As China's population ages at an accelerating pace, the silver economy is entering a critical phase of quality expansion. Leveraging digital tools and data-driven methods to upgrade the elderly care industry has become a key strategy for addressing the challenges of an aging society and improving the wellbeing of senior citizens. Although national and local statistical monitoring frameworks for the elderly care industry have been introduced this year, persistent structural issues remain.
These include fragmented data across different government departments, inconsistent statistical standards, lagging dynamic monitoring, and insufficient real-world application scenarios. Together, these gaps are holding back the high-quality growth of the elderly care sector. Zhao Ji, a member of the Standing Committee of the National Committee of the Chinese People's Political Consultative Conference (CPPCC) and a standing member of the Central Committee of the China Democratic League, has long focused on livelihood issues and the development of elderly care.
During this year's "two sessions," Zhao Ji submitted a proposal titled "On Improving the Data Support System for the Elderly Care Industry." The proposal targets four critical areas: breaking down data barriers, unifying statistical standards, building dynamic monitoring mechanisms, and unlocking the value of data. It offers systematic and targeted governance solutions to empower quality data elements in enhancing elderly care services and boosting the silver economy.
Current Situation: Elderly Care Data Scattered Across Multiple Departments and Platforms
The elderly care industry spans a long industrial chain and covers a wide range of fields. Based on your field research, what types of data are most needed to support decision-making in the development of the industry? And where is this data currently stored across different government departments?
In January of this year, the National Bureau of Statistics, together with the Ministry of Civil Affairs and two other departments, jointly issued the "Statistical Monitoring System for Aging Services and the Silver Economy," establishing a national institutional framework for industry development data. The Inner Mongolia Autonomous Region officially launched its own statistical survey of the elderly care industry in May this year. From both the national and regional perspectives, developing the industry requires data on service product distribution, academic discipline offerings, R&D personnel and funding, the scale of elderly care financial products, basic medical and long-term care insurance, and the integration of medical and nursing services. Currently, this data is scattered across multiple government bodies, including industry and information technology, education, science and technology, civil affairs, financial regulation, medical security, health, human resources and social security, and statistics departments.
In your proposal, you pointed out that different regions and departments use inconsistent statistical standards for key elderly care indicators, such as the definitions of "community elderly care service facilities," "disabled elderly," and "smart elderly care products." Could you provide specific examples of these differences, and highlight the practical problems they create?
Through our research, we discovered significant regional and departmental discrepancies in how key concepts and statistical calibers are defined. For instance, the scope of "community elderly care service facilities" varies widely. Some provinces include street-level integrated elderly care centers, day care centers, senior canteens, and rural mutual-help happiness courtyards. Other regions only count facilities with full-time or day-care functions staffed by dedicated personnel, excluding simpler amenities like senior activity rooms. Similarly, the assessment criteria for "disabled elderly" differ across departments; the civil affairs department's elderly capability assessment standards, the health commission's disability evaluation standards, and the medical security department's long-term care insurance disability classification each have their own focus. One elderly individual might receive different evaluations depending on which department conducts the assessment. Moreover, there is no unified consensus on whether products like smart wristbands, health monitoring devices, and one-button call terminals should be included in statistics. This lack of consistency is compounded by incompatible data transmission protocols and interface specifications across different devices, making interconnectivity and data sharing difficult. These inconsistent standards hinder cross-regional comparisons, obscure the national picture, and complicate business decisions, leading to unclear baseline data, resource misallocation, and service gaps across regions.
The current level of public data openness in the elderly care field is limited, making it difficult for research institutions and market enterprises to access high-quality data. Given the sensitive nature of elderly care data, how do you propose balancing the relationship between public data openness, scientific research exploration, and market application while protecting privacy and security?
I believe the key lies in "classified implementation, technological empowerment, and clear rights and responsibilities." First, we need to establish a classified and hierarchical mechanism for opening elderly care data. Data should be divided into public-level, desensitized-level, and restricted-level categories. Core sensitive data like health records and disability assessments should be strictly controlled, while non-sensitive information such as service facility distribution and demand preferences can be opened to the public as appropriate. Second, we should enable research institutions to train models without accessing raw data; enterprises can develop products based on desensitized group profiles. Finally, it is crucial to clarify the boundaries of data rights and responsibilities by establishing clear rules. A principle of "those who provide benefit, those who use are responsible" would protect the rights of data providers while constraining the behavior of users, alleviating the concerns of grassroots staff who fear being held accountable.
When Data Lags Behind, Policies and Markets Risk Systemic Misalignment
Many departments are building their own elderly care data systems, yet "data silos" persist. Why is cross-departmental data sharing so difficult in practice? Is this purely a technical challenge?
