Generating subjective, objective, assessment, and plan (SOAP)-structured medication logs using DeepSeek-R1 through...

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.

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.

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-F1 and ROUGE scores, the model's output quality remained highly dependent on input data completeness and required pharmacist review to correct errors (e.g. incomplete analyses), after which scores improved significantly.

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.

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.