Mining patient flow patterns in a surgical ward

Christoffer Olling Back, Areti Manataki, Ewen Harrison

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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 publicationProceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies
PublisherSciTePress
Pages273-283
Number of pages11
Volume5
ISBN (Print)9789897583988
DOIs
Publication statusPublished - 18 Mar 2020
Event13th International Conference on Health Informatics, part of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Valetta, Malta
Duration: 24 Feb 202026 Feb 2020
http://www.healthinf.biostec.org/?y=2020

Conference

Conference13th International Conference on Health Informatics, part of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies
Abbreviated titleHEALTHINF
Country/TerritoryMalta
CityValetta
Period24/02/2026/02/20
Internet address

Keywords

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

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