Mining patient flow patterns in a surgical ward

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Surgery is a highly critical and costly procedure, and there is an imperative need to improve the efficiency in surgical wards. Analyzing surgical patient flow and predicting cycle times of different peri-operative phases can help improve the scheduling and management of surgeries. In this paper, we propose a novel approach to mining temporal patterns of surgical patient flow with the use of Bayesian belief networks. We present and compare three classes of probabilistic models and we evaluate them with respect to predicting cycle times of individual phases of patient flow. The results of this study support previous work that surgical times are log-normally distributed. We also show that the inclusion of a clustering pre-processing step improves the performance of our models considerably.

Original languageEnglish
Title of host publicationHEALTHINF 2020 - 13th International Conference on Health Informatics, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020
EditorsFederico Cabitza, Ana Fred, Hugo Gamboa
PublisherSCITEPRESS (Science and Technology Publications, Lda.)
Publication date2020
Pages273-283
ISBN (Electronic)9789897583988
Publication statusPublished - 2020
Event13th International Conference on Health Informatics, HEALTHINF 2020 - Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020 - Valletta, Malta
Duration: 24 Feb 202026 Feb 2020

Conference

Conference13th International Conference on Health Informatics, HEALTHINF 2020 - Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020
LandMalta
ByValletta
Periode24/02/202026/02/2020
SponsorInstitute for Systems and Technologies of Information, Control and Communication (INSTICC)

    Research areas

  • Bayesian network, Data mining, Patient flows, Process mining, Surgery, Surgical workflow

ID: 250487998