Matt dives into a specific healthcare topic to help those in the industry, and those outside of it, better understand the market drivers causing today’s healthcare challenges.

This investment represents one of the most consequential shifts in modern healthcare infrastructure because AI will no longer sit on the edges of the system. It will increasingly sit inside the mechanisms that determine access, reimbursement, prior authorization, utilization management, and care navigation. The healthcare industry just accelerated toward a future where algorithms influence which patients move forward, which patients wait, and which patients face another denial letter.

That conversation should alarm every patient advocacy leader in America.

UnitedHealth covers tens of millions of Americans. The decisions this company makes ripple across providers, manufacturers, advocacy organizations, and government stakeholders. When an organization operating at that scale embeds AI deeply into operational decision-making, the impact extends far beyond internal workflows. It changes the balance of power inside healthcare.

Nobody at the patient table approved this agenda.

Nobody asked whether patient advocacy had a seat in the room where the models get trained.

Nobody publicly outlined how patients will challenge algorithmic decisions that affect their care.

And perhaps most importantly, very few advocacy leaders appear prepared for the reality that the next major access battle may not happen between a physician and an insurance representative. It may happen between a patient and a machine-learning system optimized for efficiency metrics the patient never sees.

The healthcare industry talks constantly about innovation. Far fewer people ask who benefits from that innovation, who absorbs the risk when it fails, and who gets excluded from the room where those systems get built.

That conversation starts now.

UnitedHealth already faces scrutiny over allegations that algorithmic systems denied rehabilitation care for elderly patients by overriding physician judgment with predictive models. The issue never centered on technology alone. The issue centered on power, accountability, and incentives.

When an algorithm predicts a patient no longer requires care, and that prediction moves faster than the physician’s assessment, the system shifts authority away from clinical judgment and toward operational efficiency. That dynamic fundamentally changes the relationship between patients, providers, and payers.

Now scale that capability across tens of millions of covered lives.

The concern deepens when you look at Optum. UnitedHealth does not simply use AI internally. Through Optum, it also sells analytics and AI infrastructure across the healthcare ecosystem. The same enterprise building the algorithmic infrastructure can also profit from the efficiencies those systems create.

That creates a clean-conflict problem.

When the same organization controls the decision-making framework and benefits financially from the outcomes, patients lose a clean line of defense. Advocacy groups lose visibility into the logic shaping access decisions. Physicians lose leverage against systems optimized for throughput and cost containment.

Healthcare leaders need to stop pretending these incentives operate independently. They do not.

The challenge becomes even more serious when opacity enters the equation. Most patients never understand why an algorithm flagged them for denial risk. Most physicians never receive meaningful transparency into how those systems prioritize outcomes. Most advocacy teams never receive visibility into the variables influencing patient access decisions.

That leaves patients navigating a system where the rules increasingly operate behind the curtain.

Every stakeholder in healthcare optimizes for something.

That matters because algorithms do not create values. They operationalize them.

The patient eventually becomes a variable inside someone else’s efficiency equation.

Meanwhile, the speed asymmetry becomes impossible to ignore. AI-generated denials move at machine speed while human appeals move at human speed. A patient fighting a denial cannot compete with an infrastructure designed to process decisions at scale.

That imbalance changes the nature of advocacy itself.

Traditional advocacy models often focus downstream. Teams respond after denials occur. They escalate difficult cases. They coordinate support letters. They organize patient stories after damage already happened.

That approach no longer matches the speed of the system.

Advocacy teams still relying exclusively on traditional escalation pathways will lose that race every time because the infrastructure itself evolved faster than the response strategy surrounding it.

The organizations performing best in ELAVAY data understand this shift already. The strongest advocacy functions operate upstream of access barriers rather than downstream of denial events.

These organizations consistently:

That distinction will define the next decade of advocacy leadership.

The organizations shaping tomorrow's influence will not simply react to systems. They will shape the systems before deployment

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Most advocacy organizations have never been given a chance to enter the AI governance conversation.

They manage grants. They coordinate advisory boards. They prepare patient testimony. They support awareness campaigns and community engagement initiatives. All of that work matters deeply.

But very little of it meaningfully engages the algorithmic layer now mediating patient access across the healthcare system.

That gap creates a dangerous blind spot.

Every advocacy leader should answer one question immediately:

“How does AI affect our patient population’s access to care?”

Most teams cannot answer it clearly because most advocacy structures never evolved to engage technical governance, payer analytics, predictive modeling, or AI accountability frameworks.

That means they already sit behind the curve.

The healthcare industry entered a new operating environment where algorithms increasingly influence:

Yet many advocacy organizations still position themselves primarily as disease state awareness and education rather than strategic operating partners.

This is where pharmaceutical and biotech patient advocacy functions can step in and assist patient advocacy organizations and the patient communities they serve. Without PAOs at the table, that disconnect creates long-term credibility risk.

The advocacy function that can assist PAOs engage algorithmic decision-making will eventually maximize its relevance with the very patient communities it exists to support. Patients will not care whether an advocacy team hosted another listening session if opaque predictive systems continue shaping access decisions without accountability.

Patients increasingly want advocates who can navigate systems of power, not simply document patient frustration after harm occurs.

Healthcare moved into an era where operational influence matters as much as awareness.

Advocacy must evolve with it.

The strongest advocacy organizations already shifted their posture.

Top-performing organizations in the ELAVAY benchmark integrate advocacy intelligence into commercial strategy, policy strategy, and technology strategy. They do not isolate advocacy inside communications departments or community engagement silos.

These organizations actively participate in conversations shaping operational infrastructure.

They attend AI governance meetings and encourage partners in PAOs and Professional Societies to do the same.

They build relationships with their own data science teams and advise PAOs on what is coming and what has already happened behind their back.

They engage FDA and CMS stakeholders around algorithmic accountability and transparency.

They collaborate cross-functionally with market access, legal, HEOR, policy, and technology teams.

Most importantly, they ask harder questions before systems launch.

Questions like:

Organizations performing well in the ELAVAY Advocacy Intelligence Report consistently demonstrate greater capability navigating payer-side algorithmic friction because they engage the system before denial patterns emerge.

That is the future of advocacy.

The next generation of advocacy leadership will require operational fluency alongside emotional intelligence. Advocacy professionals will need to understand data governance, reimbursement algorithms, predictive modeling, utilization management systems, and patient-impact measurement as core competencies.

The teams that build those capabilities now will gain influence.

The teams that ignore this shift will struggle to keep pace with systems moving faster than their organizational structures allow.

UnitedHealth’s $3 billion AI investment signals something much larger than technology adoption. It signals a transfer of influence. The organizations shaping these systems now will shape patient access for the next generation.

Audit your advocacy function’s engagement with AI governance immediately. Not in six months.

Not after the next wave of prior authorization denials. Right now.

If your team cannot map how UnitedHealth’s AI investment affects the patients you serve, that is your answer.