Frost & Sullivan Frost & Sullivan China officially releases 'White Paper on the Value Platform of Chinese AI Hospital Operations Service in 2026' | AI hospital operations enter a new stage of platform-based value realization (including how to obtain it)

01

Four growth drivers converging, the value of hospital online traffic entry points is being re-evaluated

The increase in the value of hospital online traffic entry points is essentially the result of combined changes in the medical supply and demand structure, policy orientation, and technical capabilities. On the supply side, China's medical service system continues to expand. In 2024, the number of hospitals reached 38,710, and the number of tertiary hospitals increased to 4,111, a growth of approximately 37% compared to 2020. However, the annual diagnostic and treatment demand of 10.15 billion patient visits still concentrated in high-quality medical resources, indicating room for further improvement in diversion and matching efficiency. On the demand side, in 2025, the population aged 60 and above in China reached 320 million, accounting for 23.0% of the total population, and there were approximately 500 million chronic disease patients. Medical services are expanding beyond single diagnoses to follow-up care, chronic disease management, and long-term health needs such as rehabilitation and physical examinations. At the policy level, "Healthy China 2030" and the high-quality development of public hospitals continue to drive hospitals to shift from "scale expansion" to "improved quality and efficiency," further enhancing the connection and operational value of online entry points. At the same time, AI-guided diagnosis, pre-diagnosis, and follow-up care have begun to be integrated into actual treatment processes. In public practice, consultation time can be reduced by approximately 40%, and waiting time can be reduced to about 12 minutes. Technological applications are moving from functional deployment to actual efficiency improvement. With the combined effect of high diagnostic and treatment demand, increased online service penetration, and upgraded operational requirements, hospital online entry points are evolving from process tools such as registration and payment to infrastructure that connects patient needs, handles in-hospital processes, and supports continuous operations.

Source: National Health Commission, National Bureau of Statistics, SULLIVAN Analysis

02

Supply Side: Total growth, structural concentration, creating three types of efficiency challenges

Continuous expansion of medical supply does not simultaneously improve resource allocation efficiency. The structural concentration of high-quality medical resources remains the core constraint for hospital operations to improve efficiency. Tertiary hospitals account for only about 11% of national hospitals, but handle 28.3% of the total patient visits. Many common diseases, follow-up care, and follow-up needs continue to concentrate in top-tier hospitals, further amplifying three types of efficiency gaps: patient diversion, doctor matching, and follow-up management. For example, in Shanghai First People's Hospital, non-emergency cases accounted for 43% of emergency cases, and incomplete chief complaint information led to a 18% error rate in triage, with an average waiting time of about 35 minutes. In the post-treatment phase, problems such as scattered patient access and insufficient continuous management are common. AI follow-up can increase the follow-up rate by more than 20%. Therefore, the focus of hospital digital upgrade is shifting from "adding entry points" to "enhancing the operational efficiency behind the entry points." By optimizing diversion, matching, and continuous management, existing medical resources are released, and the space for service delivery and non-medical insurance revenue is further expanded.

03

Demand Side: From "receiving patients" to "operating patient relationships"

The supply side requires improved efficiency, while the demand side is extending the service cycle between hospitals and patients. In 2025, the proportion of people aged 60 and above in China reached 23.0%, and it is expected to rise to approximately 30.7% by 2035. There are more than 500 million chronic disease patients, and deaths related to chronic diseases account for over 80%. Under the trends of population aging and chronic diseases becoming more common, medical needs are expanding beyond single diagnoses to chronic disease follow-up, postoperative review, long-term management such as diabetes and hypertension, as well as health services such as rehabilitation and physical examinations. As the frequency of services increases and the cycle extends, hospital operations are shifting from treating individual "patients" to continuously "operating patient relationships," with service value extending beyond single diagnoses to follow-up care, retention, and long-term health management.

