1. Ingest
- X12 837 claims and 835 remittance
- 834 enrollment and eligibility
- Pharmacy claims and encounter data
- Clinical data over FHIR R4 or C-CDA
AI in Healthcare / Payer Analytics
Payer Analytics and AI Development
Health plans hold years of claims, encounter and pharmacy data, yet risk adjustment gaps, missed quality measures and members about to leave still surface too late. Agnotic Technologies builds payer analytics and AI that finds them early, explains every flag, and routes it to the person who decides.
Risk adjustmentStar Ratings and HEDISExplainable modelsHIPAA-ready
Trusted by global innovators
What is payer analytics and AI?
Payer analytics and AI is software that turns a health plan's claims, encounter, pharmacy and clinical data into risk adjustment, quality and utilization insight, with models that explain each flag and people who review it. Agnotic Technologies builds payer analytics and AI for health plans and their vendors, integrated with the plan's data warehouse and care management systems.
The work is mostly data engineering. Member, claim and provider records arrive from many systems, in different formats and at different speeds, and every model downstream is only as good as the member match underneath it.
The AI layer sits on top: models that suggest where a chart may support an undocumented condition, which members are likely to miss a quality measure, and which are showing signs of leaving the plan. Each suggestion carries its evidence, and a person decides what happens next.
Reference architecture
How do you build payer analytics and AI?
Five layers, built in this order. Skipping the lower layers is the most common reason payer models lose the trust of the teams that use them.
2. Member and provider index
- Member matching across enrollment spans
- Provider and NPI reconciliation
- Attribution to plan, product and contract
- Lineage on every merged record
3. Analytic data model
- Condition history by service year
- Measure numerators and denominators
- Utilization by setting and episode
- Versioned code sets and value sets
4. Models and rules
- Suspected condition and HCC gap models
- Measure gap prediction
- Utilization trend and outlier detection
- Disenrollment risk signals
5. Review and action
- Coder and clinician review queues
- Care gap outreach lists
- Dashboards for Stars and finance teams
- Audit log of every decision
Which use cases do health plans start with?
Each one is a module we scope on its own, so a first release can answer one question well before the next is added.
Risk adjustment and HCC gap detection
Flags members whose claims and clinical history suggest a condition not yet documented this service year, with the evidence that points to it, for coder and provider review.
Star Ratings and HEDIS measure tracking
Numerators, denominators and exclusions tracked during the year, so the quality team sees which measures are at risk while there is still time to act.
Care gap outreach
Prioritized lists of members due for screenings, refills or follow-up visits, sent to care managers and outreach vendors with the reason each member is on the list.
Utilization trends
Admissions, emergency visits and high-cost drugs tracked by product, region and provider group, with alerts when a trend moves outside its expected range.
Member churn signals
Signals such as unresolved complaints, network changes and missed first visits combined into a disenrollment risk that retention teams can act on.
Model monitoring
Drift, data quality and reviewer agreement tracked per model, so the plan knows when a model needs retraining before its output gets worse.
Data sources
Which data does payer analytics need?
Most plans already hold this data. The work is reconciling it to one member and one provider.
Claims and payment
- X12 837 professional and institutional claims
- X12 835 remittance
- Pharmacy claims
- Capitation and encounter records
Membership
- X12 834 enrollment
- Eligibility spans and plan changes
- Demographics and language
- Complaints and appeals
Clinical
- FHIR R4 resources from provider APIs
- C-CDA documents
- Lab results
- Chart review outcomes
Quality and experience
- HEDIS supplemental data
- Member survey results
- Care management notes
- Provider directory data
Workflow
From suspected HCC gap to a documented diagnosis
The model suggests. People confirm. Only documentation from the provider who saw the member becomes risk adjustment data.
Model flags a suspected condition
Prior diagnoses, pharmacy fills and lab results suggest a condition that has not been coded this service year.
Evidence is attached
The flag shows the claims, fills and results behind it, so a reviewer can judge it in seconds rather than rebuild it.
Clinical reviewer decides
A coder or clinician accepts the flag for follow-up or dismisses it with a reason, and the reason trains the next model version.
Provider assesses the member
Accepted flags go to the treating provider as a prompt to assess at the next visit. The model never adds a diagnosis.
Documentation and submission
Only conditions the provider documents are submitted, and the chart is kept ready for a risk adjustment data validation audit.
Decision table
How much human review does each model need?
We set the review level per use case before a model is built. Anything that can change payment or a member's care gets a person in the loop.
01
Payment-affecting
Example: Suspected HCC gaps and chart review prioritization
Approach: Every flag is reviewed by a certified coder or clinician; no automated submission; evidence and reviewer decision logged for audit.
