Integrating AI into Health Economic Modelling: Enhancing Precision, Efficiency, and Relevance

Health economic modeling has been pivotal in assessing the value of healthcare interventions, influencing decisions on resource allocation, pricing, and policy. Traditional methods like Markov models, decision trees, and partitioned survival models have provided a foundation for simulating disease pathways and evaluating cost-effectiveness. However, as healthcare systems encounter increasingly complex therapies, diverse patient demographics, and a surge of real-world data (RWD), these conventional techniques are being pushed to their limits. Artificial intelligence (AI) is emerging as a transformative tool, bridging these gaps and redefining possibilities in health economic modeling

Precision: From Averages to Individualized Insights

Traditional models often depend on population-level averages and static assumptions, potentially overlooking significant variations in patient outcomes. For instance, Markov models apply fixed transition probabilities to entire cohorts, assuming uniform disease progression. AI, especially machine learning (ML), addresses these limitations by analyzing detailed datasets to reveal patterns often missed by traditional statistical methods. In oncology, ML algorithms are enhancing survival estimates in partitioned survival models by integrating genomic data, treatment histories, and socioeconomic factors. These models can forecast outcomes for specific patient subgroups, such as those with rare genetic mutations or comorbidities, resulting in more precise and accurate cost-effectiveness analyses. This transition from broad assumptions to tailored predictions marks a substantial advancement in health economic modeling.

Reinforcement learning, another AI technique, is optimizing dynamic treatment pathways in chronic diseases like diabetes. By simulating individual patient responses to interventions over time, these models consider factors such as adherence patterns, side effects, and evolving risks, thus moving beyond the “one-size-fits-all” approach of traditional frameworks. The integration of personalized treatment pathways also enhances the model’s precision in predicting long-term patient outcomes, further boosting the reliability of economic evaluations.

Efficiency: Accelerating Workflows and Reducing Uncertainty

The development of health economic models has traditionally been labor-intensive, often hindered by time-consuming tasks like data extraction, parameter estimation, and validation. AI streamlines these tasks through automation and advanced analytics, significantly improving efficiency. Natural language processing (NLP) tools, for example, can quickly scan thousands of clinical trial reports, regulatory documents, and real-world evidence (RWE) repositories to extract essential inputs—such as survival curves, utility weights, or adverse event rates. This automation reduces manual effort, allowing modelers to focus on higher-value activities, such as scenario testing and stakeholder engagement. AI also improves model calibration. Bayesian optimization algorithms, for instance, iteratively refine model parameters by comparing outputs to real-world data, enabling faster identification of biases or errors compared to manual methods.

Relevance: Aligning Models with Real-World Complexity

For health technology assessment (HTA) bodies, AI-driven models offer unprecedented flexibility and relevance. Traditional models often struggle to accurately reflect real-world clinical practice, where treatment pathways are dynamic and influenced by various factors, including provider preferences, payer policies, and patient behaviors. AI bridges this gap by incorporating RWD—such as electronic health records (EHRs) and claims data—into simulations, enabling models to better reflect the complexities of everyday healthcare delivery.

AI-powered models can also integrate data from wearable devices or mobile health apps, adding a level of granularity that traditional models often lack. For instance, in evaluating a digital therapeutic for mental health, AI can analyze anonymized data from wearable devices to quantify improvements in daily functioning—a metric typically excluded from traditional economic analyses. This integration ensures that models are aligned with the outcomes that matter most to patients and healthcare providers, ultimately strengthening reimbursement decisions.

For pharmaceutical companies, AI enhances the ability to demonstrate value in competitive markets. By identifying specific patient subgroups most likely to benefit from a therapy—such as those who respond to a targeted oncology drug—AI enables more refined and stratified cost-effectiveness analyses. These insights support value-based pricing strategies, offering a stronger case for premium pricing during negotiations with payers. In fact, some companies have already used AI to streamline market access processes by generating more compelling evidence for HTA submissions.

Challenges: Navigating Trust, Ethics, and Implementation

A prevalent criticism of AI is its "black-box" characteristic, which raises concerns among regulators and health technology assessment (HTA) bodies regarding the transparency of how models derive their conclusions. This is where explainable AI (XAI) techniques, such as SHAP (SHapley Additive exPlanations), become essential. SHAP provides a contribution value for each variable, enabling analysts to clarify the extent to which survival time, cost, or utility is influenced by each input (Lundberg & Lee, 2017). This enhances the credibility of AI-driven models during reimbursement discussions. Notably, NICE (2024) now explicitly advocates for the adoption of explainable and transparent modeling methods. They emphasize that non-transparent approaches, such as black-box models, must be thoroughly justified and used only as supplementary tools. While AI holds significant promise, its application in health economic modeling faces challenges, particularly concerning transparency. Regulators and payers often express skepticism about black-box algorithms, insisting on a clear understanding of how model inputs correlate with outputs. XAI frameworks like SHAP are crucial in addressing these transparency issues. Additionally, data quality and equity present further challenges. AI models trained on biased or incomplete datasets may exacerbate disparities, potentially leading to inequitable healthcare decisions.

The Path Forward: Collaboration and Innovation

The future of health economic modeling lies in hybrid approaches that combine the computational power of AI with the rigor of traditional methods. Federated learning, which allows algorithms to be trained across decentralized datasets without sharing sensitive patient information, could enable global collaborations while preserving privacy. Similarly, AI-powered platforms that automate HTA submissions - tailoring value dossiers to the preferences of agencies - are poised to reduce delays in market access and improve transparency.

For stakeholders across the health economics landscape, the key takeaway is clear: AI is not meant to replace human expertise, but to enhance it. By investing in interdisciplinary teams that include health economists, data scientists, and clinicians, organizations can leverage AI to develop models that are not only faster and more precise but also more equitable and actionable.

Conclusion

AI is transforming health economic modeling by turning theoretical potential into practical solutions. Its ability to enhance precision, streamline workflows, and align models with real-world complexities makes it an indispensable asset in today’s value-driven healthcare environment. However, to realize its full potential, a balance must be struck between innovation and accountability—ensuring transparency, addressing biases, and fostering collaboration across disciplines. By navigating these challenges, AI offers a pathway to models that do not simply simulate outcomes but actively improve them.