Sep 2026· Journal of Managed Care & Specialty Pharmacy· Vol 32 9, pp.
1090-1100
· 0 citations· 24 references
Medicine
TL;DR
Findings support a hybrid paradigm in which AI augments, but does not replace, health economists in value assessment and formulary decision support within managed care settings.
Abstract
Background
Health economic modeling is conceptually sophisticated but operationally repetitive and resource intensive. Recent advances in large language models suggest potential for automating components of cost-effectiveness model development.
Objective
To evaluate whether an agentic artificial intelligence (AI) system can reliably automate cost-effectiveness model development in the context of targeted therapies for anaplastic lymphoma kinase-positive (ALK+) non-small cell lung cancer (NSCLC).
Methods
We developed the Agentic Health Economic Modeling Platform (A-HEMP) to construct a cost-effectiveness model for ALK+ NSCLC therapies without access to existing models in that clinical context. Modeling decisions and extracted parameters were compared with a previously published manual cost-effectiveness analysis. A-HEMP automated PICO-based scoping, modeling approach recommendation, systematic literature review, and structured parameter extraction. Performance was evaluated across 3 domains: model structure concordance, evidence identification concordance, and parameter value alignment. Deterministic cost-effectiveness outputs were calculated externally for benchmarking.
Results
A-HEMP identified a modeling framework aligned with the published cost-effective analysis and retrieved all primary clinical trials used for clinical efficacy inputs. Concordance in model structure and assumptions was observed in 27% of modeling dimensions, with 36% partially concordant and 36% divergent. Divergences were most prominent in survival extrapolation and intracranial progression handling. Evidence identification concordance was high for primary clinical trials (100%) but moderate for cost inputs, with 25% of evidence domains fully concordant and 38% partially concordant. Parameter value alignment was high for clinical efficacy inputs and progression-free health state utilities (<2% deviation), whereas greater variability was observed for sicker health states (12% deviation) and downstream disease management costs (25%-55% deviation).
Conclusions
Agentic AI can reliably automate upstream components of cost-effectiveness model development. However, nuanced modeling decisions with less standardized methodological guidance remain areas requiring expert oversight. These findings support a hybrid paradigm in which AI augments, but does not replace, health economists in value assessment and formulary decision support within managed care settings.
INTRODUCTION
Health economic models (HEMs) provide a solid foundation for reimbursement policy decisions that shape patient access to new treatments and the allocation of scarce healthcare resources. Model development is labor-intensive and time-consuming, often requiring months of expert work. Recent advances in large language models (LLMs) prompted interest in whether artificial intelligence can support or partially automate this process, but the evidence base remains scattered and has not been mapped against the modeling workflow.
AREAS COVERED
This review examines current applications of LLMs to health economic modeling. Five proof-of-concept studies are included and mapped to an eight-stage workflow adapted from the ISPOR-SMDM Modeling Good Research Practices framework and discussed in terms of reproducibility, validation, adaptability, and technology readiness. Published work addressed model parameterization, model implementation, reporting and quality assessment, and local adaptation, while research question design, model conceptualization, uncertainty analysis, and model validation remained unaddressed.
EXPERT COMMENTARY
The evidence supports cautious optimism. Near-term gains are augmenting human modelers on decomposed, verifiable sub-tasks rather than pursuing autonomous end-to-end modeling, which remains distant given current reliability levels and the iterative, collaborative nature of model development.
Attila Imre, B. Németh, Á. Jóźwiak et al.· Expert review of pharmacoeco...· 0 citations
This systematic review provides a comprehensive analysis of XAI methods specifically applied to tabular healthcare data for classification tasks, revealing that SHAP remains the dominant post-hoc method, achieving strong model fidelity but showing inconsistent alignment with clinical expert reasoning.
Angelower Santana-Velásquez, M. B. Salazar-Sánchez· Computers· 0 citations
Patent mapping is a strategic tool for supporting decision-making within health systems and identifying emerging technological trajectories. In oncology, the growing demand for palliative care, coupled with rising treatment costs, has intensified interest in artificial intelligence (AI)-based solutions. This study reviewed patents published during the preceding five-year period to characterize AI-related technological trends in oncology, with particular emphasis on their clinical applications and economic implications. Patent searches were conducted in the databases of the Brazilian National Institute of Industrial Property, Espacenet, and the World Intellectual Property Organization between September and December 2025, using the search terms “Palliative Care” and “Cancer,” together with the International Patent Classification code G16H10/60. Of the 251 patents initially identified, 19 met the predefined eligibility criteria. The protected technologies included risk prediction models, clinical decision support systems, symptom-monitoring platforms, and natural language processing tools. These innovations were designed to improve prognostic accuracy, facilitate referrals to palliative care services, and optimize the allocation of healthcare resources. Overall, AI emerged as a key driver of innovation in oncological palliative care.
