The US hospital AI market has moved beyond the question:

“Will health systems adopt AI?”

Recent AHA data show that the share of hospitals using predictive AI integrated with their EHR increased from 66% in 2023 to 71% in 2024. KLAS also finds that large acute-care organizations are the most active purchasers of AI, with ambient documentation currently among the most adopted use cases and future investment increasingly focused on revenue cycle, patient engagement and other operational workflows.

But that does not mean every AI category has the same path into a US integrated delivery network.

In fact, the financial environment makes differentiation more important.

US hospitals saw total expenses rise 7.5% in 2025, more than twice the rate of hospital-price growth. Roughly 60% of expenses were workforce-related, workforce costs increased another 5.6%, and hospitals spent approximately $43 billion trying to collect payment from insurers for care already delivered.

Hospital margins remain thin as well. Kaufman Hall reported a 1.7% adjusted year-to-date operating margin through March 2026, while its August update said hospital performance remained under pressure from uncompensated care, payer-mix erosion, drug costs and supply inflation.

So the commercial question has changed.

It is no longer:

Does the IDN like AI?

It is:

Which KPI does this AI move, who owns that KPI, how difficult is it to deploy, and can the economic value survive procurement scrutiny?

That is the logic behind my US IDN AI Procurement Matrix.

Why an IDN is different from “selling to a hospital”

Selling AI into an integrated delivery network is not simply selling software to a large hospital.

A major IDN can contain:

acute-care hospitals, ambulatory sites, employed physician groups, specialty networks, imaging, labs, post-acute assets, population-health teams, payer relationships and centralized IT/procurement.

That creates both opportunity and complexity.

A physician can love a product.

A department head can support it.

A CIO can still stop it.

A CISO can delay it.

Procurement can require another review.

Finance can conclude that the benefits do not justify another enterprise vendor.

And increasingly, the existing EHR vendor may offer something “good enough.”

That last factor is becoming more important.

HFMA reported in July 2026 that health systems are increasingly rationalizing their AI vendor portfolios, including questioning whether specialized point solutions create enough incremental ROI to justify remaining separate from expanding Epic and other platform capabilities.

That means startups are competing against more than other startups.

They are competing against:

doing nothing + existing workflow + incumbent platform functionality.

The procurement framework I would use

For IDN AI, I would evaluate every commercial opportunity through seven gates:

KPI OWNER → WORKFLOW → INTEGRATION → GOVERNANCE → ROI → PROCUREMENT → REPLICATION

If one of those is missing, the pilot-to-contract pathway can stall.

Gate 1: KPI owner

Every AI use case should have an identifiable executive whose metric changes.

Not simply:

“Doctors will like this.”

But:

“This improves a metric the CMIO, COO, CFO, CIO, CISO or service-line executive is already accountable for.”

That is why the executive-owner column in the matrix matters.

Gate 2: workflow

Where exactly does the AI enter?

Before the encounter?

During documentation?

Inside the PACS?

Between discharge planning and bed assignment?

During coding?

At prior-auth submission?

After a remote-monitoring alert?

An AI product that creates another independent dashboard can have substantially weaker enterprise economics than one that fits inside the workflow staff already use.

KLAS repeatedly identifies workflow integration as a critical factor in scaling healthcare AI, including ambient AI and imaging AI. Imaging leaders specifically say AI needs to integrate tightly with PACS or other systems rather than becoming another application clinicians must open.

Gate 3: integration

A startup needs to know whether its deployment requires:

Epic

Oracle Health

MEDITECH

PACS

RCM systems

HL7/FHIR APIs

payer integrations

identity

device integrations

data warehouse

or bespoke interfaces.

The question for an investor is not simply:

“Can it integrate?”

It is:

“How much integration work is reusable at customer #2?”

That determines whether deployment economics improve with scale.

Gate 4: governance

Clinical AI has another buying committee that many founders underestimate.

Potential reviewers include:

clinical AI governance

information security

privacy

legal

clinical safety

model-risk management

IT architecture

and:

third-party risk.

This is becoming harder rather than easier.

A 2026 KLAS report notes that 74% of healthcare organizations in an earlier KLAS/EY study said they had been affected by a third-party breach during the preceding 24 months. Healthcare organizations are therefore putting more emphasis on continuous third-party risk management as AI expands across the vendor environment.

A startup with excellent AI and weak enterprise-risk documentation can lose the procurement race to an inferior product that is easier to approve.

