Exploring drivers of patient satisfaction using a random forest algorithm

BMC Medical Informatics and Decision Making

Nelson King¹  Noura Hamed Alhashmi¹  Elie Azar¹  Mecit Can Emre Simsekler¹  Rana Adel Mahmoud Ali Luqman²  Abdalla Al Mulla² 

[1] Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, UAE;Mubadala Healthcare, Ipic Square, 45005, Abu Dhabi, UAE;

关键词: Patient satisfaction;    Quality;    Healthcare operations;    Patient experience;    Random forests;    Data analytics;    Machine learning;   

DOI  :  10.1186/s12911-021-01519-5

来源: Springer

【 摘 要 】

BackgroundPatient satisfaction is a multi-dimensional concept that provides insights into various quality aspects in healthcare. Although earlier studies identified a range of patient and provider-related determinants, their relative importance to patient satisfaction remains unclear.MethodsWe used a tree-based machine-learning algorithm, random forests, to estimate relationships between patient and provider-related determinants and satisfaction level in two of the main patient journey stages, registration and consultation, through survey data from 411 patients at a hospital in Abu Dhabi, UAE. Radar charts were also generated to determine which type of questions—demographics, time, behaviour, and procedure—influence patient satisfaction.ResultsOur results showed that the ‘age’ attribute, a patient-related determinant, is the leading driver of patient satisfaction in both stages. ‘Total time taken for registration’ and ‘attentiveness and knowledge of the doctor/physician while listening to your queries’ are the leading provider-related determinants in each model developed for registration and consultation stages, respectively. The radar charts revealed that ‘demographics’ are the most influential type in the registration stage, whereas ‘behaviour’ is the most influential in the consultation stage.ConclusionsGenerating valuable results, the random forest model provides significant insights on the relative importance of different determinants to overall patient satisfaction. Healthcare practitioners, managers and researchers can benefit from applying the model for prediction and feature importance analysis in their particular healthcare settings and areas of their concern.

【 授权许可】

CC BY