04

Policy Side: From "building systems" to "requiring operational results"

The changing operational needs of hospitals are receiving clearer policy support. The DRG/DIP payment system reform has shifted medical insurance payments from process management to outcome management, emphasizing hospital cost control and refined operations. In November 2024, the National Medical Insurance Administration issued guidelines for setting medical service price items, including an "AI assistance" extension item for radiography, ultrasound, and rehabilitation services, providing an interface for AI to enter the pricing system. The reference guide on artificial intelligence applications in the health sector released in the same month covered 84 application scenarios across four major areas, including triage, pre-consultation, and follow-up services. "Healthy China 2030" and the national health informationization plan continue to drive public hospitals to shift from scale expansion to quality improvement. Therefore, policy guidance no longer focuses solely on completing informatization, but rather on whether digitalization can continuously improve efficiency, service quality, and patient experience, giving platforms with full-process operation capabilities clearer implementation opportunities.

05

Conclusion of the Turning Point: The Entry Has Been "Operationalized", AI Pre-diagnosis Transforms Pre-triage into Operational Results

Real Hospital Practices Further Demonstrate That AI Can Already Transform the Pre-triage Process into Quantifiable Operational Results. After deploying AI pre-diagnosis and operating for 6 months at Shanghai First People's Hospital in 2024, the triage accuracy increased from 68% to 89%, the average waiting time was reduced from 35 minutes to 12 minutes, the triage error rate dropped below 5%, the symptom collection accuracy reached 92%, the consultation time was shortened by approximately 40%, and the doctors' consultation efficiency improved by about 40%. At the same time, the proportion of non-emergency cases in emergency services decreased from 43% to 19%, and patient satisfaction increased from 82 points to 94 points. In Beijing Anzhen Hospital, the AI-guided diagnosis test showed a consultation speed increase of more than 15 minutes, and Tiantan Hospital's AI stroke imaging also reduced the interpretation time. Multiple cases collectively verify that the online entry has begun to evolve from a process-carrying tool into an operational entry capable of accumulating efficiency and service results, which also forms the real foundation for AI Hospital Operation Services to further become a separate sector.

Source: Shanghai First People's Hospital (2024, 6 months of operation), Beijing Anzhen Hospital, public reports and practical cases of Tiantan Hospital (2024–2025), Sullivan Analysis

01

Sector Definition: Along the Patient Visit Journey, Transform Pre-triage, During-triage, and Post-triage into Measurable Operational Results

AI Hospital Operation Services Are Not Just A Single AI Software or Function Set, But A New Type of Operation and Service Model Formed Around the Real Medical Visits Process of Public Hospitals. Its Core Is To Continuously Embed Tools Such As AI-guided diagnosis, pre-diagnosis, escorting, medical assistance, and follow-up Along Pre-triage, During-triage, and Post-triage, And Further Standardize and Productize the Long-term Hospital Operation Experience, So That AI Extends From a Single Function to A Continuous Operation Capability Covering the Entire Patient Journey. This Model Has Obvious Differences from the Value Logic of Traditional Smart Hospitals and Internet Medical Platforms. Traditional Smart Hospitals Focus More On Information Systems and Software Hardware Construction, And Their Value Is Usually Achieved at the Project Delivery and Acceptance Stages. Internet Medical Platforms Mainly Rely On Registration, Consultation, Traffic, or Drug Trading to Form Commercialization. AI Hospital Operation Services Emphasize Long-term Embedding in the Real Hospital Process, Continuously Delivering Measurable Operational Results. Its Value Goes Beyond "Completing System Construction"; It Relates To Whether It Can Really Improve Patient Matching, Medical Staff Efficiency, and Post-triage Management.

Public Practices Have Already Demonstrated This Result-Oriented Feature. In the Pre-triage Phase, AI-guided diagnosis and pre-diagnosis Can Increase Triage Accuracy To Approximately 89%. In the During-triage Phase, Tools Such As AI medical assistance Can Replace More Than 70% of Non-clinical Tasks. In the Post-triage Phase, Automated Follow-up Can Increase The Patient Revisit Rate By More Than 20%. Therefore, The Market Boundary Of AI Hospital Operation Services Does Not Depend On Whether AI Is Used, But On Whether AI Really Enters The Core Processes Of Pre-triage, During-triage, and Post-triage, And Continuously Forms Quantifiable, Reproducible Operational Results.

