Automating Patient Data Management and Billing Processes: A Project Case Study

Client Background

Sector: Healthcare Automation Solutions
Operational Focus: The client operates in the healthcare sector, managing patient data and billing across multiple platforms: the Zoll Billing System, Mercy Hospital Portal, and Availity. These systems handle critical functions such as patient record management, billing, and insurance verification. With rising patient volumes, the client sought an automated solution to integrate and streamline data flow across these platforms, reducing manual effort and improving operational efficiency.

Challenge Identified

The client faced several operational hurdles due to reliance on manual processes:

Time-Consuming Tasks: Staff manually retrieved patient data from Zoll, inputted it into Mercy, and cross-checked insurance details on Availity, leading to significant time delays.
Growing Workload: Increasing patient numbers amplified the volume of data to process, straining existing resources.
Error Risks: Manual data entry and transfers frequently resulted in errors, such as incorrect patient information or missing insurance details.
Scalability Constraints: The manual workflow couldn’t scale to meet growing demand without additional staffing.
Processing Delays: Slow manual handling delayed billing and insurance verification, impacting revenue and patient service delivery.

Solution: Automation of Patient Data and Billing Workflow

The project deployed an automation solution using UiPath to integrate and manage data across Zoll, Mercy, and Availity. The workflow automated patient data extraction, form population, face sheet generation, and insurance verification, with contingency steps for missing data.

Technologies Used:

Solution Overview

The solution was broken into three coordinated automation phases to ensure complete coverage:

Automated Workflow:

Extract Patient Data from Zoll: The process begins by retrieving the patient list from the Zoll Billing System, serving as the foundation for subsequent steps.
Input Data into Mercy Portal: Patient details (e.g., last name, date of birth) are automatically entered into the Mercy Hospital Portal. The system logs in (with two-step verification), fills forms, and selects "Payment" as the reason. It either selects an existing patient or creates a new record as needed.
Generate and Download Face Sheets: Face sheets, including insurance-related documents, are generated and downloaded from Mercy as PDFs.
Upload to Zoll: The downloaded face sheets are attached to the Zoll Billing System, along with invoice details, completing the billing cycle.
Fallback to Availity: If patient or insurance data is unavailable in Mercy, the system checks Availity, extracts the necessary details (e.g., insurance company, group number, ID number), generates a face sheet, and uploads it to Zoll.
Exception Handling: If data remains unavailable, an exception report is generated for manual follow-up (e.g., contacting the patient’s family or creating an invoice).

Flowchart Representation:

Start: Initiates with data extraction from Zoll.
Mercy Check: Inputs data into Mercy and downloads face sheets if available.
Availity Backup: Checks Availity if Mercy lacks data, ensuring comprehensive coverage.
Zoll Integration: Uploads face sheets and insurance details to Zoll, with exceptions flagged for manual review

Execution Strategy

The project was implemented in structured phases:

Phase 1: Planning and Analysis

Phase 2: Development and Testing

Phase 3: Deployment and Monitoring

Phase 4: Optimization

Outcomes and Benefits

Quantitative Results:

Qualitative Gains:

Improved Accuracy: Fewer errors in patient and insurance data enhanced compliance.
Scalability: The system adapted seamlessly to increased patient volumes.
Streamlined Workflow: Organized data flow improved accessibility and auditing.
Staff Efficiency: Freed staff focused on patient care rather than administrative tasks.

Lessons Learned

Consistency Through Automation: Automating repetitive tasks reduced variability and errors.
Flexible Design: Modular steps allowed easy adjustments to the workflow.
Error Preparedness: Robust exception handling ensured operational continuity.
Data Organization: Structured data storage boosted overall efficiency.

Flow chart of the process