Clinical Decision-making
Conditions
Keywords
Large Language Models, Clinical Decision Support, Rural Physicians
Brief summary
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
BEHAVIORALLLM-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.
OTHERConventional 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.
OTHERLLM 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.
OTHERDirect 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.
Sponsors
Peking University Third Hospital
Xinjiang Second Medical College
Study design
Allocation
RANDOMIZED
Intervention model
FACTORIAL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE (Outcomes Assessor)
Intervention model description
Participants will be randomly assigned to four groups according to two intervention factors: receipt of brief training in LLM use and direct access to the LLM during the initial assessment. Participants assigned to selected groups will subsequently use the LLM as a second-opinion tool to review and revise their initial responses.
Eligibility
Sex/Gender
ALL
Age
18 Years to 65 Years
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Overall Clinical Decision-Making Score | At the end of the initial 60-minute assessment | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic Judgment Domain Score | At the end of the initial 60-minute assessment | Participants' diagnostic judgment in response to primary care clinical cases will be evaluated using a predefined scoring rubric. Higher scores indicate better diagnostic judgment. |
| Clinical Warning Sign Recognition Domain Score | At the end of the initial 60-minute assessment | 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. |
| Treatment Plan Domain Score | At the end of the initial 60-minute assessment | Participants' proposed treatment plans for primary care clinical cases will be evaluated using a predefined scoring rubric. Higher scores indicate better treatment planning performance. |
| Change in Overall Clinical Decision-Making Score After LLM Review | Change from 60 to 90 minutes after the start of the assessment | 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. |
Countries
China
Contacts
CONTACTLiyuan Tao, MD
Outcome results
None listed