02

Market Potential: Three Factors of Certainty - Policy, Demand, and Technology, Driving Long-Term Expansion

The Growth Potential of the AI Hospital Operation Market Is Based On The Synchronous Maturity Of Three Factors: Policy, Demand, and Technology. At the Policy Level, The High-quality Development Of Public Hospitals And The DRG/DIP Reform Continuously Strengthen The Requirements For Improving Quality and Efficiency, Providing a Stable Demand Base For Hospital Operation Digitalization. At the Demand Level, Aging Population And Chronic Diseases Continuously Extend The Patient Service Cycle, And High-frequency Scenarios Such As Revisits, Follow-up, Rehabilitation, and Long-term Health Management Continue To Expand. At the Technology Level, Applications Such As AI-guided diagnosis, pre-diagnosis, escorting, and follow-up Are Gradually Being Productized, And Starting To Form Quantifiable Improvements In Real Medical Processes. At the Same Time, The Global "AI+Medical" Market Is Expected To Increase From $26.7 Billion In 2025 To $155.3 Billion In 2030, With A CAGR Of Approximately 35.5%, Reflecting That The Application Scope And Commercialization Depth Of AI In Medical Services Are Still In A Fast Expansion Stage. The Three Factors Together Support AI Hospital Operation To Move From Single-Point Testing To Full-Process Deployment, And Provide A Foundation For Subsequent Scale Replication.

03

Market Space: AI Hospital Drives a Billion-level Sector, Continuously Expanding from Narrow to Wide

The Market Space Of AI Hospital Operation Services Can Be Observed At Three Levels. The Digital Medical Health Comprehensive Service Market Covers Digital diagnosis and treatment, Consumer Medicine, Health management, and Data value-added Comprehensive Services. It Is Expected To Reach 74.01 billion yuan In 2030. The Digital Medical and Health Service Platform Market Focuses On Platform-based Services Connecting Hospitals, Doctors, and Patients, Covering Links to Online Consultation, Reservation, Operation, and Patient Management. It Is Expected To Reach 54.19 billion yuan In 2030. The AI Hospital Operation Market Focuses On The Operational Services Directly Formed By Deep AI Embedding In The Pre-triage, During-triage, and Post-triage Full-process Of Public Hospitals. It Is Expected To Reach 1.017 billion yuan In 2030.

The Three Levels Correspondingly Represent The Overall Market Space Of Digital Medical Health Services, The Platform-scale Expansion Space, And The Core Sub-sector Of AI Hospital Operation. The Current Penetration Rate Of The AI Hospital Operation Market Is Only About 3%, And There Is Still Approximately 97% of the Market Space To Be Opened. As AI Applications Extend From Single-point Tools To Full-process Operation, The Hospital Payment Penetration Rate And The Single-hospital Operation Value Increase Simultaneously. The CAGR For The Market From 2025 To 2030 Is Expected To Be Approximately 81.5%, Showing A Faster Growth Rate Among The Three Levels, And Also Reflecting A Larger Expansion Potential.

Source: Public Research Report Compilation, Sullivan Analysis

04

Calculation of The AI Hospital Operation Market: Base * Penetration Rate * Annual Operation Service Revenue Per Hospital

The Calculation Of The AI Hospital Operation Market Is Based On Secondary and Above-level Public Hospitals As The Service Scope, Covering Class III and IV hospitals, and Class II hospitals. It Is stratified According To The AI Operation Payment Penetration Rate And The Annual Operation Service Revenue Per Hospital, To Reflect The Process Of AI Hospital Operation Services Gradually Penetrating From Higher-level Hospitals To A Wider Range Of Hospital Levels. The Market Growth Comes From Two Aspects. On the One Hand, The Payment Penetration Rate Of The Three Types Of Hospitals Continues To Increase, Driving Potential Demand To Real actual Payment. On the Other Hand, The Service Per Hospital Is Extended From Basic Tools Such As AI guided diagnosis and pre-diagnosis To Escorting, Revisit Conversion, Patient Management, Operation Assistant, And Ecosystem Services, Driving The Enhancement Of The Single-hospital Operation Value. Driven By Both, The AI Hospital Operation Market Is Expected To Grow From 520 million yuan In 2025 To 1.017 billion yuan In 2030, With A Growth Rate Of Approximately 20 Times, And A CAGR Of Approximately 81.5%.

