Automation of Order Creation Process for US Based Healthcare
Overview of the Automation Initiative
This project involved automating patient order processing and billing for a US-based healthcare provider. The client previously relied on manual data entry, which was slow, error-prone, and risked compliance issues.We developed an automation solution using Python and UiPath RPA that connects to the client’s internal systems via API, fetches patient order data, generates Excel files, validates key fields (like DOB, insurance, and doctor info), and submits them through the billing web portal streamlining the entire workflow.
By replacing manual processes with intelligent automation, the client achieved a highly scalable, error-resilient solution with built-in logging, exception handling, and auto-recovery functionality. The result was a faster, more accurate, and compliant order processing system tailored for the healthcare domain.
Industry
Healthcare (US-based)
Project Duration
12 months
Team Size
15
Client Location
New York City
Engagement Model
Agile
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The system integrates securely with the healthcare provider’s internal API to fetch patient order data in real-time. This ensures that only the latest, most accurate information is used during billing, eliminating the delays associated with manual downloads or batch exports. The data is structured into Excel for downstream processing.
Before processing orders, the RPA engine checks and validates critical data fields like date of birth, insurance provider, physician details, and patient address. Any inconsistencies or missing information are either auto-corrected using business rules or flagged for manual intervention, drastically improving order accuracy.
Using UiPath, the bot mimics human interactions to log into the billing portal, navigate the interface, and enter patient order details. It handles dynamic web elements, popups, and validation prompts to ensure every order is submitted without errors even in portals that do not support API integration.
The system is equipped with intelligent fail-safes. If a failure occurs (due to portal downtime, data mismatch, or UI change), the bot logs the error, sends alerts, and automatically resumes processing from the last successful order. This eliminates the need to restart the entire batch manually.
At the end of each execution cycle, a detailed report is generated summarizing all processed, failed, and skipped orders. These reports are automatically emailed to stakeholders and archived for audit purposes improving visibility and compliance with internal healthcare and regulatory standards.
Real-Time Data Extraction
Automated Data Validation
Smart Portal Automation
Error Handling & Recovery
Automated Reporting
Before implementing automation, the healthcare provider faced several operational bottlenecks that resulted in inefficiencies, delays, and inaccuracies in order processing. These challenges negatively impacted the workflow, patient service timelines, and overall operational costs. Below are the primary challenges identified:
Data Inconsistency
Unstructured Input Formats: Order data from multiple sources came in varied formats, often with missing or inaccurate fields, requiring normalization before processing.
Manual Dependency
Integration Limitation
Compliance Risk
Sensitive Healthcare Data Handling: Working with patient health data required strict adherence to HIPAA, ensuring all data transfer and storage met regulatory standards.
How We Solved It
Our approach focused on building a modular, robust, and scalable automation framework using Python for data manipulation and UiPath for robotic automation. Here’s how we tackled it:
01
Modular Python Scripts
- Developed efficient Python modules to consume REST APIs securely, extract patient order data, transform it into a structured format, and generate Excel files.
02
Dynamic Field Correction
- Built logic to detect and correct invalid or incomplete entries such as age mismatches, expired insurance, or incorrect physician codes either auto-correcting them or flagging for manual review.
03
Intelligent Bot Design
- Designed UiPath bots to navigate the billing portal’s dynamic UI, using conditional flows to detect pop-ups, warnings, and validation prompts, ensuring a smooth submission process.
04
State-Based Processing Logic
- Implemented a checkpoint-based tracking system, allowing the automation to resume from the last recorded success if an error, crash, or timeout occurred.
05
Integrated Reporting Framework
- Python scripts generated a summary report (CSV/Excel/Email) after each batch execution, showing the outcome of each order (Success, Error, Skipped), reducing audit effort.
Automation & Integration
Services
Services offered in this automation project for patient order creation:
Process Automation
Automation of the entire order creation process, from data extraction to final submission.
Data Validation
Implementation of an automated data validation system to ensure the accuracy and consistency of patient information.
Portal Integration
Automation of the data entry process using UiPath RPA, allowing for seamless order creation without human intervention.
Error Logging
Automated error detection and logging, as well as the auto-restart feature, to ensure smooth process continuity even in the event of a failure.
Automated Reporting
Generation of real-time reports that track the order creation process, providing stakeholders with an up-to-date view of the operation’s performance.
Compliance Audit
Tracks all transactions to ensure regulatory compliance and maintain an audit trail.
Technology Stack
- Python: Used for API integration and data extraction, enabling seamless handling of patient order data.
- UiPath (RPA): Utilized for automating the entire process of entering data into the Billing Web Portal, ensuring consistency and reducing human intervention. Python is used for implementing complex business logic and handling backend operations.
- API Handling: Python was used to securely fetch patient data from external sources via APIs, ensuring real-time, accurate data retrieval.
- Excel Processing: Used to structure the data into a format suitable for automation, ensuring smooth processing within the RPA system.
Frontend
Backend
Database
Cloud
Security
Our Results
Performance metrics that highlight the success of the automation project.
Reduced Manual Workload
The automation eliminated repetitive tasks such as data entry and corrections, allowing billing staff to focus on more value-added responsibilities. This shift improved team efficiency and reduced human fatigue caused by routine operations.
Improved Submission Accuracy
Faster Order Processing
Stronger Compliance and Audit Readiness
Every step in the workflow was logged with detailed records of processed and corrected entries. This created a reliable and transparent audit trail, aligning the process with healthcare regulations and making audits simpler and more effective.
Robust Error Handling and Recovery
The system was designed with a smart auto-restart mechanism that allowed it to resume from the last successful step after any interruption. This ensured uninterrupted processing and reduced the risks associated with system downtime.
Greater Client Confidence and Scalability
The successful deployment of this solution demonstrated the reliability and scalability of automation. It increased the client’s trust in digital transformation and opened up opportunities for similar automation across other operational areas.
Project Goals
- Automate the entire patient order creation process to enhance operational efficiency.
- Improve data accuracy through automated validation and correction of details such as DOB, insurance, doctor info, and addresses.
- Minimize manual effort, reducing the risk of human error and processing time.
- Implement error handling mechanisms and auto-recovery features to prevent system disruptions.
- Increase transparency with real-time reporting and monitoring of the process.
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