Mining Patient Flow Patterns in a Surgical Ward

Christoffer O. Back, Areti Manataki, Ewen Harrison

2020

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.

Paper Citation

in Harvard Style

Back C., Manataki A. and Harrison E. (2020). Mining Patient Flow Patterns in a Surgical Ward. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF; ISBN 978-989-758-398-8, SciTePress, pages 273-283. DOI: 10.5220/0009181302730283

in Bibtex Style

@conference{healthinf20,
author={Christoffer O. Back and Areti Manataki and Ewen Harrison},
title={Mining Patient Flow Patterns in a Surgical Ward},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF},
year={2020},
pages={273-283},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009181302730283},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF
TI - Mining Patient Flow Patterns in a Surgical Ward
SN - 978-989-758-398-8
AU - Back C.
AU - Manataki A.
AU - Harrison E.
PY - 2020
SP - 273
EP - 283
DO - 10.5220/0009181302730283
PB - SciTePress