Gaining insight from patient journey data using a process-oriented analysis approach

Authors:
Lua Perimal-Lewis;Shaowen Qin;Campbell Thompson;Paul Hakendorf
Affiliations:
Flinders University of South Australia, Adelaide, South Australia;Flinders University of South Australia, Adelaide, South Australia;The University of Adelaide, South Australia;Epidemiology Unit, Flinders Medical Centre, South Australia
Venue:
HIKM '12 Proceedings of the Fifth Australasian Workshop on Health Informatics and Knowledge Management - Volume 129
Year:
2012

Citing 4
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Abstract

Hospitals are continually struggling to cater for the increasing demand for inpatient services. This is due to increased population, aging, and the rising incidence of chronic diseases associated with modern life. The high demand for hospital services leads to unpredictable bed availability, longer waiting period for acute admission, difficulties in keeping planned admission, stressed hospital staff, undesirable patient and family experience, as well as unclear impact on the quality of care patients receive. This study aims to gain insight into patient journey data to identify problems that could cause access block. Process mining techniques combined with statistical data analysis are adapted to discover inpatient flow process patterns and their correlation with patient types, ward types, waiting time and Length of Stay (LOS). Open source process mining software, ProM, is used in this study. The study is done in collaboration with Flinders Medical Centre (FMC) using data from their Patient Journey Database.