Cost Reduction and Efficiency Enhancement: The Role of Healthcare Big Data

In mid-December, the second batch of national pilot programs for health and medical big data centers was launched in Shandong, Anhui, and Guizhou provinces. Just a few days earlier, the Guangzhou International Artificial Intelligence Industry Research Institute was officially unveiled in the Nansha Free Trade Zone, with the first 16 laboratories—including those from Lenovo and LinkDoc Technology—moving in to focus on areas such as medical artificial intelligence.

Due to the shortcomings of the traditional healthcare industry, such as insufficient supply of physician resources, high medical costs, and high rates of misdiagnosis, the industry has been actively exploring and practicing how to leverage new technologies like big data and artificial intelligence (AI) to address existing challenges in healthcare. “Clinical guidelines and expert consensus can be integrated into data systems to form knowledge-driven clinical decision support, guiding patient medication and providing clinicians with auxiliary diagnostic and therapeutic support,” stated Zhou Maigeng from the National Center for Chronic and Noncommunicable Disease Control and Prevention under the National Health and Family Planning Commission. Speaking at the inaugural Nansha AI Summit Forum held recently in Guangzhou, he pointed out that AI is developing rapidly, and its integration with medicine, healthcare, and pharmaceuticals holds immense potential, promising endless possibilities for the future.

From the report to the 19th National Congress of the Communist Party of China, which called for “promoting the deep integration of the internet, big data, artificial intelligence, and the real economy,” to the State Council’s release of the New Generation Artificial Intelligence Development Plan, a series of domestic policies have set the tone for the development of artificial intelligence.

Big data and artificial intelligence will play a significant role for governments, hospitals, enterprises, insurance companies, and health management organizations. “For hospitals, big data and AI will enhance the quality of medical services, enable precision medicine, and support clinical research platforms. For governments and insurance institutions, the integration of big data and AI will facilitate policy analysis, promote rational drug use, and advance precision in medical insurance. For enterprises, it means leveraging real-world data analytics, improving operational efficiency through dedicated platforms, and fostering collaborations on new business models. For health management organizations, big data and AI will make risk screening and personalized care possible,” stated Zhou Maigeng.

However, the current development of artificial intelligence remains primarily at the technical level. The application of medical big data still faces bottlenecks, and the formation of a complete commercial closed loop is still under exploration. In an interview, Luo Ligang, CTO of LinkDoc Technology, told reporters that while the medical big data market holds substantial value, structural issues with medical data have yet to be adequately resolved due to the complexity of the healthcare sector and the vast volumes of data and medical records involved. “Although data collection and integration have initially established a relatively solid foundation, there are shortcomings in data application. Unlocking its full value is the direction in which the industry is striving,” he said.

“However, the role of AI (artificial intelligence) in the healthcare industry holds great promise for the future,” said Zhang Tianze, CEO of LinkDoc Technology. He noted that in clinical applications, AI can enable specialists to draw upon a broader range of reference cases when treating patients, thereby providing better treatment plans for major diseases such as cancer. Additionally, AI can make drug evaluation more efficient, allowing truly effective medications to reach their full potential while phasing out those with limited therapeutic value. This will help patients access more effective and innovative drugs. Furthermore, while homebuyers can currently obtain mortgages, medical expenses cannot be paid in installments. With the introduction of AI, financial sector involvement may become feasible, thereby reducing the financial burden on patients.

Some institutions predict that the application of data analytics in the healthcare sector will increase per capita GDP by $200, reduce national healthcare expenditures by 5%–9%, and extend average human life expectancy by one year.

Industry experts point out that aggregating patients’ clinical records and health insurance datasets, followed by advanced analytics, will enhance the decision-making capabilities of healthcare payers, providers, and pharmaceutical companies, thereby effectively improving healthcare efficiency and reducing costs. For example, in the new drug development phase, pharmaceutical companies can leverage data modeling and analysis to determine the most efficient return on investment, thus saving on R&D costs.

“Big data can shorten the R&D cycle and improve the efficiency of clinical trials; through multi-dimensional analysis of big data, new indications can be discovered; collecting and processing data information during the clinical trial phase allows for more timely prediction of clinical outcomes,” said Kevin Ke, Vice President of AstraZeneca’s Global Drug Development Department in China and Head of Oncology.

The typical drug development cycle for new medicines spans approximately 10 years. Leveraging AI and big data can help pharmaceutical companies develop more targeted drugs with higher potential market value and greater therapeutic success rates, accelerate time-to-market, and improve patient accessibility. Meanwhile, reducing R&D costs for enterprises also contributes to further lowering drug prices.

Furthermore, the application of AI and big data in clinical settings can improve healthcare outcomes. For instance, they enable early detection and diagnosis before patients exhibit symptoms, thereby facilitating timely intervention.

For the same patient, medical care methods and outcomes vary across different healthcare institutions, with significant disparities in treatment costs. For different patients, the same diagnostic and therapeutic regimen may yield varying efficacy. By analyzing and comparing large datasets comprising patient vital signs, medical expenditure, and treatment outcomes for various diagnoses, physicians can identify the most effective and cost-efficient treatment plans, potentially reducing instances of overtreatment or undertreatment. Adopting individualized diagnostic and therapeutic strategies for different patients, or adjusting medication dosages based on individual patient conditions, can also minimize adverse effects.

Consulting firm McKinsey estimates that, in certain cases, reducing prescription drug usage can lower healthcare costs by 30% to 70%. For instance, early detection and treatment can significantly reduce the burden of lung cancer on the healthcare system, as the cost of early-stage surgery is half that of later-stage treatment.

Regarding cost containment in medical insurance, rational actuarial pricing remains a primary pain point for both basic medical insurance and commercial health insurance. Experts believe that the use of big data facilitates pricing and cost control within basic medical insurance, while also fostering innovation in product offerings and mechanisms for commercial health insurance.

Source: Pharmaceutical Economic News