Towards Process Mining of EMR Data - Case Study for Sepsis Management

Gert-Jan de Vries, Ricardo Alfredo Quintano Neira, Gijs Geleijnse, Prabhakar Dixit, Bruno Franco Mazza

Abstract

Imagine you have cold shivers and a racing heartbeat and high fever. Clear thinking is impossible! Ceiling lights flash by as you are rushed to the emergency department (ED). You feel your body is getting even sicker. Doctors are doing their utmost to treat this acute and threatening condition, while they work piece together all small parts of evidence to set the diagnosis and start targeted treatment. In this situation, the clinical staff depends on a clinical pathway protocol to streamline communication and deliver care according to the latest medical evidence. Today, such clinical pathways are mainly executed and tracked using paper. Hence, there is ample opportunity for technology in a supportive role. Automated process analysis can help improve these processes of delivering standardized care beyond their current level. In this paper, we provide insight into the steps required to perform process mining to EMR data in the challenging domain of sepsis treatment and provide learnings from our preliminary analysis of these data using process mining techniques.

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Paper Citation


in Harvard Style

de Vries G., Quintano Neira R., Geleijnse G., Dixit P. and Mazza B. (2017). Towards Process Mining of EMR Data - Case Study for Sepsis Management . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: ACP, (BIOSTEC 2017) ISBN 978-989-758-213-4, pages 585-593. DOI: 10.5220/0006274405850593


in Bibtex Style

@conference{acp17,
author={Gert-Jan de Vries and Ricardo Alfredo Quintano Neira and Gijs Geleijnse and Prabhakar Dixit and Bruno Franco Mazza},
title={Towards Process Mining of EMR Data - Case Study for Sepsis Management},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: ACP, (BIOSTEC 2017)},
year={2017},
pages={585-593},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006274405850593},
isbn={978-989-758-213-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: ACP, (BIOSTEC 2017)
TI - Towards Process Mining of EMR Data - Case Study for Sepsis Management
SN - 978-989-758-213-4
AU - de Vries G.
AU - Quintano Neira R.
AU - Geleijnse G.
AU - Dixit P.
AU - Mazza B.
PY - 2017
SP - 585
EP - 593
DO - 10.5220/0006274405850593