Bridging the Gap: AI-Assisted Discharge Summaries

Every physician knows that a significant portion of their shift is consumed by routine data entry into Medical Information Systems (MIS). While these systems are designed to streamline workflows, the reality is often the opposite. Certain clinical data must be manually validated by a doctor, but much of the workload involves repetitive, boilerplate documentation that varies little from patient to patient. A prime example is the “Patient Recommendations” section of an Electronic Health Record (EHR). These are mandatory instructions provided upon hospital discharge. In cases of similar diagnoses, comorbidities, or surgical interventions, the prescribed medications and post-operative care protocols follow highly consistent patterns. This repetitive task eats into “golden time” that should be spent on direct patient care. The Solution: Domain-Specific LLM Fine-Tuning Our core objective was to fine-tune a Large Language Model (LLM) on a proprietary dataset to generate relevant, case-specific clinical recommendations. The resulting model on Hugging Face: uaritm/gemmamed_cardio, is available on Hugging Face. We have provided a GGUF quantized version (2.49 GB), which allows for local inference on standard consumer-grade laptops without a dedicated GPU. When provided with a well-structured prompt, the model demonstrates strong performance in generating Ukrainian medical text. Conclusion and Next Steps While this model is not a replacement for clinical judgment, it serves as a powerful productivity tool, automating routine documentation.

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