USTC Hospital Seeks Exclusive Licensing and Joint Development of an AI-Powered, Multi-Agent Medical Consumables Recommendation System for Disease-Specific Precision Management

Recently, the First Affiliated Hospital of University of Science and Technology of China (Anhui Provincial Hospital)“Medical Consumables Management System Based on Disease-Specific Intelligent Agent Technology for Consumable Recommendation”Public Notice on the Release of Projects for the Transformation of Job-Related Scientific and Technological Achievements. The hospital intends to license the patent technology and related systems to Lianxin Cloud (Anhui) Technology Co., Ltd. for implementation through an “exclusive licensing + collaborative technical development” model. The total amount of this transformation agreement is8.891 million yuan, of which the patent license portion amounts to RMB 1.091 million, and the technical cooperative development portion amounts to RMB 7.8 million.

The primary person in charge of this achievement is Chen Yujun, and the core inventors also include Wang Tao, Huang Cheng, Tong Guixian, He Xuemei, Yang Chunmei, Yang Dong, and other members.

The team has long been deeply engaged in the fields of smart healthcare and hospital management informatization, possessing solid research foundations and extensive practical experience in intelligent medical data processing, consumables management optimization, and the integration of clinical pathways with resource allocation.

The transferee of the scientific and technological achievements isLianxin Cloud (Anhui) Technology Co., Ltd., the company is committed to providing integrated solutions for operational management, resource optimization, and smart services to healthcare institutions through cloud computing, artificial intelligence, and Internet of Things (IoT) technologies. The Lianxin Cloud team possesses strong capabilities in technical development and practical implementation in the areas of healthcare information system integration, data governance, and intelligent decision support.

The technology proposed for commercialization in this instance isA Smart Medical Consumables Recommendation System Powered by Multi-Agent Collaboration: First, collect and analyze data related to disease types and medical consumables to build a specialized knowledge base; then leverage artificial intelligence for matching and reasoning to generate multiple recommendation options; finally, integrate these options to form personalized medical consumable recommendations and automatically trigger subsequent business processes.

The system enables end-to-end intelligent management, from disease identification to medical consumable recommendation, providing precise and dynamic configuration suggestions for different diseases. This helps hospitals achieve refined management of consumable usage and optimize cost control.

Medical consumables refer to a category of disposable medical devices used in the course of medical care for the prevention, diagnosis, treatment, monitoring, and alleviation of diseases, or for the regulation of physiological functions. These devices are utilized throughout the entire process of patient diagnosis and treatment; most are designed for single use, while those that are reusable must undergo rigorous disinfection and sterilization procedures.

The scientific and rational selection of medical consumables directly impacts treatment efficacy, healthcare cost control, and the standardization of clinical procedures.

Currently, with the gradual implementation of health insurance payment methods such as DIP (Diagnosis-Intervention Packet) and DRG (Diagnosis-Related Groups), healthcare institutions are facing greater pressure in controlling disease-specific costs. These payment models incentivize hospitals to optimize resource allocation and reduce unnecessary consumable usage by classifying diseases and establishing corresponding payment standards.

However, in the matching management of disease types and consumables, traditional methods that primarily rely on manual experience for recommendation and allocation are no longer able to meet the refined and personalized clinical demands.

In light of the above, existing medical consumables management methods often lack data-driven decision support, which can easily lead to a disconnect between consumables usage and the actual clinical needs of specific disease categories, resulting in either resource waste or inadequate allocation.

To this end, the technology proposesAn Intelligent Recommendation System Based on Multi-Agent Collaborative Decision-Making.

This method transcends the limitations of traditional reliance on manual expertise by establishing a complete closed-loop system that integrates data perception, knowledge base construction, intelligent decision-making, and business execution. It achieves end-to-end data-driven and automated management, from disease identification to medical consumable matching, providing clinicians with precise, dynamic, and interpretable consumable configuration solutions. This approach systematically addresses the dual challenges of current healthcare cost control and personalized diagnosis and treatment.

Under the traditional model, consumable recommendations rely heavily on the individual experience of healthcare professionals. This not only leads to inconsistent recommendations due to variations in experience but also makes it difficult to comprehensively and quantitatively account for increasingly complex, multidimensional constraints.