Gate 5: ROI

The strongest AI categories tend to have one advantage:

The buyer can see where the money or capacity moves.

That is why operational and administrative AI can often move faster than higher-stakes clinical AI.

KLAS' late-2025 update found healthcare organizations concentrating most heavily on well-defined, lower-risk workflows such as ambient speech, coding support and administrative automation, while being more cautious with higher-stakes clinical applications.

That pattern closely matches the matrix.

Gate 6: procurement

One question should be answered before a pilot starts:

What happens if this pilot succeeds?

Possible answers include:

enterprise agreement

departmental purchase

capital committee

security review

central IT decision

competitive RFP

system-wide contracting

or:

we will figure that out later.

The final answer is dangerous.

A technically successful pilot with no predefined route to budget can become a long unpaid reference project.

Gate 7: replication

IDN #1 should produce reusable commercial assets.

Ideally:

integration assets

security documentation

governance evidence

clinical evidence

economic evidence

implementation playbook

reference story

and:

stakeholder learning.

If IDN #2 requires exactly the same amount of founder attention, integration engineering and validation as IDN #1, the company may still have a services-heavy deployment model.

The 8 AI categories in the procurement matrix

1. Ambient documentation

Matrix position: Very High commercial priority

Likely executive owner: CMIO / Physician Operations

Integration burden: Medium

ROI visibility: Very High

Ambient documentation currently has one of the clearest AI procurement stories.

KLAS calls ambient speech the top AI use case, and its 2026 enterprise case studies show that major systems are already progressing beyond small pilots.

Examples include Cleveland Clinic scaling Ambience Healthcare, University of Iowa Health Care rolling out Nabla, University of Vermont Health deploying Abridge, and Legacy Health expanding Microsoft DAX Copilot.

KLAS' UVM Health case reported physician burnout falling from 69% to 24% after its Abridge deployment, alongside improvements in satisfaction, productivity and note quality.

Representative active vendors

Abridge

Microsoft Dragon Copilot / DAX

Ambience Healthcare

Nabla

Suki

DeepScribe

KLAS' market analysis covers those vendors and notes that buyers compare not only output quality but EHR integration, affordability, provider experience and scalability.

What wins procurement

Not:

“Our AI generates better notes.”

A stronger buyer case is:

minutes saved per encounter × encounters × clinician adoption × loaded clinician cost

plus:

reduced after-hours documentation

improved provider experience

documentation completeness

and potentially:

revenue integrity.

Biggest risk

The incumbent platform becomes “good enough.”

A standalone vendor therefore needs to prove enough incremental value to justify another contract.

2. Patient flow / throughput AI

Matrix position: High

Executive owner: COO / Throughput leadership

ROI visibility: High

This is another attractive category because the KPI is operational.

Health systems already know what poor flow costs.

Typical measures include:

ED boarding

length of stay

staffed-bed capacity

transfer declines

discharge timing

OR utilization

and:

patients leaving without being seen.

Representative active vendors

LeanTaaS

Qventus

TeleTracking

LeanTaaS says its inpatient-flow platform is used across 100+ hospitals and 30+ health systems and reports outcomes such as reduced boarding, increased admissions and approximately $10K per bed per year in ROI in its customer base. Those figures are vendor-reported, so they should be treated as case evidence rather than universal benchmarks.

Qventus similarly positions AI as an EHR-embedded operational layer. In a 2026 Erlanger case, Qventus reported the system was on track for 5× annualized ROI after increasing surgical capacity; again, this is vendor-reported customer evidence rather than an industry-wide result.

Why I rank it highly

The COO can understand:

“We created X more staffed-bed days without building another bed.”

That is much easier to buy than:

“Our model is 4% more accurate.”

3. RCM / coding AI

Matrix position: Very High

Executive owner: CFO / Revenue Cycle

ROI visibility: Very High

This may be one of the strongest enterprise-AI categories in the US.

The macro pain is enormous.

AHA estimates hospitals spent $43 billion in 2025 simply trying to collect insurer payments for care already delivered.

KLAS also reports that provider investment priorities shifted back toward revenue cycle management in 2025, with organizations viewing generative AI as a practical route to documentation, coding and margin improvement.

Representative players

CodaMetrix

SmarterDx

AKASA

Microsoft Nuance CDI

Iodine Software

Waystar

KLAS named CodaMetrix Autonomous Coding its 2026 Best in KLAS autonomous-coding solution and continues to research AKASA and AI-driven CDI platforms.

