How EviMed Powers AI-Driven Healthcare Insights with Zilliz Cloud’s Scalable Vector Search
30%
reduction in internal system operation and maintenance costs
8%
increase in customer response speed
10%
improvement in search result accuracy
Plug-and-Play Simplicity
minimizes development and DevOps overhead
After integrating Zilliz Cloud vector database service, our system performance has significantly improved. During implementation, the Zilliz Cloud expert team provided excellent support and assistance, giving our EviMed platform a strong competitive advantage in the industry.
Dr. Zeyuan Wang
About EviMed
EviMed is a healthcare technology company dedicated to empowering medical professionals with AI-driven solutions. Its platform harnesses big data and artificial intelligence to support clinical decision-making, medical research, education, and healthcare management. By delivering real-time, evidence-based medical knowledge and treatment recommendations, EviMed enables clinicians to make faster and more informed decisions at the point of care. The platform also simplifies research workflows by analyzing large volumes of medical literature, making it an indispensable tool for researchers and educators. For healthcare administrators, EviMed provides data-driven insights that support more effective management and policy development.
To date, EviMed has supported over 300 large tertiary hospitals, facilitated more than 13 million medical knowledge searches and clinical Q&A sessions, and contributed to the generation of over 800,000 pieces of medical content. With a mission to improve medical accuracy, efficiency, and outcomes, EviMed is transforming how healthcare professionals access and apply medical knowledge across the care continuum.
Challenges: Navigating the Complexities of Medical Knowledge at Scale
The healthcare industry is inherently complex—characterized by vast information asymmetry, specialized terminology systems, and highly fragmented domains of knowledge. For EviMed, these challenges were amplified by their mission to deliver fast, evidence-based answers at scale.
EviMed had built an extensive medical knowledge database with over 350 million entries, making it one of the most comprehensive in the industry. However, the scale and depth of this data introduced several technical bottlenecks:
Inaccurate or Incomplete Search Results: Traditional full-text search methods struggled to handle the nuances of medical language. With vague keyword boundaries and overlapping terminology, it was often difficult to find the most relevant results. Users frequently encountered issues with search accuracy and completeness, leading to dissatisfaction and complaints about limited coverage.
Performance and Reliability Limitations: Existing infrastructure couldn’t consistently deliver the low-latency, high-reliability performance needed for clinical environments, where seconds matter. As a result, search operations were often slow or unstable.
Scaling and Cost Concerns: With continued business growth, EviMed needed a solution that could scale elastically to handle increasing vector storage and retrieval demands without incurring skyrocketing operational overhead.
Support for Advanced Search Types: Their use cases required more than just dense vector search. The platform needed to support dense and sparse vector search, as well as hybrid keyword-vector queries, to capture the full range of medical semantics.
Technical Compatibility Requirements: EviMed's backend infrastructure heavily relied on Java, so the new solution also had to offer strong Java SDK support for seamless integration.
As Dr. Zeyuan Wang, CEO of EviMed, put it: “How to search for the most accurate medical knowledge and present it in the most reasonable way is the main technical challenge faced by our medical platform.”
Why Choose Zilliz Cloud?
After thorough evaluation and communication with the Zilliz team, EviMed identified Zilliz Cloud as the ideal solution for their needs. Their decision was based on several key factors:
Versatile Search Capabilities: Zilliz Cloud supports diverse search mechanisms, including dense vector search, sparse vector search, and keyword search.
Scalability: The platform offers elastic scaling to support growing vector storage and searching needs.
Cost Efficiency: Zilliz Cloud incurs lower operational costs compared to alternatives.
Java Compatibility: The solution offered good compatibility with their existing Java-based systems.
Expert Support: The Zilliz Cloud expert team provided excellent implementation support and assistance.
How Zilliz Cloud Transformed EviMed's Operations
EviMed has successfully migrated tens of millions of vector data to Zilliz Cloud, powering two its core business modules:
Medical Knowledge Search: Accelerates access to relevant and accurate medical knowledge.
Clinical Research Support: Enables efficient mining and analysis of medical literature, guidelines, and pharmaceutical data.
Zilliz Cloud has become a cornerstone of EviMed’s platform, delivering significant improvements in performance, intelligence, and scalability across the board.
1. Faster and More Accurate Data Retrieval
Zilliz Cloud dramatically improves both the speed and precision of EviMed’s medical knowledge search. Healthcare professionals can now get answers to clinical questions faster, leading to quicker decision-making and better patient outcomes.
2. Deeper Clinical Insight Through Advanced Data Mining
In scenarios such as clinical trials and pharmaceutical evaluations, Zilliz Cloud enables high-dimensional vector analysis to extract key insights from complex datasets—medical literature, treatment guidelines, and drug instructions—boosting the speed and quality of both qualitative and quantitative research outcomes.
3. Providing Long-term Memory for AI Agents
Zilliz Cloud also provides a critical backend capability for EviMed’s AI-powered data analysis modules. Instead of loading large volumes of context into LLMs—which is costly and inefficient—EviMed uses Zilliz Cloud to store and retrieve long-form memory for AI Agents via vector search. This not only supports more intelligent workflows but also significantly reduces operational costs.
4. Compatibility and Elastic Scalability
Zilliz Cloud’s out-of-the-box compatibility with Java and support for hybrid search—including dense vector, sparse vector, and keyword-based search—ensures seamless integration into EviMed’s existing infrastructure. Its fully managed, cloud-native architecture also allows the team to scale effortlessly with business growth.
Key Benefits and Results
The implementation of Zilliz Cloud has delivered substantial benefits for EviMed:
Improved Customer Experience
8% increase in customer response speed
10% improvement in search result accuracy
Operational Efficiency
30% reduction in internal system operation and maintenance costs
Plug-and-play simplicity minimizes development and DevOps overhead
Enhanced Search Capabilities
EviMed has eliminated the "no search results" problem that plagued their previous solution. Medical terminology is diverse, with diseases often having multiple subgroups and qualifiers. For example, a complex query like "advanced gastric or gastroesophageal junction adenocarcinoma in patients previously treated with fluoropyrimidine- or platinum-containing chemotherapy" would challenge traditional full-text search systems. Zilliz Cloud's combined vector retrieval and keyword-based full-text search capabilities enable hybrid searching that consistently delivers relevant results.
Superior Search Capabilities
Zilliz Cloud helped eliminate frustrating “no result found” problems that plagued their previous solution, especially in complex, multi-faceted queries. For example, a search like:
“Advanced gastric or gastroesophageal junction adenocarcinoma in patients previously treated with fluoropyrimidine- or platinum-containing chemotherapy” would overwhelm traditional full-text search systems.
Zilliz Cloud’s hybrid search—combining vector-based semantic search with keyword-based full-text search—delivers highly relevant results even in the most nuanced clinical contexts.
Provide Memories for Their AI Agents
EviMed's data analysis module relies on a set of AI Agents. Feeding all background information into large language models would be prohibitively expensive. Zilliz Cloud stores this background information and retrieves it through vector search according to the Agent's needs, reducing dependency on large model long context and lowering data analysis costs.
Looking Ahead
The partnership between EviMed and Zilliz has positioned the medical knowledge platform for continued growth and innovation. As Dr. Zeyuan Wang, CEO of EviMed, notes: "After integrating Zilliz Cloud vector database service, our system performance has significantly improved. During implementation, the Zilliz Cloud expert team provided excellent support and assistance, giving our EviMed platform a strong competitive advantage in the industry."
With Zilliz Cloud as a foundation, EviMed continues to advance its mission of making medical knowledge more accessible and actionable for healthcare professionals worldwide.