Streamlining Data Migration with Robotic Process Automation and Document Understanding 

In today’s fast-paced digital landscape, businesses are constantly evolving and upgrading their systems and platforms to stay competitive. One of the most daunting challenges they face is migrating large quantities of data to these updated environments. Traditional methods can be time-consuming, error-prone, and resource-intensive. However, the integration of Robotic Process Automation (RPA) and Document Understanding can revolutionize the data migration process, making it more efficient, accurate, and seamless. 

Robotic Process Automation (RPA) acts as a digital workforce, automating repetitive tasks that would otherwise require human intervention. When applied to data migration, RPA can significantly expedite the transfer of information between systems. Bots can be programmed to extract, transform, and load data with precision and speed, minimizing the risk of human error and reducing the time required for the migration process. 

Document Understanding, an advanced capability paired with RPA, takes automation to the next level by enabling bots to comprehend unstructured data. In the context of data migration, this means that the system can interpret and process data from a variety of document formats, including PDFs, images, and scanned documents. This is crucial because data is often stored in diverse formats, and manually extracting information from such documents can be a time-consuming and error-prone task. 

Challenge: A large Mid-West Healthcare provider with fifteen hospitals across two states had a large document processing problem: importing Patient Consent Forms into OnBase. This is generally not an issue for most DU (Document Understanding) tools because most of the data is structured. In this case these particular documents included a stamp or label of patient demographics added by hand and placed on the document with both structured and unstructured data. This information on these labels then needed to be read and indexed into Epic. The challenge was that these labels were being placed on the documents by clinicians and in most cases were not always positioned in a way that was effectively “read” by most DU tools.

Solution: As a trusted adviser to the client, CampTek Software brought in several DU vendors to perform a proof of concept for the provider so they could evaluate the ability of several tools to effectively read the skewed labels. Document Understanding solutions such as ABBYY, UiPath and Indico were among those reviewed.

Each product was given a sample set of documents as part of a proof of concept. From there, CampTek would analyze the ability and percentage results to present back to the client. The findings were overwhelmingly positive for Indico. Even upside down, the labels could be read immediately. For others, the test proved that if the label was more than 10% out of alignment, they had poor results. Indico immediately showed a 90% accuracy result. Indico also did not require the use of templates, like some of the other tools did.

The findings were presented to the customer and within a short time the project was approved. Also, it was stated by the project lead that once this first project was implemented, there will be additional opportunities for expansion of the Indico/CampTek managed services solution. For example, the ability to pull data and documents from Legacy systems like CPSI and replacing expensive point solutions like Edco Solarity as next projects.