Barriers to Use Artificial Intelligence Methodologies in Health Technology Assessment in Central and East European Countries

Abstract

The aim of this paper is to identify the barriers that are specifically relevant to the use of Artificial Intelligence (AI)-based evidence in Central and Eastern European (CEE) Health Technology Assessment (HTA) systems. The study relied on two main parallel sources to identify barriers to use AI methodologies in HTA in CEE, including a scoping literature review and iterative focus group meetings with HTx team members. Most of the other selected articles discussed AI from a clinical perspective (n = 25), and the rest are from regulatory perspective (n = 13), and transfer of knowledge point of view (n = 3). Clinical areas studied are quite diverse—from pediatric, diabetes, diagnostic radiology, gynecology, oncology, surgery, psychiatry, cardiology, infection diseases, and oncology. Out of all 38 articles, 25 (66%) describe the AI method and the rest are more focused on the utilization barriers of different health care services and programs. The potential barriers could be classified as data related, methodological, technological, regulatory and policy related, and human factor related. Some of the barriers are quite similar, especially concerning the technologies. Studies focusing on the AI usage for HTA decision making are scarce. AI and augmented decision making tools are a novel science, and we are in the process of adapting it to existing needs. HTA as a process requires multiple steps, multiple evaluations which rely on heterogenous data. Therefore, the observed range of barriers come as a no surprise, and experts in the field need to give their opinion on the most important barriers in order to develop recommendations to overcome them and to disseminate the practical application of these tools.

Recommendations to overcome barriers to the use of artificial intelligence-driven evidence in health technology assessment

“…A previous work product of the HTx project, including a literature review and focus group, identified a total of 29 barriers that specifically hinder the use of AI-based evidence in HTA systems in CEE countries ( 3 ). The barriers in that study were categorized as data-related, methodological, technological, regulatory and policy-related, and human-factor-related ( 3 ). The results of the study were used as a basis to select the most important barriers for the CEE countries.…”

“…Artificial intelligence has attracted much attention because of its potential in healthcare to improve access, quality and efficiency (2), but its adoption has been slow due to several factors that are specific to healthcare (e.g., legal and ethical restrictions to accessing patient level data, fragmented databases, interoperability issues with pooling data etc.) (2,3), resulting in a lag of healthcare behind other industries (4). Although AI has been applied in several areas of healthcare, such as more accurate and faster detection, prediction, and diagnosis of diseases (5,6), its use in supporting health policymaking has been limited so far (7)(8)(9).…”

“…Despite the immense potential of AI in health policy, there are substantial barriers that challenge HTA “doers” (e.g., HTA practitioners or researchers) to use AI to generate high-quality evidence and HTA “users” (e.g., reimbursement decision makers) to rely on AI-assisted evidence generation for decision-making ( 3 , 14 , 15 ). This topic is of particular importance for the Central and Eastern European (CEE) countries, as the implementation of HTA ( 16 ) and access to health databases lag behind Western European (WE) countries ( 17 , 18 ); improvements in HTA are further mandated by the poorer population health in CEE than in WE countries and the corresponding need for more advanced technology across the entire healthcare sector.…”

“…The digitalization of health data is also developing in CEE, where in most countries healthcare financing is less fragmented due to single payer systems. This provides the opportunity to track patient pathways, explore comparative effectiveness data and estimate resource use and costs for different diseases in a single national database ( 3 ). AI may accelerate the generation of locally digested evidence from electronic databases and reduce the gap in HTA implementation between CEE and WE countries.…”

“…We have two major goals. The first is to prioritize previously identified barriers developed in the context of CEE countries ( 3 ) and the second is to propose generalizable recommendations on how to overcome the most important ones.…”