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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2606.00041 |
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| _version_ | 1866910272825851904 |
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| author | Ardimento, Pasquale Bernardi, Mario Luca Cimitile, Marta Latorre, Samuele |
| author_facet | Ardimento, Pasquale Bernardi, Mario Luca Cimitile, Marta Latorre, Samuele |
| contents | This study analyzes COVID-19 care pathways using the COVID Data for Shared Learning dataset. We build a transparent, reproducible pipeline that transforms heterogeneous clinical tables into a process-mining-ready event log and applies discovery, declarative conformance checking, and outcome analysis. The reconstructed pathways highlight the monitoring backbone of inpatient care, variability at the Emergency department-admission interface, and outcome differences driven by age and exposure to intensive care units. These insights support triage standardization, capacity planning, and step-down coordination from intensive care units to lower-acuity wards, showing how process mining can inform evidence-based hospital governance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00041 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Improving Hospital Process Management through Process Mining: A Case Study on COVID-19 Clinical Pathways Ardimento, Pasquale Bernardi, Mario Luca Cimitile, Marta Latorre, Samuele Computers and Society Artificial Intelligence This study analyzes COVID-19 care pathways using the COVID Data for Shared Learning dataset. We build a transparent, reproducible pipeline that transforms heterogeneous clinical tables into a process-mining-ready event log and applies discovery, declarative conformance checking, and outcome analysis. The reconstructed pathways highlight the monitoring backbone of inpatient care, variability at the Emergency department-admission interface, and outcome differences driven by age and exposure to intensive care units. These insights support triage standardization, capacity planning, and step-down coordination from intensive care units to lower-acuity wards, showing how process mining can inform evidence-based hospital governance. |
| title | Improving Hospital Process Management through Process Mining: A Case Study on COVID-19 Clinical Pathways |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2606.00041 |