From Questions to Clinical Recommendations: Large Language Models Driving Evidence-Based Clinical Decision Making (2505.10282v1)

Abstract: Clinical evidence, derived from rigorous research and data analysis, provides healthcare professionals with reliable scientific foundations for informed decision-making. Integrating clinical evidence into real-time practice is challenging due to the enormous workload, complex professional processes, and time constraints. This highlights the need for tools that automate evidence synthesis to support more efficient and accurate decision making in clinical settings. This study introduces Quicker, an evidence-based clinical decision support system powered by LLMs, designed to automate evidence synthesis and generate clinical recommendations modeled after standard clinical guideline development processes. Quicker implements a fully automated chain that covers all phases, from questions to clinical recommendations, and further enables customized decision-making through integrated tools and interactive user interfaces. To evaluate Quicker's capabilities, we developed the Q2CRBench-3 benchmark dataset, based on clinical guideline development records for three different diseases. Experimental results highlighted Quicker's strong performance, with fine-grained question decomposition tailored to user preferences, retrieval sensitivities comparable to human experts, and literature screening performance approaching comprehensive inclusion of relevant studies. In addition, Quicker-assisted evidence assessment effectively supported human reviewers, while Quicker's recommendations were more comprehensive and logically coherent than those of clinicians. In system-level testing, collaboration between a single reviewer and Quicker reduced the time required for recommendation development to 20-40 minutes. In general, our findings affirm the potential of Quicker to help physicians make quicker and more reliable evidence-based clinical decisions.

Analyzing "From Questions to Clinical Recommendations: LLMs Driving Evidence-Based Clinical Decision-Making"

The paper entitled "From Questions to Clinical Recommendations: LLMs Driving Evidence-Based Clinical Decision-Making" introduces a novel system called Quicker, which utilizes LLMs to streamline the process of clinical decision support. The researchers aim to address the challenges posed by the time-intensive task of integrating clinical evidence into practice by automating evidence synthesis and generating clinical recommendations in alignment with established guideline development processes.

System Design and Methodology

Quicker is structured to follow a disciplined five-phase workflow that mirrors traditional guideline development:

1. Question Decomposition: This phase involves translating clinical questions into structured components using the PICO (Population, Intervention, Comparison, and Outcome) framework. The system leverages LLMs for precise question breakdown, accommodating multiple intervention-comparison pairs within a single query.
2. Literature Search: An agent-based iterative method is utilized to search for relevant literature in biomedical databases such as PubMed. The system dynamically refines its search strategies, exhibiting retrieval sensitivities comparable to those achieved by expert guideline development teams.
3. Study Selection: Quicker employs a two-stage selection pipeline divided into record screening and full-text assessment. This phase ensures the inclusion of pertinent studies with a focus on minimizing the burden through automation.
4. Evidence Assessment: Applying the GRADE methodology, Quicker assesses the quality of evidence comprehensively. Risk of bias assessments and data extraction tasks are conducted, allowing the system to assist in compiling rigorous evidence profiles.
5. Recommendation Formulation: The final phase synthesizes clinical recommendations based on the gathered evidence. The recommendations emphasize the integration of various certainty domains and present outcomes in quantifiable terms for practical application.

Experimental Findings and Outcomes

For evaluation, the researchers developed the Q2CRBench-3 dataset, which encapsulates evidence from clinical guidelines for three diseases: rheumatoid arthritis, dementia, and chronic kidney disease. Quicker demonstrated strong performance across all phases:

• In question decomposition, Quicker achieved over 75% accuracy in token-level F1 score.
• The literature search phase using an agent-based method showed sensitivities that rival manual expert strategies.
• Record screening sensitivity reached up to 94.74%, with the extraction of numerical data showing an improvement accuracy to approximately 80% when combined with human oversight.
• The generated recommendations were reported to be more comprehensive and logically aligned when compared with those constructed by clinicians of varying expert levels.

Implications and Future Directions

The introduction of Quicker as a tool for facilitating clinical decision-making lays the groundwork for further research into the deployment of AI in healthcare. The research highlights the potential for such systems to dramatically reduce the time required for clinical guideline development – crucial during rapidly unfolding public health crises. However, it also presents challenges regarding the accuracy of risk assessments and numerical data extraction that necessitate continued improvements in LLM models.

The researchers also emphasize the importance of integrating Quicker with external databases to enhance evidence retrieval while maintaining data privacy and compliance. Future work could explore the expansion of Quicker into new clinical domains and the adaptation of more sophisticated LLMs capable of processing a broader range of clinical data.

Overall, Quicker exemplifies the promising intersection of artificial intelligence and clinical medicine, underscoring the transformative potential of AI-driven tools in improving the efficiency and accuracy of healthcare practices.