AI Training for Healthcare

What large language models handle well and where they quietly get things wrong. Healthcare raises the stakes on both. Clinical and administrative staff leave this session speaking the same language about what to delegate and what to check.

• What a language model does when it drafts a note
• Where hallucination shows up in clinical text
• AI's foothold in Word and Outlook, including your EHR
• Tasks worth delegating, decisions that stay with a person
• One experiment, week one

This module teaches one repeatable structure (role, context, task, format, checks) and applies it only to documents your teams write themselves, so nothing here is a generic stand-in.

• One prompt template, reused across every clinical document type
• Giving the model context while patient identifiers stay out
• Rewriting a weak first draft instead of settling for it
• Setting tone and reading level for what a patient will read
• Building the first entries in a team prompt library

Notes, summaries, referral letters, and payer correspondence go through the same drafting flow here, one clinician-reviewed step at a time, and a review-before-sign check sits at the end of every single one before it reaches the record.

• Turning structured input into visit notes and discharge summaries
• Referral letters that carry their sources with them
• Payer and insurance correspondence, drafted and checked
• Shift handovers and meeting notes turned into structured summaries
• Reusable department-wide templates
• Review-before-sign: the last check before anything is signed

Patient-facing text is everywhere in a clinic: instructions, education material, reminders, and front-desk responses. This module covers writing all of it without losing the plain-language tone patients need. It also draws the line for triage support, marking where AI can help and where a clinician has to decide.

• Post-visit instructions at the right reading level
• Message flows for appointments, prep, and follow-up
• FAQ pages and patient education material, drafted and reviewed
• Multilingual patient communication
• Where AI can support triage, and where it must not decide
• Escalation: the point where a human takes back the case

GDPR, KVKK, and patient-privacy rules become concrete here: what can go into an AI tool, what never does, and how to write a policy staff will follow.

• What counts as personal and special-category health data
• De-identification and synthetic examples in practice
• Why a consumer chatbot and an enterprise tool handle your data differently
• Where the line sits when a workflow touches EHR data
• An AI usage policy your healthcare organization will use
• What to log when AI touches a document

This module is where individual skill turns into organizational habit: pilots, champions, and the measurement to know whether either is working, plus what keeps usage safe once we're gone.

• Picking a first pilot with time savings you can measure
• Naming champions department-wide
• Measuring adoption by what staff actually do with it
• Handling resistance and the reasonable fears behind it
• The 90-day rollout plan you build during the sessions