A particularly interesting 2026 KLAS ROI-validation case involved SmarterDx and FMOL Health. KLAS reports that the implementation expanded documentation review and was associated with an estimated $30 million increase in net revenue, along with early quality improvements.

That should get founders' attention.

Why procurement can move faster

The CFO does not have to translate “AI accuracy” into business value.

The relevant metrics already exist:

denial rate

coding cost

net revenue

documentation completeness

DRG capture

days in A/R

time-to-cash

and:

manual chart-review capacity.

4. Cybersecurity AI

Matrix urgency: Very High

Executive owner: CIO / CISO

Commercial priority: High

Procurement complexity: High

Cybersecurity is different from most categories.

The IDN may urgently need the capability while still taking longer to buy it because the solution itself becomes part of the security architecture.

Healthcare organizations are now asking IoT-security vendors for more than visibility. KLAS reports rising emphasis on actionability, automated remediation, workflow integration and measurable risk reduction.

Representative players

Claroty

Asimily

Armis

Axonius / Cynerio

ORDR

Palo Alto Networks

KLAS' 2026 analysis specifically highlights Claroty and Asimily for healthcare market energy and notes measurable-risk-reduction use cases across Claroty, Armis and others.

The ROI mistake

Cyber ROI should not be framed as:

“This saves $500K.”

It is more defensibly modeled through:

risk exposure reduced

devices discovered

remediation time

attack surface

downtime avoided

manual security effort

and:

clinical-continuity protection.

The CISO owns the risk.

The CFO still wants to understand the economics of that risk.

5. Radiology AI

Matrix priority: Medium

Executive owner: Radiology Chair / CIO

Integration burden: High

Procurement complexity: High

Radiology is one of the most mature AI clinical categories.

It is also increasingly crowded.

The FDA's AI-enabled medical-device database continues to add large numbers of radiology devices, including numerous radiology approvals in June 2026 alone.

KLAS' imaging-AI research identifies RapidAI and Viz.ai as the most adopted among its respondents, followed by Aidoc and Rad AI, while Aidoc and Nuance were among the more frequently considered platform approaches.

Representative players

RapidAI

Viz.ai

Aidoc

Rad AI

Nuance

plus a very large ecosystem of FDA-authorized point algorithms.

Why the commercial score is lower

The technical evidence bar is higher.

The buyer may need:

FDA status

local validation

PACS integration

workflow evidence

false-positive management

radiologist adoption

governance

and:

service-line economics.

KLAS' enterprise-imaging work notes that health systems increasingly want AI to be embedded in PACS/workflows and are frustrated by fragmented point solutions.

So FDA clearance is important.

It is not a procurement strategy.

6. Clinical decision support

Matrix priority: Medium

Executive owner: CMIO / Service Line

Integration burden: High

Procurement complexity: High

This category has enormous upside but a tougher enterprise path.

The higher the clinical consequence of an AI recommendation, the greater the questions around:

evidence

generalizability

bias

human oversight

liability

workflow

and:

model monitoring.

A 2026 review of AI-enabled clinical decision support found that real-world implementations can improve diagnostic precision and workflow efficiency, but evidence gaps remain around longitudinal outcomes, cross-institution generalizability and equitable performance.

Representative players / platforms

Bunkerhill Health

Evidently

Atropos Health

plus EHR-native and service-line-specific AI capabilities.

UTMB Health, for example, is using Bunkerhill's Carebricks for EHR-integrated workflows spanning incidental findings, referral prioritization and clinical interpretation.

University of Iowa Health Care has similarly scaled Evidently as an EHR-integrated clinical-data intelligence layer, with KLAS highlighting lessons around trust, workflow integration, provider experience and revenue integrity.

Atropos Health focuses on producing rapid real-world evidence for clinical and operational decision-making across health systems.

Procurement question

Do not begin with:

“Our LLM understands the chart.”

Begin with:

“Which clinical decision changes, and what measurable consequence follows?”

7. Remote monitoring / AI-enabled care management

Matrix priority: Medium

Executive owner: Population Health / Care Management

Remote monitoring looks straightforward until you examine the operating model.

A sensor does not produce ROI.

A model does not produce ROI.

An alert does not produce ROI.

A care pathway that acts on the alert may produce ROI.

Representative players

Cadence

Biofourmis / CopilotIQ

Current Health

Nexus Bedside

Cadence announced major 2026 partnerships with both Memorial Hermann and Hartford HealthCare for AI-enabled chronic-condition monitoring.

