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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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Published on: May 17, 2019

An Integrated Predictive Impact-Enhanced Process Mining Framework for Strategic Oncology Workflow Optimization: Case

Mohammad Salehi¹, Raouf Khayami¹, Reza Akbari¹

¹Department of Computer Engineering and IT, Shiraz University of Technology, Shiraz 13876-71557, Iran.

Bioengineering (Basel, Switzerland)

|December 30, 2025

Summary

Process Mining (PM) can now predict the impact of workflow changes in healthcare. Our new framework quantifies how fixing deviations reduces cycle time and workload, enabling data-driven strategic planning.

Keywords:

PM2 bioengineering chemotherapy workflow conformance checking oncology predictive impact model (PIM)predictive modeling process mining workflow optimization

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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Published on: July 11, 2025

Area of Science:

Healthcare Operations Research
Process Mining Applications
Clinical Workflow Optimization

Background:

Process Mining (PM) identifies healthcare workflow inefficiencies but lacks quantitative impact assessment.
Current methods are retrospective or use disconnected simulations, hindering evidence-based resource allocation.
A methodological gap exists in predicting the system-wide operational impact of correcting workflow deviations.

Purpose of the Study:

Introduce the PM²-Predictive Impact Model (PIM) framework to bridge the gap in quantifying the impact of workflow deviation correction.
Unify conformance checking, predictive monitoring, and scenario analysis into a closed-loop, process-native methodology.
Enable data-driven strategic planning for healthcare operations by quantifying intervention impacts.

Main Methods:

Developed the PM²-Predictive Impact Model (PIM) framework, integrating process mining techniques.
Modeled a normative seven-step pathway using event logs from an Iranian Radiotherapy and Oncology Center (Fitness=0.97, Precision=1.00).
Identified high-impact deviations (e.g., skipped approvals, resequencing) and simulated their removal using PIM.

Main Results:

The PIM framework established a causal link between deviation types and system performance.
Simulations showed statistically significant reductions in Cycle Time (8.00%) and Workload (6.00%) after deviation removal.
Results were robust to parameter uncertainty (p < 0.001), confirming reliability.

Conclusions:

The PM²-PIM framework transforms retrospective process mining diagnosis into proactive, quantitative strategic planning.
Provides a reproducible, low-cost method for oncology services to prioritize interventions.
Enables evidence-rich decision-making for sustained performance gains in healthcare operations.