AI That Fits the Clinical Workflow

Designing for Seamless Clinical Adoption: The Invisible Technology Principle

When developing AI-powered medical devices, the most sophisticated algorithms mean nothing if physicians won't use them in practice. Maulik learned this lesson early at Anumana, where the challenge wasn't just creating accurate diagnostic tools, but ensuring they could integrate seamlessly into the high-pressure environment of clinical care.

The fundamental principle that guides Anumana's product development centers on seamless integration. As Maulik explains, "The technology has to remain invisible. It cannot interrupt or come in the way of a physician's workflow or clinical decision making." This philosophy stems from a practical understanding of clinical realities: physicians often have just 10 minutes with each patient and need immediate access to information that helps them make critical decisions.

Rather than asking healthcare providers to learn new procedures, Anumana builds on workflows that already exist. For the ECG-AI LEF product, this means enhancing the electrocardiogram readings that physicians routinely perform, requiring nothing more than pressing a button in their current workflow to receive AI-powered insights. This approach recognizes that busy clinicians won't adopt solutions that add complexity to their already demanding schedules.

The key to successful adoption lies in demonstrating clear clinical value from the physician's perspective. Maulik emphasizes that medtech developers must answer fundamental questions: "What does it change in my practice? What does the data tell me? What does the clinical outcome tell me?" Technology features matter less than tangible improvements to patient care and clinical decision-making.

This focus on seamless integration extends to "last mile" thinking. Regardless of how advanced the underlying technology may be, success ultimately depends on whether it enhances healthcare providers' ability to make better decisions within their existing operational framework. For Anumana, this has meant achieving impressive clinical accuracy — with the LEF algorithm scoring 0.94 on a scale where 1.0 represents perfect accuracy — while requiring no changes to established clinical routines. 

Physicians already perform electrocardiograms as standard practice; Anumana's AI simply provides additional diagnostic insights from the same 10-second heart rhythm reading that clinicians were already capturing. By offering superior diagnostic accuracy without altering physician workflows, Anumana makes a compelling case for clinical adoption: physicians not only get insights faster, but they get better ones, too.

This approach reflects a broader lesson for medical device development: successful clinical adoption often depends more on workflow integration than technological advancement. Companies that prioritize seamless implementation over feature complexity tend to achieve higher physician adoption rates, as healthcare providers gravitate toward solutions that enhance their existing practices rather than forcing them to learn entirely new systems.

Use Risk-Benefit Balance to Build Regulatory Confidence

Successfully navigating regulatory approval for novel medical devices requires fundamentally reframing how companies approach the FDA review process. Rather than focusing solely on technological capabilities, Maulik emphasizes that regulatory success hinges on creating a compelling risk-benefit profile that justifies the reviewer's decision to approve the technology.

"Can you create a value proposition that is justifiable for a regulatory person to take a risk?" Maulik asks, highlighting the human element in regulatory decision-making. Reviewers must weigh not only the clinical evidence but also their own professional responsibility in approving completely new technologies. This creates a dynamic where regulators seek assurance that new medical devices won't create unacceptable patient risks while delivering meaningful clinical benefits.

For AI-powered medical devices specifically, this risk-benefit equation takes on additional complexity. Regulators approach AI with the perspective that while the technology may not directly harm patients, they need confidence it won't introduce new risks even as the full scope of benefits becomes established. The foundation of this equation lies in the quality and scale of supporting data.

Unlike traditional medical devices that might rely on studies involving hundreds or thousands of patients, AI algorithms require datasets encompassing hundreds of thousands of patients to demonstrate reliability across diverse clinical scenarios. "Is it robust enough that it does cover all the corner cases so that when you put it in the hands of a physician, not just the regulator, you have a high level of confidence it is completely reproducible all the time?" Maulik explains.

This comprehensive evidence base serves dual purposes: it provides regulators with the confidence they need to approve the technology while also ensuring that practicing physicians can trust the AI system's recommendations in real-world clinical settings. The scale difference is dramatic — where traditional device studies might involve a maximum of 1,000 patients, AI systems benefit from training data that spans hundreds of thousands or millions of patient records.

Anumana's regulatory strategy exemplifies this approach. The company's access to longitudinal data from over 7 million patients at Mayo Clinic, expanding to more than 20 million across multiple medical centers, provides the robust foundation that both regulators and clinicians require. This comprehensive dataset enables Anumana to demonstrate not just accuracy in controlled settings, but reliability across the full spectrum of patient presentations that physicians encounter in practice.

The regulatory landscape for AI medical devices has evolved significantly in recent years. Maulik observes a shift from default skepticism to collaborative engagement, noting "a high level of willingness to say, ‘What can we do to bring it to the market?’" from FDA reviewers. This change reflects regulators' recognition that AI tools can deliver substantial benefits to clinical care, particularly in areas where few effective diagnostic tools currently exist.

However, this increased openness doesn't diminish the importance of rigorous scientific evidence. Success still depends on demonstrating thorough development processes, proper model training, and substantial clinical validation published in peer-reviewed journals. The key difference lies in regulators' willingness to work with companies that demonstrate this level of scientific rigor, rather than viewing AI innovation as inherently problematic.

For medical device companies developing AI-powered solutions, this regulatory environment rewards those who invest early and heavily in comprehensive data collection and clinical validation, positioning robust evidence as both a regulatory requirement and a competitive advantage.