ArticleBMC medical informatics and decision making2021
Exploring drivers of patient satisfaction using a random forest algorithm.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- An interdisciplinary mixed-methods systematic review of multidimensional factors shaping the outpatient waiting experience.BMC health services research · 2026Pooled it
- Determinants of patient satisfaction in a primary healthcare facility in Bangladesh.BMC health services research · 2026Article
- Predicting recreational therapy engagement in veterans' long-term care: a machine learning approach.Frontiers in health services · 2026Article
- Identifying key influencers of patient satisfaction using an explainable machine learning approach.Scientific reports · 2025Article
- What Factors Are Associated With Trust and Experience With the Clinician?Journal of patient experience · 2025Article
- How do Chinese people perceive their healthcare system? Inequality in public satisfaction with healthcare security.Frontiers in public health · 2025Article
- A Systematic Review of the Outcomes of Utilization of Artificial Intelligence Within the Healthcare Systems of the Middle East: A Thematic Analysis of Findings.Health science reports · 2024Review
- Predicting student self-efficacy in Muslim societies using machine learning algorithms.Frontiers in big data · 2024Article
- Assessment of the organizational factors in incident management practices in healthcare: A tree augmented Naive Bayes model.PloS one · 2024Article
- Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study.Journal of business research · 2023Article
- Evaluating Drivers of the Patient Experience Triangle: Stress, Anxiety, and Frustration.International journal of environmental research and public health · 2023Article
- Immunogenic cell death-led discovery of COVID-19 biomarkers and inflammatory infiltrates.Frontiers in microbiology · 2023Article
- Factors Associated with Outpatient Satisfaction in Provincial Tertiary Hospitals in Nanchang, China: A Structural Equation Modeling Approach.International journal of environmental research and public health · 2022Article
- Resource management framework using simulation modeling and multi-objective optimization: a case study of a front-end department of a public hospital in Thailand.BMC medical informatics and decision making · 2022Article
- Identifying Patient Satisfaction Determinants in Hemodialysis Settings: A Systematic Review.Risk management and healthcare policy · 2022Review
- Reporting Inpatients' Experiences and Satisfaction in a National Psychiatric Facility: A Study Based on the Random Forest Algorithm.Journal of patient experience · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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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