15 Questions Every Hospital Should Ask Before Buying an AI Solution
Artificial Intelligence has quickly become one of the most discussed technologies in healthcare. Nearly every Revenue Cycle software company now claims to use AI, automate workflows, reduce denials, improve Patient Access, or streamline operations.
But as healthcare organizations evaluate these solutions, one question is often overlooked:
How do you evaluate an AI vendor beyond the product demonstration?
An impressive demo doesn't always translate into long-term operational success. Healthcare organizations should understand not only what an AI platform can do today, but also how it will be maintained, governed, supported, and priced over time.
Whether you're evaluating Patient Access automation, coding, prior authorization, insurance verification, call center technology, or Revenue Cycle analytics, these questions can help guide a more informed purchasing decision.
1. Who is the healthcare subject matter expert (SME) behind the AI?
AI does not manage itself.
Every healthcare AI solution should have experienced Revenue Cycle professionals who continuously review workflows, validate recommendations, and help ensure the technology reflects current payer requirements, regulations, and operational best practices.
Ask:
Who oversees the AI?
What Revenue Cycle experience do they have?
How often are workflows reviewed?
2. How are AI errors identified and corrected?
Every AI system makes mistakes.
The important question is not whether errors occur, but how quickly they are identified and corrected.
Ask vendors:
How are incorrect recommendations detected?
Is there human review?
How are recurring issues prevented?
How quickly are improvements deployed?
Will you be notified?
3. What happens when payer rules change?
Healthcare changes constantly.
Insurance policies, prior authorization requirements, Medicare guidance, and commercial payer rules evolve throughout the year.
Ask:
How frequently is the AI updated?
Who validates the updates?
Are changes automatic or manual?
How are customers notified?
4. Is your AI built specifically for healthcare?
General-purpose AI and healthcare RCM AI agents are not the same.
Solutions designed specifically for Revenue Cycle Management typically understand healthcare terminology, payer workflows, compliance requirements, and operational challenges far better than general-purpose models.
5. Does the AI integrate with our existing systems?
Integration should reduce work-not create it.
Ask whether the solution integrates with:
Epic
Your EHR
Scheduling systems
Registration platforms
Eligibility tools
Clearinghouses
Revenue Cycle workflows
The goal should be seamless workflow integration rather than requiring staff to switch between multiple applications.
6. What tasks are fully automated?
Not every workflow should be automated.
Ask business partners (vendor)s to clearly define:
What is fully automated?
What requires human review?
What generates recommendations?
What still requires manual intervention?
Understanding these distinctions helps establish realistic expectations.
7. How accurate is the AI?
Ask for measurable results.
Examples include:
Registration accuracy improvements
Denial reductions
Productivity gains
Insurance verification accuracy
Prior authorization turnaround times
Clean claim improvements
Request customer success stories supported by measurable outcomes.
8. How is patient data protected?
Healthcare AI must support strong security and privacy practices.
Ask vendors about:
HIPAA compliance
Security certifications
Data encryption
Access controls
Audit logs
Data retention policies
Security should be discussed as thoroughly as functionality.
9. How is AI priced?
AI pricing is changing rapidly.
Some vendors include AI within a fixed subscription.
Others charge based on:
API usage
Transactions
Users
Automations
AI requests
Consumption
Understanding the pricing model today helps prevent unexpected costs later.
10. Are there token or usage-based costs?
Many modern AI applications are built on large language models.
These models often incur usage costs based on the number of tokens processed or API requests submitted.
Ask vendors:
Are token costs included?
Could increasing workflow volumes increase pricing?
Are there monthly limits?
Are premium AI models priced differently?
Who absorbs future increases in AI model costs?
As AI adoption grows, understanding these operational costs becomes increasingly important during contract negotiations.
11. How is return on investment (ROI) measured?
Every implementation should include measurable objectives.
Examples include:
Reduced denials
Faster registrations
Improved cash collections
Lower staffing burden
Increased automation
Improved patient satisfaction
If success cannot be measured, it cannot be effectively managed.
12. Who provides implementation and training?
Technology alone does not create successful implementations.
Ask:
Who manages onboarding?
What training is included?
How long does implementation typically take?
What support is available after go-live?
13. Can the solution scale as our organization grows?
Healthcare organizations change.
Ask whether the platform can support:
Additional hospitals
New service lines
Higher transaction volumes
New AI capabilities
Future integrations
Selecting a scalable platform reduces the likelihood of replacing technology as your organization evolves.
14. Can we speak with healthcare clients using the solution?
References remain one of the most valuable parts of the evaluation process.
Ask to speak with organizations similar to yours in size, complexity, and operational structure.
Real-world experience often provides insights that product demonstrations cannot.
15. What makes your AI different?
Every vendor says they use AI.
Ask them to explain:
What makes their approach unique?
What problems do they solve better than competitors?
What evidence supports those claims?
How do they continue improving the platform?
The strongest vendors welcome these conversations and support their answers with measurable customer outcomes.
AI Is Only as Good as the People Behind It
One of the biggest misconceptions in healthcare technology is that AI runs independently once it is implemented.
Successful AI solutions require continuous oversight by healthcare professionals, software engineers, implementation specialists, customer success teams, and subject matter experts who understand the complexities of Revenue Cycle operations.
Technology evolves.
Payer rules evolve.
Healthcare evolves.
The best AI vendors evolve with them.