Health insurance can be complicated—especially when it comes to prior authorization (also referred to as pre-approval, pre-authorization, and pre-certification). The manual labor involved in obtaining prior authorizations (PAs) is a well-recognized burden among providers. Up to 46% of PA requests are still submitted by fax, and 60% require a telephone call, according to America’s Health Insurance Plans (AHIP). A 2018 survey by the American Medical Association (AMA) found that doctors and their staff spend an average of 2 days a week completing PAs. In addition to eating up time that physicians could spend with patients, PAs also contribute to burnout.
The objective was to identify the patterns from data to create clinical decision making in Pre-Auth and improve the accuracy in a clinical decision based on historical data analysis.
Two use cases were identified. Use Case 1 - Supervised Learning Model - to aid clinicians in UM decision making. Tasks - Ingest Pre-authorization data from Mongo DB into the analytical environment, Exploratory Data Analysis and Feature Engineering, Train supervised analytical models, model validation and model selection, Create a web service to be plugged into the case processing flow to call the model, and Display the recommendation from the model on UI on the authorization review screen. Use Case 2 - Unsupervised Learning Model - to generate insights from the pre-authorization data. Tasks - Ingest Pre-authorization data from Mongo DB into the analytical environment, Cluster analysis, univariate and multivariate analysis, and Generate insights and display insights on the dashboard.
Final Deliverables - Model re-training (batch mode), validation and deployment code (python scripts) with Unix command line support, Documentation - PPT, Recorded video, Technical document, Flask API backend system, HTML/PHP Web App frontend UI integration, and Plotly Dash Supervised/Unsupervised learning and insights generation dashboard.
