Ischemic stroke: Process perspective, clinical and profile characteristics, and external factors

Area of Science:

• Health Informatics
• Data Science
• Public Health

Background:

• Ischemic stroke requires immediate treatment and risk factor management for prevention.
• Understanding healthcare processes is crucial for improving patient outcomes.
• Electronic Health Records (EHRs) offer rich data for healthcare process analysis.

Purpose of the Study:

• To introduce and validate a novel analysis method (PDM) for healthcare processes.
• To apply PDM analysis to an ischemic stroke dataset for improved understanding.
• To identify factors affecting ischemic stroke patient care and outcomes.

Main Methods:

• Combined process mining and data analysis techniques.
• Analyzed 4,830 ischemic stroke EHRs from 2010-2017.
• Grouped patient data by primary care units (PCUs).

Main Results:

• Developed the Process, Data, and Management (PDM) analysis method.
• Identified factors contributing to a 3.4-hour increase in hospital admission time for urgent care patients.
• Found patient social class to be a key differentiator in secondary stroke rates across PCUs.
• Visualized pre-stroke risk factors by PCU for targeted prevention.

Conclusions:

• PDM analysis provides a comprehensive view of the healthcare process, integrating treatment flow and patient characteristics.
• Results support health managers in developing targeted action plans for stroke care improvement and prevention.
• PDM analysis is adaptable to various disease contexts and aids in policy formulation and resource allocation.