Case study
Prevent unrealized revenue loss with Gen AI
How we supported a revenue cycle management suite for the healthcare industry improve the success rate of appeals for payment completion and thus increase their overall revenue using Gen AI
65% BOOST IN ACCEPTANCE RATES
30% INCREASE IN RECOVERED REVENUE
5x FASTER RESPONSE TIMES
5% DECREASE IN EMPLOYEE ATTRITION RATE
The Business Challenge
Healthcare providers engage in the appeals process to ensure equitable compensation for services rendered by providing supplementary information and compelling evidence.
Appeal letters are crafted through manual compilation of essential details to substantiate the claim and achieve a positive resolution for the denial.
These details encompass information about the patient, healthcare provider, claim specifics, insurance details, as well as pertinent medical necessity documentation.
Manual composition of these letters often results in inaccuracies, omissions, or grammatical errors. These human errors in turn lead to the denial of about 70% of these letters, resulting in revenue leakage.
The Solution
The appeal letters were of a wide variety. These letters were studied thoroughly to understand the content and the way details were mentioned in them. Based on the exploratory data analysis, the letters were segregated into different groups. Amplify created a deep learning-based model to classify these appeal letters in each group as successful or likely to be rejected.
To further identify the likelihood words and sequence of words used in these appeal letters likely result in success or rejection, an Explainable AI model was created. Based on the explainability of the words, a new sequence of words was suggested to further improve the verbiage of these letters, which could in turn result in improving the overall acceptance rate of the appeal letters sent by the hospitals and health systems, maintaining the correct semantics and syntax as per English Grammar rules.
Our Approach
The methods used
Decay scores were created for some relevant engagement features: Email sent, Email open, Email click & Page visit. The model doesn’t only count the number of important engagement features but is assigning some scores to the time at which the actions were done. If somebody visits a page today, he/she would be given higher importance than somebody who visited the same page a month back. These decay scores are calculated using an exponential equation.
Decay rates are different for the diverse engagement features: the decay rate for “Email open” is slower than the decay rate for “Email sent”.
ML Model deployment
This machine learning model was deployed successfully on Azure Cloud with no bugs and is being used by the client’s sales team.
Amplify pulled data from the client’s marketing applications such as SFDC and Pardot and used the Databricks platform, to train multiple models and MLflow, part of Databricks, to track experiments.
The fresh scores are then pushed back to the Salesforce application in an automated fashion.
Business impact
The case study showcases how Amplify Analytix provided a solution to the client using Text Classification and NLP Analytics. This helped in revising the content and format for the letters sent by healthcare service providers to appeal for payment to insurance companies. The new and improved appeal letters resulted in a better acceptance rate, and payment completion by insurance companies further helped in improving overall revenue.
Related content
Want to know more?
Gen AI
Unlock unparalleled customer satisfaction and retention with our Gen AI Chatbot solution.
