The healthcare industry is a fascinating paradox. On one hand, it’s a field driven by cutting-edge scientific discovery, constantly pushing the boundaries of what’s possible in medicine. On the other, it can be remarkably resistant to change, particularly when it comes to adopting new technologies. This push and pull is especially evident in the realm of revenue cycle management (RCM), where the promise of artificial intelligence (AI) and automation often collides with ingrained skepticism, particularly from the very professionals whose lives it aims to simplify: physicians.
At Magical, we’ve been spending a lot of time thinking about this challenge. We know AI and automation are rapidly transforming healthcare, helping organizations manage vast amounts of data, improve efficiency, and optimize workflows in areas like patient registration, claims processing, and denials management. But we also know that for these advancements to truly take root, we need to address the very real concerns of the medical professionals on the front lines.
This isn't just about implementing new software; it's about building trust, fostering transparency, and ultimately, creating a collaborative environment where technology enhances, rather than hinders, patient care and financial stability. In a recent podcast episode, we sat down with Dr. Greg Hobbs, an emergency physician and co-founder of Milagro, a fully autonomous coding solution, to dive deep into the roots of physician skepticism and explore how truly transparent AI-powered medical coding can win over even the most cautious practitioners.
I. The Deep Roots of Physician Skepticism Towards Technology
Physicians are, by nature, a cautious and discerning group. Their daily work involves critical decision-making with direct impacts on human lives, which naturally fosters a healthy skepticism toward anything that might introduce uncertainty or risk. This inherent caution, combined with past experiences, forms the bedrock of their resistance to new technologies in healthcare.
A. Historical Resistance to New Technologies in Healthcare
It’s no secret that healthcare, while innovative in clinical advancements, often lags in technological adoption. Dr. Hobbs himself noted this persistent challenge:
“Physicians are resistant to it. I mean, not all of us, but I know a lot of physicians who retire rather than…I even still now in 2025 work with some physicians who hand write their orders and give them to a nurse or sales struggling with that.”
This isn’t just anecdotal; it points to a deeper issue. The medical field has seen its share of technological shifts, from the introduction of electronic health records (EHRs) to the latest AI algorithms. Each change, no matter how promising, requires a significant shift in established routines, a steep learning curve, and a leap of faith. For professionals trained to rely on precision and tangible evidence, embracing an abstract, code-driven solution can feel counter-intuitive.
B. The "Tech Run That Didn't Work"
One of the most significant contributors to physician skepticism is the memory of past technological disappointments. Many doctors have been promised revolutionary tools that ultimately failed to deliver on their grand claims. They've invested time, effort, and institutional resources into systems that were pitched as "the best thing since sliced bread," only to find them cumbersome, unreliable, or simply ineffective.
Imagine being told a new system will "solve all your problems" and "you're going to have to work to start playing golf again." Then, after months of implementation and training, the system falls short. This cycle of overpromise and under-delivery leaves a lasting scar, making physicians wary of the next big buzzword. They’ve learned to protect their time and their patients from disruptive technologies that don’t genuinely improve care or efficiency. It’s not about being anti-progress; it’s about demanding real, demonstrable value.
C. Workflow Disruption and Learning Curves
Physicians operate in fast-paced, high-stakes environments where every second counts. Introducing a new technology often means fundamentally altering well-established workflows, which can be a significant source of stress and inefficiency, at least initially. Dr. Hobbs highlighted this directly: “When you bring in a new technology that might disrupt their workflow, they have to learn something, then that’s a challenge for them.”
The idea of "learning something new" might sound trivial to an outsider, but for a busy physician, it means taking time away from patient care, administrative tasks, or personal life to grapple with unfamiliar interfaces and procedures. If the perceived benefit doesn't immediately outweigh the upfront cost in time and effort, resistance is inevitable. The ideal solution must integrate seamlessly, minimizing disruption and offering intuitive pathways to adoption.
D. The "Black Box" Problem: Why Physicians Mistrust AI Algorithms
Perhaps the most profound source of mistrust, especially concerning AI, is what Dr. Hobbs refers to as the "black box" problem. This is the inherent opacity of complex algorithms, where the inputs go in, and outputs come out, but the intermediate steps are invisible.
“As a practice in physician, I’m uncomfortable with some algorithm where I can’t see what happened. A bunch of information went in, an answer came out, I can’t tell how they got the answer. And now I’m supposed to apply that to the treatment of a patient. And it’s telling me to do something different than I would have done.”
