The Noise in Healthcare Automation: Who Will Actually Deliver?
By Andrew Smith, Inflect President & Vituity Chief Operations Officer
Healthcare is in the middle of a digital transformation, with automation promising to solve some of the biggest pain points across the industry. Physician workflow automation, infrastructure automation, and revenue cycle management (RCM) automation have become the focus of countless companies, each claiming to have the next big solution. On paper, the problem statement is clear — healthcare is inefficient, clinicians are overburdened, administrative tasks drain time, and revenue cycles are overly complex. Automation seems like the perfect answer to these challenges. But here’s the reality: there’s so much noise in the automation space that it’s almost impossible to know who will actually deliver on the promise. The market is flooded with companies touting AI, machine learning, and automation as the future of healthcare, but sorting out the real solutions from the vaporware is a daunting task.
The Problem: Too Much Noise, Not Enough Substance
The buzzwords are flying — AI-driven solutions, machine learning, robotic process automation (RPA). The sales pitches are shiny, and the PowerPoints are polished. Everyone seems to have the “next big thing” to streamline physician workflows, automate hospital infrastructure, or fix RCM inefficiencies. But the truth is, while the problem statement is clear, who can actually get it done?
Healthcare automation is incredibly complex because humans are complex. Unlike other sectors, it’s not just about efficiency and cost reduction. The stakes are much higher. Lives are at stake, and every decision has profound implications for patient care. And yet, many of the solutions in the market today are disconnected from the actual workflow realities of healthcare providers and the patients they serve.
This lack of real-world understanding makes it nearly impossible for most solutions to deliver on their promises. And as the noise in the space grows louder, healthcare organizations face decision paralysis. Who do you trust? Who can really execute?
Physician Workflow Automation: The Perfect Problem, But Who’s Solving It?
One of the hottest areas in automation is physician workflow automation. Physicians are drowning in administrative tasks — documentation, order entry, prescription management — taking them away from what they do best: caring for patients. Automation here will be a game-changer, improving efficiency and reducing burnout. But here’s the problem: without real-world clinicians involved in training these models and designing these solutions, it’s all smoke and mirrors. Physicians work in fast-paced, high-stakes environments where context is everything. A model trained on theory alone won’t understand the nuances of a clinical decision, the way a patient interaction flows, or the critical steps that must never be automated.
So many companies believe they can solve this with AI, but their models are disconnected from real-life medical practice. If physicians aren’t directly involved in the training and refinement of these systems, they won’t work. It’s that simple. Models need to be trained on real workflows, real clinical interactions, and real patient complexities. Otherwise, you’re left with a solution that might work in a demo but fails miserably in the chaotic reality of a hospital or clinic.
Infrastructure Automation: Can We Trust It?
On the infrastructure side, automation promises to optimize everything from hospital staffing, clinic management, to supply chain logistics. And, again, it sounds like a perfect solution — why wouldn’t we want our hospitals and clinics to run more efficiently?
The problem? Many of these tools are disconnected from how hospitals and clinics actually function. Healthcare infrastructure is like a living organism; it’s dynamic and unpredictable. Automating infrastructure without understanding the day-to-day flow of operations often leads to inefficiencies, bottlenecks, or worse, critical gaps in care.
Once again, without the involvement of on-the-ground healthcare staff — those who live the reality of healthcare operations — automated infrastructure solutions are doomed to fail. It’s not just about replacing manual processes with automated ones; it’s about deeply understanding how healthcare environments operate and where automation can help, rather than hinder.
RCM Automation: Solving Complexity with Simplicity?
Revenue cycle management (RCM) is another prime target for automation. The RCM process is notoriously complex, with billing, claims processing, and reimbursement requiring immense resources. Automation here will help reduce manual work, prevent denials, and speed up payments. But here’s the catch: the complexity of RCM comes from the endless variability in billing codes, payer requirements, and regulatory changes. While some automation tools promise to cleanly solve these issues, the reality is far messier. Many solutions oversimplify RCM, failing to adapt to the intricacies of healthcare finance, leading to inaccurate billing, rejected claims, and potential revenue loss. Humans are still in the loop.
And here, too, real-world clinicians must be involved. RCM automation isn’t just a tech problem; it’s deeply connected to clinical decision-making and documentation. If these systems aren’t properly aligned with how clinicians document care, automation could introduce errors instead of efficiencies.
Without Real-World Clinicians Training the Models, It Won’t Work
In all of these areas — physician workflow, infrastructure, and RCM automation — the solutions will only be as good as the data they are trained on. And if that data doesn’t come from diverse clinical settings, the automation will fail. It’s not enough to understand the theory of healthcare workflows; you need to understand the lived experience.
Physicians and frontline healthcare providers must be involved in training these AI models. They are the ones who can provide the context, the nuance, and the understanding that automation needs to succeed. Otherwise, we risk building solutions that add to the burden, rather than easing it.
Conclusion: Cutting Through the Noise
The promise of healthcare automation is undeniable, but the current landscape is filled with more noise than substance. The only way to truly solve these complex problems is by involving the right people in every step of the process in multitudes of settings. At the end of the day, the companies that will succeed in this crowded space are those that can bridge the gap between technology and the real world of healthcare.
