JMIR Publications

ABSTRACT

Background:

Patient recruitment remains a major bottleneck in clinical trials. Large language models (LLMs) show promise for patient–trial matching and eligibility screening, but existing work emphasizes algorithmic performance in controlled settings. Less is known about how LLMs should be embedded in multi-stakeholder recruitment workflows where human oversight, process design, and economic incentives jointly determine outcomes.

Objective:

To propose a theory-grounded conceptual framework—the LLM-Embedded Clinical Recruitment Architecture (LECRA)—that explains how LLM integration in clinical trial recruitment may influence operational and economic outcomes through intermediate mechanisms involving data complexity, LLM processing, and human–AI collaboration.

Methods:

We synthesize evidence from recent empirical and review studies on LLM-based patient–trial matching, human–AI teaming in clinical settings, and AI governance in healthcare. Socio-technical systems theory is used to justify joint optimization of technical and social subsystems, and transaction cost economics is used to interpret information-processing costs, uncertainty, and scale effects. The framework is specified as four interdependent layers, and six propositions are derived for future empirical testing.

Results:

LECRA distinguishes (1) data complexity, (2) LLM processing, (3) human–AI collaboration, and (4) economic impact. The framework posits that LLM value is not solely a property of model accuracy; collaboration design (e.g., teaming mode, oversight, explainability) mediates how technical capability translates into recruitment performance, and economic returns may increase with scale as fixed deployment costs are spread across cases. The propositions link complexity, teaming, and scale to expected differences in processing efficiency and cost-related outcomes.

Conclusions:

This conceptual work reframes LLM-enabled recruitment as a socio-technical and economic problem, not only an algorithmic one. It offers a structured basis for empirical studies—especially quasi-experimental evaluations in organizational settings—and for practice by highlighting workflow integration and governance choices that determine whether LLM adoption realizes its potential benefits. Clinical Trial: Not applicable. This manuscript presents a conceptual framework and literature synthesis; it does not report a prospectively registered interventional clinical trial.