Claims AI Assessment: Take the test now
Claims AI Assessment: Take the test now
HEALTH
80% of claims recommended straight through, with no investigation work from a handler. The remaining 20% arrives with the confidence gap already identified.
Overview
Health claims volume increases faster than the teams that handle them. Every claim needs someone to read the policy, read the evidence, and form a view, so throughput is capped by headcount, and cost per claim rises with volume rather than falling.
Sprout.ai interprets the policy and the claim together and recommends the coverage outcome, with the relevant clause applied and the evidence used. Clear claims resolve on an automated recommendation.
The challenge
Health claims are judgement work: does this treatment, for this member, under this policy, qualify, and for how much?
Claims and supporting documents arrive from hundreds of providers in inconsistent formats, channels, and languages – some handwritten, some machine-printed, many a mix of both on the same page.
Deciding a claim means knowing what a diagnosis implies, which treatments follow from it, and how the two map to a coding space running to tens of thousands of ICD, CPT, pharmacy, and surgery codes.
When claims require review inside the core claims management system, handling time per claim sets the ceiling. Growing volume means growing headcount just to hold turnaround steady.
Your most experienced adjuster and your newest adjuster should reach the same answer on the same claim.
sprout.ai understands and interprets
| Claim dimension | What Sprout.ai contextualizes | What it returns |
|---|---|---|
| Coverage | Policy documents, riders, endorsements, and benefit schedules, against the treatment claimed | Whether the treatment is covered, partially covered, or excluded, and the clause that says so |
| Eligibility | Membership records, policy dates, waiting periods, network status, and cost-sharing terms | Whether the member was covered on the date of treatment, and the portion they owe |
| Clinical evidence | Doctors' reports, discharge summaries, treatment records, and prior authorization letters | The facts that decide the claim, plus any conflicts or gaps in the evidence |
| Coding and billing | Medical invoices and bills, itemised line items, diagnosis and procedure codes | Whether the codes match the treatment documented, and which lines are payable |
| Claim integrity | The full claim file, alongside claim and provider history | Duplicates, unbundling, and patterns worth investigating |
| Completeness | Everything submitted, against what the policy requires | What is missing, and who needs to supply it |
| Recommendation | All of the above, taken together | The recommended outcome, a confidence score, and the automation path - straight through, or adjuster review |
How sprout.ai improves claims performance
Reads every document in the file, including claim forms, provider invoices, and clinical records, and establishes what the claim actually says.
Maps the treatment to the benefit schedule and applies the policy terms – limits, sub-limits, exclusions, waiting periods, cost-sharing, network status
Examines patterns across documents in the file and across claim history, surfacing duplicates, unbundling, and anomalies that single-claim review cannot see.
The agents’ findings resolve into one output per claim: the recommended outcome, the clause applied, the evidence considered, and a confidence score.
Above your configured confidence threshold, claims resolve without review. Below it, they route to an adjuster with the work already done and the specific gap identified
Works from the claim context, the claim form, the invoice, the clinical record, to determine the ICD, CPT, and pharmacy codes that represent the treatment given
Every override an adjuster makes is feedback. Sprout.ai learns from the decisions your team actually makes, so accuracy improves against how you interpret your own policies, rather than how a rule was written
The impact of AI-powered claims processing
I had the opportunity to lead the implementation at MetLife Chile, working alongside the Sprout.ai team. It was a great experience of collaboration and innovation, delivering excellent results for customer experience.
Pablo Guajardo
|
Claims Manager & Operational Transformation Lead, Metlife Chile
AdvanceCare is continuously looking for innovative ways to improve the insurance claims experience. We selected Sprout.ai as our partner, as they have demonstrated that their technology and approach can take us one step forward in our digitalization and efficiency journey.
Sergio Melro
|
Chief Operating Officer, AdvanceCare
Our partnership with Sprout.ai is another example of how MetLife is innovating with high tech solutions to address real customer needs.
Venkata Natarajan
|
Chief Information Officer, MetLife
Our customers highly value the digital reimbursement solution that we provide through our app and web portal. Focusing on efficiency also means focusing on time resolution and the quality of service we provide to our customers. This is why we see AI as a useful tool and look for solutions that contribute to this, such as Sprout.ai.
Liliana Silva
|
Operations Director, AdvanceCare
We know making a claim can be stressful, so we're dedicated to processing them quickly and smoothly. By partnering with Sprout.ai, we're using cutting-edge AI and NLP to free up our claims handlers, allowing them to focus on delivering empathetic support to customers.
David Russell
|
Senior Manager, Claims & Underwriting Strategy, Scottish Widows (Lloyds Banking Group)
Sprout.ai can capture much more data, then format it so it is structured, allowing analysis into needs, leakage, and care quality.
Joanne Richardson
|
Former Health Director, AXA
Book a demo to see how Sprout.ai surfaces policy, medical, and claims evidence before a decision is made.
ai for health claims
How does AI reduce leakage in health, life, and disability claims?
Sprout.ai reads every document in the claim file against the relevant policy wording, surfaces coverage-relevant facts, and flags inconsistencies before a decision is made.
How does Sprout.ai reduce compliance risk in health, life, and disability claims?
Sprout.ai applies relevant policy logic to each claim and shows the evidence considered, policy clause applied, outcome rationale, role of the adjuster, and role of AI. This creates a traceable, defensible audit record showing who did what, why, and when, helping reduce manual variation and support internal, international, state, and NAIC review.
Can AI handle complex medical evidence reviews?
Yes. Sprout.ai summarizes clinical documentation so adjusters can see what the policy requires, what the medical record shows, and where gaps exist. Adjusters spend time deciding, not reading.
How quickly can Sprout.ai integrate with our existing claims management system?
Sprout.ai connects via API to existing claims infrastructure, including Guidewire, Duck Creek, and other platforms, without core system replacement.