This lack of visibility is a fundamental barrier to trust. When an AI suggests a course of action or generates a medical code, physicians need to understand the underlying logic. They need to trace the "work" of the algorithm, just as they would a human colleague's reasoning. Without this transparency, AI algorithms become a source of anxiety rather than a reliable tool.
II. Legal Responsibility and the Imperative for Trust in Coding
Beyond personal comfort or efficiency, there’s a much more significant reason for physicians’ deep concern about AI in medical coding: legal responsibility. This isn't a minor detail; it's a foundational aspect of their profession that underscores the absolute imperative for trust in any coding solution.
A. Physicians' Legal Liability for Case Coding
Most people outside the medical profession don't realize the gravity of this aspect. Physicians are ultimately on the hook for the accuracy of their patient’s medical records and the codes derived from them. Dr. Hobbs emphasized this crucial point: “Most people don’t understand that physicians are legally responsible for how their cases were coded. This is why a lot of physicians continue to do their own coding.”
The medical code assigned to a patient's case isn't just an administrative detail; it dictates billing, informs future treatment decisions, and becomes a permanent part of their health history. Errors can lead to significant financial penalties, audits, and even legal repercussions for the physician. In a world where precision means everything, outsourcing such a critical function to an opaque system is a massive ask.
B. The Challenge of Trusting Algorithms vs. Human Coders
The shift from human coders to AI presents a significant psychological and professional hurdle. When a physician delegates coding to a human coder, there's a clear line of communication and accountability. If there's a question or a discrepancy, they can simply approach the coder and ask for clarification, review the documentation together, and reach a consensus. That human element provides a pathway to query, understand, and correct.
“If you’re ending that off to a person, you could go and say, "Hey, can you show me this case?" If you’re trusting an algorithm, in a algorithm, to do it for you, that’s a whole other level of trust that you’re asking for.”
This "whole other level of trust" is precisely what AI solutions need to earn. It's not enough for the algorithm to be accurate; it must also be explainable and auditable in a way that allows physicians to confidently take legal responsibility for its output. Without this, AI coding will remain a non-starter for many.
C. Nurses' Fight for the Right to Say No
The skepticism isn't limited to physicians; other medical professionals share similar concerns. The precedent set by nurses is a powerful indicator of this widespread need for transparency and comfort: “Nurses have fought and won the right to say no to using AI algorithms because they weren’t comfortable with the answers they were getting and the things I were being told to do.”
This highlights a critical point: clinical judgment and comfort with a system’s recommendations are paramount. If AI is perceived as an unchallengeable black box, or if its outputs contradict a clinician's expert opinion without clear justification, it will face significant resistance. Any successful AI integration must respect and facilitate the professional autonomy and accountability of healthcare providers.
III. Building Trust Through Unwavering Transparency
Given the deep-seated skepticism and the heavy legal burden on physicians, building trust isn't just a nice-to-have; it's an absolute mandate for any AI-powered coding solution. And the cornerstone of this trust is unwavering transparency.
A. The Mandate for Fully Transparent AI Systems
To truly address physician skepticism, an AI coding system cannot be a black box. It must be designed from the ground up to be completely transparent. Dr. Hobbs put it plainly: “In order to address that skepticism, the system has to be fully transparent, able to confirm that the answers that you’re getting, the codes that you’re getting are accurate or at least is accurate if not more than with the human covers.”
This means enabling physicians to peel back the layers of the algorithm and understand exactly how it arrived at a particular conclusion. It’s about demystifying the technology and making its internal logic accessible, rather than relying on blind faith.
B. Demonstrating Accuracy: Head-to-Head Comparisons
Transparency also extends to proving accuracy. It’s not enough to claim high accuracy rates; an AI coding solution must be able to demonstrate its performance empirically. This often means conducting rigorous “head-to-head” comparisons against human coders or existing processes, using real-world data.
This kind of direct comparison allows healthcare organizations to validate the AI's capabilities with their own data and their own people, building confidence in its reliability. If the system can consistently match or exceed human accuracy, and prove it in a transparent, auditable manner, it goes a long way toward alleviating concerns about its effectiveness.
C. Showing the "Work": Providing Data Elements Supporting Codes
The core of true transparency lies in the ability to "show the work." Just as a student must show their calculations in math, an AI coding system must be able to present the specific data elements that informed its coding decisions. Dr. Hobbs stressed this: "You have to be able to show the physician if they wanted to look, show the sergeant data elements that support the codes."
This functionality directly counters the "black box" problem. If a physician can easily access the specific phrases, diagnoses, or procedural notes from the medical record that led the AI to assign a particular code, they can verify its accuracy, understand its rationale, and ultimately, feel comfortable taking legal responsibility for it. This level of detail empowers physicians to maintain oversight and control.
