The AI process in the healthcare industry seems to be facing a structural contradiction: single-point tools are constantly emerging, yet it is difficult to penetrate the complexity of the industrial scene. When GM's big models poured into the medical circuit, most products were limited to fragmented applications such as auxiliary diagnosis and intelligent customer service, and lacked deep adaptation to the entire link of pharmaceutical circulation, commercial decision-making, and terminal services. Data silos, fragmented scenarios, and high compliance thresholds have made the implementation of AI on the industrial side always “win over the seat”.
On August 13, during the Xipu Conference, Wu Han, chairman of Zhongkang Technology (02361), faced the judgment given in an exclusive media interview: China's health industry is standing at a breaking point between the decline of old kinetic energy and the rise of new kinetic energy. Whether AI can evolve from a “tool” to an “operating system” will determine whether the industry can cross this gap.
Faced with the question “What kind of layout has been made in terms of smart devices in recent years”, Wu Han gave a clear road map. On top of the two major models, an AI intelligent platform adapted to the healthcare industry has already been formed and blossomed in five key scenarios. Business decision-making intelligence is probably the area that pharmaceutical businesses are most concerned about. It allows the industry to use the same set of data language and the same brain to complete analysis and judgment in the face of a rapidly changing market environment. The conclusions are generated based on massive data integration and are highly supportive. The R&D decision-making intelligence body is like an intelligence officer around the clock. By tracking R&D intelligence and progress in various pipelines around the world, it provides comprehensive support for pharmaceutical companies' R&D strategy judgments.
In the doctors' work scene, the integration of software and hardware has brought about more intuitive changes. He specifically mentioned the “AI medical sound card” launched as a companion — this hardware can collect conversation content in real time and convert it into medical documents during communication between doctors and patients, implant part of the doctors' intelligence into the hardware, and provide functions such as patient management, evidence-based support, and scientific research support.
The core of the pharmacy intelligence system is the member intelligence module, which integrates pharmacy membership management and marketing management capabilities based on Zhuomniao's medical model to support the transformation of pharmacies from “simply selling pharmaceutical products” to “health posts.”
In addition, a future-oriented health management smart system is positioned as a 7×24 hour health management expert. It can output comprehensive health management recommendations covering the dimensions of exercise, nutrition, diet, etc. in real time based on consumers' physical examination reports, diagnosis reports, and dynamic data generated by wearable devices. In the future, it will also open up a doctor database to provide accurate guidance for consumers to seek medical treatment.
Such a large layout inevitably makes people wonder: the healthcare industry covers a very wide range, and it is not easy to break through a single point. Is there too much coverage of the entire scenario? In response, Wu Han explained that at present, many AI companies can produce high-quality single-point products, but due to a single function, they are unable to fully adapt to industry scenarios, and lack of high-quality medical data, they are often unable to implement independently. Zhongkang's solution is to build an AI ecosystem center as an industrial connector to integrate the technical capabilities of the AI technology community into the AI application system of the healthcare industry. “Through the architecture of 'one base+five major scenario intelligents+one ecological connector', we will eventually build an AI operating system suitable for the healthcare industry.” he added.
At the same time, the pharmaceutical industry is in a stage of transformation from the implementation of a strategic blueprint to an industrial reality. Currently, pharmaceutical companies are undergoing multiple changes with continuous deepening collection, accelerated expansion of the out-of-hospital market, and refined marketing trends. In response to the question of “how can AI strategies match changes in the industry cycle”, Wu Han said bluntly that the most direct impact brought about by collection was a sharp decline in gross profit, and the extreme fragmentation of the out-of-hospital market and significant regional differences also caused many pharmaceutical companies to “disagree” from within the hospital to outside the hospital. In this context, the ability to make accurate, efficient, and low-risk decisions has become critical.
In fact, in the overall transformation of retail terminals, the transformation of pharmacies is particularly noteworthy, and Wu Han has not shied away from this topic. He pointed out that over the past three years, pressures such as declining passenger flow, declining gross profit, and weak members' ability to monetize have continued to loom over the industry, but the essence of this is not a decline in the industry; rather, the old business model is no longer able to match the new health needs. More than 600,000 pharmacies across the country form the health service terminal with the most intensive network and the most frequent face-to-face contact with consumers in the healthcare field in China. Their future role should shift from “selling pharmaceuticals and products” to “health posts” providing health services to community residents, and become community health entrances.
A set of data is quite convincing: a diabetic's total health consumption expenditure is about 10,000 to 13,000 yuan a year, but the drug consumption in pharmacies is only about 2,000 yuan. The space in the fields of adjuvant drugs, nutritional health products, health services, etc. is far greater than simply selling drugs, and this part of the value has just been overlooked in the past.
The Zhitong Finance App also noticed that in order to support business development, Zhongkang is trying to build a dual ecosystem. On the one hand, it integrates high-quality technology suitable for pharmacy scenarios in the field of AI technology to build a complete AI technology ecosystem; on the other hand, it relies on the conference platform to integrate payer resources such as pharmaceuticals, devices, health services, medical services and commercial insurance to create a service system that can meet consumers' health needs throughout the life cycle, and provide ecological resource support for pharmacies to build health management capabilities throughout the life cycle.
Of course, with regard to Zhongkang's current strategic upgrade and transformation, Wu Han firmly answered, “While closing a window, it also opened a new door. What we need to do is help the industry find that door faster.”