Skip to content

Chapter 6 Quiz: Payer Perspective and Insurance

Test your understanding of insurance payer modeling and claims processing concepts.

  1. What is the primary advantage of using graph databases to model insurance claims and coverage relationships?

    1. To reduce claim processing time
    2. To represent complex relationships between members, policies, providers, and claims
    3. To eliminate claim denials
    4. To automate premium calculations
    Show Answer

    Answer: B - To represent complex relationships between members, policies, providers, and claims

    Graph databases excel at representing the complex, interconnected relationships in insurance systems: members belong to policies, policies have coverage rules, claims link members to providers and services, claims may have multiple line items, and authorization requirements connect diagnosis-procedure combinations to coverage policies. These multi-dimensional relationships are naturally expressed in graphs, enabling queries like "find all claims for a member under a specific policy that required prior authorization." The graph model makes these complex relationships explicit and queryable, whereas relational models require numerous join tables and complex SQL to express the same concepts.

    See: Insurance Data Modeling

  2. In a payer-centric graph model, which relationship would connect a Claim node to a Provider node?

    1. COVERED_BY
    2. SUBMITTED_BY
    3. ENROLLED_IN
    4. AUTHORIZED_FOR
    Show Answer

    Answer: B - SUBMITTED_BY

    The SUBMITTED_BY relationship connects a Claim node to the Provider node representing the healthcare provider who submitted the claim for payment. This relationship captures the billing relationship and would include properties such as submission date, rendering provider NPI, billing provider tax ID, and claim submission method. COVERED_BY typically connects members to insurance policies, ENROLLED_IN connects members to health plans, and AUTHORIZED_FOR might connect authorization requests to procedures or services. The SUBMITTED_BY relationship is essential for analyzing provider billing patterns and claim submission behavior.

    See: Claims Relationships

  3. What property would be MOST important on a COVERS relationship between a Policy node and a Service node?

    1. Policy premium amount
    2. Coverage limits, copay, and deductible information
    3. Policy effective date
    4. Member demographics
    Show Answer

    Answer: B - Coverage limits, copay, and deductible information

    Coverage limits, copay amounts, and deductible information are the most important properties on a COVERS relationship because they define the specific financial terms under which a service is covered by the policy. These properties determine how much the payer will reimburse for the service versus the member's cost-sharing responsibility. This information is essential for claims adjudication, member cost estimation, and benefit verification. Policy premium and effective dates are typically properties of the Policy node itself, while member demographics are properties of the Member node rather than the coverage relationship.

    See: Coverage Modeling

  4. How would graph traversal help identify members who are at risk of exceeding their out-of-pocket maximum?

    1. By counting total policy enrollments
    2. By summing claim amounts along HAS_CLAIM relationships and comparing to policy OOP max
    3. By listing all member demographics
    4. By calculating average claim amounts across all members
    Show Answer

    Answer: B - By summing claim amounts along HAS_CLAIM relationships and comparing to policy OOP max

    Graph traversal can identify members approaching their out-of-pocket maximum by following the path: Member → HAS_CLAIM → Claim, aggregating the member_responsibility amounts from each claim, and comparing the total to the out_of_pocket_max property on the member's Policy node. This analysis combines relationship traversal with property aggregation to track year-to-date cost accumulation. The graph can also traverse to upcoming scheduled procedures to predict when members might hit their OOP maximum, enabling proactive member communications about benefit transitions. This type of integrated analysis across multiple entities is natural in graph databases.

    See: Benefits Analytics

  5. In modeling prior authorization workflows, what is the best approach for representing authorization requests and approvals?

    1. Store all authorizations as properties on the Member node
    2. Create Authorization nodes connected to members, procedures, and claims with status properties
    3. Use a separate relational database for authorization tracking
    4. Embed authorization data as JSON in claim records
    Show Answer

    Answer: B - Create Authorization nodes connected to members, procedures, and claims with status properties

    Creating Authorization nodes as first-class entities in the graph enables comprehensive tracking and analysis of the authorization lifecycle. An Authorization node connects to the Member requesting service, the Procedure or Service requiring authorization, the Provider submitting the request, and eventually to the Claim that results from the authorized service. Properties on the Authorization node track request date, approval/denial status, authorized quantity, authorization period, and denial reasons. This approach enables queries like "find all denied authorizations for a specific procedure" or "identify claims submitted without required prior authorization." Authorization nodes provide the flexibility to model the complete authorization workflow.

