Identifying and prioritizing factors affecting doctors' professional performance for effective performance...

Efficient and effective healthcare delivery relies on the optimal performance of healthcare workers. By understanding the key performance factors, decision-makers can make data-driven decisions and implement targeted improvements to achieve the best results. In the literature, no comprehensive framework yet exists that simultaneously captures the relative importance, hierarchical interactions and prioritized structure of the performance factors specific to doctors. This study aims to fill this gap by systematically identifying, structuring and weighting the key drivers of doctors' professional performance using an advanced fuzzy decision-making methodology.

This study proposes an interval-valued Fermatean fuzzy analytic hierarchy process (AHP)-based decision-making framework to examine the decision problem. Within the model, five main factors are defined: "personal factors," "workplace conditions," "social factors," "management factors" and "career and development factors." Each main factor is broken down into various subfactors. The interval-valued Fermatean fuzzy AHP method is used to compare and prioritize the factors.

According to the results, "personal factors" is the most significant main factor. The five most important subfactors are determined as "education and experience," "skills and competencies," "quality of the working environment," "technological infrastructure and resources" and "knowledge management and communication."

The findings of this study serve to inform healthcare decision-making, facilitate policy development and design an effective performance management process. Additionally, they provide a valuable guide for doctors to self-assess and enhance their effectiveness.

This study formulates the evaluation of doctors' professional performance as a complex fuzzy multicriteria decision-making problem. It pioneers the application of the interval-valued Fermatean fuzzy AHP method in healthcare and contributes by adding new dimensions to the existing body of literature. The originality of this research lies not only in the application of an advanced fuzzy methodology but also in the integration of key factors into a hierarchical structure and their systematic prioritization. The study contributes new perspectives to both theory and practice in healthcare performance management.