This study will evaluate whether, relative to conventional information retrieval approaches, direct large language models (LLM) access and LLM use training can improve the overall clinical decision-making ability of rural physicians in low-resource grassroots healthcare settings.
Rural physicians play an essential role in the diagnosis and management of common and frequently occurring conditions, referral decision-making, chronic disease management, and patient education. In resource-constrained primary care settings, they often face limited access to medical information and specialist support, delays in updating clinical knowledge and guidelines, and substantial pressure in clinical decision-making. These challenges are particularly relevant in northwestern China, where primary care resources are relatively limited. Improving rural physicians' abilities in diagnostic assessment, recognition of clinical warning signs, and rational prescribing is therefore an important priority for strengthening primary healthcare services. Large language models (LLMs) can support medical information retrieval, organization of diagnostic and management approaches, differential diagnosis, medication-related decision-making, patient education, and follow-up planning, and may therefore serve as accessible tools for supporting clinical decision-making in primary care. However, general-purpose LLMs were not specifically developed for use in resource-constrained primary care settings and have not been adequately evaluated among rural physicians. Their responses may contain factual errors or fabricated evidence, overlook warning signs, provide insufficient medication safety warnings, or recommend investigations and treatments that are not feasible in local primary care settings. Without adequate verification skills, physicians may fail to benefit from LLM assistance and may even introduce new safety risks. It is therefore important to evaluate how rural physicians use LLMs and whether structured training can improve the safe and effective use of these tools before their wider implementation. This randomized controlled trial will evaluate the effects of LLM assistance and brief training on clinical decision-making among rural physicians. Participants will complete clinical cases involving common conditions encountered in primary care, with tasks assessing diagnostic judgment, recognition of warning signs, rational treatment, and patient education. Some participants will also use the LLM as a second-opinion tool to review and revise their initial decisions. All responses will be independently evaluated by reviewers blinded to group assignment using standardized scoring criteria to assess overall clinical decision-making performance and safety.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE
Enrollment
240
Before completing the clinical cases, participants receive brief structured training on the safe and effective use of LLMs. The training covers the role and limitations of LLMs, structured prompting and follow-up questioning, identification of warning signs and referral indications, medication safety, verification of LLM-generated information, high-risk situations in which LLMs should not be relied upon, and protection of patient privacy.
During the initial 60-minute assessment, participants complete primary care clinical cases using conventional non-LLM resources only, including clinical guidelines, textbooks, drug labels, training materials, medical websites, and standard search engines. Participants are not permitted to use LLMs during this phase.
Xinjiang Second Medical College
Karamay, Xinjiang Uygur Autonomous Region, China
Overall Clinical Decision-Making Score
Participants' responses to primary care clinical cases will be evaluated using a prespecified scoring rubric. The overall score will reflect performance across key components of clinical decision-making. Higher scores indicate better overall clinical decision-making performance.
Time frame: At the end of the initial 60-minute assessment
Diagnostic Judgment Domain Score
Participants' diagnostic judgment in response to primary care clinical cases will be evaluated using a predefined scoring rubric. Higher scores indicate better diagnostic judgment.
Time frame: At the end of the initial 60-minute assessment
Clinical Warning Sign Recognition Domain Score
Participants' ability to identify clinically important warning signs in primary care clinical cases will be evaluated using a predefined scoring rubric. Higher scores indicate better recognition of clinical warning signs.
Time frame: At the end of the initial 60-minute assessment
Treatment Plan Domain Score
Participants' proposed treatment plans for primary care clinical cases will be evaluated using a predefined scoring rubric. Higher scores indicate better treatment planning performance.
Time frame: At the end of the initial 60-minute assessment
Change in Overall Clinical Decision-Making Score After LLM Review
Among participants assigned to the LLM second-opinion phase, the change in overall clinical decision-making score will be calculated as the score after LLM-assisted review minus the score before LLM-assisted review. Positive values indicate improvement in overall clinical decision-making performance.
Time frame: Change from 60 to 90 minutes after the start of the assessment
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After completing and submitting their initial responses using conventional non-LLM resources, participants receive an additional 30 minutes to use the study-provided DeepSeek-V4 as a second-opinion tool. They may review, verify, and revise their initial clinical decisions before submitting their final responses.
During the initial 60-minute assessment, participants may use the study-provided DeepSeek-V4 to assist with medical information retrieval, diagnostic and management reasoning, identification of warning signs, referral decisions, rational prescribing, patient education, and follow-up planning. Participants remain responsible for their final clinical decisions and responses.