Evaluating the Consistency of ChatGPT's Responses in Outpatient Triage Guidance: A Comparative Study

Konsep Inti

The study evaluates the consistency of responses generated by ChatGPT-3.5 and ChatGPT-4.0 in providing outpatient triage guidance, highlighting the potential and limitations of integrating large language models in healthcare operations.

Abstrak

The study aimed to assess the consistency of responses generated by ChatGPT-3.5 and ChatGPT-4.0 in providing outpatient triage guidance in a Chinese healthcare setting. The researchers collected 52 questions representing patient-reported symptoms and inputted them into both ChatGPT versions three times each to analyze within-version and between-version consistency.
Key findings:

ChatGPT-4.0 demonstrated significantly higher internal consistency in its responses compared to ChatGPT-3.5 (p=0.03).
Both versions showed moderate consistency in their top recommendations, with ChatGPT-4.0 being slightly higher (71.2%) than ChatGPT-3.5 (59.6%).
The between-version consistency was relatively low, with a mean score of 1.43 out of 3 and a median of 1, indicating few recommendations matched between the two versions.
Only 50% of the top recommendations matched perfectly between the two ChatGPT versions.
ChatGPT-3.5 responses were more likely to be complete than those from ChatGPT-4.0 (p=0.02), suggesting potential differences in information processing and response generation.
The findings offer insights into the potential and limitations of integrating large language models like ChatGPT in outpatient triage operations. While ChatGPT-4.0 demonstrated higher internal consistency, the between-version variability highlights the need for careful optimization and alignment with specific healthcare needs. Future research should focus on enhancing LLM performance and integration based on human factors principles to support effective outpatient guidance.

Statistik

58.4% of the top recommended departments had a recommendation rate higher than 50%.
61.1% of the top recommended departments had a probability higher than 50% of successfully treating the symptom.
ChatGPT-3.5 had a significantly higher completeness rate in its responses compared to ChatGPT-4.0 (p=0.02).

Kutipan

"ChatGPT-4.0 demonstrated significantly higher internal consistency in its responses compared to ChatGPT-3.5 (p=0.03)."
"The between-version consistency was relatively low, with a mean score of 1.43 out of 3 and a median of 1, indicating few recommendations matched between the two versions."
"Only 50% of the top recommendations matched perfectly between the two ChatGPT versions."

Wawasan Utama Disaring Dari

Evaluating the Application of ChatGPT in Outpatient Triage Guidance: A Comparative Study

by Dou Liu,Ying... pada arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.00728.pdf

Pertanyaan yang Lebih Dalam

How can the prompt engineering process be optimized to improve the consistency and accuracy of ChatGPT's responses in outpatient triage?

Prompt engineering plays a crucial role in enhancing the consistency and accuracy of ChatGPT's responses in outpatient triage. To optimize this process, several key strategies can be implemented:

Clear and Detailed Prompts: Providing clear and detailed prompts is essential to ensure that ChatGPT understands the required information accurately. Specific and well-structured prompts can guide the AI model to generate more precise responses tailored to the outpatient triage context.

Standardized Prompt Format: Establishing a standardized format for prompts can help maintain consistency in the information provided to ChatGPT. Consistent prompts enable the AI model to interpret the input consistently, leading to more reliable responses.

Prompt Validation: Before inputting prompts into ChatGPT, conducting prompt validation by domain experts can help ensure the accuracy and relevance of the information provided. Experts can verify that the prompts align with the intended questions and symptoms, reducing the risk of misinterpretation by the AI model.

Prompt Iteration: Iterating on prompts based on feedback and performance analysis can refine the prompt engineering process over time. By analyzing the responses generated by ChatGPT and adjusting prompts accordingly, healthcare providers can continuously improve the accuracy and effectiveness of the AI system.

Contextual Prompts: Incorporating contextual information into prompts, such as patient history, symptoms, and specific triage requirements, can help ChatGPT generate more contextually relevant responses. Contextual prompts enable the AI model to consider relevant factors when providing guidance, leading to more personalized and accurate recommendations.

Prompt Customization: Tailoring prompts to the specific needs of outpatient triage settings can enhance the relevance and effectiveness of ChatGPT's responses. Customizing prompts to address common outpatient scenarios and symptoms can improve the AI model's ability to provide accurate and timely guidance.