Source: National Health Commission, Company Information, Sullivan Analysis

01

AI Medicine Enters Deep Water: Capital Judgments Shift From "Concept" To "Realization"

As AI medicine enters the commercial verification stage, The Value Judgment Of The Capital Market Is Shifting From "Concept Layout" To "Commercial Realization". The Platform Value Depends More On Whether Real Scenarios, Process Embedding, And Result Delivery Can Form a Complete Closed Loop. The Relationship Among Hospitals, Doctors, And Patients Determines the Authenticity of Demand, The Continuity of Data, And The Frequency of Service Use. Only After AI Further Enters Daily Diagnosis and Operation Processes Can It Continuously Accumulate Usage Stickiness, Data, And Migration Costs. Whether Results Such As Revenue Growth, Operation Efficiency Improvement, And Patient Retention Can Be Continuously Quantified Determines Whether Technical Ability Can Finally Be Transformed Into Commercial Value. Thus, The Competition Focus Of Internet Medical Platforms Is Shifting From Traffic Scale And Technical Labels To Real Scenarios, Depth of Process, And Profit Quality.

Source: Sullivan Analysis

02

Six Types of Ecological Positions: The Deeper The Operation and Data Barriers, The More Difficult It Is to Copy Short-term

New Value Judgment Criteria Further Promote Clearer Ecological Position Differentiation in the Digital Medical Health Service Industry. Different Models Are Mainly Shaped By Core Entry Assets, Resource Endowments, And Revenue Methods, Gradually Forming Representative Paths Such As Pharmaceutical e-commerce, AI medical solutions, B-side marketing And doctor services, Chronic disease management And prescription retail, HMO health management, And Public hospital entry And AI operation. As the exclusivity of resources, data accumulation, And operation embedding Degree Continue To Deepen, The Barriers Between Different Ecological Positions Are Also Further Widened. Compared With Models That Mainly Base On Products, Traffic, Subscriptions, Or Membership Systems, Public hospital entry And AI operation Depend More On Long-term hospital relationships, Real diagnosis data, And full-process operation capabilities, Thus Forming A Longer cycle And More difficult to be copied Short-term.

Source: Public Information Search, Sullivan Analysis

03

Benchmarking: Who Enters the Real Medical Process, Who Has Greater Long-term Value

The Difference In Ecological Positions Is Further Reflected In The Position And Depth Of Platforms Entered into The Medical System. E-commerce And HMO platforms mainly Enter From the Transaction, Supply Chain, Or Payment Side. The AI Hospital Operation Model Further Enters The Real public hospital online entry, Pre-triage, During-triage, And Post-triage Real processes. Overseas market development Is Relatively Leading. Its practices Have Already Showed That Platforms Deeply Embedded in Medical Workflows Are More Likely To Form Continuous Revenue And High profit quality, While Models That Mainly Depend On Traffic Or Large-scale Consultation Are More Vulnerable To Pressure After Competition Intensifies. Thus, Whether the entry Is Deeply Entered into the Real medical process, And Whether It Can Form Continuous operation ability, Is Becoming An Important Basis For The Long-term Value differentiation of platforms.

04

Ability Portrait: Each Model Has Its Advantages, And The AI Hospital Operation Model Is Most Fit for "Full-process Operation"

Different entries Further Shape Different Ability Combinations. E-commerce platforms Are Known For Their Supply chain And fulfillment abilities. HMO platforms Have Advantages In Payment And insurance collaboration. The ability focus Of the AI operation model Is More On The hospital entry, Doctor connection, And operation scenarios During pre-triage, during-triage, And post-triage. For AI hospital operation services, A Single technology or resource advantage Is Not Enough To Support full-process operation. Hospital entry, Doctor resources, And continuous operation ability Need To Form Synergy, So That AI Can Really Embed In the medical process And Continuously Receive operation results. Therefore, The Three types of models Have Their Comparative Advantages, And The ability combination Of the AI operation model Has A Higher match with the full-process hospital operation needs.