Biofourmis operates remote and home-care infrastructure across hospitals and payers and says it partners with more than 50 health systems and payer organizations.

KLAS is also studying inpatient monitoring models such as Nexus Bedside, where AI and remote monitoring support nursing workflow and clinical oversight.

Why the matrix stays at Medium

The economics depend heavily on:

who monitors alerts

which cohort is enrolled

intervention rate

reimbursement

risk contracts

readmission economics

staffing

and:

EHR integration.

Without the care model, remote monitoring risks becoming a device/data project rather than a scalable service.

8. Prior authorization / administrative automation

Matrix priority: High

Executive owner: CFO / Operations / Access

ROI visibility: High

Few healthcare workflows have such an obvious burden.

The AMA's 2025 survey, released in 2026, found that physicians handle roughly:

40 prior authorizations per week

consuming around:

13 hours of physician and staff time per week.

Forty percent reported employing staff dedicated exclusively to prior authorization, and 95% said prior authorization delays access to care.

The regulatory tailwind is also getting stronger.

CMS' 2024 final rule requires impacted payers to implement several interoperability APIs, including a Prior Authorization API, generally beginning January 1, 2027. CMS announced in May 2026 that 29 healthcare organizations had already joined its electronic-prior-authorization early-adopter effort.

Representative players

Waystar

Humata Health / R1

Infinx

plus payer and EHR-native automation layers.

A 2026 KLAS collaboration involving Waystar demonstrated touchless prior-authorization workflows for imaging and cardiology, with reduced administrative burden and faster decisions.

Humata continues to expand AI-driven prior authorization across large health systems and, in August 2026, announced an agreement to be acquired by R1.

Infinx also offers AI-enabled determination, submission, status and denial-risk workflows across provider organizations.

Why this category is commercially interesting

The baseline is easy to measure.

Before implementation:

13 staff hours

X open authorizations

Y delayed procedures

Z denial rate

After implementation:

measure again.

That makes procurement evidence much easier to defend.

The matrix in one view

These are representative active-market examples, not an exhaustive vendor ranking.

Why lower-risk AI often wins first

There is a pattern underneath all eight categories.

KLAS says healthcare organizations currently use AI more for operational efficiency than to reinvent clinical care, and buyers remain more cautious around higher-stakes clinical applications.

That creates an important commercial lesson.

The fastest route to IDN revenue is often not the AI that does the most intellectually impressive thing.

It is the AI that makes the executive buyer say:

“I know this problem. I own this KPI. I can measure the baseline. I can see what this saves. And implementation risk looks manageable.”

That explains why the matrix puts:

ambient documentation

RCM/coding

patient flow

and:

administrative automation

ahead of some more sophisticated clinical applications.

The ROI model I would use

The accompanying calculator uses:

Annual IDN Value

=

Labor / staff-capacity value

Revenue-cycle / throughput value

Avoided cost / denials / downtime

−

Annual vendor cost

−

Implementation

−

Integration + security

That is deliberately broader than:

hours saved × salary.

Because different AI categories create different types of value.

A worked example

Suppose an AI product creates:

60 staff hours/week of capacity

at an estimated loaded labor value of:

$70/hour.

Annual capacity value:

60 × $70 × 52 = $218,400

Now assume:

$250,000 annual throughput/revenue-cycle value

plus:

$150,000 avoided cost.

Gross annual value:

$618,400

Assume first-year spending is:

$150K contract

$75K implementation/change

$50K integration/security

Total:

$275,000

Net first-year value:

$618,400 − $275,000 = $343,400

Illustrative first-year ROI:

125%

and implied payback:

approximately:

5.3 months

These are illustrative assumptions, not a benchmark or expected result.

The point is to replace them with actual buyer-specific numbers.

Hard-dollar ROI and capacity ROI should not be mixed

This distinction matters.

If ambient AI saves 60 clinician hours per week but the health system does not:

reduce overtime

increase appointment capacity

reduce locum spend

or:

reallocate staff,

the entire $218K should not automatically be called a cash saving.

Part of it may be:

capacity ROI

rather than:

hard-dollar ROI.

I would report three layers.

A buyer will usually trust a conservative model more than an inflated one.

The second ROI calculation: cost of procurement delay

Founders should model their economics too.

Suppose the commercial organization burns:

$50,000/month

and an unclear procurement route creates:

6 months of avoidable delay.

Then:

$50,000 × 6 = $300,000 of runway exposure

That happens before counting:

deferred revenue

integration rework

pilot-support cost

founder time

or:

the opportunity cost of ignoring a better IDN.

