Token Limits and Context Windows in Health Documentation

A token is roughly one word in AI language (actually smaller—about 1.3 words per token on average, but don't get bogged down in that). Every AI model has a context window—a maximum number of tokens it can process in a single conversation or request. For ChatGPT-4, that's 128,000 tokens. Claude can handle up to 200,000. Older models cap out at 4,000-8,000. In caregiving, this matters because your patient's entire medical history, care plan, and recent notes might exceed these limits.

Why It's Critical in Care Coordination

Imagine you're trying to summarize a patient's complete medical record for a new specialist. Five years of appointment notes, lab results, imaging reports, medication history, allergy documentation—that's easily 50,000+ tokens. If you try to feed all of it into an older model with an 8,000-token limit, the AI can't see most of it. It's like handing a doctor a filing cabinet but only letting them open three drawers.

Different models have different trade-offs: more context window means better awareness of full history (fewer missed connections), but also higher cost and sometimes slower response times. Claude's 200,000-token window costs more per request than GPT-4's 128,000-token window, but covers more ground.

Practical Strategies for Caregiving Workflows

Chunking: Break your patient data into logical segments. Instead of feeding the AI the entire 5-year record at once, provide the last 12 months of appointments, current medications, and known allergies. Process older historical data separately if needed. This fits within smaller context windows and reduces noise.

Retrieval-Augmented Generation (RAG): Store your patient's full history in a searchable database (like Notion or a vector database), then ask the AI to retrieve only the relevant pieces for the current task. If you're preparing for a cardiology visit, pull cardiology-related notes, EKGs, and blood pressure logs—not the dermatology records from three years ago. This keeps your active context lean and focused.

Prioritization layering: Load the AI's context window strategically. Put most recent data first, then active conditions, then current medications, then historical context. If you hit the limit, the most critical information is already there.

Cost consideration: Longer context windows cost more per request. A 100,000-token request to Claude costs roughly $3. A 4,000-token request to an older GPT model costs cents. For routine tasks (scheduling, simple summaries), use smaller context windows. For complex reasoning (care plan adjustments, interaction analysis), invest in larger windows.

Common Pitfall

Caregivers often assume "more data is always better." It's not. Oversized contexts confuse AI models—they struggle to distinguish signal from noise, and relevant patterns get buried. A focused 10,000-token history almost always outperforms an unfocused 50,000-token dump.

Try this: Audit your next caregiving task. List all the documents and information you'd want the AI to consider. Roughly estimate tokens (rule of thumb: 750 words ≈ 1,000 tokens). If it exceeds your model's limit, practice chunking: extract the last 6-12 months of data, current medications, and known conditions into one focused document. Run your task with just that. Then run it with the full historical dump using a model with a larger context window (Claude). Compare results. You'll likely find the focused version is clearer and cheaper.