The Wave of AI Large Models Accelerates Medical Digitalization: New Opportunities for Digital Transformation in Healthcare
-
The butterfly effect is triggering chain reactions across every corner of the healthcare industry, revealing previously overlooked issues. Meanwhile, recognition of the value of digitalization and AI large models is rapidly gaining momentum in China's medical sector.
At a local people's hospital in Chongqing, IT director Wang Nan (pseudonym) is hurriedly gathering his materials for a meeting. Unlike previous meetings, this one focuses on "leveraging technology to advance hospital information transparency and digitalization."
"There's a clear trend of hospitals deploying SDP systems, elevating medical digital intelligence to new heights," Wang observed.
The secondary market indirectly validates Wang's observation, with stocks like Zhejiang Zhenyuan (000705) and Saili Medical (603716) hitting limit-up, reflecting the growing strength of the SPD concept.
Over the past few years, medical digitalization and smart healthcare have been frequently discussed and implemented. Yet, in this complex industry, many processes and nodes remain "untouchable" by digital transformation, with some pain points even being deliberately avoided.
Through his account, we learned that the hospital has established four intelligent systems covering pharmaceuticals, consumables, medical services, and healthcare insurance, forming a transparent digital network.
"This time it feels serious," Wang told industry insiders, contrasting current efforts with past superficial digital transformations done merely to meet quotas.
Such changes suggest that medical digitalization is poised to enter a new phase.
The butterfly effect continues to ripple through healthcare, making neglected issues increasingly visible while bringing both opportunities and challenges.
I. Digital Disconnects, Bottlenecks, and Unhealthy Practices
As healthcare becomes a hot topic, China's medical digitalization challenges are once again under scrutiny.
"A medical equipment agent purchases a device from the manufacturer for 5.79 million yuan, then sells it to a hospital for 11.7 million yuan—doubling the price..." This excerpt from the anti-corruption documentary Zero Tolerance exposes systemic issues.
Behind such unhealthy medical practices lie opaque operations enabled by broken processes.
"From procurement to usage, feedback, and follow-up—any break in this chain requires manual intervention, making accountability nearly impossible."
Xie Zhaohui, CEO of Aichuang Technology, shared with us that his company specializes in 'one product, one code' solutions, leading the pharmaceutical industry in product traceability and serving over 3,000 pharmaceutical companies globally. Although Xie doesn't interact with hospitals frequently, his extensive experience in the pharmaceutical sector gives him deep insights into the challenges of digital transformation in healthcare.
The core of digitalization in the healthcare industry lies in structuring the accumulated information and data, analyzing and utilizing it to improve efficiency and support business growth.
However, in healthcare, difficulties in data collection, governance, and application hinder the realization of data value, creating barriers between data and operations, leading to disconnections across various stages.
'There is a significant gap between the accumulation of medical data and its practical application,' a point also confirmed in discussions with Yunzhisheng.
Specifically, in healthcare, most information involves sensitive patient health and privacy data, making data security a major challenge for medical institutions.
According to IBM Security's '2020 Cost of a Data Breach Report,' the average global cost of healthcare data breaches in 2020 alone reached $7.13 million, a 10% increase from 2019.
In China, data from the Chinese Hospital Association's Information Management Committee shows that in a 2018-2019 survey, only 43.95% of the 839 participating hospitals passed the graded protection assessment, with 75% of hospitals below the tertiary level never having undergone such an assessment.
Given the severity of medical data security issues, most healthcare data has long been kept in closed environments to ensure safety.
Additionally, due to the unique nature of healthcare services, hospitals in different regions and departments have their own specific needs, making data integration and interoperability challenging.
Relevant data indicates that the construction rates for provincial, municipal, and county-level regional health information platforms in China are 63.3%, 39.2%, and 23.8%, respectively. The proportions of these platforms achieving cross-departmental data sharing with medical insurance agencies are 66.7%, 30.5%, and 26.1%, respectively. Furthermore, 29.6% of tertiary hospitals and 45.4% of secondary hospitals have not achieved information sharing and exchange within their regions.
Overall, the non-standardized, heterogeneous, and isolated nature of medical data makes it difficult to apply, understand, analyze, and derive value from it.
As a representative from Yilian noted, 'Digital healthcare, driven by internet technology, has primarily addressed spatial and connectivity issues but has yet to solve the shortage of doctors.' In their view, progress in digital healthcare has been limited over the decades.
For healthcare digitization, the challenges are becoming increasingly clear.
Is SPD the Solution?
A fact is that while emerging technologies such as 5G, big data, cloud computing, and the Internet of Things (IoT) are gradually being applied to various scenarios of medical digitization, the complexity of the healthcare industry and regulatory requirements have led to uneven progress in digital transformation across different scenarios.
For example, in the pharmaceutical and medical device sector, policy requirements for drug traceability have accelerated the implementation of full lifecycle management. Aichuang Technology is a leading player in this field.
Additionally, the rapid rise of early internet healthcare has driven the digitization of outpatient services. Products like Medlinker's electronic medical records and intelligent prescription review systems, as well as Unisound's medical voice interaction solutions and intelligent follow-up systems, are typical examples in this scenario.