This is why buyer intelligence can be worth much more than another 500 healthcare contacts.

The 25-IDN scenario

Suppose you start with:

25 carefully selected IDNs

rather than a national hospital spreadsheet.

Assume:

30% qualify

20% probability-weighted conversion among those qualified

$150K annual first contract.

Then:

25 × 30% × 20% × $150K

=

$225,000 probability-weighted pipeline scenario

Again, this is not a forecast.

It is a prioritization model.

Its value is making the commercial assumptions visible.

The account-scoring framework I would use

For each IDN, score:

1. KPI urgency

Is the problem currently strategic?

2. Executive ownership

Who carries the number?

3. Platform fit

Will the product fit the EHR / PACS / RCM / care stack?

4. Governance burden

How much AI, security and privacy review is likely?

5. ROI visibility

Can value be measured within 90–180 days?

6. Procurement path

Does the account have a plausible budget and contracting route?

7. Reference value

If you win this IDN, does IDN #2 become easier?

That gives you a much stronger target list than:

largest US health systems by revenue.

What investors should diligence

For investors evaluating an AI company selling to IDNs, I would ask:

How many pilots are paid?

How many become enterprise contracts?

Which executive owns the KPI?

What is median time from pilot to contract?

What internal IT hours are required per deployment?

How much integration code is reusable?

How long does security review take?

How often does the incumbent EHR compete?

Does gross margin improve at the second deployment?

Can sales close without the founder?

Is customer #2 easier than customer #1?

The last question is particularly important.

A company can have five impressive health-system pilots and still lack a scalable enterprise business.

A better definition of traction

I would separate:

LOI

Interest.

Pilot

Evaluation.

Successful pilot

Evidence.

Paid production

Commercial validation.

Enterprise rollout

Organizational validation.

Renewal

Persistent value.

Second independent IDN

Market repeatability.

Those milestones should not be treated as equivalent in a fundraising deck.

How I would spend the first 30 days of US IDN market entry

Week 1: narrow the use case

Choose:

one primary KPI

one executive owner

one buyer narrative.

Do not sell:

“AI for hospitals.”

Sell:

“We reduce documentation burden for physician operations.”

or:

“We recover revenue lost to incomplete documentation.”

or:

“We increase staffed-bed capacity without adding beds.”

Week 2: identify the right 25 IDNs

Score systems on:

use-case fit

digital maturity

relevant strategic initiatives

existing platforms

buyer accessibility

financial pain

AI governance maturity

and:

reference value.

Week 3: map 15 decision-makers

Depending on category, this might include:

CMIO

Chief Medical Officer

COO

CFO

CIO

CISO

Chief Digital Officer

Revenue Cycle VP

Physician Operations

Patient Access

Radiology Chair

Population Health

Care Management

AI Governance Lead

and:

Procurement.

Do not assume the most senior person is always the best entry point.

Week 4: create the account thesis

For every top target:

Why this IDN?

Why this problem?

Why now?

Who owns the KPI?

What platform must we integrate with?

What evidence will they require?

What could security reject?

What budget might own the purchase?

What should happen after the pilot?

That turns lead generation into commercialization intelligence.

Where I help

I do not think HealthTech teams need another database containing hundreds of hospital names.

The higher-value missing layer is usually:

which IDNs are worth founder time and why.

That is where my work sits.

I can help translate a broad US market into:

priority IDN segments

25 high-fit buyer organizations

15 relevant decision-makers

buyer/KPI mapping

commercial triggers

integration and procurement friction

evidence gaps

and:

a buyer-specific ROI thesis.

The objective is not to replace the sales team.

It is to give the team fewer, better accounts to pursue.

That is also the logic behind the HealthTech Buyer Pipeline Sprint: 25 Buyers + 15 Decision-Makers.

HealthTech Buyer Pipeline Sprint: 25 Buyers + 15 Decision-Makers

The final procurement equation

I would summarize the entire US IDN matrix as:

KPI ownership × ROI visibility × workflow fit × integration × governance × procurement clarity × replication = commercial deployability

Great technology can survive a weak score in one area.

It usually cannot survive a zero.

The US IDN market is already buying AI.

Large acute-care systems are already among its heaviest purchasers.

The competitive question for 2026–27 is no longer:

Who has AI?

It is:

Who can prove enough value, with little enough friction, to survive the entire buying committee?

That is where I expect the fastest paths to repeatable IDN revenue to emerge.