Highlights
- •Extracting patient journeys patterns factually and objectively from occurrences of processes and activities is arguably one of the most important aspects of analyzing patient journeys.
- •This study describes the use of process mining as a tool to obtain a deep understanding of executed care processes from electronic health records.
- •Process mining–based patient journey patterns are used to derive and discuss patients’ length of stay, as a crucial piece of patient experiences and of particular importance for measuring operational performance of hospitals.
Abstract
Objectives
Given the increasing availability of electronic health records, it has become increasingly
feasible to adopt data-driven approaches to capture a deep understanding of the patient
journeys. Nevertheless, simply using data-driven techniques to depict the patient
journeys without an integrated modeling and analysis approach is proving to be of
little benefit for improving patients’ experiences. Indeed, a model of the journey
patterns is necessary to support the improvement process.
Methods
We presented a 3-phase methodology that integrates a process mining–based understanding
of patient journeys with a stochastic graphical modeling approach to derive and analyze
the analytical expressions of some important performance indicators of an emergency
department including mean and variance of patients’ length of stay (LOS).
Results
Analytical expressions were derived and discussed for mean and variance of LOS times
and discharge and admission probabilities. LOS differed significantly depending on
whether a patient was admitted to the hospital or discharged. Moreover, multiparameter
sensitivity equations are obtained to identify which activities contribute the most
in reducing the LOS at given operating conditions so decision makers can prioritize
their improvement initiatives.
Conclusions
Data-driven based approaches for understanding the patient journeys coupled with appropriate
modeling techniques yield a promising tool to support improving patients’ experiences.
The modeling techniques should be easy to implement and not only should be capable
of deriving some key performance indicators of interest but also guide decision makers
in their improvement initiatives.
Keywords
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Article info
Publication history
Published online: May 25, 2022
Accepted:
April 8,
2022
Identification
Copyright
© 2022 International Society for Pharmacoeconomics and Outcomes Research, Inc. Published by Elsevier Inc.
