County-Level Healthcare Spurs Sub-Center Development, Creating New Opportunities for AI: Aetos Imaging Advances with IPO Filing

This year marks the third year of implementation of the “Work Plan for Enhancing the Comprehensive Capabilities of County Hospitals under the ‘Thousand Counties Project’ (2021–2025).” How to facilitate the downward flow of high-quality medical resources to the county level, address deficiencies in the medical service delivery and management capabilities of county hospitals, and further leverage the role of county-level medical centers has become a core issue for the high-quality development of county hospitals in the coming years.

Based on this premise, over 1,000 party secretaries, presidents, and academic leaders from county- and city-level hospitals gathered at the “5th China Healthy County Conference & High-Level Forum on Enhancing the Comprehensive Capabilities of County Hospitals in the New Era” to engage in vigorous discussions on the aforementioned issues.

At the forum on AI-empowered construction of county-level sub-centers for medical and health services, supported by Airdoc, experts focused on two key issues: “How to build county-level sub-centers for medical and health services” and “How artificial intelligence can empower healthcare.” They sought to integrate technological enablement with organizational reform to further promote high-quality development of county-level hospitals.

Construction requirements for county-level medical and health sub-centers vary across regions, but they can all be summarized into a tiered development model driven by “county-level public hospitals – county-level medical sub-centers – general township health centers – central village clinics – administrative village clinics.”

In this model, county-level hospitals play a leading role by establishing medical sub-centers based on existing township health centers. These sub-centers liaise upward with the lead hospitals of the medical consortium and provide downward technical guidance and support to three to five general health centers in the surrounding areas (or one to three in some regions), thereby constructing a “30-minute key disease treatment service circle” in rural areas.

During the construction process, county-level medical sub-centers may adopt the service capacity of secondary general hospitals as a benchmark, formulating standards based on the Detailed Implementation Rules for the Accreditation Standards for Secondary General Hospitals (2012 Edition), in conjunction with the Capacity Standards for Township Health Centers (2022 Edition) and the Construction and Management Standards for Township Health Centers in Shandong Province (Revised 2018). The aim is to address shortcomings and weaknesses at the primary level, such as small individual facility scale, inadequate equipment and infrastructure, and limited service capacity. By leveraging advantages in regional location, population distribution, and transportation accessibility, these centers seek to break through the existing layout of healthcare resources, enhance primary care service delivery and health management capabilities, and further expand their scope of services.

High-quality county-level medical sub-centers should, in principle, serve a population of 100,000 or more. They must not only act as “gatekeepers” of health for grassroots communities but also demonstrate the expansion and balanced distribution of high-quality medical resources within the county. This includes providing diagnosis, treatment, rehabilitation, and nursing services for common and frequently occurring diseases to surrounding populations, as well as emergency care and referral services for acute, critical, and complex cases.

Since 2022, more than 100 county-level sub-centers for medical care have been under construction. As these sub-centers gradually achieve scale, China’s primary healthcare system will see further upgrades in clinical diagnosis and treatment capabilities, emergency care capacity, and health management services.

Organizational restructuring can effectively unlock the development potential of primary healthcare; however, to fully realize this potential, primary healthcare still faces numerous challenges, including the development of county-level talent pools and the establishment of specialized disciplines.

At the conference, a physician discussed the talent situation at their community health center, where the retention rate for high-quality personnel was only 20%. The institution has long been trapped in a dilemma of being unable to recruit or retain staff. Even with the acquisition of effective medical equipment, the facility could not secure physicians capable of utilizing these tools for diagnosis, thereby hindering the effective implementation of primary healthcare functions.

Artificial Intelligence has long been regarded as the key to resolving the shortage of healthcare professionals in primary care settings. On one hand, this technology can enhance the service quality of primary healthcare institutions by assisting less experienced physicians in making diagnoses and facilitating their learning during the diagnostic process. On the other hand, it can significantly improve the efficiency of diagnosis and treatment, thereby alleviating the problem of insufficient medical personnel in primary healthcare facilities.

Furthermore, patients treated by primary care institutions often exhibit distinct regional variations in disease prevalence. Residents living near mining areas tend to have a higher incidence of pneumoconiosis, while those in townships adjacent to water bodies frequently suffer from bone and joint pain. Given this distribution of diseases, primary care institutions can address the vast majority of medical needs by effectively diagnosing and treating a few common conditions. In such scenarios, procuring artificial intelligence solutions tailored to specific diseases proves simpler and more reliable than seeking external specialists who meet their requirements.

Taking Airdoc as an example, the company’s independently developed AI-based retinal imaging detection system can rapidly diagnose and assess various ocular diseases—including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, age-related macular degeneration, and pathologic myopia—from a single fundus image. It also evaluates associated health risks related to the cardiovascular and cerebrovascular systems, nervous system, and endocrine and metabolic systems, thereby effectively addressing the needs of primary healthcare settings characterized by personnel shortages and high screening volumes.

This capability also supports the development of county-level medical sub-centers and helps primary healthcare institutions establish specialized clinical strengths. Specifically, Airdoc enables healthcare institutions aiming to develop ophthalmology specialties to rapidly acquire diagnostic capabilities for eye diseases comparable to those of tertiary hospitals, thereby addressing the shortage of specialized talent at the primary care level. It also assists primary healthcare institutions in conducting early screening and health management for chronic disease patients across departments such as endocrinology, cardiology, and physical examination.

Currently, numerous primary healthcare institutions are leveraging Airdoc’s artificial intelligence technology to build specialized capabilities in departments such as ophthalmology and endocrinology. During a conference, one institutional leader stated that they would deepen collaboration with Airdoc on high-prevalence conditions like glaucoma and cataracts, accelerating the development of ophthalmic subspecialties.

In addition to conducting in-depth research and development in artificial intelligence software, Airdoc is further exploring the application potential of integrated AI hardware and software solutions in primary healthcare.

At the conference, Airdoc showcased its portable fundus camera as a prime example of this trend. Weighing only 2 kilograms, the device can be easily carried in a backpack by primary care physicians, enabling AI-based eye screenings for residents with limited mobility and further eliminating blind spots in screening and prevention at the grassroots healthcare level.

Beyond retinal imaging, AI solutions for common diseases such as lung cancer, diabetes, and coronary heart disease have also been widely implemented in primary care settings. These technologies help address the shortage of healthcare professionals at the grassroots level while supporting the development of specialized clinical departments.

As the application of medical AI deepens in real-world scenarios and continues to penetrate the vast grassroots healthcare market, the value of medical AI enterprises will become increasingly prominent. In the upcoming county-level secondary healthcare centers, a mature AI ecosystem may play a more significant role, fundamentally addressing the various challenges faced by grassroots healthcare.