An AI service can now investigate a case with growing sophistication. It reads clinical notes and supporting documents, queries claims, eligibility and provider systems, extracts the facts that matter. That flexibility is powerful. It also raises a policy question :
The AI service can assemble the case. Who decides whether the case can be resolved, and how ?
Knowledge keeps the rules outside the model. Existing platforms continue to own the workflow and the execution. The AI capability determines how to gather what a case needs ; Knowledge determines what the policy says once the context is in.
How the operational pipeline changes
Knowledge sits between the AI capability that assembles the case and the operational path that follows from the decision. The claims platform, the preauthorization workflow, the clinical systems and any human reviewers stay in place.
What Knowledge can be in your healthcare stack
Two shapes cover most payer and TPA deployments.
| Shape | How it works |
|---|---|
| Knowledge as the decision authority | For a given case type, Knowledge determines the outcome (allow, deny, benefit-specific determination, approval required) from the assembled context and the encoded policies. The claims or workflow platform acts on the outcome. |
| Knowledge as a complement to what you already run | For decisions the existing claims platform already produces, Knowledge can add a governed layer for specific cases : a firm-specific exception rule, a jurisdiction or plan overlay, an approval workflow, an audit surface. |
Healthcare engagements often use both shapes at once.
Three outcomes Knowledge produces
Not every healthcare decision should be automated. Knowledge distinguishes cases that can be resolved from explicit policy from those that genuinely require human judgment.
| Outcome | What happens next |
|---|---|
| incomplete + required_context | The applicable policies still need specific information. The existing system (or the AI capability behind it) obtains it and re-consults Knowledge. |
| complete + deterministic verdict | The encoded policies resolve the case without human review. The existing workflow acts on the verdict ; the signed envelope authorizes the downstream operational call. |
| complete + approval_required | The encoded policies explicitly route the case to human review. The workflow escalates with the policy-relevant context already assembled. |
For TPAs operating across multiple payers and plans, the same model keeps different policy sets governed separately while exposing a consistent decision interface to the systems processing cases.
The policy patterns Knowledge represents
Healthcare organizations operate under different regulatory frameworks, plan structures and operating models. Knowledge does not prescribe those policies. It provides a governed way to encode and evaluate the recurring decision patterns behind them.
| Policy pattern | What it governs |
|---|---|
| Eligibility and coverage | Whether the member, policy, benefit or service falls within the applicable coverage conditions |
| Information completeness | Which information or documentation is required before the applicable policies can reach a verdict |
| Plan and benefit conditions | Limits, exclusions, network conditions, benefit-specific requirements and other plan rules |
| Claims and approval rules | The organization's encoded conditions for resolving a claim or approval request |
| Exceptions and escalation | When an exception applies or when the encoded policies require additional review |
| Human-review routing | When a case cannot or should not be resolved automatically and must be sent to an appropriate reviewer |
Your organization owns the policy content. Knowledge provides the governed decision layer through which those policies are evaluated.
Let the policy drive the case investigation
Progressive Context turns the case-preparation into an active loop rather than a static form. The caller (an AI capability, an operational application, a workflow node) sends what it has. Knowledge determines what the applicable rules still require. The caller acquires it and re-consults.
A preauthorization case in progress :
Round 1. The caller sends what the case already carries : member_plan, service_code, provider_id.
Round 2. Knowledge asks for supporting_documentation_status and provider_network_status. The caller retrieves both, one from the document repository, one from the provider directory.
Round 3. Knowledge asks for medical_necessity_review_flag (governed by the benefit policy for this service code). The caller queries the appropriate system.
Result. approval_required. The workflow routes the case to a medical reviewer with all policy-relevant context assembled.
The caller does not encode the complete dependency tree. As context arrives, Knowledge determines which policy branches remain relevant and which additional information is required. See Progressive context.
Insertion patterns
Healthcare engagements typically use one of four patterns.
| Pattern | How it works |
|---|---|
| Shadow (typical starting point) | Knowledge evaluates the same cases in parallel with the existing process. Compare outcomes over a defined window before Knowledge holds operational authority. |
| Overlay | Existing decision outcomes become part of the context Knowledge evaluates. Add new governed policies without migrating the underlying platform. |
| Selective routing | Knowledge handles a new decision scope (a new plan, product, benefit or region) while existing flows remain on the current stack. |
| Primary | Knowledge is the decision layer from the start, typical for a new operational service or a greenfield deployment. |
Start with one decision. Encode the applicable policy. Run Knowledge alongside the existing process. Measure what changes.
Make the decision enforceable
For the operational call that follows a Knowledge verdict, Knowledge can issue signed authorization bound to the exact decision the policy produced. The claims API, the preauthorization service, or a governed workflow node verifies the signature and refuses if the operation does not match what the policy authorized.
See Enforcement for the model.
Reconstruct why a case was decided
Every consultation writes a Consultation record that freezes the applicable rule versions, the precedence trace, the overrides in force, and the exact context that resolved. Reviewers, auditors and regulators asking "why was this case allowed / denied / escalated ?" get a business-view of the frozen state at decision time, not an approximation stitched together from logs.
See Auditability for the mechanism.
What Knowledge does not do
Being explicit about the clinical boundary is part of the contract.
Knowledge evaluates the policies your organization has encoded. It does not diagnose patients, recommend treatment, determine medical necessity or replace clinical judgment. It does not execute operational actions ; your organization decides whether a verdict results in automated processing, a request for additional information, escalation, or human review.
Where clinical judgment is required (medical necessity determinations that go beyond encoded policy, disputed cases, unusual presentations), the encoded policy should route the case to the human reviewer with the right qualifications, and Knowledge records that routing decision as it does any other.
Where we are starting
We explore Healthcare with payers and TPAs that operate policy-heavy coverage, claims and approval processes, particularly where existing platforms already run but decision logic requires unnecessary review, depends on incomplete information, or needs to become safely accessible to AI-driven workflows.
The initial focus is on organizations operating complex multi-plan or multi-payer environments.
Start with one decision
Pick one operational decision your teams still resolve manually or where an AI capability could gather the case but the policy determination should remain outside the model. Run it in shadow mode against the current process. Cut over when parity and audit meet your bar.
Discuss your use case · See pricing
Related
| Read next | Why |
|---|---|
| For AI product teams | The team building the AI capability behind operational workflows |
| For compliance officers | The policy-ownership angle : versioning, coverage, approvals |
| Progressive context | The /resolve loop the case-assembly caller navigates |
| Enforcement | Signed verdicts and PEP for the operational boundary |
| Auditability | Consultation record, RuleVersion, precedence trace |