Related Experiment Video
Updated: Aug 8, 2026
10:39
Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
Published on: August 29, 2025
Quality measurement across borders: needs and options
1Joint Commission International Europe.
World Hospitals and Health Services : the Official Journal of the International Hospital Federation
|May 4, 2006
Summary
Patients seek cross-border healthcare for cost, access, and proximity. However, healthcare quality and data collection methods vary significantly between countries, posing challenges for policymakers and providers.
Area of Science:
- Healthcare policy
- International health systems
- Quality improvement
Background:
- Patients increasingly seek healthcare services across national borders, driven by factors like cost, access, and proximity.
- Significant variations exist in healthcare quality and regulatory approaches (e.g., licensure, accreditation) internationally.
- Current public and private quality evaluation systems are inconsistent, yielding non-comparable data.
Purpose of the Study:
- To investigate the challenges in assessing healthcare quality for international patients.
- To highlight the variability in quality data collection and regulatory frameworks across countries.
- To propose the use of international standards for evaluating cross-border healthcare quality.
Main Methods:
- Analysis of traditional regulatory approaches (licensure, accreditation, certification) in different countries.
- Review of private sector quality evaluation systems and their comparability.
- Examination of survey data (e.g., Joint Commission International) on electronic medical record adoption and adverse event reporting.
Main Results:
- Wide variations observed in the use of electronic medical records and adverse event data collection globally.
- Traditional regulatory and private quality assessment methods differ significantly, leading to non-comparable quality data.
- Existing systems struggle to provide consistent quality metrics for cross-border healthcare services.
Conclusions:
- International healthcare travel is common, but quality assessment remains a significant challenge.
- The variability in data collection and regulatory oversight hinders the evaluation of cross-border healthcare quality.
- Adoption of international standards focusing on process-outcome relationships is crucial for managing risks and ensuring quality in international healthcare.
Related Concept Videos
01:19Quality Assurance
Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
01:05Quality Control
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
01:11Variability: Analysis
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
The range is a simple measure of variability, indicating the difference between the highest and...
01:00Detection of Gross Error: The Q Test
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
03:38Measurement: Standard Units
Every measurement provides three kinds of information: the size or magnitude of the measurement (a number), a standard of comparison for the measurement (a unit), and an indication of the uncertainty of the measurement. While the number and unit are explicitly represented when a quantity is written, the uncertainty is an aspect of the errors in the measurement results.
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...