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Exploring drivers of patient satisfaction using a random forest algorithm

Authors
  • Simsekler, Mecit Can Emre1
  • Alhashmi, Noura Hamed1
  • Azar, Elie1
  • King, Nelson1
  • Luqman, Rana Adel Mahmoud Ali2
  • Al Mulla, Abdalla2
  • 1 Khalifa University of Science and Technology, Abu Dhabi, UAE , Abu Dhabi (United Arab Emirates)
  • 2 Mubadala Healthcare, Ipic Square, Abu Dhabi, 45005, UAE , Abu Dhabi (United Arab Emirates)
Type
Published Article
Journal
BMC Medical Informatics and Decision Making
Publisher
Springer (Biomed Central Ltd.)
Publication Date
May 13, 2021
Volume
21
Issue
1
Identifiers
DOI: 10.1186/s12911-021-01519-5
Source
Springer Nature
Keywords
License
Green

Abstract

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.

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