Augesys

Healthcare

Streamlining EMR and EHR Data Processing for a Healthcare Payer Agency

Client Overview

A leading healthcare payer agency was tasked with processing tens of thousands of EMR and EHR data records received from various healthcare providers. These records, in the form of Excel spreadsheets and other data types, were transmitted through SFTP, emails, and other means. The agency faced significant challenges in managing this influx of data, which required manual validation and coding before processing and payment.

Data Ingestion Challenges:

The agency received large volumes of EMR and EHR data from multiple sources, including FTP, emails, and other channels. Some of these processes were automated, but many still required manual intervention.

Manual Validation and Coding

Data records had to be manually validated for accuracy and categorized appropriately for processing and payment. This manual effort was time-consuming and prone to errors.

High Operational Costs

Hundreds of service agents working round-the-clock shifts were required to handle the large volume of records, leading to high labor costs and inefficiencies.

Error-Prone Processes

Manual handling of data increased the risk of errors, which could lead to delays in processing and payment, affecting overall efficiency and satisfaction.

Digital Transformation Solutions

With the CIO as the primary stakeholder of the agency, and with the support of their business leaders, subject matter experts, and technical architects, we studied their pain points and developed the following solutions:

Automated Data Ingestion

We implemented a robust system to automate the collection of EMR and EHR data from various sources, including SFTP, emails, and other channels. This solution utilized Intelligent Document Processing (IDP) and Robotic Process Automation (RPA) to streamline data ingestion.

Automated Validation and Coding

Using advanced AI and machine learning algorithms, we developed an automated system to validate the accuracy of the data and categorize it appropriately along with an exceptions category. This system significantly reduced the need for manual intervention.

Enhanced Workflow Management

e integrated workflow automation tools to manage the end-to-end process, from data ingestion and validation to coding and processing. This ensured a seamless and efficient workflow.

Automated Data Ingestion

Implemented real-time monitoring and reporting tools to provide visibility into the processing status and identify any bottlenecks or issues promptly.

Results

  • Increased Data Accessibility by 50%: The shift from individual Excel files to a centralized web application significantly improved data accessibility, allowing more users to access and manage the data in real-time.
  • Reduced Data Entry Errors by 35%: The user-friendly forms and standardized data input processes reduced the incidence of data entry errors, leading to more accurate and reliable data.
  • Improved Collaboration: The centralized system enabled better collaboration among departments, as data could now be easily shared and accessed by all relevant stakeholders.
  • Streamlined Reporting: The built-in reporting tools provided instant access to critical information, reducing delays and improving decision-making across the institution.

Conclusion

By developing and implementing web applications to replace decentralized Excel spreadsheets, the institution was able to transform its data management processes. The new system not only improved data accessibility and user interaction but also reduced errors and streamlined reporting. This digital transformation enabled the institution to operate more efficiently, collaborate more effectively, and make better-informed decisions.

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