Particularly against the backdrop of the comprehensive implementation of healthcare payment reforms based on Diagnosis-Intervention Packet (DIP) and Diagnosis-Related Groups (DRG), medical institutions must strictly control costs while ensuring the quality of diagnosis and treatment, a challenge for which traditional methods have proven inadequate. The advantages of this technology are first reflected in its comprehensive data awareness and deep integration capabilities.

The system, through specially designed"Data-Aware Agents"(such as HIS agents, LIS agents, and PACS agents), proactively extract and integrate multi-source data from various heterogeneous hospital information systems, including disease diagnoses, patient-specific information, clinical pathways, historical medical consumables usage data, and health insurance payment data.

This breaks down the common data silos within hospitals, laying a solid data foundation for intelligent decision-making.

More importantly, this technology enablesFrom “Single-Point Decision-Making” to “Multi-Agent Collaborative Game-Theoretic Decision-Making”. The system does not rely on a single algorithmic model; instead, it deploys multiple AI decision-making agents with specialized functions, such as the “Cost Prediction Agent” responsible for forecasting expenses, the “Anomaly Detection Agent” for identifying unusual usage patterns, the “Resource Optimization Agent” for balancing cost, quality, and efficiency, and the “Risk Assessment Agent” for evaluating risks.

These agents perform specialized functions, akin to a top-tier medical expert team, analyzing and providing recommendations for the same case from diverse perspectives, including clinical efficacy, economic benefit, resource efficiency, and compliance safety. They conduct reasoning and matching based on a “Medical Consumables Knowledge Base” that integrates foundational information on medical consumables, disease-associated rules, clinical guidelines, and cost-effectiveness data.

Ultimately, the system integrates the outputs of various agents through a coordination mechanism to generate a consumable recommendation plan that balances multiple stakeholders' demands and achieves comprehensive optimality. This architecture makes system decisions more comprehensive and robust, enablingSimultaneously achieving the dual management objectives of “precision medicine” and “precision payment.”

Additionally,This technology features significant dynamic optimization and continuous learning capabilities, which constitute its long-term adaptive advantage.The system goes beyond one-time recommendations; it leverages a “Business Execution Agent” to translate recommended strategies into concrete actions such as inventory allocation and cost control, while continuously monitoring actual consumable usage outcomes and clinical feedback.

These feedback data form a closed loop, which is used to continuously optimize the upper-level AI decision-making models. This means that the system can continuously learn from real-world applications, adapt to changes in new diagnostic and treatment technologies, consumable products, and medical insurance policies, achieve self-evolution of the agent, and make the recommendation strategy more accurate over time.

Meanwhile, the advanced nature of this patented technology is reflected in the fact that it is not a simple improvement on existing technologies, but ratherA series of forward-looking innovations have been implemented, ranging from architectural design to algorithmic integration, representing the direction of evolution in medical consumables management toward the stage of “cognitive intelligence.”

Its primary advancement lies in proposing a modular, scalable “multi-agent collaboration” system architecture.In the field of medical artificial intelligence, although large language models (LLMs) have been applied to build agents for handling tasks such as appointment registration and question-answering, or to create specialist physician agents to assist in surgical planning, the systematic application of the multi-agent paradigm to consumable management—a scenario involving complex resource allocation and multi-objective optimization—represents a significant architectural innovation.

This architecture decomposes complex management challenges into subtasks such as perception, decision-making, execution, and feedback, with specialized agents assigned to handle each function. This design ensures clear system logic and facilitates maintenance and upgrades (e.g., allowing for the independent enhancement of an individual agent’s algorithms).

More importantly, by leveraging collaboration and game-theoretic interactions among agents, it simulates the decision-making process of human multidisciplinary consultations. When addressing multi-objective problems with inherent conflicts (e.g., optimal efficacy vs. minimal cost), it generates solutions that exhibit superior trade-off intelligence, surpassing those produced by any single model.

At the core algorithm level, the advancement of this technology is prominently demonstrated by its deep utilization of “multimodal feature fusion” and “interpretable reasoning” techniques.The system does not merely perform simple data matching when making decisions.

