“The future of medicine is not about replacing doctors
with machines, but about empowering them with intelligent tools.” – Dr.
Eric Topol, Cardiologist and Author
Introduction: The Rise of AI in Clinical Encounters
In recent years, the integration of artificial intelligence
(AI) into healthcare has accelerated, leading to significant advancements in
diagnostics, treatment recommendations, and administrative processes.
AI-powered tools, such as ambient scribe technologies and clinical decision
support systems, are now commonplace in many healthcare settings. These systems
can transcribe patient interactions, suggest diagnoses, and even recommend
treatment plans. However, this rapid adoption raises a critical question: when
an AI system autonomously diagnoses and prescribes without direct human
clinician input, who is responsible for the billing?
This article delves into the complexities surrounding
AI-only clinical encounters, exploring the implications for medical billing,
provider accountability, and the evolving role of healthcare professionals in
an AI-driven landscape.
The Current Landscape of AI in Healthcare
1. AI-Powered Medical Scribes
AI-powered medical scribes have become integral in reducing
clinician burnout and improving documentation efficiency. For instance, The
Permanente Medical Group's ambient AI scribes have been used over 2.5 million
times in one year, significantly easing the documentation burden and enhancing
communication between providers and patients.
2. Autonomous Diagnosis and Prescription Systems
Platforms like Doctronic offer AI-driven consultations,
providing users with potential diagnoses and treatment plans. These systems
operate without direct clinician oversight, raising questions about the
appropriate billing practices when AI is the primary decision-maker.
3. AI in Revenue Cycle Management
AI is also transforming revenue cycle management by
automating coding, claim submissions, and denial management. Tools like AWS
HealthScribe leverage generative AI to enhance medical coding accuracy and
streamline administrative processes.
Who Is the Billable Provider?
The question of who is the billable provider in AI-only
clinical encounters is multifaceted and varies based on several factors:
1. Legal and Regulatory Framework
Current billing practices are primarily designed around
human providers. The Centers for Medicare & Medicaid Services (CMS) and
private insurers have yet to establish standardized guidelines for reimbursing
AI-driven services. As a result, healthcare organizations often bill under the
supervising or treating clinician's credentials, even if their direct
involvement is minimal.
2. AI as a Tool vs. AI as a Provider
If AI is considered a tool assisting a clinician, the human
provider remains the billable entity. However, if AI systems are deemed to
operate autonomously, questions arise about their status as providers and their
eligibility for reimbursement.
3. Hybrid Models
Some institutions are adopting hybrid models where AI
systems assist clinicians in decision-making, but the final diagnosis and
treatment plan are determined by a human provider. In these cases, the
clinician is typically the billable provider, with AI serving as a supportive
tool.
Key Statistics on AI in Clinical Encounters
- 2.5
million: The number of times The Permanente Medical Group’s ambient AI
scribes were used in one year, significantly reducing clinician
documentation burden (AMA).
- 30–40%:
Estimated reduction in administrative workload for clinicians using
AI-powered scribe technologies, freeing time for direct patient care.
- $243
million: Funding raised by Ambience Healthcare for AI scribing
technology, reflecting investor confidence in AI-driven clinical support (Business Insider).
- 85%:
Accuracy rate reported by certain AI-assisted diagnostic platforms
compared to traditional clinician-only diagnoses, emphasizing the
potential and current limitations of AI.
- 2025:
The year in which multiple AI reimbursement policies are expected to be
piloted, indicating regulatory evolution for AI-driven encounters.
- 50%:
Reduction in claim denials achieved by some healthcare organizations using
AI-enabled revenue cycle management systems for coding and billing (AWS).
Expert Opinions on AI-Only Clinical Encounters
Dr. Eric Topol, Cardiologist and Author
Dr. Topol emphasizes the importance of maintaining human
oversight in AI-driven healthcare. He argues that while AI can enhance
diagnostic accuracy, it should not replace the clinician's role in
decision-making. "AI should augment, not replace, the physician's
judgment," he asserts.
