Health-Links Success Story: Arabic Sentiment Analysis Solution
公司规模
11-200
地区
- Middle East
国家
- Saudi Arabia
产品
- Repustate Sentiment Analysis Solution
- Press Ganey Solutions
技术栈
- API Integration
- Cloud-based Solution
- Machine Learning
实施规模
- Enterprise-wide Deployment
影响指标
- Customer Satisfaction
- Digital Expertise
- Productivity Improvements
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
- 应用基础设施与中间件 - 数据交换与集成
适用行业
- 医疗保健和医院
适用功能
- 商业运营
- 质量保证
服务
- 软件设计与工程服务
- 系统集成
关于客户
Health-Links is a specialized healthcare consultancy based in Jeddah, Saudi Arabia. The company partners with the Ministry of Health, KSA, and other healthcare leaders to improve the quality of healthcare in the Gulf region. Health-Links focuses on identifying gaps in care services and mapping the patient journey within hospitals using data-backed insights. The company collaborates with Press Ganey Associates for inpatient experience measurement, performance analytics, and strategic advisory solutions. Operating throughout the Middle East, Health-Links leverages Press Ganey’s solutions under a localized model to enhance healthcare delivery.
挑战
Health-Links faced several challenges in analyzing patient and employee feedback. The primary issue was the need for a robust, cloud-based sentiment analysis solution that could natively understand Arabic and handle mixed Arabic-English responses. The client needed to analyze a massive amount of unstructured text from over 12 million surveys annually. Manual interpretation was prone to human bias and errors, leading to inaccurate insights. The client required a solution that could provide sentiment scores for themes specified in the Saudi Complaints Taxonomy, enabling healthcare organizations to understand the pros and cons of their operations.
解决方案
Repustate provided Health-Links with a customized sentiment analysis solution capable of handling hundreds of API calls in seconds. The cloud-based solution offered aspect-based sentiment scores for each topic specified by the KSA Ministry of Health. The model was designed to natively analyze Arabic text without relying on English translations, classifying comments into Positive, Negative, Neutral, and Mixed categories. Repustate trained the model according to the Complaints Taxonomy and discovered new relevant topics during development. The model was reviewed and re-trained based on feedback from the Ministry, achieving an accuracy rate of 81% within a month and three iterations.
运营影响
数量效益
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