A Process Mining Application for the Analysis of Hospital-at-Home Admissions

Area of Science:

• Healthcare Management
• Business Process Management
• Data Science

Background:

• Hospital-at-home services are growing, requiring efficient patient admission processes.
• Traditional process modeling methods can be time-consuming and subjective.
• There is a need for data-driven approaches to optimize healthcare operations.

Purpose of the Study:

• To apply process mining techniques to analyze and improve the patient admission process for hospital-at-home services.
• To demonstrate the utility of automatic process discovery using hospital information system data.
• To enhance business process management in healthcare through simulation and scenario analysis.

Main Methods:

• Extraction of process information from hospital information system data to create an event log.
• Application of automatic process discovery algorithms using specialized process mining tools.
• Focus on the patient admission subprocess for service case discrimination.

Main Results:

• Successful application of process mining to discover the actual patient admission process.
• Identification of potential areas for improvement in the existing workflow.
• Demonstration of a data-driven approach for process analysis.

Conclusions:

• Process mining offers a powerful, automated method for analyzing and optimizing hospital admission processes.
• This approach can supplement traditional methods, leading to more efficient healthcare service delivery.
• The framework supports business process simulation for future service improvements and scenario planning.