Healthcare organisations handle huge volumes of referrals, letters, results and forms. AI can help extract key details, route documents and draft summaries. Because the data is sensitive, the design has to start with protection, not finish with it.
Principles to build on
- Data minimisation. Only process the fields needed for the task.
- Clear lawful basis. Agree the purpose with your data protection lead before building.
- Controlled hosting. Know where data is processed and stored, and under which agreements.
- Access control and audit. Record who saw what and when.
Keep clinicians in control
AI should prepare, not decide. Extracted fields and summaries are presented for review, with links back to the source document. Uncertain results are flagged rather than guessed.
Good first use cases
- Classifying incoming documents and routing them to the right team.
- Extracting patient identifiers and dates for indexing.
- Drafting summaries of long correspondence for review.
- Finding missing information before an appointment.
Trust in healthcare AI is earned through transparency: every answer should show where it came from.
Test with real, governed samples
Measure accuracy on a reviewed set of documents, under the same governance as production. Record error types, not just overall scores, so the team understands where human review matters most.
Next steps
Start with a narrow workflow, involve information governance from day one, and integrate the output into the existing document management system so staff do not have to switch tools.
