CASE STUDY · HEALTHCARE
Documented RPA Use CaseFrom 25 minutes to approximately 4 minutes per transaction.
Automating document intake, extraction, validation and application updates with UiPath.
The business problem
The process before automation
- 01Manually downloading PDF attachments from emails
- 02Checking whether the PDF already existed in the application
- 03Extracting required details from the document
- 04Updating the application manually
- 05Preparing and sharing status reports
Documented challenges
- High manual effort
- Approximately 25 minutes per file
- Inconsistent data extraction
- Risk of human error
- Delays in application updates
- Difficulty scaling during peak document volumes
Before / after
See the process transform
Switch between the manual workflow and the automated one. In automated mode, click any step to see what it does.
Illustrative workflow replay
Watch a transaction move through the automation
This animates the architecture, not real production timestamps.
Press replay to step through the automation, one stage at a time.
Comparison
What changed after automation?
- Download document
- Check application
- Extract information
- Enter data
- Update application
- Prepare report
- Monitor mailbox
- Validate documents
- Create queue items
- Extract information
- Validate extracted data
- Update application
- Record transaction status
- Generate processing report
Where humans remain involved
Exceptions and validation can require human intervention. Automation does not remove people from the process entirely — it changes what they spend their time on.
Resilience
What happens when something goes wrong?
Click a node to see what happens at that stage.
Technical architecture
How the pieces fit together
Click any component to see its purpose, inputs and outputs.
Purpose
Entry point for incoming documents.
PDF documents sent as email attachments.
New messages available for the dispatcher to check.
Technical deep dive
How the solution works
UiPath
The automation platform used to build the dispatcher and performer robots that run this process.
Why it mattersProvides the runtime and Orchestrator that coordinate both bots.
Orchestrator Queues
Stores transactions that are ready for processing so the performer bot can process them reliably.
Why it mattersKeeps work items in order and available again if something needs to be retried.
Document Understanding
Extracts structured information from PDF documents and makes the data available to the automation workflow.
Why it mattersRemoves the need for someone to read each document and re-type its contents.
API Integration
Updates the target application using API calls.
Why it mattersApplication updates happen directly, without manual data entry.
Validation
Checks extracted data before it is used to update the application.
Why it mattersPrevents uncertain or incorrect data from being written to the application.
Exception Handling
Routes transactions that fail validation through retries and, where needed, human review.
Why it mattersAutomation keeps running reliably even when a document doesn't extract cleanly.
Logging
Records transaction status and processing details for every document handled.
Why it mattersGives a clear trail of what happened to each document.
Reporting
Generates a processing report summarising the outcome of each run.
Why it mattersKeeps the status of processed documents visible without manual compilation.
Results
Measured before and after
Average processing time per file/transaction
Documented case resultTechnology stack
What this automation runs on
Business impact
Why the automation mattered
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