By implementing these strategies, healthcare providers can optimize the prompt engineering process to improve the consistency and accuracy of ChatGPT's responses in outpatient triage, ultimately enhancing the quality of patient care and operational efficiency in healthcare settings.

What are the potential ethical and legal implications of relying on AI-generated medical advice in outpatient settings, and how can these be addressed?

The reliance on AI-generated medical advice in outpatient settings presents several ethical and legal implications that need to be carefully considered and addressed:

Patient Safety and Liability: One of the primary concerns is ensuring patient safety when using AI-generated advice. Healthcare providers must consider the potential risks of inaccurate or misleading recommendations from AI systems and the associated liability if patient outcomes are compromised. Clear guidelines and protocols should be established to mitigate these risks and ensure accountability.

Informed Consent and Transparency: Patients should be informed about the use of AI in providing medical advice and understand the limitations of AI systems. Transparency about the role of AI in decision-making is essential to maintain patient trust and autonomy. Healthcare providers must ensure that patients are adequately informed and consent to AI-generated recommendations.

Data Privacy and Security: AI systems rely on vast amounts of patient data to generate recommendations. Protecting patient privacy and maintaining data security are critical considerations. Healthcare organizations must adhere to strict data protection regulations and implement robust security measures to safeguard patient information from breaches or misuse.

Bias and Fairness: AI algorithms can inadvertently perpetuate biases present in the data used for training. Healthcare providers must address bias in AI systems to ensure fair and equitable treatment for all patients. Regular bias assessments and algorithm audits can help identify and mitigate bias in AI-generated medical advice.

Professional Oversight and Decision-Making: While AI can assist in medical decision-making, it should not replace the expertise and judgment of healthcare professionals. Physicians and clinicians must retain ultimate responsibility for patient care and use AI-generated advice as a supplementary tool rather than a substitute for clinical judgment.

To address these ethical and legal implications, healthcare organizations should establish comprehensive guidelines and protocols for the use of AI in outpatient settings. This includes implementing robust data governance practices, ensuring transparency and informed consent, addressing bias and fairness issues, and maintaining professional oversight in decision-making processes.

Given the observed differences in performance between ChatGPT versions, how can healthcare systems effectively monitor and adapt to the evolving capabilities of large language models to ensure reliable and safe integration?

Healthcare systems can adopt several strategies to monitor and adapt to the evolving capabilities of large language models like ChatGPT for reliable and safe integration:

Continuous Evaluation and Validation: Healthcare systems should regularly evaluate the performance of different ChatGPT versions in outpatient settings through rigorous testing and validation processes. Monitoring key metrics such as response consistency, accuracy, and patient outcomes can help identify any discrepancies and guide decision-making on model selection.

Version Control and Updates: Implementing a robust version control system for AI models like ChatGPT enables healthcare systems to track changes, updates, and improvements in performance over time. Regularly updating to the latest versions of ChatGPT can ensure access to enhanced capabilities and features for improved outpatient triage guidance.

Feedback Mechanisms: Establishing feedback mechanisms for healthcare providers and patients to report issues or provide input on AI-generated recommendations is essential. Gathering feedback on the usability, accuracy, and relevance of ChatGPT responses can inform system improvements and adaptations to better meet the needs of users.

Training and Education: Providing training and education for healthcare professionals on the use of AI in outpatient settings is crucial. Healthcare systems should offer resources and guidance on how to effectively leverage ChatGPT for triage guidance, interpret AI-generated recommendations, and integrate them into clinical decision-making processes.

Collaboration with AI Experts: Collaborating with AI experts, data scientists, and researchers can help healthcare systems stay informed about the latest advancements in AI technology and best practices for integration. Engaging with experts in the field can provide valuable insights and guidance on optimizing the use of large language models in outpatient settings.

Adaptive Policies and Protocols: Developing adaptive policies and protocols that accommodate the evolving capabilities of AI models is essential. Healthcare systems should have flexible frameworks in place to adjust to changes in AI technology, regulatory requirements, and best practices to ensure the safe and reliable integration of ChatGPT in outpatient triage.

By implementing these strategies, healthcare systems can effectively monitor and adapt to the evolving capabilities of large language models like ChatGPT, ensuring reliable and safe integration in outpatient settings while maximizing the benefits of AI-assisted healthcare delivery.