Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

6.6K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.6K
Patient-centered Care01:13

Patient-centered Care

2.6K
Patient-centered care involves delivering care beyond inpatient hospitalization. Reflective practice can enhance a patient-centered approach. Reflective practice is a process of reasoning that considers all aspects of the present situation, including practicalities, learning from personal practice, and consideration of patient needs. Patients appreciate care decisions made while considering their input. Involving the patient in their care provides the patient with a sense of contribution rather...
2.6K
Response Surface Methodology01:16

Response Surface Methodology

354
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
354
Survival Tree01:19

Survival Tree

206
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
206
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.2K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.2K
Cancer Survival Analysis01:21

Cancer Survival Analysis

493
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
493

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Adenoid cystic carcinoma of the trachea: an uncommon presentation of a rare malignancy.

Journal of surgical case reports·2026
Same author

The Role of Artificial Intelligence in Reducing Dispensing Errors for Patient Safety and Quality: A Systems Approach.

Risk management and healthcare policy·2026
Same author

Applying Quality Improvement Science to Patient Safety: Strategies, Frameworks, and Sustainable Solutions.

Risk management and healthcare policy·2025
Same author

AI-Driven Decision Support Framework for Preventing Medical Equipment Failure and Enhancing Patient Safety: A New Perspective.

Journal of multidisciplinary healthcare·2025
Same author

Optimizing Emergency Department Operations: A Simulation Framework for Managerial Decision-Making.

Journal of nursing management·2025
Same author

Streamlining Patient Fall Prevention and Management Through Human-Centered AI-Based Decision Support Systems.

Risk management and healthcare policy·2025
Same journal

Established machine learning matches tabular foundation models in clinical predictions.

BMC medical informatics and decision making·2026
Same journal

Explainable AI machine learning framework for chronic kidney disease prediction utilizing electronic health records.

BMC medical informatics and decision making·2026
Same journal

Interpretable SHAP-based machine learning framework for patient satisfaction prediction: a case study in Thammasat University Hospital.

BMC medical informatics and decision making·2026
Same journal

Automated generation of structured breast ultrasound reports using BreastViT and ChatGPT.

BMC medical informatics and decision making·2026
Same journal

Shared decision-making and medication adherence among community adults with chronic diseases: a cross-sectional study in Hubei Province, China.

BMC medical informatics and decision making·2026
Same journal

Classification of periapical radiographic findings for root canal therapy decision support using deep neural networks.

BMC medical informatics and decision making·2026
See all related articles

Related Experiment Video

Updated: Nov 5, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

617

Exploring drivers of patient satisfaction using a random forest algorithm.

Mecit Can Emre Simsekler1, Noura Hamed Alhashmi2, Elie Azar2

  • 1Department of Industrial and Systems Engineering, Khalifa University of Science and Technology, P.O. Box 127788, Abu Dhabi, UAE. emre.simsekler@ku.ac.ae.

BMC Medical Informatics and Decision Making
|May 14, 2021
PubMed
Summary
This summary is machine-generated.

Patient age is the primary driver of satisfaction. Provider attentiveness and registration time also significantly impact patient satisfaction, with demographics most influential in registration and behavior in consultations.

Keywords:
Data analyticsHealthcare operationsMachine learningPatient experiencePatient satisfactionQualityRandom forests

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.5K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

306

Related Experiment Videos

Last Updated: Nov 5, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

617
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.5K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

306

Area of Science:

  • Healthcare Quality Improvement
  • Health Services Research
  • Patient Experience

Background:

  • Patient satisfaction is a critical indicator of healthcare quality.
  • Previous research identified various patient and provider factors influencing satisfaction, but their relative importance is not well-defined.

Purpose of the Study:

  • To determine the relative importance of patient and provider-related factors on patient satisfaction.
  • To analyze satisfaction drivers during hospital registration and consultation stages using machine learning.

Main Methods:

  • Employed a random forest machine-learning algorithm with survey data from 411 hospital patients in Abu Dhabi, UAE.
  • Analyzed determinants of patient satisfaction across registration and consultation phases.
  • Utilized radar charts to assess the influence of question types (demographics, time, behavior, procedure) on satisfaction.

Main Results:

  • Patient age emerged as the leading determinant of satisfaction in both registration and consultation stages.
  • 'Total time for registration' and 'physician attentiveness' were key provider-related factors for registration and consultation, respectively.
  • Demographics most influenced registration satisfaction, while patient behavior was most influential during consultations.

Conclusions:

  • The random forest model effectively identified the relative importance of determinants impacting patient satisfaction.
  • Findings offer valuable insights for healthcare practitioners, managers, and researchers for predictive analysis and improving patient experience.