TY - JOUR
T1 - Exploring drivers of patient satisfaction using a random forest algorithm
AU - Simsekler, Mecit Can Emre
AU - Alhashmi, Noura Hamed
AU - Azar, Elie
AU - King, Nelson
AU - Luqman, Rana Adel Mahmoud Ali
AU - Al Mulla, Abdalla
N1 - Funding Information:
This publication is based upon work supported by the Khalifa University of Science and Technology under Award No. RCII-2019-002, Center for Digital Supply Chain and Operations Management. The funding body had no direct involvement in the design, data collection, analysis, and interpretation or in writing the manuscript.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - Background: Patient 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. Methods: We 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. Results: Our 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. Conclusions: Generating 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.
AB - Background: Patient 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. Methods: We 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. Results: Our 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. Conclusions: Generating 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.
KW - Data analytics
KW - Healthcare operations
KW - Machine learning
KW - Patient experience
KW - Patient satisfaction
KW - Quality
KW - Random forests
UR - http://www.scopus.com/inward/record.url?scp=85105819077&partnerID=8YFLogxK
U2 - 10.1186/s12911-021-01519-5
DO - 10.1186/s12911-021-01519-5
M3 - Article
C2 - 33985481
AN - SCOPUS:85105819077
SN - 1472-6947
VL - 21
JO - BMC Medical Informatics and Decision Making
JF - BMC Medical Informatics and Decision Making
IS - 1
M1 - 157
ER -