Aiden Tech Leverages AI to Enhance Healthcare Efficiency Amid DRG/DIP Payment Reform

The state has been exploring healthcare reform for many years.

In 2004, the Ministry of Health issued the “Notice on Launching Pilot Programs for Diagnosis-Related Group (DRG) Payment Management,” initiating healthcare reform through changes in medical insurance payment mechanisms.

In June 2016, the Ministry of Human Resources and Social Security issued the “Guiding Opinions on Actively Promoting the Linked Reform of Medical Care, Health Insurance, and Pharmaceuticals,”It was proposed to prioritize payment method reform in healthcare system reform and actively promote the application of Diagnosis-Related Group (DRG) payment.

Subsequently, the pace of healthcare reform gradually accelerated. Policies related to diagnosis-related group (DRG) payment reform were successively introduced, including the "Opinions on Promoting the Reform of Medical Service Prices," the "Notice on Promoting Case-Based Charging," and the "Notice on Issuing the List of National Pilot Cities for Diagnosis-Related Group (DRG) Payment." Relevant policies further explicitly stipulated that all pilot cities must fully implement actual DRG-based payments by the end of 2021.

With this, the DRG policy has been fully implemented.

Policies are clearly favorable to this development, and DRG will inevitably be gradually rolled out as policies advance. However, challenges such as chaotic data sources, inconsistent standards, and difficulties in DRG grouping remain urgent issues to be addressed in the application of DRG.

Beijing Yading Information Technology Co., Ltd. (hereinafter referred to as “Aideng Technology”) is one of the early domestic enterprises to enter the DRG field, having begun its strategic layout in DRG since 2016.

The company leverages “artificial intelligence,” “disease classification,” and “medical record quality control” as entry points to build an AI-driven “Disease Taxonomy Knowledge Graph,” aiming to address current DRG-related challenges, enhance healthcare efficiency, and improve healthcare equity.

Guided by the ultimate goal of “enhancing medical efficiency and improving healthcare equity,” Aideng Technology has developed the Aideng Hospital Collaboration Management Platform, a system supported by multiple foundational technologies.

The platform encompasses seven major product centers within the hospital, ranging from medical record quality control to health insurance settlement, cost analysis, and operational decision-making. By standardizing data, streamlining business processes, and modularizing functionalities, it provides efficient support for inter-departmental collaboration and assists functional departments in application and decision-making analysis related to DRG/DIP payment reform.

Furthermore, the platform also provides hospitals' various departments and key stakeholders with systematic capabilities to address DRG/DIP requirements.

It can be said that the DRG/DIP collaborative management platform not only meets the specific needs of individual departments but also maximizes the overall management and operational efficiency of the hospital.

Leveraging its underlying technological advantages, Aideng Technology’s services have expanded from an initial coverage of 26 hospitals to more than 3,000 hospitals.

Aideng Technology’s successful practices in DRG exploration stem from its corporate positioning.

In the interview, Zhu Wei, Chairman of the Company, shared with VCBeat:“Few companies in the market mention ‘using AI to improve health insurance efficiency,’ but our positioning is precisely to address healthcare efficiency issues based on disease taxonomy, leveraging technologies such as artificial intelligence and big data. In my view, this is the most efficient and direct way to enhance healthcare equity.”

Zhu Wei, Chairman of Aideng Technology

Following the logic of Aideng Technology, improving medical efficiency is the goal, DRG is the means, and proper disease classification is the key to streamlining the entire process.

Aideng Technology ensures the achievement of this objective by enhancing the accuracy of its AI-powered “Disease Taxonomy Knowledge Graph” and accelerating its overall iteration speed.

Zhu Wei believes that disease classification holds significant importance for today’s healthcare reform, stating:“Disease taxonomy is the foundation of medicine. Only through the standardized use of disease classification can a unified language be established within the medical community; furthermore, disease classification can realize its full potential only when integrated with extensive data within its framework. For the operational workflow of disease taxonomy—encompassing coding, computation, and application—medical records serve as the starting point. However, a thorough understanding of the significance and depth of disease taxonomy, along with mastery of relevant knowledge and skills, is essential to meaningfully engage in the entire business process.”

Since 2016, Aideng Technology has been building a disease taxonomy knowledge graph. After five years of refinement, it now serves over 3,000 hospitals and has become one of the most widely adopted AI tools for disease taxonomy in terms of market share.

Meanwhile, to ensure that disease classification standards remain aligned with DRG reforms, advancements in medical technology, and national requirements for healthcare payment reform, its AI algorithms are upgraded and iterated more than 20 times per year.

The application of DRG is gradually becoming the industry mainstream. Overall, there are three major benefits to promoting DRG.

First, for payers, the implementation of Diagnosis-Related Groups (DRGs) can alleviate the financial pressure on medical insurance funds to a certain extent, while DRG data is also expected to serve as a critical source for healthcare cost containment and medical big data.

Secondly, for hospitals, clinical pathway management and diagnosis-related group (DRG)-based payment are increasingly becoming essential requirements. The application of DRGs can not only improve hospital management efficiency but also increase hospital revenue;

Third, for patients, DRG can to some extent alleviate the difficulty of accessing medical care and improve the quality of care.

However, reflections on DRG should not remain superficial; instead, we must “open it up” and look inward.

Whether it pertains to healthcare reform or payment system reform, the fundamental objective is to enhance medical efficiency through cost-containment measures within health insurance reform. This facilitates a transition of the healthcare system from past volume-driven expansion to efficiency-oriented scaled development, ultimately addressing issues of equity in healthcare.

To advance the current healthcare system reform, standardizing and applying medical big data serves as the foundation and key. Consequently, data integration standards, code validation, disease classification, and imputation of missing data are critical areas warranting attention in this field. Aideng Technology leverages data to develop AI-driven tools for disease classification, enhancing hospital management and operational efficiency while meeting the specific needs of various clinical departments.

Under the vision statement of Aideng Technology, it reads: “The right to health for all.”

For Tianjin Aideng Technology Co., Ltd., disease taxonomy serves as the cornerstone, artificial intelligence and big data research methodologies in healthcare serve as the means, improvements in medical efficiency and health insurance fund utilization efficiency constitute the process, while achieving healthcare equity is the ultimate goal.

Amid favorable policies, market entrants have contributed their expertise to this sector, offering solutions from diverse perspectives. While Aideng Technology’s entry point is undoubtedly unique, whether the company can capitalize on this opportunity to surge ahead will depend on its subsequent strategic moves and layout.