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Sustainable Business Models of China's Tech-enabled Elderly Care Industry Under the '9073' Pattern and Payment Capacity Constraints

#tech_elderly_care #sustainable_business_model #payability_constraint #long_term_care_insurance #industry_analysis #business_model #head_enterprise_analysis
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January 1, 2026

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Sustainable Business Models of China's Tech-enabled Elderly Care Industry Under the '9073' Pattern and Payment Capacity Constraints

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Sustainable Business Models of China’s Tech-enabled Elderly Care Industry Under the ‘9073’ Pattern and Payment Capacity Constraints

  1. Industry Background and Core Data (with Sources)
  • Population size and aging degree: As of the end of 2024, China’s population aged 60 and above was approximately 310 million, accounting for about 22.0%; those aged 65 and above were about 220 million, accounting for 15.6% [2].
  • ‘9073’ Pattern: Approximately 90% home-based care,7% community-based care,3% institutional care; current actual market distribution:52.9% home-based,26.5% community-based,20.6% institutional [2].
  • Payment capacity: Consumers’ ideal monthly retirement expenditure ranges from 3,000 to 5,000 yuan; about 35% of elderly people have a monthly income exceeding 5,000 yuan; approximately 85% of children are willing to buy smart elderly care devices for their parents [3].
  • Market size: In 2024, China’s smart elderly care market size was approximately 6.8 trillion yuan, and it is expected to reach 7.21 trillion yuan in 2025 [2]; additionally, the elderly care industry size is projected to reach about 19.5 trillion yuan by 2029 [3].
  • Long-term care insurance (LTC Insurance): As of September 2025, it covers approximately 190 million people with expenditures exceeding 85 billion yuan, and is expected to become the ‘sixth social security insurance’ [4].
  1. Framework of Sustainable Business Models (Focused on Payment Capacity and Service Stratification)
    1. Diversified Payment Channels:
    • Insurance + Medical-Care Linkage (Taikang Model): Integrates capital and service ends through a closed loop of ‘payment + service + investment’. In 2024, Taikang Life’s operating revenue was approximately 271.101 billion yuan (YoY +15.3%), with a comprehensive solvency ratio of about 335% [3]; its subsidiary Taikang Home has deployed 47 projects in 37 cities nationwide, of which 27 are in operation with over 20,000 residents [3].
    • Policy-based Payment Tools: Long-term care insurance uses multi-channel financing (unit contributions, individual contributions, fiscal subsidies, medical insurance funds, etc.), pay-as-you-go, with funds unified into the pooling account; severely disabled elderly are generally covered, and both institutional and home care models are supported [4].
    • Membership and Subscription Models: Enhance user stickiness and repeat purchases through membership systems, monthly/annual subscriptions, or ‘community points + service redemption’ to lower the threshold for single consumption.
    1. Service Stratification and Scenario Pricing:
    • High-end Institutional: Taikang Home and others provide one-stop medical-care services (medical/rehabilitation/leisure), with relatively high fees targeting customers with strong payment capacity [3].
    • Mid-end Community: Provide meal assistance, cleaning assistance, health care, etc., through ‘15-minute elderly care service circles’ and community stations, using basic service packages + value-added items, combined with government subsidies and PPP models to reduce out-of-pocket costs [1].
    • Inclusive Home-based: Smart monitoring (fall detection radar, sleep monitoring, emergency call, etc.) + telemedicine + IoT aging-adaptation transformation, with one-time equipment investment + subscription or pay-per-use pricing, suitable for mainstream home scenarios to improve coverage and accessibility [1].
    1. Cost Reduction and Efficiency Improvement Paths:
    • Technology Replacing Labor: For example, smart monitoring devices can reduce nursing labor costs by about 30%; telemedicine reduces single service costs by about 50% [2].
    • Equipment Standardization and Supply Chain Concentration: Wearable devices like smart bracelets/smart watches form industrial clusters (e.g., Guangdong) to reduce costs and improve iteration efficiency [1].
    • Data-driven Operations: AI data platforms are used for risk early warning, nursing quality control, and resource scheduling to reduce accidents and disputes and improve service consistency [1].
  1. Analysis of Scaling and Inclusiveness of Smart Medical-Care by Leading Enterprises like Taikang
  • Scaling of Taikang Model:
    • Deployment and Occupancy: As of 2025, Taikang Home has deployed 47 projects in 37 cities nationwide, with 27 in operation and over 20,000 residents [3].
    • Medical-Care Collaboration: Five medical centers provide over 5,000 medical beds, building a service network of ‘elderly care community + general hospital + rehabilitation’ to improve critical care response and chronic disease management capabilities [3].
