arXiv:2512.03787v2 Announce Type: replace 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 t
The increasing sophistication of process mining techniques, combined with the growing availability of clinical data, makes this AI-driven approach to healthcare optimization feasible now.
This development allows for the automated identification and improvement of clinical pathways, leading to more efficient, cost-effective, and standardized patient care, especially for complex cases.
Healthcare providers can move from manual, static clinical guideline development to dynamic, data-driven optimization of treatment protocols, potentially reducing variations in care and improving outcomes.
- · Hospitals and healthcare providers
- · Patients with complex conditions
- · AI/process mining software developers
- · Healthcare data analytics firms
- · Inefficient healthcare systems
- · Manual clinical guideline development roles
- · Legacy healthcare IT systems
Healthcare systems adopt process mining tools to analyze and optimize existing clinical pathways.
Improved patient outcomes and reduced healthcare costs become measurable leading to broader adoption of AI in healthcare operations.
The development of 'adaptive' or 'personalizable' clinical pathways that continuously learn and adjust based on individual patient data and real-world outcomes.
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