PIORS: Using Large Language Models and Simulation to Improve Outpatient Reception in China

Belangrijkste concepten

This paper introduces PIORS, a system that uses a large language model (LLM) trained with a novel simulation framework to improve the efficiency and quality of outpatient reception in Chinese hospitals.

Samenvatting

• Bibliographic Information: Bao, Z., Liu, Q., Guo, Y., Ye, Z., Shen, J., Xie, S., ... & Wei, Z. (2024). PIORS: Personalized Intelligent Outpatient Reception based on Large Language Model with Multi-Agents Medical Scenario Simulation. arXiv preprint arXiv:2411.13902.
• Research Objective: This paper presents PIORS, a system designed to enhance the efficiency and quality of outpatient reception in Chinese hospitals by integrating a large language model (LLM) trained with a novel simulation framework.
• Methodology: The researchers developed PIORS, which consists of an LLM-based reception nurse and a collaboration between the LLM and the hospital information system (HIS). To train the LLM, they created SFMSS (Service Flow aware Medical Scenario Simulation), a framework that simulates real-world outpatient reception scenarios using real patient data and predefined actions for both patients and nurses. The effectiveness of PIORS was evaluated through automatic assessments and human evaluations involving both users and clinical experts.
• Key Findings: Automatic evaluations showed that PIORS-Nurse outperformed baseline models, including GPT-4o, in accuracy of department guidance, information-gathering ability, and efficiency. Human evaluations revealed that users and clinical experts preferred PIORS-Nurse over baseline models, highlighting its ability to align with human preferences and clinical needs.
• Main Conclusions: PIORS, with its integrated LLM and innovative simulation training framework, offers a promising solution to address the challenges of outpatient reception in China. The system demonstrates the potential of LLMs in improving healthcare efficiency and patient experience.
• Significance: This research significantly contributes to the field of healthcare by demonstrating the practical application of LLMs in real-world clinical settings. The development of SFMSS provides a valuable framework for training LLMs in other healthcare domains.
• Limitations and Future Research: The study acknowledges limitations in data generalizability due to regional variations and a relatively small sample size. Future research could focus on validating PIORS in real clinical environments, expanding the training data to encompass diverse medical specialties, and exploring the system's applicability in other healthcare systems.

Statistieken

In 2023, China recorded 9.3 billion outpatient visits, a 13% increase from the previous year.
Receptionist nurses in China are required to address nearly one case per minute, communicating an average of 2,149.2 words per hour.
The study used a dataset of 2,400 Chinese hospital outpatient records for training and 500 records for testing.
PIORS-Nurse demonstrated an 18% increase in accuracy relative to the Qwen2-7b backbone model.
PIORS-Nurse achieved a win or tie ratio of over 81% compared to the best baseline model in user evaluations.

Citaten

"In China, receptionist nurses face overwhelming workloads in outpatient settings, limiting their time and attention for each patient and ultimately reducing service quality."
"Given the remarkable capabilities and cost-effectiveness of large language models (LLMs), integrating them into the outpatient reception workflow presents a promising solution for these issues."
"By streamlining outpatient reception processes and improving communication with PIORS, our study contributes to a more efficient and patient-centered healthcare experience."

Belangrijkste Inzichten Gedestilleerd Uit

PIORS: Personalized Intelligent Outpatient Reception based on Large Language Model with Multi-Agents Medical Scenario Simulation

by Zhijie Bao, ... om arxiv.org 11-22-2024

https://arxiv.org/pdf/2411.13902.pdf

Diepere vragen

How can the ethical considerations of using LLMs in healthcare, such as data privacy and potential biases, be addressed in the development and deployment of systems like PIORS?

Addressing ethical considerations in LLM-driven healthcare systems like PIORS is paramount. Here's a breakdown of key strategies:
Data Privacy:

De-identification: Rigorously de-identify all patient data used for training and operation, removing Personally Identifiable Information (PII) to prevent re-identification.
Federated Learning: Explore federated learning techniques to train models across multiple institutions without directly sharing sensitive patient data.
Secure Data Storage: Implement robust data encryption and access control mechanisms to safeguard patient information throughout the system's lifecycle.
Transparency and Consent: Provide clear and understandable information to patients about how their data is being used and obtain informed consent for data usage.
Potential Biases:

Diverse Training Data: Ensure the training data encompasses a wide range of demographics, medical conditions, and social determinants of health to minimize bias in the model's outputs.
Bias Detection and Mitigation: Employ bias detection tools and techniques to identify and mitigate biases during model development and deployment.
Human Oversight and Validation: Incorporate human oversight by healthcare professionals to review and validate the system's recommendations, particularly in critical decision-making processes.
Continuous Monitoring: Continuously monitor the system's performance for potential biases and implement mechanisms for feedback and improvement.
Additional Considerations:

Explainability: Develop methods to make the LLM's decision-making process more transparent and understandable to healthcare providers and patients.
Accountability: Establish clear lines of responsibility and accountability for the system's actions and decisions.
Regulation and Guidelines: Adhere to relevant healthcare regulations and guidelines, such as HIPAA in the United States, to ensure ethical and legal compliance.
By proactively addressing these ethical considerations, developers can foster trust in LLM-driven healthcare systems and ensure their responsible and beneficial use.

Could the reliance on LLMs for outpatient reception lead to a decrease in the quality of human interaction and empathy in healthcare settings?

While LLMs like PIORS-Nurse offer efficiency and consistency, the potential impact on human interaction and empathy in outpatient reception is a valid concern. Here's a balanced perspective:
Potential Drawbacks:

Reduced Human Connection: Over-reliance on LLMs could lead to fewer opportunities for patients to engage with human receptionists, potentially diminishing the sense of personal connection and emotional support.
Misinterpretation of Emotions: LLMs, while improving, may not always accurately interpret subtle emotional cues from patients, potentially leading to insensitive or inappropriate responses.
Exacerbating Healthcare Disparities: If not carefully designed and implemented, LLM-based systems could exacerbate existing healthcare disparities by failing to adequately address the needs of diverse patient populations.
Mitigating the Risks:

Hybrid Approach: Implement a hybrid approach that combines the strengths of LLMs with the irreplaceable value of human interaction. LLMs can handle routine tasks, freeing up human receptionists to focus on providing empathetic care and addressing complex patient needs.
Empathy Training: Train LLMs on datasets that include examples of empathetic communication and emotional intelligence to enhance their ability to respond sensitively to patients.
Human Oversight: Maintain human oversight to monitor interactions, intervene when necessary, and provide emotional support to patients who require it.
Patient Education: Educate patients about the role of LLMs in the reception process and emphasize that human interaction remains an integral part of their care.
The key is to leverage LLMs strategically to enhance, not replace, the human element in healthcare. By prioritizing empathy, human oversight, and a patient-centered approach, we can harness the power of LLMs while preserving the essential qualities of human interaction in healthcare settings.

What are the potential applications of SFMSS, the simulation framework used to train PIORS-Nurse, in other industries or domains beyond healthcare?

SFMSS, with its ability to simulate complex scenarios and train AI agents, holds significant potential beyond healthcare. Here are some promising applications:
Customer Service:

Training Chatbots: SFMSS can train sophisticated chatbots capable of handling diverse customer inquiries, resolving issues, and providing personalized support.
Simulating Customer Interactions: Businesses can use SFMSS to simulate various customer interactions, test different service strategies, and optimize customer experience.
Education and Training:

Personalized Learning Environments: SFMSS can create immersive and personalized learning environments, simulating real-world scenarios for students to practice skills and receive feedback.
Training for High-Stakes Professions: SFMSS can be used to train professionals in fields like aviation, law enforcement, and emergency response, providing a safe and controlled environment to practice critical decision-making.
Finance and Business:

Financial Modeling and Analysis: SFMSS can simulate market conditions, test investment strategies, and assess financial risks.
Training for Negotiations and Sales: SFMSS can create realistic simulations for training employees in negotiation tactics, sales techniques, and customer relationship management.
Human Resources:

Interview Simulations: SFMSS can create realistic job interview simulations to help candidates prepare and improve their interviewing skills.
Onboarding and Training: SFMSS can develop interactive onboarding programs and training modules that simulate real workplace scenarios.
Other Domains:

Legal Research and Analysis: SFMSS can assist in legal research by simulating case scenarios and predicting legal outcomes.
Social Sciences Research: SFMSS can be used to study human behavior, social dynamics, and the impact of policies in simulated environments.
The adaptability of SFMSS to various domains stems from its core strengths:

Realistic Scenario Simulation: SFMSS can create complex and dynamic scenarios that closely resemble real-world situations.
Personalized Agent Training: SFMSS allows for the training of AI agents with specific roles, behaviors, and goals tailored to the target domain.
Data Generation and Analysis: SFMSS can generate large amounts of synthetic data, enabling the development and evaluation of AI models in data-scarce domains.
By adapting SFMSS to different industries, we can unlock the potential of AI to improve efficiency, enhance training, and drive innovation across various sectors.