    • Integration of Payment and Service: Use ‘new life insurance’ (payment + service + investment) to connect capital and services, enhance customer acquisition and retention, and increase customer lifetime value [3].
  • Boundaries and Challenges of Scaling:
    • High-end Positioning and Payment Capacity: Taikang Home is relatively high-end, with monthly fees usually above the range of 7,000–20,000 yuan, which has a large gap with the monthly disposable income of most elderly (mostly 3,000–5,000 yuan), limiting penetration into the mass market [3].
    • Land and Operation Costs: Large complex projects have heavy investment and long cycles; rapid replication is constrained by location and capital; the operation end needs professional medical-care teams, with a significant talent gap.
  • Paths to Inclusiveness (Options for Taikang and the Industry):
    • Product Matrix Stratification: Beyond high-end communities, launch community-embedded small and medium stations and home service packages, combined with long-term care insurance and government subsidies to improve accessibility [4].
    • Spillover of Tech-driven Cost Reduction: Output AI early warning, standardized processes, and equipment integration solutions from smart medical-care platforms to community and home scenarios, making them inclusive through SaaS or integrated equipment subscription [1].
    • Linkage with Long-term Care Insurance: Actively align with long-term care insurance payment standards, promote inclusion of home visit services and telecare in reimbursement catalogs, reduce users’ out-of-pocket ratio, and accelerate inclusive implementation [4].
    • Cooperation with Internet/Platforms: Collaborate with internet vendors and smart hardware manufacturers (e.g., wearables, smart home, voice assistants) to form an integrated equipment-service-operation ecosystem, improving user experience and stickiness [1].
  1. International Practices and Insights for Reference
  • US-Inspiren: Focuses on AI prediction engines and smart hardware (e.g., AUGi bone monitoring) for senior communities to improve care efficiency and safety. It has been active in financing recently, completing a $100 million Series B in September 2025; launched the industry’s first complete AI-driven senior living ecosystem in March 2025 [1]. Insight: Use AI and IoT to solve the ‘monitoring-warning-response’ closed loop, improving operational efficiency and safety levels.
  • Japan-PARO Therapy Robots: Government and enterprises invest in care robot technology for emotional companionship and cognitive intervention. Insight: Strengthen ‘companionship + rehabilitation’ scenarios, improve elderly quality of life and care compliance through human-machine interaction [1].
  1. Risks and Uncertainties
  • Regulation and Standards: Currently, there is a lack of unified industry standards and data interoperability norms, with obvious information silos restricting large-scale replication and data value mining [1].
  • Data and Privacy: Health data and location information are highly sensitive; need to improve encryption, permission management, and compliance frameworks to reduce risks [1].
  • Long Commercialization Cycle: Large upfront investment and long return cycle; enterprises need to do good capital planning and rolling investment; if the payment end is over-reliant on a single policy tool, it is vulnerable to policy adjustments.
  • Insufficient Aging Adaptation: Complex device operation and non-aging-friendly interfaces affect usage rate and satisfaction [1].
  1. Conclusions and Recommendations
  • Key to Breaking the Problem: Under the ‘9073’ pattern, institutions, communities, and home-based care each adapt to different payment capacities and risk levels; sustainable coverage is achieved through the combination of ‘high-end institutional premium + mid-end community package + home-based basic subscription’.
  • Diversified Payment and Policy Collaboration: Strengthen the coverage and reimbursement scope of long-term care insurance, encourage inclusion of eligible smart elderly care devices and services in medical insurance/long-term care insurance catalogs; reduce supply-side costs through tax incentives, subsidies, and PPP models [4].
  • Technical Standards and Interoperability: Promote unified industry standards and data interfaces, connect institutional, community, and home platforms, and build a full-life-cycle service chain [1].
  • Talent and Operation: Jointly train ‘medical + care + tech’ composite talents by universities and enterprises, improve vocational training and incentive mechanisms, and enhance service quality and supply stability [1].

References
[1] Internet Search and Industry Materials:

[2] News and Media (Brokerage News/Internet Search Results):

[3] Taikang and Related Enterprises (Internet Search and Brokerage News):

  • Nanfang+ - From Having Money to Spend to Having Quality: Taikang’s Thirty Years of Building Certainty in Later Life with New Life Insurance (2025-12-25, including revenue, solvency, layout and occupancy): https://www.nfnews.com/content/Ko7DJg5nye.html
  • 21st Century Business Herald -
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Insights are generated using AI models and historical data for informational purposes only. They do not constitute investment advice or recommendations. Past performance is not indicative of future results.