Use AI to review your draft patient discharge summary before sending it to catch missing info and unclear language.

Before you send a discharge summary to a referring physician, case manager, or next provider, let AI review it as your first editor. This workflow helps you catch missing information, unclear language, or gaps in continuity before the document leaves your hands. You'll paste your draft (with all patient info removed or changed to fake data), ask AI to review it against best practices, and get specific feedback you can use to strengthen the final version. 1. Remove or anonymize all patient data from your draft discharge summary—change names to 'Patient A,' remove dates of birth, change specific locations. You're only sharing the structure and clinical language, not real protected information. 2. Paste your anonymized draft into ChatGPT or Claude and ask it to review the summary for completeness, clarity, and professionalism. Specify what matters most: Are treatment goals and outcomes clear? Is the plan for continued care specific enough? Will the next provider understand what was done and what's needed next? 3. Review the AI's feedback and look for patterns—missing elements like functional status at discharge, vague language about home program compliance, or unclear recommendations for follow-up care. 4. Revise your original draft based on the feedback that makes clinical sense. Add specifics where the summary was too general, clarify any confusing phrasing, and fill in gaps the AI identified. 5. Do a final read-through yourself to confirm the tone is professional, the clinical reasoning is sound, and all key information is included. Remember: AI helps you spot issues, but your clinical judgment determines what stays and what changes. Never put real patient data into any public AI tool. Always verify that your final discharge summary reflects accurate clinical information and meets your documentation standards.

February 24, 2026