5 min readSEYON Insights
Clinical data operations include file intake, interface monitoring, code mapping, reconciliation, and report refresh. Several of those steps are repetitive. Automation and, in some cases, machine learning can take the repetitive ones. They should not be asked to invent a clinical conclusion from incomplete data.
Good candidates
- Detecting a feed that stopped arriving.
- Checking required fields and accepted code values.
- Suggesting a terminology map for a person to confirm.
- Drafting a reconciliation list between two identifiers.
- Refreshing a warehouse once quality checks pass.
Keep a person on the exception
When a record fails a rule, route it to a queue with the reason. Store the model or rule version that made the suggestion. Avoid sending real patient data to a tool that has not been approved for that purpose. A useful automation program is boring on purpose: fewer missed files, fewer silent map errors, and a clear record of what changed.