Dr. Adam Oskowitz, Co-founder of Doctronic
Dr. Oskowitz highlights the potential of AI to democratize
healthcare access. He notes that platforms like Doctronic can provide
immediate, cost-effective consultations, especially in underserved areas.
However, he acknowledges the need for clear guidelines on billing and provider
accountability in AI-only encounters.
Dr. Nikhil Buduma, Co-founder of Ambience Healthcare
Dr. Buduma discusses the integration of AI in administrative
tasks, such as medical scribing and coding. He believes that AI can
significantly reduce clinician burnout and improve efficiency. However, he
stresses the need for regulatory bodies to establish frameworks for reimbursing
AI-driven services.
Tactical Advice for Healthcare Providers
- Stay
Informed on Regulatory Changes: Keep abreast of evolving guidelines
from CMS and private insurers regarding AI-driven services.
- Implement
Hybrid Models: Consider integrating AI systems that assist but do not
replace clinician decision-making, ensuring human oversight remains
central.
- Collaborate
with Legal Experts: Work with legal professionals to navigate the
complexities of billing and provider accountability in AI-only encounters.
- Advocate
for Clear Policies: Engage with professional organizations and
policymakers to advocate for standardized billing practices for AI-driven
services.
Myth Busters: Debunking Common Misconceptions
Myth 1: AI can replace human clinicians entirely.
Fact: AI is a tool that can assist clinicians but
cannot replicate the nuanced judgment and empathy that human providers offer.
Myth 2: AI-driven services are not reimbursable.
Fact: While reimbursement policies are evolving, some
insurers are beginning to cover AI-assisted services, especially when human
oversight is involved.
Myth 3: AI systems are infallible.
Fact: AI systems can make errors, and their
recommendations should be reviewed by qualified clinicians to ensure patient
safety.
Myth 4: AI automatically improves diagnostic
accuracy.
Fact: AI accuracy depends on the quality of data and algorithms. Poorly
trained models can generate incorrect recommendations, so clinician review is
essential.
Myth 5: Patients prefer AI over human interaction.
Fact: While AI can offer faster responses, most patients value human
judgment, empathy, and accountability in their care.
Myth 6: Using AI eliminates clinician responsibility.
Fact: Legal and ethical responsibility remains with the supervising
clinician or healthcare organization. AI is a support tool, not a replacement
for human oversight.
Myth 7: AI-only encounters are ready for widespread
regulatory approval.
Fact: Regulatory frameworks are still evolving. Many jurisdictions
require human oversight or hybrid models for safe deployment.
Myth 8: AI reduces all healthcare costs immediately.
Fact: Initial investment in AI infrastructure can be high. Cost savings
often appear over time through efficiency gains, reduced errors, and optimized
billing processes.
Myth 9: AI models are universally applicable across
all patient populations.
Fact: AI models may be biased or less accurate for underrepresented
groups. Continuous validation and inclusive training data are critical to avoid
disparities in care.
FAQs
Q1: Can AI systems be billed as the primary provider?
A1: Currently, most billing practices require a human
provider to be listed as the primary billable entity. However, this may change
as regulations evolve.
Q2: How can healthcare organizations prepare for AI
reimbursement?
A2: Organizations should stay informed about
regulatory changes, implement hybrid models, and collaborate with legal experts
to navigate billing complexities.
Q3: Are there any AI-specific CPT codes?
A3: Yes, the American Medical Association has
developed specific CPT codes for certain AI-driven services, though their
application is still limited.
Q4: What liability issues arise when AI makes
diagnostic or treatment decisions?
A4: Liability generally falls on the supervising clinician or healthcare
organization. AI recommendations should always be reviewed by a qualified human
provider to ensure patient safety and compliance with legal standards.
Q5: Can insurance reimbursements differ if AI is
involved in patient care?
A5: Yes, reimbursement policies vary. Some insurers may cover
AI-assisted services when human oversight is documented, while others may not
recognize AI-driven encounters as reimbursable.
Q6: How do patients perceive AI-only encounters?