Our research shows that each department operates its own independent data system with different standard interfaces and lacks efficient, mandatory sharing and coordination mechanisms. The root causes lie in institutional barriers, interest structures, and security concerns. First, elderly care data involves multiple verticals like civil affairs, health, medical security, and human resources. Each department has its own information systems and evaluation systems, and data has become a form of departmental "resource" and "discourse power." Some departments worry that sharing data will weaken their functions. Without a higher-level coordination mechanism, relying on negotiations between peer departments alone makes it difficult to break through the "departmental ownership" mindset. Second, elderly care data includes highly sensitive content like health conditions, family financial information, and service records. A leak could have serious consequences. Currently, the definition of data security responsibilities is unclear, and the fear of being held liable makes grassroots officials cautious. Third, these systems were built in different eras with different technical architectures, so data field definitions, coding rules, and interface specifications are incompatible. For example, the same elderly person may have different identity codes in the civil affairs system and the medical security system. Even if sharing is desired, it requires significant effort for data cleaning and mapping, which is costly.
Currently, data in the elderly care field is mainly based on annual static statistics. There is a lack of dynamic monitoring for new business forms like home-based care services, residential elderly care, and time banks. Does this shortage of high-frequency, real-time data lead to misalignment between policy and the market?
Most core data currently relies on annual statistical reports, yet new forms like home services, residential care, and time banks are precisely the areas that change most rapidly and require real-time insight. When data lags behind, policy and markets can easily fall out of sync. First, it widens the time gap between policy supply and real demand. For example, demand for home-based services fluctuates significantly with seasons and circumstances. In northern regions, demand for home nursing surges in winter when mobility is difficult. Similarly, summer triggers a boom in residential care, shifting resources between source and destination regions. Without capturing these fluctuations, we might see a mismatch of "insufficient supply during peak seasons and idle resources during off-peak periods." Second, monitoring of new business forms is not timely. Time banks rely on volunteer mutual aid with service records scattered across communities and personal phones, lacking a unified data entry point. Residential care involves cross-regional flows with no information sharing between sending and receiving areas. These forms are essentially unaccounted for, leaving them outside policy coverage and regulatory oversight. Third, business entities lack decision-making support. Static data cannot answer questions like "how many elderly people need home services this year" or "what is the actual volume of residential care traffic." This leads to companies either entering blindly and incurring losses or hesitating to invest due to insufficient information, dampening market vitality.
Many local elderly care platforms are built but their data remains confined to internal administrative use, failing to feed back into service quality improvement. What do you think causes this "emphasis on collection but neglect of application"?
There are three main reasons. First, data quality is often low. Grassroots data collection relies heavily on manual entry, which is labor-intensive and ambiguous, plagued by duplicate inputs, outdated information, and missing fields. This makes conclusions seem unreliable, so nobody wants to use it. Second, the application ecosystem is underdeveloped. Grassroots civil affairs staff are swamped with administrative duties and lack data analysis skills. Technology vendors move on after project completion, and nobody continues operations. The result is platforms with data but no users, systems with no scenarios, rendering them "digital decorations." Third, demand-side participation is inadequate. The ultimate value of elderly care data lies in serving seniors, but most platforms are designed from a "manager's perspective" rather than a "user's perspective." Elderly people and their families cannot use the platform to inquire about services or provide feedback, and service personnel cannot access real-time information about the elderly.
Transforming "People Seeking Services" into "Services Finding People"
In your proposal, you suggest piloting monthly and quarterly reporting systems for the elderly care industry and promoting electronic work orders to collect service process data. Compared to traditional annual reports, what specific blind spots in industry development is this dynamic monitoring approach intended to address?
The biggest problem with traditional annual reports is their "lag effect." By the time annual data is available, the market has already changed and problems have accumulated. Dynamic monitoring aims to fill three types of blind spots. First, the "fluctuation blind spot" of market operations. The elderly care industry, especially new business forms, changes rapidly. Demand for home-based services can fluctuate dramatically with seasons and emergencies, residential care has clear peak and off-peak seasons, and nursing home occupancy rates shift in real-time. Annual reports can only show yearly averages and cannot capture these fluctuations; a rapid reporting system can address this. Second, the "effectiveness blind spot" of policy implementation. Many policies are introduced, but whether they achieve the desired effect is unclear. For example, after issuing operating subsidies, has service volume increased? After piloting long-term care insurance, how many disabled seniors are actually benefiting? Annual reports only provide final outcomes and cannot reflect problems during implementation, leaving policy adjustments without timely basis. A rapid reporting system can capture policy implementation effects more promptly. Third, the "early warning blind spot" for industry risks. Elderly care services directly affect seniors' rights and interests. In the event of disputes, safety incidents, or abnormal business operations, immediate action is required. Dynamic monitoring can capture these signals early, allowing for proactive intervention to de-risk and prevent small problems from escalating into major incidents.
You also advocate for releasing data value through pilot programs, data sandboxes, and phased desensitized opening. Why must elderly care data openness follow a path of "controllable pilots, safety-first, and gradual progress" rather than direct full-scale opening?
