Process Mining: a new approach for improving patients' care path in radiation oncology department
Federico Mastroleo,
Italy
OC-0593
Abstract
Process Mining: a new approach for improving patients' care path in radiation oncology department
Authors: Federico Mastroleo1, Matteo Pepa1, Roberto Gatta2, Giorgia Capobianco3, Laura Dima3, Stefania Volpe1, Mattia Zaffaroni1, Massimo Sarra Fiore1, Elena Rondi1, Anna Maria Ferrari1, Marco Krengli4, Giulia Marvaso1, Barbara Alicja Jereczek-Fossa1
1IEO, Radiation Oncology, Milan, Italy; 2University of Study of Brescia, Clinical and experimental sciences , Brescia, Italy; 3University of Milan, Radiation Oncology, Milan, Italy; 4University of Piemonte Orientale, Radiation Oncology, Novara, Italy
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Purpose or Objective
Process mining is a machine learning-based methodology which can help clinicians to discover, monitor and improve processes by exploiting the daily collected real-world data for accurate and efficient workflow prototyping and visualization. The aim of this work is to perform a process mining - machine learning based analysis of the major events involving patients’ path of cure in a high-flow Radiation Oncology department.
Material and Methods
All patients treated from 2017 to 2021 in our department have been enrolled in the study. An analysis of the main events representative of the care path has been performed. Main events included are: first consultation, dose prescription, CT simulation, plan contouring, start and end of the treatment. Treatment suspensions/cancellation have been analyzed by categorizing underlying causes. A custom script has been developed to refine and convert data coming from our institutional database to event log template. Process mining analysis is expected to be performed by pMineR v.045b.
Results
More than 10,000 patients, that gave informed consent, were included and more than 100,000 events were considered in the study.
Our data refining framework involved data collection and a preliminary analysis for event logs consolidation. The former step involved patient’s age, site of tumor, prescribed dose, dates of the main events, linear accelerator, priority of the treatment. Cause of suspension/cancellation of the treatment were collected for patients undergoing these events. Later, the events and the attributes have been converted in an event log template suitable for machine learning analysis.
The results of the preliminary analysis have showed that the 1 out of 5 radiotherapy treatment candidates has undergone treatment suspension/cancellation. Among the most common categories we found logistic issues and clinical impairments, both accounting for more than 20% of the involved cases. Among logistic issues, the most common causes were the rescheduling of the treatment due to machine ordinary and extraordinary maintenance, while among the clinical causes, 1 out of 3 patients had tumor-related complications (e.g. chemotherapy schedule not ended, etc).
Conclusion
Our work clearly demonstrates the importance of the establishment of a process mining methodology that would allow clinicians to gain more awareness on the actual workflow to apply mitigation strategies and optimize patients’ care path.