By Itay Mishan, CTO, Wisedocs —
After processing more than 100 million medical records and claims documents at Wisedocs, we’ve learned things you can only learn at scale. Claims data is genuinely unpredictable: the formats, quality, and volume per case are never consistent. A single complex claim can span thousands of pages, dozens of providers, and years of fragmented history. You can’t engineer your way around that unpredictability with a generic tool. You have to build for it.
The question we hear most from claims professionals, attorneys, and medical reviewers alike isn’t “how accurate is your model?” It’s “can you show me where that came from?” Across the claims ecosystem, an output you can’t trace to a source document isn’t just incomplete; it’s unusable. It can’t support a reserve decision. It can’t stand up in legal proceedings. It can’t back a clinical opinion in an IME report. The stakes are too high for anything short of a direct hyperlink to the record it came from.
This is where generic AI tools fail. Language models without domain grounding produce fluent, confident-sounding summaries that contain statements with no basis in the underlying documents: no citations, no audit trail. In high-stakes claims environments, that’s not a minor limitation. It’s a structural failure.
The trust gap has a technical cause
There’s a well-documented trust problem with AI in claims. Research we conducted showed that only 16% of claims professionals report medium or high trust in AI-generated outputs, with just 2% reporting high trust. Those numbers reflect real experience. Many organizations have tried off-the-shelf AI in claims workflows and encountered inconsistent, indefensible results. The professionals in this space are not being unreasonably skeptical; their distrust is calibrated. Fixing it requires a fundamentally different approach to system design: one built around defensibility from the ground up.
Three layers of accuracy
When we talk about accuracy at Wisedocs, we mean three distinct things. The first is data extraction: the combination of ML models and language models that read, classify, and structure raw documents. The second is defensibility: every insight we surface is accompanied by a direct hyperlink to the source record and page that supports it. We’re not asking users to trust us. We’re giving them the means to verify every output themselves.
The third layer is human-in-the-loop validation. Expert clinicians review AI-generated outputs, correct ambiguities, and flag errors, and every correction feeds back into the models. The loop closes. AI scales the analysis, human experts ensure the accuracy, and the feedback continuously improves the system. Each layer matters individually. Together, they produce outputs defensible enough to use in claims and legal workflows.
Why human experts aren’t going away
There’s a view that AI will eventually replace human review entirely. I don’t think that’s right. The reason is the data itself. Medical records are unstructured, messy, and unpredictable in ways that are genuinely difficult to anticipate: the contradictions, the missing records, the ambiguous clinical language, the jurisdictional nuances. These are exactly where errors compound and where the cost of being wrong is highest.
What AI changes is the nature of that human work. As AI handles both scale and intelligence (extraction, deduplication, and chronology on one side, and surfacing risk signals, claim inconsistencies, and treatment outliers on the other), the human validation role becomes more context-heavy and more specialized, not less necessary. The skills required to review AI outputs in this environment are actually higher than those required to manually process documents. That’s not work that diminishes with better AI. It evolves.
From summarization to decision intelligence
For years, the value proposition of AI in claims was speed: faster summarization, faster indexing, faster extraction. That was a real improvement, but it wasn’t the end state. The direction the industry is moving, and what we’ve built Wisedocs 2.0 around, is the shift from document processing to decision intelligence. The question isn’t just how fast can we get through the file. It’s what does this file tell us about risk, inconsistency, and the decisions that need to be made?
That means surfacing treatment outliers, flagging litigation risk signals, identifying conflicts across records earlier in the lifecycle, before the file escalates and cost compounds.
A top 10 P&C carrier using Wisedocs reduced average turnaround from 14 days to 2 and cut cost per case by 3x. A workers’ compensation defense firm‘s paralegal and ops team now delivers files to attorneys two weeks ahead of deadline, freeing them to focus on legal strategy instead of document review. An IME practice cut total time per assessment from roughly 22 hours to under 10, with weekly assessment capacity more than doubling. Those outcomes come from a system built to reach the decision, not just describe the documents.
In claims, trust is the product. The technology is only as valuable as the confidence professionals can place in what it produces, and what they can defend when challenged. That’s the standard the new Wisedocs platform is built around.
To learn more about Wisedocs Claims Decision Intelligence, visit wisedocs.ai/product/claims-decision-intelligence.
About the Author
Itay Mishan is the Chief Technology Officer (CTO) at Wisedocs. He joined the Wisedocs team in 2024 after being introduced by interim CTO George Papayianis, who was impressed by Itay’s deep technical expertise, emotional intelligence, and approach to building teams. Itay has more than two decades of experience across the tech industry, including plenty of time spent in the Toronto startup world. He holds a bachelor’s degree in Computer Science and Economics, as well as an MBA, which has given Itay a robust technical background and a strong foundation in traditional business disciplines. Blending the two, Itay can be found using his leadership skills to support his passion for Machine Learning and software architecture – resulting in building creative, high-performing teams.
He has led Product and Technology organizations through rapid growth, replatforming, and transformation, owning technical vision, organizational design, delivery, reliability, and unit economics. He specializes in helping companies move from “it works” to “it scales” – which includes designing platforms and teams for enterprise customers, translating technical debt into board-level decisions, and building execution confidence across leadership, investors, and customers.
Prior to joining Wisedocs, Itay Mishan founded two startups in the computer vision space. He has worked and studied across subsects of AI, including real-time embedded systems, web technology, system architecture, computer vision, and IoT. Itay built and led the R&D office for eBay in Toronto, where he led a high-performing team through complex technological challenges and contributed to the continued success of eBay’s global brand. As a consultant, Italy has leveraged his tech knowledge for startups and new companies, offering mentorship and insight into everything from technology expertise to the next investment move.
At Wisedocs, Itay uses his extensive knowledge of technology, including AI, and Machine Learning, to innovate and drive strategic growth. Combined with his 20 years of experience as an engineering leader, Itay brings technological robustness – and a stellar engineering team – to advance the Wisedocs mission to reshape the future of claims.
About Wisedocs
Wisedocs is the decision intelligence platform for insurance claims. The company helps insurers and organizations across the claims ecosystem transform complex claim documents into structured, decision-ready intelligence.
Using artificial intelligence combined with expert human oversight, Wisedocs organizes, analyzes, and summarizes large volumes of medical, legal, and billing records so claims teams can identify risk earlier, make faster decisions, and improve claim outcomes.
Founded in 2019, Wisedocs serves insurance carriers, TPAs, law firms, medical evaluators, and government programs. The platform is built for highly regulated environments and supports enterprise deployments with SOC 2 Type II compliance, HIPAA alignment, and expert-validated AI workflows. To learn more, visit wisedocs.ai.
Source: Wisedocs
Tags: Artificial Intelligence (AI), claims, InsurTech, InsurTech Spotlight, platform, Wisedocs