Leveraging RPA to Optimize Parsing Reports for a Healthcare Company

Client

The client is a US-based healthcare company that embarked on a transformative journey to reduce billing errors, streamline manual report verification processes, and significantly accelerate the parsing process.

Challenge

The client had to deal with manual information extraction from various healthcare documents, including CIOMS and Takeda forms, and encountered obstacles such as unstructured documents, diverse formats, including XML, and even scanned documents. The complexity was compounded by multiple languages, including Japanese, Spanish, and English, requiring intricate document manipulation techniques.

The client aimed to automate the information extraction from healthcare documents while ensuring accuracy, language compatibility, and seamless integration with the client's existing CRM system. Thus, it was imperative to mitigate human errors associated with manual data entry, incorrect values, and misinterpretation of information inherent in traditional parsing methods.

Solution

By leveraging automation tools and advanced technologies, DataArt combined the power of robotic process automation (RPA) with sophisticated data extraction techniques, improving not only operational efficiency but also enhancing the accuracy and reliability of the client's parsing processes.

Utilizing UIPath for data extraction and integration with the CRM system, DataArt’s solution allowed streamlining workflows and reduced manual intervention. Additionally, recognition algorithms were developed to automatically identify different report types and assign them to relevant stakeholders within the CRM, further enhancing efficiency and accountability.

Outcomes

Thanks to DataArt’s approach, the client gained a significant reduction in processing time, with the time required for parsing documents slashed by an impressive 600%. Robotic process automation implementation also led to the auto-recognition of over 50 reports daily, alleviating the burden on administrative staff and accelerating decision-making processes. Furthermore, they achieved a remarkable reduction in the time required to recognize individual documents, cutting the processing time from 3 hours to just 30 minutes per document. Additionally, the project addressed the challenge of language diversity by providing recognition support for CIOMS forms in Japanese, further expanding the capabilities of the parsing system and ensuring compatibility with a global healthcare landscape.