Shanlian Hu, MD, MSc, Professor of Health Economics, School of Public Health, Fudan University, Shanghai, China
At present, with the rapid development of artificial intelligence (AI) technology, the demand for computing power is becoming increasingly urgent. How to realize the application of AI in Health Technology Assessment (HTA) and pharmacoeconomics is a proposition worth studying. The author believes that in the near future, AI can also be used to reform the DRG/DIP payment methods in medical insurance and negotiate drug prices, assist medical insurance payers to calculate the maximum fair price more accurately while helping pharmaceutical companies calculate the optimal pricing.
Enhance Hospital Services
AI promotes the development of the biomedical industry, which requires the coordinated development of medical insurance, medical care, and pharmaceutical policies. At present, there are still many gaps in AI between China and other developed countries. But the good news is that many hospitals in China have begun to introduce large medical models and established AI medical diagnosis and treatment evaluation systems to enhance the hospital's service capabilities. Taking medical imaging as an example, As the population ages, the number of patients increases, and innovative treatments develop, the numbers of CT and MRI examinations surge. In Canada, in the ten years from 2010 to 2020, these two examinations increased by 31% and 62%, respectively, causing an overburdened workload on radiologists, and also indirectly affecting the efficiency and quality of work on radiologists. In 2023, the Canadian HTA Organization published a "Report on the Impact of ChatGTP on Radiology Workflows", which summarized the effects of ChatGPT in making clinical decisions, improving diagnosis, and saving time. But it also brings certain risks, such as reliability and protection of patient privacy. The accuracy of ChatGPT reports depends on the collection of reading data quality.
According to incomplete statistics, China has made more than 40 large AI models which are catching up with the ChatGDT 4.5 model, and the market size of generative artificial intelligence (AIGC) is expected to exceed 10 trillion yuan in 2030.
Promote Market Access
In recent years, AI has begun to play a significant decision-making role in the pharmaceutical industry's product development, drug prescription, market access, drug pricing and reimbursement. Many pharmaceutical companies use AI to simulate the company's business operations to assist in market entry decision-making research on existing products and future new products.
Firstly, AI can quickly integrate and analyze medical data, clinical trial results and market access information improve the conditions for drug launch. Identifying market trends, treatment standards and guidelines, assisting in collecting suggestions from opinion leaders in different fields, collecting and organizing patient information, understanding unmet clinical needs, and collecting optimal pricing and providing medical insurance providers with drug value information are all powerful applications.
Secondly, AI helps pharmaceutical companies predict the price of negotiations and the results of negotiations with medical insurance parties and HTA agencies and develop influential value propositions and price prediction tools. The UK has established a platform called ValueScope to help companies predict the prices and results of negotiations with HTA agencies such as the UK's NICE and Germany's IQWiG. This is a virtual model was built by collecting data on more than 1,700 drugs marketed in Europe. It can predict the value of outcome evaluation and the prices of negotiation with a reported accuracy of 90%.
Thirdly, AI helps payers make decisions. In 2021, Humana Technologies partnered with IBM Watson Health Commercial Insurance Payers to Collect information on health costs, treatment benefits, medical costs, and patient information. Through the AI platform, patients’ healthcare costs are predicted to reduce expenses for insured persons.
Fourthly, AI helps design outcome-based contracts and assist payers with value-based compensation programs. This could include helping identify appropriate patient populations and subgroups, improve treatment yield, simultaneously predicting treatment outcomes and costs, determining clinical metrics, and evaluating treatment outcomes. For example, in 2020 Centene Corporation applied Apixio Payer for a value-based reimbursement platform to examine patient data to provide quality information and value-based reimbursement.
Lastly, AI predicts new drug prices and pricing strategies. This includes predicted procurement price (PAC) and optimized, maximum allowable drug cost (MAC). PAC was once used by the state of Oklahoma in the United States to manage the state's Medicaid drug prices to build a multi-level predictive analysis model to evaluate drug purchase prices, Including the drug price list published by MAC Buchmarks, existing benchmark prices, dispensing costs, drug supply and demand, and purchase prices.
As medical data becomes increasingly available, AI will play an increasingly significant role in drug pricing and reimbursement, and market access.
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