Fragmented claim documents
Scanned hospital packages, tariff schedules, and coding references arrive as unstructured PDFs across paper, portals, and legacy systems.
Evidence-first AI for healthcare insurance claims
Sahabat ClaimCare turns claim packages into validated, risk-scored, citation-backed evidence for payer reviewers — deterministic rules own the facts, AI stays advisory, and reviewers keep final authority on every claim.
Claims review does not scale on manual effort alone
Inconsistent tariff and coding checks
Manual ICD-10, ICD-9-CM, and INA-CBG lookups create reviewer variance and slow turnaround on routine claims.
Fraud that hides in volume
Abuse patterns and tariff variance are difficult to spot at manual review speed across thousands of claims a month.
Audit and compliance pressure
Payers, regulators, and auditors expect defensible, evidence-backed decisions with a traceable rationale — not opaque approvals.
A claim intelligence pipeline reviewers can trust
Sahabat ClaimCare combines document intelligence, medical and tariff validation, explainable risk scoring, and a reviewer workspace on one evidence-first platform — with every AI output attributed back to its source document.
Document intelligence
Automated document extraction turns scanned claim packages into structured, validated fields with per-stage confidence scoring.
Medical and tariff validation
ICD-10, ICD-9-CM, and INA-CBG semantic mapping with confidence scores, plus hospital tariff normalization against fair-price registries.
Explainable risk scoring
Weighted fraud and abuse indicators route claims to auto-approve, manual review, or investigation, with rationale documentation on every score.
Reviewer workspace
Evidence visualization over source documents, an optional context-grounded AI assistant, and idempotent, audit-logged reviewer actions.
Evidence before inference, at every stage
| Stage | What it covers |
|---|---|
| Intake and extraction | Automated intake from hospital and payer submissions with document extraction and data validation. |
| Coding and tariff validation | ICD-10 / ICD-9-CM / INA-CBG mapping, medical necessity checks, and tariff normalization against fair-price registries. |
| Risk intelligence | Explainable fraud and abuse scoring with relationship mapping and disposition routing across auto-approve, review, and investigation. |
| Reviewer workspace | Evidence-linked review screen, advisory AI assistant, and decision actions with immutable audit trails. |
| Multi-tenant access control | Payer, provider, and personal workspace scoping with role-based and claim-scoped access control. |
| Monitoring and audit | End-to-end tracing, operational dashboards, and before/after decision records for every reviewer action. |
AI recommends. Reviewers decide.
Evidence before inference
Deterministic rules establish tariff validity, coding correctness, and claim facts before any AI interpretation is applied.
AI is advisory and attributable
Every AI-assisted suggestion links back to the extracted evidence it was generated from — no unsupported recommendations.
Least-privilege, fail-closed
Access is scoped per claim and workspace, and unsupported or low-confidence evidence routes to human review by default.
Every stage is observable
Processing, validation, scoring, and reviewer decisions are traced and logged end to end for compliance and tuning.
Frequently asked questions
Does Sahabat ClaimCare replace claims reviewers?
No. AI is advisory only. Deterministic rules establish tariff validity, coding correctness, and claim facts, while human reviewers retain final authority on every disposition.
Which coding and tariff standards are supported?
ICD-10, ICD-9-CM, and INA-CBG semantic mapping with confidence scoring, plus hospital tariff normalization against fair-price registries.
Can it integrate with our existing claims system of record?
Yes. Automated intake handles submissions from hospitals and payers, and the platform produces a defensible recommendation and audit trail for your settlement system to consume.
How is fraud and abuse risk scored?
A weighted, explainable indicator model routes claims to auto-approve, manual review, or investigation, with documented rationale and relationship mapping behind every score.