Machine learning is helping drive efficiency and accuracy across healthcare operations by enabling systems to learn from data and automate complex processes. At Claritev, this technology is used to prioritize claim reviews and enhance payment integrity, allowing teams to focus on high-value tasks, reduce manual effort, and scale operations while delivering greater value to clients.
A large healthcare carrier faced the challenge of enhancing protection against balance billing while preserving the cost advantages of its reimbursement model. Through strategic negotiation parameters—balancing provider incentives and member value—Claritev delivered more transparent agreements, achieving significantly higher savings with minimal impact on negotiation success rates.
Claritev analysis helped a third-party administrator defend against a competitor’s push to implement Medicare pricing by: significantly improving savings for the employer and its members; engaging important providers in the new process; and preserving the community’s provider-patient relationships.
Claritev analysis uncovered opportunities for a third-party administrator using a variety of PPO networks to: improve payment accuracy – too much was being missed in the existing editing process; gain control over clinical billing waste and abuse; and minimize provider abrasion to preserve leased network relationships.