This is a critical question. Elderly care data openness cannot be a blanket "one-size-fits-all" full release; it must follow a cautious and prudent path for three reasons. First, the sensitivity of this data far exceeds that of general public data. It encompasses health conditions, medical histories, family economic situations, residential addresses, daily activity patterns, and even cognitive and mental state assessments—covering nearly all privacy dimensions of a person's later years. A full open release could lead to data misuse or leaks, infringing on seniors' rights and potentially enabling fraud or inducement scams. Second, the security infrastructure for full-scale release is not yet ready. Security capabilities of elderly care data systems vary across regions, and data desensitization technical standards are not uniform. Opening data prematurely without a solid "security base" would expose seniors' privacy to uncontrollable risks. Third, institutional rules need gradual refinement. Data openness involves a series of arrangements covering rights division, usage norms, and accountability mechanisms. These cannot be designed flawlessly on paper in one go. Pilot programs can expose problems and accumulate experience on a small scale. I suggest launching national-level pilot projects for integrated elderly care data applications in regions with better data foundations, such as Shanghai, Zhejiang, and Chengdu, Sichuan. Building on this, we can plan the construction of a national elderly care big data center, opening basic datasets to research institutions and compliant enterprises in batches after desensitization, gradually promoting data transparency.
In your view, what changes can a mature and complete data system for the elderly care industry bring to the daily lives of ordinary seniors and the healthy development of the silver economy?
Ultimately, the value of a data system lies not in the data itself. Our goal is to ensure every elderly person can access elderly care services fairly, conveniently, and with dignity, and that every business entity can thrive under clear market signals. For ordinary seniors, the most fundamental change is shifting elderly care services from "people finding services" to "services finding people." Currently, many seniors don't know what policies they qualify for or what services are available nearby. Adult children trying to find suitable care facilities or home nursing for their parents often have to visit multiple departments and make numerous calls to piece together information. With an integrated data system, seniors' demand profiles, service records, and policy utilization are interconnected. Community workers can proactively identify the needs of key groups—such as those living alone, disabled, or of advanced age—and precisely push suitable services. For the entire silver economy, the core change is moving from "blind men feeling the elephant" to "precision navigation." Once the data system matures, critical information like regional demand distribution, service consumption preferences, and acceptable price ranges becomes transparent and accessible. Companies can make accurate siting decisions, price reasonably, and optimize products. Capital can also find tracks with genuine demand support. This will effectively drive the silver economy from crude expansion to high-quality development.
Proposal Highlights from the 2026 Two Sessions
The proposal "On Improving the Data Support System for the Elderly Care Industry" was submitted by Zhao Ji, a Standing Committee member of the CPPCC National Committee and a Standing Committee member of the Central Committee of the China Democratic League. Improving the data support system is a foundational and pioneering project for implementing the national strategy of actively responding to population aging and promoting high-quality development of the silver economy. However, current practice faces several urgent problems: data barriers and poor sharing; inconsistent statistical standards and difficult horizontal comparisons; inadequate dynamic monitoring and slow market response; and insufficient application depth and limited value extraction.
To accelerate the construction of a unified, standardized, efficient, dynamic, and value-driven data support system for the elderly care industry, the proposal recommends the following. First, strengthening top-level design and establishing mandatory sharing mechanisms. The National Bureau of Statistics or the National Data Administration should take the lead in formulating and issuing a "Responsibility List and Management Standards for Sharing Elderly Care Government Data Resources," clarifying the data provision responsibilities, sharing scope, update frequency, and quality standards for civil affairs, health, medical security, human resources, and other relevant departments. This includes promoting the establishment of national or provincial-level elderly care data exchange platforms and reforming grassroots data collection through a "single form for elderly care data at the grassroots level," utilizing unified information collection terminals or apps to achieve "one-time entry, multi-departmental verification, and shared use."
Second, accelerating standard unification to improve data comparability. The National Bureau of Statistics, together with the Ministry of Civil Affairs, the National Health Commission, and other core departments, should benchmark against the "Statistical Classification of the Elderly Care Industry" to quickly formulate and release a "National Core Indicator System and Standard Specifications for Statistical Surveys of the Elderly Care Industry" covering the entire industry chain.
Third, innovating collection methods and building a dynamic monitoring network. For home and community elderly care services receiving government subsidies, electronic work order systems should be promoted to automatically record key service information. The Ministry of Civil Affairs and the National Bureau of Statistics, together with relevant industry associations and leading enterprises, should pilot a "monthly (quarterly) rapid reporting system for elderly care industry operations," focusing on leading indicators like occupancy rates, service order volumes, price indices, and the scale of emerging business forms, with regular publication of industry operation analysis.
Fourth, deepening data applications and promoting orderly open development. Support the launch of national-level pilot projects for integrated elderly care data applications in regions with better data foundations, such as Shanghai, Zhejiang, and Chengdu, Sichuan. Building on this, plan the construction of a national elderly care big data center. Study and formulate a "Public Elderly Care Data Resource Opening Catalog" and open basic datasets in batches, after desensitization, to research institutions and compliant enterprises.