Contract & Benefit Optimization

Improve your rules.
Based on what actually happened.

Audits explain what went wrong. Monitoring prevents repeat errors. Optimization uses verified historical data to improve how benefit and contract logic is written going forward.

Rule Analysis
Eligibility Rule A
12,450 triggersActive
Pricing Rule B
8,920 triggersOver-broad
Threshold Rule C
4,210 triggersActive
Modifier Rule D
0 triggersUnused
Override Rule E
2,890 triggersActive
Optimization opportunity
2
Rules to refine
$420K
Potential savings
Based on 18 months of verified dataFull simulation available
Evidence-based
Verified
Real claims data

Rules are rarely designed with real usage in mind.

When rules are written
Real-world usage
Unknown
Edge cases
Theoretical
Contract pressure
High

Logic gets added but rarely removed. Thresholds drift from their original intent.

What accumulates over time
SPD exclusion codes
Never updated after plan change
Prior auth rule
Still auto-approving 52 days later
Benchmark methodology
Stale trend factor, outdated cohort

These patterns are invisible in aggregate reporting.

Optimization requires more than intuition. it requires verified evidence.

Why optimization is hard without verified data.

Teams have experience, data, and tools. But optimization decisions still rely on incomplete information about how rules actually behave.

1
Manual reviews
Rule coverage
Reviewed (3)
Not covered (21)

What it does

Expert judgment on select rules

The gap

Can't cover hundreds of rule interactions

2
Analytics
Spend analysis
What happened
Why?Unknown

What it does

Spend forecasts and trend reports

The gap

Shows outcomes, not why rules fired

3
Rule changes
Rule change impact
threshold: $50 → $75
Expected: 12% cost reduction
Surprise: Rule B fires 3x more
Surprise: Edge case now $180K

What it does

Threshold adjustments on paper

The gap

Unintended consequences appear at scale

Optimization works best when you know exactly how rules behave. not just what outcomes look like.

From contract terms to bottom-line impact.

We tie every clause in your contracts to real historical usage, so you can see exactly what drives cost, test changes safely, and optimize for the outcomes you want.

Map clauses to impact

See exactly which contract terms drive cost. Every clause is tied to historical claims and quantified.

Every clause quantified
18 months of data
Clause Impact Map
High
Medium
Low
Unused
§3.1Rate Escalator (Hospital)
12,450 claims$412K
§7.3Carve-out Threshold (Hospital)
4,210 claims$189K
Exh BNetwork Discount (Self-Funded)
8,920 claims$267K
§4.1Stop-loss Specific (Self-Funded)
2,890 claims$175K
App AAttribution Logic (VBC)
1,240 claims$95K
§9.2Volume Bonus (Hospital)
0 claims$0
Clauses analyzed across personas$1.1M total impact identified

Test changes before deploying

Run what-if scenarios to see how specific changes would have affected outcomes, including side effects.

Side effect detection
Safe simulation
What-If Scenario
Simulating
Change being tested
§3.1 Rate Escalator:3% annualCPI-U linked
Claims affected
12,450
650 CPT codes
Cost delta
-$127K
vs fixed escalator
Side effects
1
Clause affected
Downstream effects detected
§7.3 Carve-out: threshold shifts relative to new base rate

Optimize for your goals

Define your constraints and objectives. The solver finds the optimal configuration across all your rules.

Custom constraints
Optimal solutions
Optimization Solver
Solution found
Objective: Minimize total cost
Subject to: No network disruption, Attribution accuracy maintained
No network disruption (self-funded)Satisfied
Attribution accuracy maintained (VBC)Satisfied
Escalator terms enforceable (hospital)Satisfied
Optimal configuration found
Recommended changes
• §3.1: CPI-U linked escalator
• §4.1: Raise stop-loss specific
• §9.2: Deprecate volume bonus
Projected savings
$340K
per year
Deterministic
Same input → same output
Evidence-backed
No guesswork or projections
Quantified
Every trade-off made explicit

The hidden costs inside your logic.

Most contract and benefit logic accumulates inefficiencies over time. These patterns show up clearly when you analyze verified rule behavior.

Rule Usage Analysis
Eligibility Rules42 rules
Pricing Logic28 rules
Threshold Conditions15 rules
Active & appropriate
Over-broad (fires too often)
Unused (never triggers)
85
Total rules
19
Need review
Quantified
Each opportunity
Rules that fire too oftenClauses that never triggerEdge cases with outsized impactInteractions that create surprises

What you get from optimization.

Every recommendation is backed by data that has already been verified against the rules.

Scenario Comparison
Current logic
Rule triggers
8,420
Cost impact
$1.8M
Edge cases
312
Optimized logic
Rule triggers
5,210
Cost impact
$1.4M
Edge cases
47
$400K saved with 85% fewer edge cases
Based on 18 months of verified claims
Ranked optimization opportunitiesEvidence tied to verified outcomesSide-by-side scenario comparisonsClear explanation of trade-offs

The decisions you're already making.
Now with verified data.

These moments happen every year. Walk in with evidence, not assumptions.

Escalator drift
$412K
Fee schedule mismatch
$267K
Volume bonus
unused
Documented leverage$868K
Contract Renewal

Enter negotiations with documented evidence

Hospital going into renewal with documented escalator drift, fee schedule errors, and unused clauses as concrete leverage.

Simulating
Stop-loss: $175K to $200K specific
Current premium
$142/mo
Projected
$118/mo
3 high-cost claimants would exceed
Benefit Redesign

Model changes against real claims

Self-funded employer modeling new stop-loss thresholds and Rx formulary changes against historical claims data.

Settlement gap
$1.2M vs payer
Attribution312 patients excluded
Benchmarkstale trend factor
Root cause documented
Post-Audit Action

Trace settlement gaps to root cause

VBC organization tracing a settlement gap to stale benchmark methodology and attribution errors, with 60-day dispute window.

Prior auth tighten$180K
Modifier rule update$140K
Policy sync cadence$95K
Addressable opportunity$415K
Strategic Planning

Test policy changes before they go live

Payer testing prior auth tightening and modifier rule updates against historical claims to predict downstream effects.

One question.
Verified answer.

Pick a contract term or benefit rule. We'll show you exactly how it performed, and what would change if you adjusted it.

Days to first insight, not months
No system integration required
Every recommendation tied to verified data
Start with one rule

See how this applies to you

optimization query
Analyzing
§3.1 Rate Escalator Structure
Claims analyzed12,450
CPT codes affected650
Optimization opportunity$127K identified
CPI-U linked escalator recommended