Artikel
A Pipeline for the Usage of the Core Data Set of the Medical Informatics Initiative for Process Mining – a Technical Case Report
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Autoren
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Hauke Heidemeyer
- Medizinische Fakultät der RWTH Aachen, Aachen, Germany
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Leo Antonio Auhagen
- Institute of Medical Informatics, Medical Faculty of RWTH Aachen, Aachen, Germany
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Raphael W. Majeed
- Medizinische Fakultät der RWTH Aachen, Aachen, Germany
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Marco Pegoraro
- RWTH Aachen University, Aachen, Germany
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Jonas Bienzeisler
- Medizinische Faculty, RWTH University, Aachen, Aachen, Germany
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Viki Peeva
- RWTH Aachen University, Aachen, Germany
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Harry Beyel
- RWTH Aachen University, Aachen, Germany
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Rainer Röhrig
- Medizinische Fakultät der RWTH Aachen, Aachen, Germany
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Wil M. P. Van der Aalst
- RWTH Aachen University, Aachen, Germany
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Behrus Puladi
- Universitätsklinikum RWTH Aachen, Aachen, Germany
| Veröffentlicht: | 6. September 2024 |
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Gliederung
Text
Process Mining (PM) has emerged as a transformative tool in healthcare, facilitating the enhancement of process models and predicting potential anomalies. However, the widespread application of PM in healthcare is hindered by the lack of structured event logs and specific data privacy regulations. This paper introduces a pipeline that converts routine healthcare data into PM-compatible event logs, leveraging the newly available permissions under the Health Data Utilization Act to use healthcare data. Our system exploits the Core Data Sets (CDS) provided by Data Integration Centers (DICs). It involves converting routine data into Fast Healthcare Interoperable Resources (FHIR), storing it locally, and subsequently transforming it into standardized PM event logs through FHIR queries applicable on any DIC. This facilitates the extraction of detailed, actionable insights across various healthcare settings without altering existing DIC infrastructures. Challenges encountered include handling the variability and quality of data, and overcoming network and computational constraints. Our pipeline demonstrates how PM can be applied even in complex systems like healthcare, by allowing for a standardized yet flexible analysis pipeline which is widely applicable. The successful application emphasize the critical role of tailored event log generation and data querying capabilities in enabling effective PM applications, thus enabling evidence-based improvements in healthcare processes.
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: WvdA has an affiliation with the commercial software vendor Celonis SE. However, this affiliation did not influence this work in any way or affect objectivity. The other authors declare no conflict of interest according to ICMJE recommendations.
The authors declare that a positive ethics committee vote has been obtained.