D. Addressing Fears of Systematic Upcoding and Undercoding
Physicians carry valid concerns about potential systematic errors, particularly "upcoding" (coding for a higher reimbursement than appropriate) which can lead to severe penalties from payers. But Dr. Hobbs also revealed a lesser-known but common fear: "I’ve never talked to a physician who didn’t believe their cases were consistently undercoded, and it’s like, 'I’m the last thing they deserve.' That’s true. Well, I heard that saying, really, but all this, I don’t think they’re, I think they’re missing stuff. You can’t undercode. That’s why the software has intentionally been built, remove it to them, you know, head to head comparison with their current process."
An effective AI solution must address both fears. It should be built to eliminate both systematic upcoding and undercoding, ensuring that cases are consistently and accurately coded for the services rendered. By proving its ability to optimize revenue by catching missed codes without inflating charges, AI can truly become a trusted partner in financial health.
IV. The Role of Human Oversight and Partnership
While the promise of fully autonomous AI is compelling, the reality, especially in sensitive areas like medical coding, often involves a nuanced blend of AI and human expertise. Achieving optimal results isn't about replacing humans but empowering them through smart collaboration.
A. Why Pure AI Isn't Enough: Combining AI with Human Expertise
The idea of "pure AI" handling everything might sound ideal, but the complexities of medical documentation and the constant evolution of coding guidelines mean that AI alone has its limits. As Dr. Hobbs noted, “In our experience, AI by itself cannot get you to 95% accuracy, when looking at vendors and technology, if they’re promising the 95%, what else are you using? But we’ve seen it where you combine it with other technology or other approaches, you can get to that level of accuracy with complex surgeries and procedures.”
This insight is crucial. The most effective solutions don't rely solely on AI but strategically combine it with other technologies, and critically, human oversight. AI excels at repetitive, rules-based tasks and identifying patterns, but complex, nuanced, or rare cases still benefit from the critical thinking and experience of human coders. This hybrid approach ensures both efficiency and accuracy, leveraging the strengths of both worlds.
B. Defining Rules for Human Review
A truly intelligent AI coding solution understands its limitations and is designed to seamlessly hand off cases to human experts when needed. This isn't a flaw; it's a feature. Solutions should allow for customizable rules that trigger human review based on predefined criteria. Dr. Hobbs highlighted this practical application: “The solution is a combination of software that leverages AI in very intelligent ways, but it’s not pure AI, but then can also incorporate the human element we needed can say, all cases above this dollar amount go to review, all cases for a new doctor for the first month, whatever rules you want.”
This capability empowers healthcare organizations to maintain control and ensure compliance. It allows them to set guardrails, ensuring that high-value cases, new provider cases, or particularly complex scenarios always receive the expert human touch. This selective human intervention builds confidence while still maximizing AI's efficiency gains.
C. Vendor as a Partner: Openness to Feedback and Adapting to Rule Changes
The relationship with an AI coding vendor shouldn’t be transactional; it should be a true partnership. The healthcare landscape is dynamic, with coding guidelines and regulations constantly evolving. A vendor must be agile and responsive to these changes, as well as to client feedback.
"The vendor needs to be a partner with you. How is the vendor going to know about local changes? Who's going to communicate that? How's that knowledge from between vendor and clients in any work together to make sure that you're compliant?"
A good partner is open to communication, feedback, and enhancement suggestions. They should view themselves as an extension of your team, working collaboratively to adapt the AI solution to specific organizational needs and the ever-changing regulatory environment. This ongoing collaboration ensures that the technology remains effective, compliant, and continually improving.
V. Fostering Adoption: Beyond Skepticism to Collaboration
Once the foundations of transparency and partnership are laid, the path to wider adoption becomes much clearer. It’s about demonstrating tangible value, addressing underlying fears, and making the integration process as smooth as possible.
One of the most immediate concerns often voiced by coding professionals is the fear of job displacement. When AI is introduced, the first thought might be, "Am I going to lose my job?" It's a real fear, but the reality is quite different, especially in a healthcare landscape plagued by staffing shortages.
AI in coding is not about firing skilled professionals. Instead, it addresses the persistent labor gaps in healthcare, including a 30% vacancy rate in the coding workforce. It can act as a "backfill," taking on the high volume of routine cases that currently burden human coders. This allows organizations to manage staffing challenges, and as people retire, AI can ease the pressure of finding immediate replacements.
