Contributor: The Promise of AI in Health Care

Artificial Intelligence (AI) is rapidly transforming the health care landscape. It is improving treatment algorithms, enhancing diagnostics, and accelerating drug development. AI holds significant promise in its application to clinical trial design and real-time monitoring, which can lead to more personalized and efficient patient care. These advancements are poised to reshape health economics and outcomes research (HEOR), especially in cost-effectiveness analysis (CEA) and Health Technology Assessment (HTA).

While AI's role in clinical trial design and real-time monitoring is well-known, it also brings unprecedented potential in reshaping health economics and its assessment models. AI-driven tools can help us look at the entire health care system with fresh eyes, creating new connections between vast amounts of previously disconnected data. This ability to reveal hidden patterns can lead to a more nuanced understanding of value—something that is critical as health care systems worldwide grapple with escalating costs and the need for sustainable health care systems.

Jason Spangler, MD, MPH, FACPM | Image credit: Center for Innovation and Value Research

Evidence Generation and Interpretation

An exciting area where AI is making waves is in evidence generation and interpretation. In traditional health care models, evidence is often gathered through large-scale clinical trials or observational studies, a process that can take years and costs millions of dollars. AI has the potential to streamline this process, allowing us to generate evidence more quickly and with greater precision. Machine learning algorithms, for example, can sift through vast troves of data—from electronic health records to genetic information—extracting insights that were previously unattainable.

Data and information iteration in AI, particularly large language models (LLMs), is a powerful tool that can enhance information perspective and analysis. This is particularly relevant in the context of evidence generation. AI does not merely collect data, it learns from each piece of information, refining each time. This iterative process will revolutionize how we approach clinical trial designs. Imagine trials that adapt dynamically to new data inputs—changing protocols or endpoints in real time based on statistically sound predictive insights from AI. The implications for personalized medicine are profound, offering the possibility of truly individualized treatment plans based on a continuously evolving dataset.

Predictive Modeling in Health Technology Assessment (HTA)

Another area where AI shows tremendous promise is predictive modeling, which is integral to HTA. Health systems worldwide are faced with assessing the long term cost-effectiveness of new treatments and interventions, often based on limited short-term data. AI’s ability to model complex interactions and project long-term outcomes can significantly enhance this process. In many ways, AI helps us simulate potential futures based on present data, providing policymakers and health care providers with more accurate assessments of how different interventions will impact health outcomes and costs over time.

Predictive modeling allows us to move beyond the limitations of linear thinking. Traditional CEA tends to focus on static, point-in-time assessments, but AI helps us think more dynamically and longitudinally. The ability to run simulations that account for a wide range of variables, from demographic shifts to evolving disease patterns, gives health care stakeholders a more holistic view of potential outcomes. This is where the intersection of AI and health care economics can lead to genuinely transformative change. AI’s ability to evolve and simulate different scenarios can help policymakers navigate the complexities of modern health care, leading to better and more sustainable decision-making.

John Nosta | Image credit: Center for Innovation and Value Research

Ensuring Fairness and Transparency

However, as pointed out in the literature (including previous writings from one of the authors), the adoption of AI in health care must be approached cautiously. The promise of AI is enormous, but so are the ethical challenges it presents. The risk of bias in AI algorithms is particularly concerning in health care, where decisions can directly impact patients' well-being. If incorrectly designed AI systems can inadvertently reinforce existing health disparities by using biased data or creating unfair algorithms.

In the context of HEOR and HTA, these concerns are especially relevant. For instance, predictive models based on biased datasets could lead to inequitable health care decisions, where certain patient populations—such as those from underrepresented racial or socioeconomic groups—may be overlooked or underserved. This highlights the need for AI algorithms to be as inclusive and transparent as possible, a theme we repeatedly emphasized when discussing the ethical dimensions of AI.

AI in health care should focus on efficiency and uphold principles of fairness and transparency. This requires rigorous validation processes, diverse datasets, and continuous oversight to ensure that AI tools contribute to equitable health care delivery. While technology holds immense potential, it must be developed and deployed in ways that prioritize patient-centered outcomes and ethical integrity.

Balancing Innovation and Caution

The journey from “big data” to actionable, AI-driven insights represents one of the most significant transformations in modern health care. As AI becomes more integrated into clinical trial design, real-time monitoring, and HTA, it offers the potential for more personalized care, improved cost-effectiveness, and better long-term health outcomes. Thoughtful regulation, rigorous validation, and ethical considerations must guide AI’s integration into health care systems.

The broader narrative of AI's role in health care is not just about revolutionizing systems but ensuring that this revolution leads to a more equitable, transparent, and patient-focused future. The promise is immense but realizing that promise requires a delicate balance of innovation and responsibility. As we continue down this path, AI’s role in reshaping health economics and outcomes research will likely define the next frontier of medical advancement, ensuring that both the clinical and ethical dimensions of health care are elevated to new heights.