Large Language Model Assistance for Clinical Decision-Making Among Rural Physicians

Official title

Effect of Large Language Model Assistance on Clinical Decision-Making Among Rural Physicians: A Randomized Controlled Trial

Overview

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.

Detailed description

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.

Interventions

• Behavioral LLM-Use Training
  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.
• Other Conventional Non-LLM Resources
  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.
• Other LLM Second-Opinion Review
  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.
• Other Direct LLM Assistance
  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.

Primary outcome measures

• Overall Clinical Decision-Making Score [Time frame: At the end of the initial 60-minute assessment]

Secondary outcome measures (4)

• Diagnostic Judgment Domain Score [Time frame: At the end of the initial 60-minute assessment]
• Clinical Warning Sign Recognition Domain Score [Time frame: At the end of the initial 60-minute assessment]
• Treatment Plan Domain Score [Time frame: At the end of the initial 60-minute assessment]
• Change in Overall Clinical Decision-Making Score After LLM Review [Time frame: Change from 60 to 90 minutes after the start of the assessment]

Eligibility criteria

Inclusion criteria

• Currently engaged in clinical practice at a rural primary healthcare institution in northwestern China.
• Has received formal medical education and holds a relevant diploma or degree.
• Able to read and understand clinical case materials in Chinese.
• Able to use a computer to complete the study tasks.
• Willing to participate and able to provide written informed consent.

Exclusion criteria

• Previously involved in the development of the clinical case tasks, reference answers, or scoring rubric for this study.
• Previously participated in pilot testing involving the same clinical case tasks or study procedures.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Allocation

Randomized

Model

Factorial

Masking

Single blind

Primary purpose

Health services research

Study locations

China · 1 center

• Xinjiang Second Medical College — Karamay

Identifiers

NCT: NCT07711600 · IRB00006761-M20260614

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