It first leverages technologies such as medical knowledge graphs, graph neural networks (GNN), and logical reasoning engines to conduct in-depth knowledge reasoning on disease characteristics, individual patient characteristics, clinical characteristics, and cost characteristics, respectively.

Subsequently,Innovatively adopting advanced machine learning techniques such as “attention mechanisms” and “feature fusion networks”, organically integrating the reasoning results derived from different dimensions and data structures (modalities). The “attention mechanism” enables the model to dynamically focus on the most critical features of the current case (for example, for an elderly patient with comorbidities, individual risk characteristics may be more important than conventional disease-specific features), thereby enhancing the personalization accuracy of recommendations.

Meanwhile, integrating the reasoning process of knowledge graphs enhances the interpretability of decision-making. The system can provide the logical chain and medical evidence behind its recommendations, which is crucial for gaining the trust of clinicians and meeting healthcare regulatory requirements, thereby effectively mitigating the “black box” decision-making risks commonly associated with AI models.

Finally, this technology demonstrates innovation in the integration and application of cutting-edge machine learning algorithms.In specific agents, this patent integrates a variety of cutting-edge algorithms currently prevalent in the field of artificial intelligence.

For example, the “Cost Prediction Agent” employs deep reinforcement learning to enable the model to autonomously optimize its prediction strategies; meanwhile, the “Anomaly Detection Agent” integrates multiple unsupervised learning algorithms, such as Isolation Forest and AutoEncoder, to conduct parallel detection and fuse results, thereby more accurately identifying complex and latent anomalous usage patterns.

This approach of “tailoring” the integration and optimization of the most suitable algorithms to the specific characteristics of each task demonstrates profound technical expertise. It ensures that the system achieves industry-leading performance at every granular functional node, thereby establishing significant technical barriers and overall technological advancement.

Currently, leading medical technology companies and research institutions both domestically and internationally are actively strategizing to address the core pain points prevalent in hospital operations, including resource misallocation, soaring costs, efficiency bottlenecks, and data silos.

In the international market,Qure AIIt is a medical AI company from India, primarily focused on the healthcare sector. It specializes in providing AI-powered solutions for medical imaging diagnosis, disease screening, and collaborative diagnosis and treatment, covering early detection and management of various diseases such as tuberculosis, lung cancer, and stroke. The company is committed to making precise diagnosis more accessible and affordable.

In the technical field related to medical consumables recommendation, its core achievement is the qTrack module within the Medical Imaging Agent Platform.Leveraging multi-agent collaboration capabilities, this module enables correlative analysis of medical imaging data and consumable usage data. For instance, in the diagnosis and treatment of conditions such as tuberculosis and lung cancer, once the AI-powered imaging system detects lesions, the qTrack module can automatically match corresponding recommendations for diagnostic consumables (e.g., biopsy supplies) and therapeutic consumables (e.g., interventional therapy supplies). Meanwhile, it monitors consumable usage efficiency and reduces unreasonable consumption through customized dashboards, while supporting integration with hospital information systems to facilitate consumable demand forecasting and inventory alerts.

The medical imaging AI agent platform and the qTrack module are currently in the commercial deployment phase.

In China，Sun Yat-sen Memorial Hospital, Sun Yat-sen UniversityIn the field of medical consumables technology, we intend to procure a “Medical Consumables Intelligent Agent Platform.” This platform features multiple core functionalities related to consumables management and recommendation, including predicting consumables demand and enabling intelligent restocking by leveraging AI models combined with historical consumption data and surgical schedules; real-time monitoring of consumables usage to minimize waste; providing decision support through multi-platform price comparison; and automatically verifying compliance with medical insurance policies and other regulatory requirements. Additionally, the platform must offer open information interfaces to facilitate hospital IoT infrastructure development.

In the future, the maturity of technology will depend not only on advancements in algorithms themselves but also on deep integration with existing hospital information systems, the construction of high-quality medical knowledge graphs, and seamless adaptation to clinical workflows. Achieving the transition from “auxiliary recommendation” to “trustworthy decision-making,” and validating clinical value through real-world cost-effectiveness data, will be key challenges common to all technological approaches, ultimately determining their depth of application and market prospects.