ReviewAdvances in therapy2025
Integration of Generative AI with Human Expertise in HEOR: A Hybrid Intelligence Framework.
Review in Advances in therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionHealth economics and outcomes research (HEOR) is pivotal in shaping healthcare policies, optimizing decision-making, and ensuring effective resource allocation. However, current HEOR workflows often struggle to keep pace with the growing complexity of data, constrained resources, and the need for adaptable, real-time analysis. Generative artificial intelligence (Gen-AI) offers a transformative opportunity to address these challenges by augmenting human expertise with advanced computational capabilities. Despite its potential, the integration of Gen-AI into HEOR workflows remains largely unexplored, leaving professionals uncertain about how to effectively leverage its capabilities. This study bridges this gap by introducing a novel hybrid intelligence framework that integrates Gen-AI with human input to enhance critical HEOR tasks, including health economic model conceptualization, evidence synthesis, and patient-reported outcome (PRO) assessment.
methodsBuilding on established adoption theories such as the technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT), the framework emphasizes key enablers like perceived usefulness, ease of use, organizational readiness, and social influence to support seamless integration within HEOR workflows. The framework incorporates two implementable approaches: human-in-the-loop (HITL), where AI takes the lead with human validation and refinement, and AI-in-the-loop (AITL), where human professionals remain in control, leveraging AI for verification and enhancements. Advanced tools like retrieval augmented generation (RAG) and Graph RAG are employed alongside techniques such as prompt engineering to ensure outputs are reliable, contextually grounded, and aligned with HEOR needs.
conclusionBy combining computational efficiency with human insight, this hybrid approach contributes to the evolving integration of AI in HEOR, fostering innovation and driving actionable outcomes. This research sets a foundation for practically integrating Gen-AI into HEOR, offering an actionable pathway to transform workflows, improve healthcare decision-making, and ultimately enhance patient care.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.