More importantly, it frees up your best, most experienced coders to focus on the truly complex, challenging cases—like inpatient, reconstructive, or trauma surgeries—where their unique expertise is absolutely essential. Think of it as elevating their role, allowing them to apply their skills where they matter most, rather than on repetitive "routine screening colonoscopy cases." AI isn't about replacing; it's about optimizing human potential.
The Game-Changing Impact of Real-Time Coding: Eliminating Prior Authorization Mismatch Denials
Beyond efficiency and staff optimization, AI-powered autonomous coding offers a game-changing benefit that directly impacts hospital revenue: solving the "monster called preauthorization mismatch times."
Here’s the scenario: A patient comes in for a procedure, say a routine colonoscopy, which is preauthorized. But during the procedure, the GI doctor finds a polyp and removes it, or performs a biopsy. Now, the actual procedure differs from what was preauthorized. Some payers will automatically deny these cases, leading to a massive financial headache. While payers might offer a short window (24-72 hours) for reauthorization, manual or traditional automated coding often can't process the case and identify the new CPT code in time.
The result? “80 plus percent of these cases just ended up getting written off millions of dollars for sending new hospitals and tens or hundreds of millions of big health systems every year.” This is pure lost revenue, simply due to a mismatch in timing and workflow.
This is where true autonomous coding shines.
By leveraging agentic AI, solutions like Magical can code the case in real-time, often within minutes of the operative note being dictated. This immediate processing identifies the correct CPT code for the procedure actually performed. Then, the system can automate the workflow to push this updated information directly to the pre-authorization team. They have the right code and supporting clinical information to reach out to the payer for reauthorization before the claim is denied.
This capability is revolutionary. At a large teaching hospital, this approach “eliminate[d] 95 percent of their pre-authorization denials.” Imagine the impact on your revenue cycle! This isn't just about small gains; it's about stopping massive revenue leakage cold.
If you’re looking to slash through manual denials, speed up claims processing, and boost revenue cycle efficiency by putting your RCM workflows on autopilot, Magical’s Agentic AI employees can help automate entire processes end-to-end, often with zero human oversight required. You can even book a demo to see how Magical can integrate with your existing systems and transform your team's most time-consuming workflows faster and more flawlessly.
Seamless Integration and Robust Security
Another hurdle to adoption, especially for large healthcare organizations, is the complexity of integrating new technology into existing workflows and systems. Historically, these integrations could be lengthy, resource-intensive IT projects requiring months of development and significant internal bandwidth.
The modern approach to AI automation, particularly with tools like Magical, flips this script. Rather than demanding months of IT time, Magical is designed for rapid deployment, allowing users to set up RPA workflows in a matter of minutes. This dramatically reduces the burden on already stretched IT teams, who are often "already busy all day long". The goal is to make the vendor responsible for 90% of the work, with the hospital's IT team primarily providing secure access to the necessary data.
Security, of course, is paramount. Healthcare deals with highly sensitive patient data, making cyberattacks an ever-present threat. Any new solution must offer robust data protection. It's critical to ensure that data remains secure and ideally stays within the hospital's firewall, backed by the vendor's security protocols. Magical, for example, is built with security in mind, and "doesn't store keystrokes or store any patient data, meaning there is zero risk of any data breaches." This level of security is non-negotiable for building trust and ensuring compliance.
The Power of a True Partnership
Ultimately, fostering adoption comes down to the quality of the partnership. As you navigate the selection process for an AI coding solution or any RCM technology, ask tough questions. Don't just accept promises; demand specific examples of how the vendor will support you post-implementation. Will you have a dedicated contact? How will they handle issues or discrepancies that arise?
As Dr. Hobbs highlighted, look for smaller technology companies who are "really laser focus[ed] in this one area and aren't trying to solve for gazillion problems". These companies are often more passionate, more responsive, and more likely to stick with you until the problem is truly solved. They become an extension of your team, not just a software provider.
The journey to AI adoption in medical coding is not without its challenges. Physician skepticism is real, rooted in valid concerns about past tech failures, workflow disruption, legal liability, and the "black box" nature of some algorithms. However, by embracing unwavering transparency, demonstrating provable accuracy, fostering true human-AI collaboration, and building strong vendor partnerships, we can move beyond skepticism to a future where AI empowers physicians, streamlines revenue cycles, and ultimately, enhances patient care.
If you're ready to explore how fully autonomous, agentic AI can transform your revenue cycle management workflows and address critical challenges like prior authorization denials, it's time to see the magic in action.
Book a Free Demo and discover how Magical can help your organization streamline operations, improve financial health, and free your valuable team members to focus on what matters most: your patients.
