Generating subjective, objective, assessment, and plan (SOAP)-structured medication logs using DeepSeek-R1 through prompt engineering and multi-source clinical information.

Objectives: This study evaluated the feasibility of using the open-source large language model (LLM) DeepSeek-R1 to generate standardized Subjective, Objective, Assessment, and Plan (SOAP)-format medication logs and its potential to support clinical pharmacists. Materials and methods: Thirty complete oncology medication profiles were collected, from which 80 days of logs were extracted and converted into simulated pharmacist-patient dialogues. The experiment compared single-information-source inputs (dialogue only) with multi-information-source inputs (dialogue plus patient information, records, and test results), using five prompts of increasing complexity. Performance was measured using the Bidirectional Encoder Representations from Transformers (BERT) score and Recall-Oriented Understudy for Gisting Evaluation (ROUGE), alongside a blinded expert evaluation based on the Seven-Dimension Index (7DI) metric. Results: DeepSeek-R1 effectively generated structured SOAP medication logs when integrated with multi-source information and complex prompts (especially Prompts 4 and 5). Both machine scores and manual 7DI evaluation confirmed the superiority of multi-source inputs over single-source dialogues. While Prompt 4 achieved the highest BERT- Discussion: This study confirms DeepSeek-R1's utility in generating SOAP medication logs using multi-source data and structured prompts, potentially enhancing pharmacists' efficiency. Limitations such as oncology-specific scope and artificial intelligence (AI) hallucinations necessitate pharmacist review and future validation across specialties, alongside comparisons with closed-source LLMs and explainable AI integration. Conclusion: This study demonstrates that DeepSeek-R1 can generate structured SOAP-format medication logs when guided by prompt-engineered multi-source clinical information, while highlighting that output quality depends on input completeness and that pharmacist review remains essential for clinical reliability.