In contrast, the penetration rate of digitization in hospital settings is much lower.
For instance, the actual implementation of SPD traceability systems in hospitals is far from ideal. Currently, only a single-digit percentage of hospitals in China have deployed such software. "Around 3% to 5%, and some are only partially implemented," Xie Zhaohui told Industrial Insights.
Aichuang Technology's approach is to use a 'one-item-one-code' system to connect pharmaceutical companies with distributors, hospitals, pharmacies, doctors, medical insurance, and patients, creating a digital ecosystem for the entire industry. "However, for the healthcare industry to develop healthily, establishing an in-hospital SPD traceability system is a fundamental issue that must be addressed."
Notably, the trend of SPD software adoption has become increasingly evident in recent years. Data from the Medical Device Supply Chain Branch of the China Federation of Logistics & Purchasing shows that the number of SPD projects launched in the first ten months of 2022 alone was nearly equal to the total number implemented from 2019 to 2021.
In simple terms, SPD is an integrated management system for medical supplies that adopts modern supply chain management methods to help hospitals reduce costs and improve efficiency. It is a critical component of in-hospital digital transformation.
It's important to note that drug and consumable costs are the largest expense for hospitals, typically accounting for over 50% of total costs. For example, a tertiary hospital may stock no more than 1,500 types of drugs but could use over 10,000 types of consumables, each with different management requirements. Under the SPD model, the costs associated with managing consumables—such as space, labor, and operations—can be transferred to SPD service providers, separating logistics from clinical operations.
Under the SPD management model, transparency between in-hospital and out-of-hospital medical devices, as well as patients, can be significantly improved.
This is no easy task.
"Many hospital systems are old and complex, practically held together like paper—any touch could cause problems." In Xie Zhaohui's view, this is a massive undertaking with numerous challenges that will take time to resolve.
Therefore, relying solely on hospitals' internal motivation to drive transformation is unlikely to sustain progress for most institutions.
The industry generally agrees that solutions lie in two approaches: first, implementing mandatory policies within hospitals similar to drug traceability; second, SPD vendors promoting healthier industry development.
Currently, while some regions have included SPD in tertiary hospital evaluations, progress is slow and long-term. In contrast, the latter approach may yield more immediate results.
In China, SPD vendors operate under two business models: those engaged in operational activities (typically pharmaceutical companies providing in-hospital services) and pure third-party service providers.
The third-party model eliminates operational involvement, enhancing transparency while reducing hospital management pressures and risks. Hospitals can leverage SPD system data to evaluate suppliers objectively, optimizing benefits – potentially steering the industry toward sustainable growth.
However, as mentioned, only a handful of hospitals have deployed SPD systems currently. The focus remains on leveraging new technologies, continuous optimization, and connecting operational nodes in healthcare digitization.
This transformation is giving rise to new medical ecosystems.
AI Large Models and Healthcare Digitalization
Healthcare has a unique characteristic: patients often lack clarity about treatment costs or expense breakdowns. In many regions, insured individuals don't even receive itemized bills, making cost transparency challenging.
Digitalization can establish cost-effective healthcare networks, creating patient-centered incentives that encourage providers to deliver optimal care. An ideal ecosystem is emerging.
With full-process integration across regulation, hospitals, pharmaceuticals, devices, and insurance, even a single anesthetic dose becomes fully traceable – from raw material suppliers to manufacturers, purchasers, administering doctors, and patients.
Furthermore, low-quality service providers will struggle to survive. Insurers may exclude underperforming doctors (with poor outcomes, negative patient reviews, or unjustifiably high costs) from their networks.
Additionally, previous incentive mechanisms encouraged healthcare providers to conduct as many tests as possible on patients, some of which were entirely unnecessary and did not necessarily meet the actual needs of patients.
Data shows that in the U.S. alone, the cost of unnecessary treatments and tests totals up to $600 billion, while in Switzerland, the figure stands at 2 billion Swiss francs.
The new digital incentive mechanisms will bring about more rational and streamlined treatment processes.
In the digital era, treatment methods can also shift from standardized approaches to personalized ones. CloudMinds believes that 'AGI can analyze vast amounts of personal health data.'
This personalized treatment is based on comprehensive real-world patient data (such as blood test results, X-rays, CT scans, MRI images, omics data, microbiome data, and vital signs).
Under this trend, data about patients, their diseases, and treatment methods will play a critical role. This data will no longer be just a byproduct of the treatment process; it will hold equal importance to diagnosis and medication, becoming a key factor in effective treatment. Much medical research will revolve around patient data, accelerating progress and reducing costs.
Medical data and digital infrastructure will become the lifeblood of the 21st-century smart healthcare system.
Currently, this trend is already evident amid the explosion of AI large models.
For example, YiLian has launched the medical large language model MedGPT, aiming to liberate doctors' productivity and truly balance medical resources, while CloudMinds uses AGI to understand, analyze, and reason with data, offering solutions for complex medical conditions.
Although healthcare digitization still faces many challenges today, it is gradually being reshaped by industry participants and emerging technologies. As the head of YiLian stated, the ultimate solution to the 'effective, accessible, and affordable' healthcare trilemma will emerge with the advent of MedGPT.