Process data analytics for hospital case-mix planning

Robert Andrews; Kanika Goel; Paul Corry; Robert Burdett; Moe Thandar Wynn; Donna Callow

doi:10.1016/j.jbi.2022.104056

Process data analytics for hospital case-mix planning

J Biomed Inform. 2022 May:129:104056. doi: 10.1016/j.jbi.2022.104056. Epub 2022 Mar 23.

Authors

Robert Andrews¹, Kanika Goel², Paul Corry³, Robert Burdett³, Moe Thandar Wynn², Donna Callow⁴

Affiliations

¹ School of Information Systems, Queensland University of Technology, Brisbane, Australia. Electronic address: r.andrews@qut.edu.au.
² School of Information Systems, Queensland University of Technology, Brisbane, Australia.
³ School of Mathematics, Queensland University of Technology, Brisbane, Australia.
⁴ Queensland Children's Hospital, Brisbane, Australia.

PMID: 35337944
DOI: 10.1016/j.jbi.2022.104056

Abstract

The composition and volume of patients treated in a hospital, i.e., the patient case-mix, directly impacts resource utilisation. Despite advances in technology, existing case-mix planning approaches are mostly manual. In this paper, we report on a solution that was developed in collaboration with the Queensland Children's Hospital for supporting its case-mix planning using process mining. We investigated (1) How can process mining capabilities be used to inform hospital case-mix planning?, and (2) How can process data be used to assess hospital capacity assessment and inform hospital case-mix planning? The major contributions of this paper include (i) an automated workflow to support both process mining analysis, and capacity assessment, (ii) a process mining analysis designed to detect process performance and variations, and (iii) a novel capacity assessment model based on limiting-resource saturation.

Keywords: Capacity assessment; Hospital case-mix planning; Process mining.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Child
Data Science*
Diagnosis-Related Groups*
Hospitals
Humans
Workflow