02
Care-affecting
Example: Care gap and outreach lists, high-risk member identification
Approach: Care managers review the list and can override priority; outreach wording approved by the clinical team; fairness checks across member groups.
03
Operational
Example: Utilization trend alerts and member churn signals
Approach: Analysts review alerts weekly; thresholds tuned with the business owner; no individual member decision made by the model alone.
04
Descriptive
Example: Stars and HEDIS dashboards, finance reporting
Approach: Reconciled against source systems on a schedule, with data quality checks shown next to every figure.
Failure points
Where do payer models go wrong?
These failures come from the data and the workflow more than the algorithm, so the design has to handle them from the first sprint.
Challenge
Duplicate or split member records
Agnotic approach
A member index with match rules the plan can inspect, and lineage on every merge, so gaps and measures count each member once.
Challenge
Gap flags that coders stop trusting
Agnotic approach
Every flag shows its evidence, and dismissed flags with reasons are fed back so the next version stops repeating the same mistake.
Challenge
Code sets and value sets changing mid-year
Agnotic approach
Code sets and measure value sets are versioned by date, and models are re-checked against each new release before they are used.
Challenge
Claims lag hiding recent care
Agnotic approach
Supplemental clinical data and pharmacy fills fill the gap, and dashboards show how complete each month's data is.
Challenge
Models that work for some members and not others
Agnotic approach
Performance and outreach rates are reported by age, language, region and product, and differences trigger a review.
Challenge
Black-box scores no one can explain
Agnotic approach
Each score lists its top contributing factors in plain language, and model cards record data, limits and owners.
Compliance as engineering
HIPAA compliant payer analytics, by design
Analytics platforms hold more member data than almost any other system at a plan. We treat the controls as product decisions.
BAA and least privilege
Agnotic signs a business associate agreement before any PHI is shared. Analysts, coders and care managers see only the members and fields their role needs.
People decide
Models suggest and prioritize. Payment and care decisions stay with coders, clinicians and care managers, and the product enforces that boundary.
Audit trail
Every flag, view, override and export is logged with user and time, so a compliance review or a RADV audit can replay what happened.
AI risk management
We map, measure and manage model risk using the NIST AI Risk Management Framework, with a model card and monitoring plan for each model.
Compliance-First Healthcare App Development Services Backed by Global Standards
01
HIPAA
Health Insurance Portability and Accountability Act
Protect PHI with privacy-first architecture, encrypted storage and transmission, strict access controls, and traceable audit logs.
02
GDPR
General Data Protection Regulation
Implement lawful consent flows, data minimization, retention controls, and secure processing for sensitive health data.
03
FHIR
Fast Healthcare Interoperability Resources
Enable standardized health data exchange across apps, care teams, and systems through standards-based FHIR APIs.
04
HL7
Health Level Seven International
Support enterprise-grade interoperability with HL7-based integrations for records, events, and clinical messaging workflows.
05
HITRUST
Health Information Trust Alliance
Align security programs to healthcare-specific control and risk management practices trusted by providers and ecosystem partners.
06
HITECH
Health Information Technology for Economic and Clinical Health Act
Design with breach notification readiness, digital record safeguards, and operational controls that support regulated care programs.
07
SaMD
FDA Software as a Medical Device
Plan software quality, traceability, and documentation pathways for products that may require SaMD review and submission.
08
MDR (EU)
Medical Device Regulation (European Union)
Prepare EU market-ready processes for risk classification, evidence tracking, and lifecycle governance under MDR expectations.
09
SAMHSA
Substance Abuse and Mental Health Services Administration
Apply confidentiality controls and consent-aware sharing models for behavioral and mental health data experiences.
Scope drivers
What drives payer analytics and AI cost?
We do not quote a price before scoping. These are the factors that move effort most, and we size each one on the call.
Number of data sources
Each claims system, pharmacy benefit manager, lab feed and provider API adds mapping, validation and monitoring.
Member matching quality
Clean enrollment data keeps the index simple. Many legacy systems and plan mergers mean more matching work up front.
Use cases in the first release
One use case, such as HCC gap detection for one product line, is a far smaller build than gaps, Stars and churn together.
Review workflows
Coder queues, care manager lists and provider prompts each need their own screens, roles and audit trail.
Existing warehouse
Building on a plan's current warehouse is usually faster than a new platform, if its data model can hold history by service year.
Startup or established plan
Startups selling analytics to plans need multi-tenant design from day one. A single plan's internal build does not.
