Johnson, OA orcid.org/0000-0003-3998-541X (2017) Process Mining and Analytics for Care Pathways: Proposal for a Tutorial at ICHI2017. In: Giraud-Carrier, C, Facelli, J, Nakajima, H, Cummins, M and Meixner, G, (eds.) Proceedings: 2017 IEEE International Conference on Healthcare Informatics (ICHI). 5th IEEE International Conference on Healthcare Informatics (ICHI 2017), 23-26 Aug 2017, Park City, UT, USA. Institute of Electrical and Electronics Engineers , Los Alamitos, CA, USA , xvii. ISBN 9781509048816
Abstract
This tutorial demonstrates the application of a range of process analytics techniques to the study and improvement of clinical care pathways. The past decade has seen increasing interest in care pathway design, documentation and dissemination but formal methods for describing, monitoring and assessing pathways have yet to be established. Outside of healthcare other industries have well established techniques for business processes and there is much scope for translating these to fit the unique nature of healthcare. In particular data analytics, data mining, and machine learning have converged on a set of technologies called process mining which has the potential to lead to a step-change in using e-health record data to mine and manage care pathways. The tutorial presents an iterative method developed with the UK NHS and the Connected Health Cities programme that combines process mining with other process analytics methods including process modeling, process simulation and business process improvement. The session is highly interactive and based on a series of hands-on exercises around a worked example supplemented by case studies of completed work. Links to further study are provided and the aim is encourage further research and build a global community of practice.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. This is an author produced version of a paper published in Proceedings: 2017 IEEE International Conference on Healthcare Informatics (ICHI). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | care pathways; data analytics, process mining, business process improvement, e-health records |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number NHSA Northern Health Science No Ext Ref |
Depositing User: | Symplectic Publications |
Date Deposited: | 03 Jan 2018 15:43 |
Last Modified: | 04 Jan 2018 03:03 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/ICHI.2017.104 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:125723 |