Towards a Comparative Assessment of Data-Driven Process Models in Health Information Technology

Hilda Klasky; Ozgur Ozmen; Olufemi Omitaomu; Mohammed Olama; Merry Ward; Angela Laurio; Jonathan Nebeker

doi:10.1109/ICHI57859.2023.00052

Towards a Comparative Assessment of Data-Driven Process Models in Health Information Technology

Hilda Klasky, Ozgur Ozmen, Olufemi Omitaomu, Mohammed Olama, Merry Ward, Angela Laurio, Jonathan Nebeker

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Process mining for conformance analysis focuses on comparing a reference process model against a data-driven process model that is generated via log files from information technology systems. While this approach is helpful when there is an existing process model in an organization, it leaves the question of what to do in the absence of a complete reference process model unanswered. In this paper, we present a comparative assessment approach that combines process mining, process mapping for dimensionality reduction, and statistical analysis. Our goal is to find similarities and dissimilarities in data-driven process models among U.S. Veterans Health Administration (VHA) facilities to assess process conformance among different healthcare facilities, which can help assess the standardization of care. We illustrate our approach by applying it to two clinical radiology order process models generated by two similar facilities. Our results demonstrate statistical similarities in the standardization of care among those two facilities.

Original language	English
Title of host publication	Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	338-343
Number of pages	6
ISBN (Electronic)	9798350302639
DOIs	https://doi.org/10.1109/ICHI57859.2023.00052
State	Published - 2023
Event	11th IEEE International Conference on Healthcare Informatics, ICHI 2023 - Houston, United States Duration: Jun 26 2023 → Jun 29 2023

Publication series

Name	Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023

Conference

Conference	11th IEEE International Conference on Healthcare Informatics, ICHI 2023
Country/Territory	United States
City	Houston
Period	06/26/23 → 06/29/23

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access- ACKNOWLEDGMENT This work is sponsored by the U.S. Department of Veterans Affairs. We also acknowledge the helpful discussions with Hong-Jun Yoon and Blair Christian about the Statistical Analysis Section.

Keywords

Process mining
anomaly detection
conformance analysis
data mining
health information systems

Access to Document

10.1109/ICHI57859.2023.00052

Cite this

Klasky, H., Ozmen, O., Omitaomu, O., Olama, M., Ward, M., Laurio, A., & Nebeker, J. (2023). Towards a Comparative Assessment of Data-Driven Process Models in Health Information Technology. In Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023 (pp. 338-343). (Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI57859.2023.00052

Klasky, Hilda ; Ozmen, Ozgur ; Omitaomu, Olufemi et al. / Towards a Comparative Assessment of Data-Driven Process Models in Health Information Technology. Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 338-343 (Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023).

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abstract = "Process mining for conformance analysis focuses on comparing a reference process model against a data-driven process model that is generated via log files from information technology systems. While this approach is helpful when there is an existing process model in an organization, it leaves the question of what to do in the absence of a complete reference process model unanswered. In this paper, we present a comparative assessment approach that combines process mining, process mapping for dimensionality reduction, and statistical analysis. Our goal is to find similarities and dissimilarities in data-driven process models among U.S. Veterans Health Administration (VHA) facilities to assess process conformance among different healthcare facilities, which can help assess the standardization of care. We illustrate our approach by applying it to two clinical radiology order process models generated by two similar facilities. Our results demonstrate statistical similarities in the standardization of care among those two facilities.",

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Klasky, H, Ozmen, O, Omitaomu, O , Olama, M, Ward, M, Laurio, A & Nebeker, J 2023, Towards a Comparative Assessment of Data-Driven Process Models in Health Information Technology. in Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023. Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023, Institute of Electrical and Electronics Engineers Inc., pp. 338-343, 11th IEEE International Conference on Healthcare Informatics, ICHI 2023, Houston, United States, 06/26/23. https://doi.org/10.1109/ICHI57859.2023.00052

Towards a Comparative Assessment of Data-Driven Process Models in Health Information Technology. / Klasky, Hilda; Ozmen, Ozgur; Omitaomu, Olufemi et al.
Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 338-343 (Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Process mining for conformance analysis focuses on comparing a reference process model against a data-driven process model that is generated via log files from information technology systems. While this approach is helpful when there is an existing process model in an organization, it leaves the question of what to do in the absence of a complete reference process model unanswered. In this paper, we present a comparative assessment approach that combines process mining, process mapping for dimensionality reduction, and statistical analysis. Our goal is to find similarities and dissimilarities in data-driven process models among U.S. Veterans Health Administration (VHA) facilities to assess process conformance among different healthcare facilities, which can help assess the standardization of care. We illustrate our approach by applying it to two clinical radiology order process models generated by two similar facilities. Our results demonstrate statistical similarities in the standardization of care among those two facilities.

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Klasky H, Ozmen O, Omitaomu O , Olama M, Ward M, Laurio A et al. Towards a Comparative Assessment of Data-Driven Process Models in Health Information Technology. In Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 338-343. (Proceedings - 2023 IEEE 11th International Conference on Healthcare Informatics, ICHI 2023). doi: 10.1109/ICHI57859.2023.00052

Towards a Comparative Assessment of Data-Driven Process Models in Health Information Technology

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