Large Language Models for Outpatient Referral: Problem Definition, Benchmarking and Challenges
Summary: arXiv:2503.08292v4 Announce Type: replace-cross
In recent years, large language models (LLMs) have emerged as a significant technological advancement in the field of healthcare, particularly in the context of outpatient referrals. As these models are increasingly integrated into healthcare systems, understanding their effectiveness and the challenges they pose becomes essential for optimizing patient care.
Introduction
The application of LLMs in outpatient referral tasks aims to streamline the process of directing patients to appropriate healthcare providers. However, the evaluation of these models is hampered by the absence of standardized criteria. This paper aims to systematically explore the capabilities and limitations of LLMs within Intelligent Outpatient Referral (IOR) systems, while proposing a robust evaluation framework tailored for their assessment.
Research Objectives
The primary objectives of this research are as follows:
- To define the problem space surrounding the use of large language models in outpatient referral processes.
- To benchmark the performance of LLMs against traditional models, such as BERT-like architectures.
- To highlight the challenges faced in the implementation of these models in dynamic healthcare environments.
Evaluation Framework
To effectively assess LLMs in IOR systems, the study proposes a comprehensive evaluation framework comprising two key components:
- Static Evaluation: This aspect focuses on the accuracy and reliability of predefined outpatient referrals. It examines how well LLMs can generate correct recommendations based on fixed input data.
- Dynamic Evaluation: This component assesses the model’s ability to refine referral recommendations through iterative dialogues. It emphasizes the importance of interactive communication in enhancing the patient referral experience.
Findings
The findings from this research indicate that while LLMs provide certain advantages, they do not significantly surpass the performance of existing BERT-like models in static evaluations. However, LLMs demonstrate notable potential in facilitating effective question-asking during dynamic interactions. This capability can enhance patient engagement and lead to more tailored referral outcomes.
Challenges and Future Directions
Despite the promising aspects of LLMs in outpatient referral systems, several challenges remain:
- The need for standardized evaluation metrics that accommodate the unique dynamics of healthcare interactions.
- Integrating LLMs into existing healthcare workflows without disrupting patient care processes.
- Addressing ethical concerns related to data privacy and model transparency in clinical settings.
Future research should focus on refining the evaluation framework and exploring the integration of LLMs into real-world outpatient referral systems. By addressing the outlined challenges, healthcare providers can leverage LLMs to improve the efficiency and effectiveness of patient referrals.
Conclusion
As LLMs continue to evolve, their application in outpatient referral systems offers a promising avenue for enhancing healthcare delivery. By establishing robust evaluation criteria and addressing existing challenges, stakeholders can fully realize the potential of these advanced models in improving patient outcomes.