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. 2021 May 13;21(1):157.
doi: 10.1186/s12911-021-01519-5.

Exploring drivers of patient satisfaction using a random forest algorithm

Mecit Can Emre Simsekler  1 , Noura Hamed Alhashmi  2 , Elie Azar  2 , Nelson King  2 , Rana Adel Mahmoud Ali Luqman  3 , Abdalla Al Mulla  3
Affiliations

Exploring drivers of patient satisfaction using a random forest algorithm

Mecit Can Emre Simsekler et al. BMC Med Inform Decis Mak. .

Abstract

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.

Keywords: Data analytics; Healthcare operations; Machine learning; Patient experience; Patient satisfaction; Quality; Random forests.

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Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the authorship and/or publication of this article.

Figures

Fig. 1
Fig. 1
Model 1: feature importance summary for the registration stage
Fig. 2
Fig. 2
Radar chart for the registration stage
Fig. 3
Fig. 3
Model 2: feature importance summary for the consultation stage
Fig. 4
Fig. 4
Radar chart for the consultation stage
See this image and copyright information in PMC

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