Surface and prioritize nonpayables
to complete more reviews, faster.
Getting started with health plan payment integrity review automation
CAVO Predict’s algorithms review and recommend prioritization of denied charges – and detail reasons for flagged charges. Claim reviewers easily accept or reject each suggestion, and CAVO Predict utilizes these decisions to continuously refine the machine learning model for that health plan’s reviews. Leverage CAVO Predict to streamline claims review processes, and to begin or enhance health plan payment integrity automation initiatives.
3 BENEFITS OF CAVO PREDICT
Positively impact health plan financials
Proactively identify often missed, high-frequency charges
Move providers to prepay claim reviews
Increase payment integrity review productivity
Leverage your review team’s skillsets and time
Continuously refine machine learning and NLP models
Enhance models with reviewers' ongoing decisions
Customize prediction rules with health plan policies and guidelines
WHY CHOOSE CAVO PREDICT
Leverage Our Resource Trifecta for Machine Learning
Healthcare Data Science Expertise
Data scientists specializing in healthcare claims review models customize algorithms to your objectives
Health Plan Claims Review-Specific Technology
CAVO enables continuous machine learning model refinement based on your ongoing review decisions
Claims Review
Clinical Expertise
Clinicians are the liaisons and SMEs to help build and initially test the machine learning model
Partner with one payment integrity vendor Ι Minimize plan review time investment
Itemized Bill Review NLP Example
In most cases, tubing is used for IVs and is reusable. However, when tubing is used as a feeding tube, it is only usable for a single patient. Using Natural Language Processing (NLP), CAVO Predict – Itemized Bill Reviews flags non-feeding tube charges for prioritized payer review and enables efficient decisions using our ibill review platform’s sort and filter features.
