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Project Overview
This project involved developing a robust Python-RPA (UiPath) solution to automate and optimize the patient order creation process for a US-based healthcare provider. The solution seamlessly integrates API-based data extraction, validation, and automated processing through a Billing Web Portal, ensuring efficiency, accuracy, and reliability.
Key Features & Workflow
1. Data Extraction
- Patient order data is fetched via secure API integration using Python.
- The extracted data is structured into an Excel file for further processing.
2. Data Validation & Entry
- UiPath RPA bot processes the extracted data and enters it into the Billing Web Portal.
- Automated data validation and correction include:
- Date of Birth (DOB) standardization
- Insurance provider verification
- Doctor information validation
- Address normalization based on predefined rules
3. Order Processing & Error Handling
- Orders are automatically created in the system.
- Any failed transactions due to incorrect or missing data are logged and flagged for review.
4. Automated Reporting & Monitoring
- A summary report is generated post-execution, outlining successful and failed orders.
- Reports are automatically shared with the relevant stakeholders for visibility and follow-up.
5. Intelligent Error Recovery (Auto-Restart Mechanism)
- In the event of an error or system failure, the RPA bot resumes processing from the last successful step, ensuring business continuity and reducing downtime.
Technologies & Expertise Utilized
- Python – API integration, data extraction, and Excel processing
- UiPath (RPA) – Web portal automation, data validation, and workflow execution
- API Handling – Secure data retrieval and processing
- Data Processing & Error Handling – Ensuring accuracy and process resilience
Business Impact & Outcomes
✔ 80% reduction in manual data entry efforts
✔ Significant improvement in order processing speed and accuracy
✔ Minimized errors through automated validation and correction
✔ Enhanced reporting for improved operational transparency
✔ Increased efficiency with automated failure recovery
Conclusion
This automation project demonstrates proficiency in Python-based API integration, RPA-driven process automation, and data-centric workflow optimization, significantly enhancing operational efficiency in the healthcare sector. By reducing manual intervention and ensuring data accuracy, the solution minimizes processing errors and accelerates order fulfillment. Additionally, the automated reporting and intelligent error recovery mechanism provide greater transparency and reliability in the end-to-end workflow.
