In recent years, Artificial intelligence (AI) has been promoted in the healthcare sector to accelerate product development and improve the health outcomes, quality of healthcare services, patients’ access, and efficiency of care. AI can be used in almost all phases of the product lifecycle, from R&D to market access and reimbursement.
This talk is about health technology assessment (HTA) and its requirements, the role of AI in health and HTA so far, and potential areas that AI might help in HTA, the challenges of HTA agencies and AI in the healthcare sector. Decision-makers make reimbursement decisions based on HTA bodies’ recommendations. HTA bodies assess new health technologies from different perspectives including clinical, economic , ethical, social, and legal. The clinical perspective is any changes in morbidity (efficacy, effectiveness, quality of life, and safety) and/or mortality due to a health intervention. The clinical evidence measures the health outcomes which are basedmainly on clinical trials and real-world evidence (RWE). AI tools are employed to collect and analyze the outcomes data from different sources. However, there are several challenges to use AI for measuring health outcomes in the decision-making processes. These challenges include the risk of bias and the quality of data we use to train machines, the appropriateness of the AI algorithm to analyze this data, its representativeness, safety and security. Another critical challenge is trust which is not easy among practitioners and decision-makers in the health system because the impact might be irreversible.
On the other hand, investing in improving health outcomes saves lots of costs and causes the sustainability of the health system in the long term. Building the health economic models is another sensitive and time-consuming element of HTA. AI might be able to build or even adjust the health economic models and accelerate the HTA processes leading to expedited access to new health technologies.