Developing a Novel Integrated Generalised Data Envelopment Analysis (DEA) to Evaluate Hospitals Providing Stroke Care...

Area of Science:

• Healthcare Operations Research
• Medical Informatics
• Public Health

Background:

• Stroke is a leading cause of adult disability and mortality in the US.
• Early stroke treatment significantly impacts long-term recovery and prognosis.
• Operational research methods are underutilized in addressing stroke care challenges.

Purpose of the Study:

• To propose a novel approach integrating Data Envelopment Analysis (DEA) and Multi-Objective Linear Programming (MOLP) for stroke care efficiency.
• To enhance decision-making in hospitals by selecting the most efficient stroke care strategies.
• To address limitations of existing DEA models by incorporating decision-maker preferences.

Main Methods:

• Developed a novel approach by transforming a generalized DEA (GDEA) model into MOLP using the max-ordering approach.
• Utilized the step method (STEM) to interactively solve the problem and incorporate decision-maker preferences.
• Applied the most preferred solution (MPS) to identify the most efficient stroke care approach.

Main Results:

• The proposed method successfully integrates DEA and MOLP, overcoming limitations of traditional DEA models.
• The approach allows for interactive problem-solving and considers decision-maker preferences without requiring predetermined information.
• Demonstrated the practical application of the method through a case study of hospitals providing stroke care.

Conclusions:

• The novel DEA and MOLP integrated approach offers a powerful tool for optimizing stroke care efficiency in hospitals.
• This method provides a more flexible and preference-informed way to evaluate and improve healthcare service delivery.
• The study highlights a new experimental direction for applying operations research in stroke management.