Adaptive Identification and Modeling of Clinical Pathways with Process Mining

TL;DR

This paper introduces a process mining-based two-phase method for modeling clinical pathways from healthcare data, enabling dynamic updates and improved accuracy in representing treatment variations.

Contribution

It presents a novel approach combining conformance checking with process mining to automatically extend clinical pathway models based on real patient data.

Findings

Achieved up to 95.62% AUC in conformance accuracy

Maintained 67.11% arc-degree simplicity in models

Demonstrated effectiveness using SARS-CoV-2 treatment data

Abstract

Clinical pathways are specialized healthcare plans that model patient treatment procedures. They are developed to provide criteria-based progression and standardize patient treatment, thereby improving care, reducing resource use, and accelerating patient recovery. However, manual modeling of these pathways based on clinical guidelines and domain expertise is difficult and may not reflect the actual best practices for different variations or combinations of diseases. We propose a two-phase modeling method using process mining, which extends the knowledge base of clinical pathways by leveraging conformance checking diagnostics. In the first phase, historical data of a given disease is collected to capture treatment in the form of a process model. In the second phase, new data is compared against the reference model to verify conformance. Based on the conformance checking results, the knowledge base can be expanded with more specific models tailored to new variants or disease combinations. We demonstrate our approach using Synthea, a benchmark dataset simulating patient treatments for SARS-CoV-2 infections with varying COVID-19 complications. The results show that our method enables expanding the knowledge base of clinical pathways with sufficient precision, peaking to 95.62% AUC while maintaining an arc-degree simplicity of 67.11%.