How Anterior Accelerates Prior Authorization and Clinical Decision-Making with 99%+ Accuracy

“Without solid document extraction, everything else crumbles.”
— Anuj Iravane, AI Research Lead at Anterior

In the healthcare industry, where decisions carry life-altering consequences, automation can’t come at the expense of accuracy. For AI-driven companies working with patient medical records, insurance claims, and clinical workflows, the biggest bottleneck is often at the very beginning: making sense of the messy, unstructured documents that power critical decisions.

That’s the exact challenge Anterior set out to solve.

Anterior is a fast-growing health technology company streamlining clinical and administrative tasks, starting with accelerating prior authorization workflows with their AI agent, Florence. But unlocking this kind of process improvement requires one crucial prerequisite: reliable document ingestion infrastructure. 

Using Reducto, Anterior has processed over 20,000 clinical documents for medical necessity reviews in real time, with 95% completed within a 1-minute SLA, with fewer than 0.1% of medical necessity reviews having had flaws attributable to document ingestion. That level of accuracy has been key to building trust in AI-assisted decision-making—proving that AI-enabled workflow transformation can deliver both speed and clinical-grade reliability.

The Problem: Healthcare’s Document Chaos

In the U.S. healthcare system, prior authorization is a critical, but notoriously painful administrative process. Before many medical procedures can happen, providers must submit documentation to prove that the treatment is medically necessary. Payers (insurance companies) review these documents to decide whether the requested service is covered under the patient’s policy and is necessary and appropriate based on the latest medical guidelines and standards.

For patients, delays in this process can mean waiting days or even weeks for essential care to be authorized, whether that’s a diagnostic test, surgery, or access to a specialist. Every prior authorization decision starts with a giant blob of unstructured data: scanned records, handwritten notes, and fragmented patient histories. 

A Parsing Engine Built with Accuracy First

The Anterior team knew that they would need a document ingestion engine as the first step in the development process. 

The team put together a strong rubric for their evaluation with three key criteria:

• Accuracy: The solution needed to extract information with high fidelity to the original document. In a highly regulated domain like healthcare, even minor inaccuracies or hallucinations are unacceptable—outputs must reflect exactly what is on the page.
• Layout Understanding: Robust support for complex structural elements was essential. This included accurate extraction of checkboxes, tables, and form fields, as well as the ability to preserve semantic relationships—such as associating paragraphs with the correct section headers or medical categories.
• Granular Bounding Boxes: To enable targeted citations within clinician-facing tools, Anterior required sentence-level bounding boxes. This level of granularity ensures that extracted insights can be precisely located and verified within the original document.

Then the team found Reducto, which quickly proved capable of extracting structured data from scanned PDFs, capturing complex layouts like checkboxes and tables, and preserving sentence-level citations and section context—perfect for RAG pipelines and clinical review.

Anterior’s engineering team worked closely with Reducto, regularly sharing feedback that directly shaped feature development. 

“One of our engineers requested a specific feature from the Extract API, and there was a one day turnaround time for the Reducto team to ship the feature which is crazy to me.” said Anuj.

This tight collaboration allowed both teams to move faster and ensured the roadmap stayed aligned with real-world clinical needs.

Overcoming AI Skepticism with Proven Accuracy

In healthcare, AI adoption faces a higher bar than most industries. Clinical leaders are rightly cautious as automation in high-stakes workflows like prior authorization demands trust, transparency, and proof.

Anterior earned that trust by letting performance speak for itself. They ran side-by-side tests using real patient cases and payer workflows. The results were clear: 99.24% accuracy (compared to 85% human accuracy rates) with minimal need for manual escalations. That level of accuracy made it easier for stakeholders to buy in.

With document ingestion used in many of its workflows powered by Reducto, Anterior was able to guarantee traceability, cite exact sources, and confidently automate approvals in minutes for patients and providers.

What’s Next: Synthetic Clinical Data and Adapters

Looking ahead, Anterior is investing in several key initiatives to advance the performance and safety of healthcare AI. These include building tools to generate synthetic clinical datasets for evaluation and benchmarking, and building adapters for intelligent representations of medical guidelines in the wild.

“The last mile of performance in healthcare AI is incredibly nuanced and the details really matter. That’s where we’re focused—getting those hard edge cases right,” said Anuj.

Each of these initiatives depends on accurate extraction of raw documents—an area where Reducto has been a valuable and consistent partner within Anterior’s broader intelligence stack.