05

Pattern Conclusion: Operation results Determine Differentiation - "Traffic / Selling drugs" vs "Embedded operation"

Different ability combinations correspond to different revenue methods, And Are Further Reflected In Profit quality. Although E-commerce And HMO platforms Have Larger revenue volumes, Costs Such As products, fulfillment, And customer acquisition Are High, And the scale advantage May Not Simultaneously Convert Into profit advantage. The embedded operation model Is More Likely To Realize Revenue Through SaaS And operation share. After stable embedding in hospital processes, the marginal delivery cost Is Lower, And It Is Also More conducive To Forming High gross margin. The gross margin Of E-commerce And HMO digital solutions Is Approximately 24%. The gross margin Of the embedded operation type Is 78.1%. In The Development Path Of Public hospital entry And AI operation In China, Health160 Is A Representative Head platform. Its high gross margin business Has Contributed Approximately 96% of the total gross margin, And achieved positive adjusted net profit for the first time in 2025. Thus, The Long-term differentiation of platform value Depends Not Only On Revenue volume, But Also On Whether It Can Continuously Convert Operation results Into High-quality revenue And sustainable profit.

01

Health160: From Online Registration to AI Hospital Operation Service Provider and Industry Value Platform

Entering the Chinese public hospital scenario, Health160's evolution also reflects a progressive logic from entry points to operations, and then to an ecosystem. The connection layer is based on online medical and health services, continuously building relationships between hospitals, doctors, and patients, forming a stable online entrance to public hospitals. The empowerment layer, based on the existing connection, further productizes hospital operation experience, embedding AI triage, pre-consultation, accompaniment, medical assistance, and follow-up services into the entire process before, during, and after treatment, shifting the platform from "connecting resources" to "improving operations." When the size of the entry and operational capabilities become mature, the ecosystem layer introduces third-party supplies such as medical devices, traditional Chinese medicine, testing, and health management, expanding the platform's scope of meeting patient needs and industry resources. Through the gradual expansion from "connection - empowerment - ecosystem," Health160's value extends beyond entry resources to operational efficiency and industrial collaboration, forming a more diverse income structure through SaaS, operating royalties, and ecosystem commissions. Throughout this evolution, the platform always adheres to three principles: not replacing doctors, deeply integrating into hospital business ecosystems, and not relying on a single income source.

Source: Company materials, company prospectus, Frost & Sullivan analysis

02

Connection (Foundation Layer): An Indistinguishable Entry Network

The connection layer forms the basis for Health160's subsequent AI operations and ecosystem expansion. As of June 30, 2026, Health160 had connected 45,100 medical and health institutions, including approximately 14,700 hospitals, covering over 898,000 healthcare workers. The platform had 61.9 million registered users, with an average monthly active user of about 3.4 million, creating a broad network of hospital, doctor, and patient connections. As the coverage of hospitals and doctors expands, patients choose and use more frequently, further accumulating real needs and operational data, supporting service optimization and deeper hospital cooperation.

Practices such as "Healthy Shenzhen" further demonstrate the practical value of this connected network in enhancing hospital efficiency, medical services, patient experience, and the utilization of public healthcare resources. For Health160, this long-established entry network also provides a continuous real-world foundation for further integrating AI capabilities into pre-diagnosis, during-treatment, and post-treatment processes.

03

Enabling (Service Deepening Layer): 160AI Hospital Productizes Operational Capabilities

Based on the existing entrance network, the enabling layer further productizes hospital operation experience. 160AI Hospital integrates AI capabilities into pre-diagnosis, during-treatment, and post-treatment processes. Pre-diagnosis, it enhances triage and information collection efficiency through AI-guided consultation and pre-diagnostic interviews. During-treatment, it handles some non-clinical tasks via AI accompanying doctors and medical assistance. Post-treatment, it supports follow-up visits and patient retention through automated follow-ups, extending services from individual tools to full-process operations.

