Patient Flow
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Accepted author manuscript, 1.34 MB, PDF document
Intelligent systems play an increasingly central role in healthcare systems worldwide. Nonetheless, operational friction represents an obstacle to full utilization of scarce resources and improvement of service standards. In this paper we address the challenge of developing data-driven models of complex workflow systems - a prerequisite for harnessing intelligent technologies for workflow improvement. We present a proof-of-concept model parametrized using real-world data and constructed based on domain knowledge from the Royal Infirmary of Edinburgh, demonstrating how off-the-shelf process mining, machine learning and stochastic process modeling tools can be combined to build predictive models that capture complex control flow, constraints, policies and guidelines.
|Title of host publication||Biomedical Engineering Systems and Technologies - 13th International Joint Conference, BIOSTEC 2020, Revised Selected Papers|
|Editors||Xuesong Ye, Filipe Soares, Elisabetta De Maria, Pedro Gómez Vilda, Federico Cabitza, Ana Fred, Hugo Gamboa|
|Publication status||Published - 2021|
|Event||13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020 - Valetta, Malta|
Duration: 24 Feb 2020 → 26 Feb 2020
|Conference||13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020|
|Periode||24/02/2020 → 26/02/2020|
|Series||Communications in Computer and Information Science|
© 2021, Springer Nature Switzerland AG.
- Bayesian network, Data mining, Patient flow, Petri nets, Process mining, Simulation, Surgery, Surgical workflow