João Rafael Lisboa Rêgo Brito, Jussara Secundo dos Santos, Letícia Gabriele Secundo dos Santos et al.· Revista Inclusiones· 0 citations
Objectives
To evaluate the efficiency and reliability of a large language model (LLM) as a decision-support tool in hospital compounding pharmacy for pediatric extemporaneous preparations requiring assessment of drug crushability, regulatory compliance, and formulation feasibility.
Methods
A proof-of-concept study compared a structured LLM-assisted workflow with the traditional manual information retrieval process in a hospital pharmacy setting. The LLM (Claude Sonnet 4.6) was configured with a standardized prompt to extract and consolidate data from multiple authoritative sources: the Friuli Venezia Giulia "Do Not Crush" list, the Italian Medicines Agency (AIFA) database for Summary of Product Characteristics (SmPC), AIFA Law 648/96 off-label use lists, and the Stabilis database for oral liquid formulation stability. Two representative drugs, propranolol hydrochloride and imatinib mesylate, were analyzed. For each drug, the model generated a structured output including crushability, regulatory information, off-label status, and extemporaneous formulation data. The same queries were manually performed by an experienced hospital pharmacist. Primary outcome was information retrieval time; secondary outcomes included completeness and accuracy.
Results
The LLM-assisted workflow reduced retrieval time to less than 2 minutes per drug (mean 1 minute 45 seconds), compared with a mean of 20 minutes (range 15-25 minutes) for the manual process, plus an additional 5 to 10 minutes for transcription. Output completeness was 100%, with all predefined fields correctly populated. The model accurately classified drug crushability and correctly identified Law 648/96 regulatory status. For propranolol, the system identified crushability, pediatric off-label authorization, and SyrSpend-based formulations with stability data of up to 146 days at room temperature. For imatinib, the model highlighted cytotoxic handling precautions, identified the absence of SyrSpend formulations, and retrieved alternative formulation stability data (30 days refrigerated).
Conclusions
A properly configured LLM can function as an effective decision-support tool in hospital compounding pharmacy, improving efficiency while maintaining high standards of completeness, accuracy, and regulatory compliance. These preliminary results support further investigation into the integration of the LLM system into routine pediatric galenical preparation practice.
E. Castellana, MR Chiappetta· Hospital Pharmacy· 0 citations
ObjectiveWith increasing emphasis on value-based healthcare and rising costs, it is essential to assess the economic impact of artificial intelligence (AI). This study systematically reviews the evidence on the cost-effectiveness of AI applications in healthcare.MethodsA systematic search of PubMed, Scopus and Web of Science was conducted up to 21 August 2025. Studies that had full text and were peer-reviewed articles in English and reported formal economic evaluations, including cost-effectiveness, cost-utility or cost-benefit analysis of AI-based healthcare interventions, were included in this study. Key economic indicators such as incremental cost-effectiveness ratios (ICERs), quality-adjusted life years (QALYs) and disability-adjusted life years (DALYs) were extracted. Methodological quality was assessed using the Health Economics Consensus Criteria (CHEC-list). Data were qualitatively combined.ResultsA total of 26 studies met the inclusion criteria. Applications of AI in screening, diagnosis, treatment decision support and rehabilitation were assessed. Deep learning approaches were the most common approaches studied. Nine studies were classified as cost-effective, six as cost-neutral or somewhat cost-effective and eleven as not cost-effective. Overall methodological quality ranged from low to high, with considerable heterogeneity in model structure, perspective and time horizon.ConclusionsAI in healthcare, particularly in screening and early diagnosis, shows promising cost-effectiveness. However, the strength of current evidence is moderate and highly context-dependent, and the results are sensitive to methodological assumptions, implementation costs, and healthcare system characteristics.
Ali Imani, Elham Monaghesh· Health Informatics Journal· 0 citations
A scoping review with systematic evidence mapping across five electronic sources, screened 1,649 exportable records, and provisionally included 557 unique studies that met predefined criteria for goal-directed task execution, tool use, interaction with external resources, feedback-based refinement, or multi-agent collaboration.
Zheng Tong, Yang Liu, Wanshu Fan et al.· 0 citations
