Ensuring Faster Authorization Decisions through Prior-auth Automation
Facing slower approval cycles and rising authorization
volumes, a healthcare payer used ML-driven decision
support to improve speed and consistency.
For a healthcare payer, prior-authorization processing was slowed by manual drafting and review workflows. Without predictive support, approval cycles were longer and difficult to scale efficiently.
Manual Review Workflows
Authorization requests required manual drafting and review, extending processing timelines
Delayed Approval Cycles
Inefficient workflows slowed down authorization decisions and impacted overall efficiency
Limited Decision SupportLack of predictive insights reduced the ability to streamline and standardize approvals
To accelerate authorization decisions, the organization introduced ML-based decision support. This enabled predictive processing, reduced manual effort, and improved the speed and consistency of approvals.
Cognitive Document Processing
Automated analysis of hospital and diagnostic records along with supporting data
Structured Medical Summaries
Generated consistent, multi-format summaries for easier sharing and review
Streamlined Clinical Workflows
Reduced manual effort across
prior-auth, care management, and appeals processes
60% automated approvals
Reduced reliance on manual review processes
30% assisted approvals
Improved efficiency with predictive decision support
80% reduction in turnaround time
Significantly accelerated authorization processing
