Aspect-based sentiment analysis helps extract granular insights from data analytics in healthcare. It helps healthcare providers prioritize important decisions that can be critical to various departments that need better operational efficiencies. Pharmaceutical companies, hospital chains, biochemistry and research agencies, and such organizations need fine-grained analysis of content from video, audio and text channels that incorporate both present and historical data. Not only for patient care enhancement, but this data is also important for providing employee satisfaction and a sustainable work environment for critical staff.
Nahdi Medical Company is a major healthcare provider that has a network across Saudi Arabia, covering 145 cities and villages. It provides advanced care in radiology, oncology, cardiology, and other areas, along with a robust pharmacy chain.
Nahdi needed a high-accuracy sentiment analysis model powered with Arabic natural language processing (Arabic NLP) that could perfectly understand Arabic and its dialects. This was important so that they could study and analyse the valuable feedback they collect continually from employees, patients, and caregivers, over a range of topics.
Repustate provided the customer with an aspect-based sentiment analysis model for granular data analytics in healthcare, specifically for Arabic. The model can process patient voice and Voice of the Employee (VoE) data, seamlessly from audio and video recordings, as easily as with text data.
In the client's words,
“Repustate's Arabic semantic analysis has helped Nahdi get a better grasp of what our customers and employees are saying quickly and accurately. Arabic text data is not easy to mine for insight, but with Repustate we have found a technology partner who is a true expert in the field.”
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