    See: Prior Authorization Modeling

  6. Which graph algorithm would be MOST useful for detecting coordinated billing fraud schemes across multiple providers?

    1. Shortest path
    2. PageRank
    3. Community detection
    4. Degree centrality
    Show Answer

    Answer: C - Community detection

    Community detection algorithms identify clusters of providers, members, and claims that are more densely connected to each other than to the rest of the network, which is a common pattern in coordinated fraud schemes. For example, a fraud ring might involve multiple providers billing for the same phantom patients, creating a densely connected subgraph. Community detection can reveal these suspicious clusters even when individual claim patterns might appear normal. The algorithm identifies structural patterns that suggest collusion or coordination, such as providers sharing patient populations in unusual ways or claim patterns that suggest kickback arrangements or upcoding schemes.

    See: Fraud Detection Algorithms

  7. Analyze this modeling decision: Representing formulary drug tiers as separate nodes (Tier1, Tier2, Tier3) versus as properties on medication nodes. Which is preferable?

    1. Separate tier nodes - enables flexible tier rules and member-specific formularies
    2. Properties on medication nodes - simpler and more efficient
    3. Both approaches are equally effective
    4. Neither - use external reference tables
    Show Answer

    Answer: A - Separate tier nodes - enables flexible tier rules and member-specific formularies

    Modeling formulary tiers as separate nodes provides flexibility to represent complex formulary structures where tier assignments vary by policy, medical condition, or member attributes. A Medication node can have different ASSIGNED_TO_TIER relationships for different formularies, and tier nodes can have their own properties defining copay amounts, prior authorization requirements, and quantity limits. This structure supports queries like "find all medications in tier 1 for this member's policy that treat diabetes." While storing tiers as medication properties seems simpler, it cannot accommodate the reality that the same medication may be in different tiers for different insurance plans or member segments.

    See: Formulary Modeling

  8. How can graph analysis help payers identify high-cost members who would benefit from care management programs?

    1. By counting the total number of members
    2. By traversing member claim patterns to find complex chronic conditions and high utilization
    3. By listing member demographics only
    4. By calculating average premium payments
    Show Answer

    Answer: B - By traversing member claim patterns to find complex chronic conditions and high utilization

    Graph analysis identifies candidates for care management by traversing from Member nodes through their HAS_DIAGNOSIS relationships to find multiple chronic conditions (diabetes, heart failure, COPD), then analyzing claim patterns for indicators like frequent ER visits, hospital readmissions, multiple specialist relationships without care coordination, and polypharmacy (many concurrent medications). The graph can calculate risk scores based on the complexity of the member's clinical network - members with many diagnoses, providers, and high-cost services form dense, expensive subgraphs that indicate need for care coordination. This multi-dimensional pattern analysis is more sophisticated than simple cost ranking.

    See: Member Risk Stratification

  9. What is the advantage of modeling provider networks and network adequacy requirements in the same graph as member coverage?

    1. It reduces database storage costs
    2. It enables real-time analysis of whether members have access to in-network providers for their conditions
    3. It eliminates the need for provider credentialing
    4. It automatically approves all prior authorization requests
    Show Answer

    Answer: B - It enables real-time analysis of whether members have access to in-network providers for their conditions

    Integrating provider network relationships with member coverage in the same graph enables sophisticated network adequacy analysis. The graph can answer questions like: "Do members with diabetes in ZIP code 12345 have access to in-network endocrinologists within 30 miles?" This requires traversing from Member nodes with diabetes diagnoses to their Policy, checking the policy's IN_NETWORK provider relationships filtered by specialty and geographic proximity. Real-time network adequacy analysis supports regulatory compliance reporting, identifies network gaps that require recruitment, and helps members find appropriate in-network providers. This integrated analysis is difficult in systems where network, coverage, and member data are siloed.

    See: Network Adequacy Analysis

  10. Evaluate this approach: Modeling explanation of benefits (EOB) as a separate document node versus including EOB data as properties on claim nodes. What are the trade-offs?

    1. Separate EOB nodes allow versioning and complex member communications tracking
    2. EOB properties on claims are simpler but less flexible
    3. Separate EOB nodes enable tracking member viewing and understanding of benefits
    4. All of the above
    Show Answer

    Answer: D - All of the above

    Modeling EOB as separate nodes provides important advantages despite increased complexity. EOB nodes can have properties tracking when the EOB was generated, mailed, viewed online, and whether the member called with questions - valuable data for member engagement analytics. EOB nodes can also maintain versioning history when claims are reprocessed, connecting to Claim nodes through EXPLAINS relationships with effective dates. Additionally, EOB nodes can link to member actions like DISPUTED_BY or PAYMENT_MADE relationships that track the member's response to the benefits explanation. While including EOB data as claim properties is simpler for basic scenarios, separate EOB nodes provide the flexibility needed for comprehensive member communications and dispute management workflows.

    See: Claims Communication Modeling