From actual deployment, the relevant practices can reduce consultation time by 30% to 50%, increase follow-up visits by more than 20%, and replace over 70% of non-clinical tasks. To date, Health160 has established partnerships with 163 public hospitals, including 78 third-class grade-A hospitals and 120 hospitals of level three or higher. The coverage of hospitals at a certain scale and quantifiable operational results indicate that their operational capabilities are gradually transforming from project experience into deployable, reusable standardized services.

04

Ecosystem (Value Integration Layer): Extending from the hospital entrance to become the central point for value exchange in the medical health industry.

The core of the ecosystem layer is not simply adding more service categories, but rather organizing the hospital access, real patient needs, and operational capabilities accumulated in the previous two layers into a value exchange network involving multiple parties. Health160 relies on the online access of public hospitals to meet the needs of follow-up consultations, chronic disease management, specialized rehabilitation, and self-paid health services. It then incorporates third-party offerings such as medical devices, health check-ups, traditional Chinese medicine, and beauty treatments. Through follow-up visits, patient engagement, content delivery, and guidance, it identifies needs, matches supply and demand, and facilitates service conversion.

As this mechanism continues to operate, hospitals can expand their online service capabilities and non-medical insurance revenue sources. Patients will receive more continuous health services. Third-party service providers can enter reliable medical scenarios and reach customers more precisely. The government can improve the efficiency of regional medical resource allocation through platform collaboration. A closed loop of "demand accumulation—supply integration—operation conversion—value sharing" is thus formed. Based on operational results such as GMV, follow-up consultations, and customer retention, the platform participates in the distribution of incremental value, enabling Health160 to extend its operations by hospitals to become a supply-demand organization and value exchange platform for the medical health industry.

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Benchmark Case: From process integration to result delivery, demonstrating replicable operational value.

某华东头部三甲医院案例进一步验证了健康160模式在公立医院场景中的可落地性、可复制性与商业化潜力。平台以公立医院线上入口为基础，进一步叠加AI运营工具、患者触达与服务转化能力，并根据患者持续沉淀的真实需求，引入专科及慢病管理等第三方服务供给，逐步形成从入口承接、流程运营到生态供给和结果交付的完整闭环。

Based on actual operational results, within approximately 4 months, the hospital's GMV monthly compound growth rate reached about 155.7%. The chronic disease management project contributed 4.3 million RMB in GMV, and service commission income based on operational results was also generated. This case demonstrates the application value of the combination of "public hospital entrance + AI operational tools + service conversion" in real hospital scenarios. It also shows that Health160 can further transform the online entrance into measurable operational results and continuous revenue, providing an observable practical foundation for replicating the model in more hospital environments.

06

Investment Value Positioning: Health160 is entering the revaluation window of the "AI Hospital Operation Value Platform".

The value position of Health 160 can be observed along five levels: "Entry Barriers—Expansion Replication—Result Realization—Profit Leap—Industry Positioning". The long-term cooperation network of public hospitals established over time constitutes a scarce entry resource for entering the real medical process, and provides a continuous scenario foundation for AI operational capabilities. 160AI Hospital has already operated in collaboration with 163 public institutions, transforming years of hospital operation experience into a fully deployable, reusable, and scalable end-to-end AI operational service system. Quantifiable results such as improved consultation efficiency, enhanced follow-up services, and substitution of non-clinical tasks further demonstrate the feasibility of expanding operational capabilities to create incremental value.

The commercialization progress further reflects the above-mentioned capability evolution. In 2025, Health160 achieved revenue of 652 million yuan, and its adjusted net profit turned positive for the first time. Digital solutions contributed approximately 96% of the total gross profit, indicating that high-margin businesses are increasingly supporting the overall profitability structure. As the hospital entrance, AI operations, and medical health service ecosystem become more interconnected, the value the platform can deliver expands from single-hospital operations to broader industry supply-demand collaboration. Health160's role has evolved from an online registration platform to an AI hospital operation value platform, progressing towards an AI infrastructure and value exchange center for the medical health industry.