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UnitedHealthcare · Ethical AI · 2025

The industry built AI to process millions of claims. It never built accountability for what they decided.

A strategic proposal on one reframe: making AI decisions legible is not the same as making them accountable. Every structural choice follows from that.

My role
Strategy, financial modeling, AI architecture, vendor governance
Team
3-person graduate project, Pratt Design Management
Scope
8.5-month Florida pilot, $2.5M modeled
Focus
Structural accountability for AI claim decisions
01 / Context

Three events in 2024 made the status quo untenable, and opened a window to lead, not react.

None of it was a messaging problem. Standing still had quietly become the expensive option.

$0M
annual fraud exposure in Florida, the hardest market to prove accountability in
Source · CMS / state fraud reporting
0%
of patients would prefer AI-assisted billing clarification
Source · KFF consumer survey
0.0mo
to build, deploy, and show measurable signal
Scope · modeled pilot
What forced the window
DOJ Investigation·2024
AI denials under federal scrutiny

AI auto-denied post-acute claims in 1 to 2 days, regardless of medical necessity.

Leadership exit·2024
A CEO departs under pressure

The window opened to respond structurally, not defensively.

KFF Research·2023
A systemic deficit

72% of Medicare seniors can't understand their denial letters.

02 / Market Gap

Everyone optimized how the decision feels. Nobody built the layer that makes it auditable.

The whole category competed on the same surface. The accountability layer underneath was open ground.

The evidence

Aetna, Cigna, and Humana each invested in the front-end experience of a denial: clearer letters, friendlier portals, better scripts. None built the internal layer where a decision can actually be examined.

Why it holds

That layer is organizational and legal, not a feature. It survives cost-cutting because it is written into how the company is accountable to itself, which is why a competitor can’t close it in a quarter.

UnitedHealthcare’s opening: patient transparency and internal compliance on one data layer. The first to build the layer underneath.

03 / The Reframe

Making a denial letter clearer does not make the system more accountable.

What the patient experienced

Legibility

A denial letter written at a 12th-grade reading level
A 1-800 line staffed by agents who saw the same denial
A 72% appeal-denial rate, with no visibility into why
What accountability requires

Accountability

Patients see why a decision was made, not only that it was
Compliance catches anomalies in real time, not at the annual audit
Governance lives in contracts, not guidelines that erode
The principle this is built on

When AI decides whether to cover your MRI, accountability cannot be designed into the interface. It has to be built into the governance structure: the architecture, the org design, and the legal agreements that hold when cost pressure arrives.

04 / The Decisions

Four decisions, one platform built to be audited.

One platform, two surfaces, one shared data layer, so the regulator can audit the exact decision the patient sees explained. Choosing dual-sided over patient-facing only is what gave the work something to be accountable to.
Fraud detection compliance dashboard, Florida pilot, with claim-level risk scores and a regional risk heatmap
The compliance surface · fraud detection on the shared data layer, Florida pilot
01Platform architecture

Dual-sidedPatient chatbot and compliance dashboard on one shared data layer.

Patient-facing only Leaves the regulator no way to audit the decision.

Patient assistant chatbot explaining an ER bill and why a physical therapy session was only partially covered
02Pilot market

Florida21% Medicare, bilingual from day one.

A softer market Only proves the system works when nothing is at stake.

$0Mfraud exposure, the hardest market to prove it in
03Infrastructure

Palantir FoundryHIPAA-native, CDC and UK NHS trusted, modular.

Building in-house Costs 18 to 24 months UHC does not have under scrutiny.

18-24mosaved versus building in-house
04Where governance lives

In the contractFour enforceable legal mechanisms, not product guidelines.

Product guidelines Get deferred the first cost-cutting quarter. Contracts don't.

4enforceable mechanisms, not guidelines
05 / Trade-offs

What we consciously gave up.

01

Dual-sided over patient-only

Gave up

A validated single-team playbook.

Got

The only position a regulator can't dismiss as a comms fix.

02

Florida over a softer market

Gave up

The safety of a clean success story.

Got

A real signal: hold here, hold anywhere.

03

Palantir over in-house

Gave up

Full ownership and lower licensing cost.

Got

18 to 24 months faster, with CDC and NHS credibility.

04

Governance in the contract

Gave up

Vendor flexibility and a standard timeline.

Got

Accountability that survives a budget cut.

05

Embedded chatbot

Gave up

A clean UX free of the legacy portal.

Got

Adoption with no new app to download.

The honest limit

Real implementation starts with the organizational design question, before the technology. This proposal does not go there.

The barrier between patient experience and compliance at UHC’s scale is structural and regulatory, not a communications problem. A stakeholder liaison and sprint demos are not enough, and saying so is part of the work.

06 / Financial Model

The AI was not the risk. The behavior was.

The $4.5M swing between best and worst case is not the AI, the infrastructure, or the contract. It is whether Medicare seniors change how they ask for help.

Net gainNet loss
Modeled net outcome at 8.5 months, by senior adoption
What the model actually plans for
0%

chatbot adoption, the rate the model actually plans for, between the 15% that loses and the 65% that pays. The whole swing turns on this one behavior.

Source · ROI model, realistic adoption scenario
Worst case · 15% adoption0.0%ROI
+Call savings$48K
+Fraud recovery$195K
Investment (8.5-mo build & run)$2.50M
=Net loss-$2.26M
Best case · 65% adoption+0.0%ROI
+Call savings$3.33M
+Fraud recovery$1.46M
Investment (8.5-mo build & run)$2.50M
=Net gain+$2.29M
The build, to first measurable signal8.5 months
01Mo 0-1
Setup and governance
Oversight board live
02Mo 1-3
Compliance and legal
Four contract mechanisms
03Mo 3-5
Pilot activation
Florida cohort live
04Mo 5-6
Public rollout
Full Florida market
05Mo 6-7.5
Feedback and audit
First ethics audit
06Mo 7.5-8.5
Reporting
Results to regulator
Mo 8.5
First measurable signal
The payoff
07 / What I’d Measure

Did accountability shift, or only the interface?

Projected targets, not outcomes, from the financial model and CMS standards. What the system was designed to reach, and what I’d be held to.

The floor · non-negotiable
0%

CMS reporting accuracy

Any error recreates the exposure. Everything else is a target; this is the line that cannot move.

Five targets I’d be held to · two consecutive misses force a renegotiation
0%AI fraud detection accuracyContractual
0%Inbound billing inquiriesFinancial
+0%Florida NPSTrust
0%+Chatbot satisfactionAdoption
<0hTime to resolutionService
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