A6: Patient acceptance varies. Studies indicate many patients appreciate
faster, accessible care but still value human judgment, empathy, and
accountability in clinical decisions.
Q7: What safeguards exist to prevent AI errors in
clinical care?
A7: Best practices include continuous AI model validation, clinician
oversight, clear documentation of AI recommendations, and integration of AI
alerts for unusual or high-risk scenarios.
Q8: How soon will AI-only encounters become
mainstream?
A8: Adoption is accelerating, but fully autonomous AI encounters remain
limited due to regulatory, legal, and ethical considerations. Hybrid models
with human oversight are more common today.
Q9: Are there ongoing studies evaluating outcomes of
AI-only encounters?
A9: Yes, research is ongoing. Early studies focus on diagnostic
accuracy, efficiency, patient satisfaction, and billing implications. Outcomes
are mixed but generally indicate AI can improve efficiency when supervised by
clinicians.
Step-by-Step Guide: Implementing AI in Clinical
Encounters
Step 1: Assess Organizational Readiness
- Evaluate
current workflows, clinician workload, and administrative processes.
- Identify
areas where AI could reduce burden or improve efficiency.
- Ensure
data privacy and security compliance before integrating AI systems.
Step 2: Select the Right AI Tool
- Choose
platforms with proven accuracy in diagnostics or medical scribing.
- Consider
integration with EMR/EHR systems for seamless documentation.
- Review
vendor regulatory compliance and certification.
Step 3: Define Human Oversight
- Determine
which clinicians will review AI-generated diagnoses or treatment plans.
- Establish
clear accountability policies for billing and liability.
- Ensure
patients are informed about AI involvement in their care.
Step 4: Train Staff and Clinicians
- Provide
hands-on training on AI tools and interfaces.
- Educate
clinicians on interpreting AI recommendations, recognizing
limitations, and documenting oversight.
- Include
training on legal, ethical, and billing implications.
Step 5: Implement Hybrid Models
- Start
with AI-assisted encounters rather than fully autonomous AI-only
interactions.
- Use AI
to draft documentation, suggest diagnoses, or flag coding opportunities,
with clinicians making final decisions.
Step 6: Monitor and Evaluate
- Track accuracy,
patient satisfaction, and billing outcomes.
- Adjust
AI algorithms and workflows based on performance data.
- Regularly
audit for bias, errors, and compliance with regulatory
requirements.
Step 7: Engage with Payers and Regulators
- Keep
updated on reimbursement policies and CPT codes for AI-assisted
services.
- Advocate
for standardized billing and legal frameworks supporting AI in
clinical practice.
Step 8: Scale and Optimize
- Expand
AI integration to additional departments or specialties once efficacy and
compliance are proven.
- Continuously
update training and workflows to incorporate new AI capabilities and industry
best practices.
Final Thoughts
The integration of AI into clinical encounters presents both
opportunities and challenges. While AI has the potential to enhance efficiency
and accessibility, it also necessitates careful consideration of billing
practices and provider accountability. As the healthcare landscape continues to
evolve, it is crucial for stakeholders to collaborate in establishing clear
guidelines that ensure both innovation and patient safety.
References
- "Here's
the exclusive pitch deck Ambience Healthcare used to raise $243 million as
the AI scribing gold rush hits new highs" – Business Insider. Link
- "That
AI scribe your doctor is using? It may make your bill go up" – STAT
News. Link
- "Generative
AI enabled Medical Coding on AWS" – AWS for Industries. Link
About the Author
Dr. Daniel Cham is a physician and medical consultant with
expertise in medical technology, healthcare management, and medical billing. He
focuses on delivering practical insights that help professionals navigate
complex challenges at the intersection of healthcare and medical practice.
Connect with Dr. Cham on LinkedIn to learn more: linkedin.com/in/daniel-cham-md-669036285
Hashtags: #AIinHealthcare #MedicalBilling #ClinicalAI
#HealthTech #DigitalHealth #MedicalInnovation #AIReimbursement
#HealthcarePolicy #MedicalScribing #AIandMedicine
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