In healthcare, accuracy isn’t just important. It’s regulated.
For health plans and providers that are paid based on quality and outcomes, patient complexity shapes reimbursement, care planning, and population health strategy. When the process breaks down, the effects ripple across the system, from compliance risk to missed care opportunities.
Yet many supporting workflows haven’t kept up. They remain manual and time intensive. And while AI has promised speed, it has introduced a new challenge: outputs that are difficult to explain, verify, or defend.
That gap, between speed and trust, is the problem RAAPID is built to address.
Why healthcare AI can’t be generic
RAAPID was founded on a simple premise: in highly regulated industries, AI must be purpose-built.
“I have spent more than 20 years in healthcare,” said founder and . “What I kept seeing was that the existing platforms were not purpose-built … the output was often a black box.”
Many systems, whether rules-based or powered by newer large language models, are optimized to identify possible diagnoses. But in healthcare, finding a diagnosis is only the start. Each one must be backed by clear clinical evidence.
RAAPID takes a different approach. Its platform combines neural AI with symbolic reasoning, often called neuro-symbolic AI, which blends pattern recognition with rules-based logic.
The result is a system designed not just to generate answers, but to show how it reached them.
Turning AI into something you can trust
RAAPID describes its system as a “glass box,” not a black box.
Each diagnosis surfaced by the platform links directly to supporting clinical documentation, down to the specific encounter and evidence used. That transparency matters when decisions must stand up to audits and regulatory scrutiny.
“Anyone using our platform should be able to see exactly which clinical evidence supports it,” said Parikh. “Hallucination is not allowed in healthcare. Period.”
The platform is also designed to find missed diagnoses while removing unsupported ones. The goal is not just speed, but defensibility over time.
Real impact: faster cycles, higher confidence
In practice, that approach is already changing how plans review patient records.
One health plan managing approximately 100,000 covered lives reduced its retrospective review cycle from eight to twelve weeks down to two.
The impact extended beyond speed:
- Initial accuracy exceeded 80%, improving to more than 98%.
- RAAPID identified approximately $3 million in net revenue.
- The platform reduced unsupported codes, lowering compliance risk.
- On the prospective side, RAAPID analyzes multiple years of patient history, including labs, medications, and patterns in how care has been used, to surface potential care gaps before a visit.
That shifts the work from retrospective correction toward proactive care.
Building for trust in healthcare
In healthcare, adoption hinges on more than technical performance. It depends on trust.
“The hardest moments in healthcare are never the technical ones,” Parikh said. “They are the trust moments.”
That principle shaped RAAPID’s early validation with UPMC Enterprises. Known for its rigorous evaluation standards, UPMC not only selected RAAPID as a preferred partner after extensive testing but also invested in the company.
The experience reinforced a key insight: AI must be built for the people who use it, including coders, clinicians, and compliance teams, not just for model performance.
For a coder or a clinician, that means seeing the diagnosis, encounter, and supporting evidence in one place before taking action.
Accelerating with Microsoft
Microsoft support has helped RAAPID build on its healthcare validation and prepare for enterprise-ready deployment.
With backing from Microsoft’s M12 venture fund and participation in the Microsoft Pegasus program, RAAPID has been able to work within Microsoft’s startup and enterprise ecosystem as it scales.
That support extends to Microsoft Azure Marketplace, where healthcare organizations can deploy RAAPID in their own environments, helping them maintain control of sensitive data while using Azure for security, scale, and faster rollout.
RAAPID has also achieved Microsoft’s Healthcare AI Certified Software designation, reflecting alignment with privacy, clinical relevance, and responsible AI expectations.
Together, those connections have helped RAAPID validate its approach, strengthen its infrastructure, and reach enterprise healthcare customers through Microsoft’s broader ecosystem.
What comes next
Risk adjustment may be RAAPID’s starting point, but the broader opportunity is larger.
As Parikh describes it, this work is central to how value-based care functions. When the underlying data is incomplete or inaccurate, the system struggles to deliver on its promise.
“If we do this well,” he said, “health plans can reallocate resources from audit defense to care management—and patients get providers who are looking at their full clinical picture.”
The next phase of healthcare AI won’t be defined by speed alone. It will depend on systems that are transparent, explainable, accountable, and built for real-world complexity.
And in an industry where every decision must stand up to scrutiny, trust and responsible AI may become the foundation for what comes next.
Join our Founder Friday LinkedIn Live on July 31 featuring RAAPID CEO Chetan Parikh. Register here.
