Exploring Patient Decision-Making In Hospital Choice: A Statistical Evaluation Using Exploratory Factor Analysis And Logistic Regression

Chaitali Dey; T. Jayanth Kumar; Anita Chaturvedi; Suresha Ramareddy; K J Ghanashyam

doi:10.29284/ijasis.12.1s.2026.74-84

Authors

Chaitali Dey Department of Data Analytics and Mathematical Sciences, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru -562112 India
T. Jayanth Kumar Department of Data Analytics and Mathematical Sciences, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru -562112 India
Anita Chaturvedi Department of Data Analytics and Mathematical Sciences, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru -562112 India
Suresha Ramareddy Department of Data Analytics and Mathematical Sciences, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru -562112 India
K J Ghanashyam Department of Data Analytics and Mathematical Sciences, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bengaluru -562112 India

DOI:

https://doi.org/10.29284/ijasis.12.1s.2026.74-84

Keywords:

Healthcare; Hospital Selection; Logistic Regression; Quantitative method; Patient satisfaction; Verbal communication; Social media; Service quality.

Abstract

This study investigates the determinants influencing patients' hospital selection for healthcare services, aiming to identify the most pertinent factors and categorize the decision-makers involved in choosing between private and public hospitals. A pre-designed questionnaire was employed to obtain data from patients utilizing multispecialty hospitals in Bengaluru, India. The study developed a logistic regression model by using Exploratory Factor Analysis. The environment, convenience and economic feasibility, personal experiences, care efficiency, referrals and suggestions, and professional healthcare services significantly affect patients' decisions. The results established five factors such as Hospital Service Quality Dimensions, Hospital Quality and Service Indicators, Patient Care Experience and Amenities, Healthcare Service Assessment Criteria, and Patients’ facility and Service Criteria. Although the model is moderate it is not suitable for prediction since the overall accuracy level is only 53%. Findings from this study could support predicting, as well as enhancing the understanding of criteria influencing hospital selection.

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