Implementing for hassle free Anesthesia Claim Processing
Client intro and Requirement
Our client was a provider organization that offers advanced urology services in Georgia, USA. They have 9 different facilities on their premises. All of these were conducting several surgeries daily. They were struggling to keep up with the manual process of collecting anesthesia claims and storing it under the respective patient’s encounter history in the EHR. The client chose Nalashaa to solve this issue.
Client Challenge
The client had been receiving anesthesia claims from all their nine facilities. Although all surgeries don’t require anesthesia, a considerable number of them do. Their present system was capable of creating and storing professional/service claims of encounters in the ECW EHR. But the problem arose when they wanted to create and store the anesthesia claims. They had to manually collect the information flocking from nine facilities and then populate the respective fields in the EHR. This was time-consuming and often resulted in an overlap of information.
Claims for 9 different facilities
Lack of automation resulted in tedious manual work
Manual work created overlap of information
Decrease in revenue due to inaccurate claims
Our Solution
The entire process of automating the anesthesia claims creation workflow was done in two phases.
Phase 1
Nalashaa developed a bot to download patient schedules from all the nine facilities and create a master file. Then, the bot was trained to create blank shell claims. This step was important as the process of blank shell creation has more than 10 manual steps. This freed up valuable provider time.
Phase 2
After phase 1 the bot learned how to download schedules from the nine facilities and create blank claims for new encounters. Now it was trained to read the patient docs in the patient hub. Patient docs contain information such as ASC chart and professional claim which are important for creating the final claim. Our bot utilized Omni Page’s OCR technology to extract ASC and the ICD & CPT data from these documents.
The extracted information is segregated into the following types: provider’s name, resources used, ICD & CPT codes and the latest fee schedules. This information was then populated in the required fields of the new claim.
After phase 2 implementation, the bot could now create a blank shell claim for encounter which doesn’t have ASC chart or professional claim information in the patient doc. And for encounters with all the relevant information, it could create partial claim (ACS or CPT only. In case one is missing) and complete claim (Both ASC and CPT). Nalashaa also made sure that the bot was capable of sending updates by the end of everyday about the number of claims created and their status via email to the respective authorities.
How We Made a Difference
95% of manual tasks reduced
85% reduction in claim rejection
Increase in revenue collection
Improved patient satisfaction
Technology stack
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