Build or buy
Should you build or buy payer analytics?
Most plans do both: buy where a standard product fits and build where the plan's own data, workflow or product is the difference.
| Your situation | Buy a product | Build with Agnotic |
|---|---|---|
| You need standard Stars and HEDIS reporting | Often the faster path | Build only the parts the product does not cover |
| Your risk adjustment workflow is a differentiator | You adapt to the vendor's workflow | Built around your coders, providers and evidence rules |
| You sell analytics to plans as a startup | Not an option; it is your product | We build the product with you, multi-tenant from the start |
| Your data sits in many legacy systems | Depends on the vendor's connectors | Ingest and matching built for your actual sources |
| You want models your team can inspect and retrain | Depends on vendor access | Your code, your models, your model cards |
Either way, coders, clinicians and care managers stay in the loop, and the plan's compliance program still applies.
Who it is for
Who we build payer analytics for
The same layers, aimed at different teams.
Medicare Advantage plans
Risk adjustment, Star Ratings and RADV readiness on one member index.
Medicaid and commercial plans
Quality measures, care gaps and utilization trends by product and region.
Payer analytics startups
Multi-tenant analytics products sold to plans, with each plan's data kept apart.
Risk-bearing provider groups
Gap and utilization views for groups that share risk with a plan.
Care management vendors
Outreach lists and member risk signals delivered into the vendor's workflow.
Plan data and finance teams
Reconciled reporting that ties analytics back to claims and payments.
How we work
How does an engagement work?
Three stages, with a fixed first release agreed before build starts.
1.
Scoping call
We pick one use case, list the data sources it needs, and agree how success is measured and who reviews the model's output.
One use case
2.
Fixed-scope first release
Ingest, member index, the first model and its review queue, built on de-identified or synthetic data first, then on plan data under a BAA.
Fixed scope
3.
Scale and support
More use cases on the same index, model monitoring, and retraining as code sets and measure specifications change each year.
Monitored
Standards it speaks
Standards in a payer analytics stack
X12 837 / 835X12 834FHIR R4HEDISICD-10-CMNIST AI RMF
Facts and sources
Which rules shape payer analytics and AI?
Short facts we design around, each linked to its primary source.
Each Medicare Advantage organization must submit to CMS the data needed to characterize the context and purpose of each item and service provided to an enrollee, and must obtain that risk adjustment data from the provider or practitioner who furnished the service.
Source: 42 CFR 422.310(b) and (d)(3) (eCFR)Medicare Advantage organizations and their providers must submit a sample of medical records to validate risk adjustment data, and must remit improper payments found in risk adjustment data validation (RADV) audits.
Source: 42 CFR 422.310(e) (eCFR)CMS calculates an overall Star Rating and Part C and Part D summary ratings for each MA-PD contract on a 5-star rating system, assigning stars to each measure at the contract level and weighting them.
Source: 42 CFR 422.162(b) (eCFR)HEDIS, the Healthcare Effectiveness Data and Information Set, is a performance improvement tool maintained by the National Committee for Quality Assurance, which collects HEDIS data from health plans and other health care organizations.
Source: NCQA, HEDISThe NIST AI Risk Management Framework is a voluntary framework that organizes AI risk work into four functions: Govern, Map, Measure and Manage.
Source: NIST AI 100-1
Frequently Asked Questions
Not in the products we build. Risk adjustment data has to come from the provider who furnished the service, so the model only suggests where a chart may support a condition. A coder or clinician reviews each flag, and only conditions the provider documents are submitted.
Fraud detection looks for suspicious claims and providers. Payer analytics looks at members: undocumented conditions, quality measure gaps, utilization trends and churn risk. They share the same claims data, so plans often build them on one data layer.
Yes. We ingest from your claims, enrollment and pharmacy systems, provider FHIR APIs and your current warehouse, and send results to your care management and coding tools. We build to the interfaces you already have.
Yes. Agnotic signs a business associate agreement before any PHI is shared. Early development uses synthetic or de-identified data, and member data stays in your environment or HIPAA-ready infrastructure you approve.
It depends on scope; we agree a fixed first release on the scoping call. One use case on clean data is a smaller first release than several use cases across many legacy systems.
Yes, and we recommend it. A typical first release covers one use case, such as HCC gap detection for one product line, with its data sources, member index, model and review queue.
Each score lists its top contributing factors and the source records behind them, and each model has a model card describing its data, limits and owner. Reviewers can see why a member was flagged before acting.
Engineers use AI tools for drafts and test generation, then review every change. AI tools never see real member data, and every pipeline and model is tested against synthetic data before it touches plan data.