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Discharge Summary Forms

Discharge Summary Forms

The discharge summary is crucial as it enables a smooth transition from a healthcare facility to a patient's home or another care setting. This critical document facilitates seamless continuity of care, ensuring patients receive the necessary support and attention.
Here's a detailed look at the key components and guidelines for preparing a discharge summary:

Guidelines for Nursing Documentation I

Guidelines for Nursing Documentation I

Quality documentation and reporting share essential characteristics that ensure they are practical and valuable resources for those who use them. These characteristics are:
Factual:
The following points emphasize the significance of upholding accurate and unbiased documentation in healthcare.

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Evaluating the Performance of Large Language Models for Generating Emergency Department Discharge Instructions.

Patricia Hernández¹, Giovanni Rodriguez², Chanel Fischetti²

¹Department of Emergency Medicine, Massachusetts General Hospital, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.

The Journal of Emergency Medicine

|April 9, 2026

Summary

This summary is machine-generated.

Large language models (LLMs) can generate emergency department (ED) discharge instructions, but readability varies by model and often misses health literacy targets. Careful selection and oversight are key for improving patient comprehension.

Keywords:

artificial intelligence discharge instructions electronic medical record health equity health literacy large language models transitions in care

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Area of Science:

Artificial Intelligence in Healthcare
Medical Informatics
Patient Communication

Background:

Effective emergency department (ED) discharge instructions are crucial for patient safety.
Creating health-literate, patient-centered materials presents ongoing challenges.
The utility of large language models (LLMs) in ED patient communication is largely unexplored.

Purpose of the Study:

To assess the feasibility of using LLMs for generating ED discharge instructions.
To compare the readability of discharge instructions produced by various LLMs.
To evaluate clinician-perceived quality of LLM-generated instructions.

Main Methods:

Discharge instructions were generated for 20 ED diagnoses across Emergency Severity Index (ESI) levels using multiple LLMs (GPT-4, GPT-4o, GPT-5.2, Gemini 2.5 Pro, Gemini 3 Pro, Gemini 3 Flash).
Instructions were compared against electronic medical record (EMR) stock templates for readability using eight indices.
Medical accuracy, clarity, completeness, and understandability of GPT-4 instructions were evaluated by blinded emergency medicine attendings, with inter-rater reliability assessed.

Main Results:

Readability often exceeded recommended 6th-8th grade health literacy targets across models.
Gemini models generally produced lower grade-level outputs compared to GPT-5.2.
Physician ratings for clarity, understandability, completeness, and accuracy were generally high (>4/5), but completeness and understandability had some lower scores (≤3/5).
Inter-rater reliability was fair (κ = 0.40), with lower agreement for lower ESI levels.

Conclusions:

LLMs show potential for generating high-quality ED discharge instructions, as rated by clinicians.
Readability performance varies significantly among LLM models and metrics, frequently not meeting health literacy standards.
Model selection, prompt engineering, and human oversight are essential for ensuring accurate, accessible, and complete patient discharge communication.