HFMA ANI 2022
Looking for a slot?
By successful submission of below form we will ensure your RSVP
Location
Cocktail Reception
Hyatt Regency,
Colorado Convention center
Event Date & Time
27th June 2022 - 6PM to 9PM
Last Date for RSVP
23rd June 2022
Visiting a doctor’s office used to be an enormous task, starting from filing the claim then waiting till the approvals and finally the billing process. This process could sometimes take hours if not days thus making it very hazardous let alone the mistakes that are very common when you handle such sensitive data in bulk.
We are using AI/ML techniques for predictive analytics, which helps healthcare organizations identify where they can improve their coding for better reimbursement. Using machine learning and natural language processing (NLP) to analyze claims data can give a more accurate picture of the risk profile for each patient.
For any patient we can cross refer their claim information with their health information based on the ICD10 medical code for better risk assessment which helps with accurate pricing and better revenue.
Waterlabs AI has revolutionized the entire RCM industry with its state of the art proprietary software solutions. Our smart AI powered APIs like HINES can perform the entire insurance claim process without any human intervention and combining it with Transform AR makes the process even more accurate and faster.
Hines is a rest API, and it consists of 4 main parts.
- Post Claimstatus: First it takes the insurance providers details one by one and provides a token id
- Get Claimstatus: Then with the help of the token id, the user gets to see all the details of the insurance claim.
- Post Coverages: Next with the details of the insurance holder the user receives a token.
- Get Coverages: At last with the token id the user receives the details of the claim whether it is approved or under process etc
Transform AR is an API that makes managing this process easier like never before. It can handle millions of accounts and keep track of the process and allocate the accounts to the associates, and provide regular reports.
As Revenue Cycle professionals know, the submission of a claim can become abandoned for a number of different reasons. This is mostly due to the patient’s failure to provide the required information or their inability to pay for services rendered. With our AI/ML enabled smart RPAs it’s just a matter of seconds to track claims at every stage of the revenue cycle, this ensures zero abandoned claims.
We also have our own proprietary AI and ML bots like LASIT 2.0, Artemis, Danny etc. these smart bots make the process flawless and decrease the requirement of human intervention.
Lasit 2.0 can extract text from any kind of data be it a pdf, image, hand written etc and creates an excel sheet to be provided to bot for insurance processing. This entire process from taking the raw data and completing the insurance claim process and creating on time reports does not need any human intervention.
With techniques like OCR and NLP ARtemis is able to identify anomalies and spelling mistakes and also fixes them, ensuring accuracy that was nearly impossible with any human workforce.
Our AI/ML solutions play an important role in improving billing accuracy by correcting common transcription mistakes identified during data entry and validation checks. This ensures that there are no delays in claim submission or denied claims due to invalid information.
Making the entire RCM process automatic with cutting edge AI/ML techniques reduces any chance of man made errors, and improves the revenue cycle management as a whole.
In today’s digital age and the huge influx of highly sensitive patient data it is very important to ensure data safety and security. Even here Waterlabs AI is ahead with our state of the art security measures that we follow at every stage of data processing ensuring zero data leakage. We do not store any personal information, with all our HIPAA compliant software solutions waterlabs AI provides the most secure environment for a fully automated smart RCM process.