Presentation
Beyond the Algorithm: How Systems Thinking Unlocks AI’s Potential in Medical Device Innovation
DescriptionArtificial intelligence (AI) continues to dominate the conversation in medical device innovation, yet many promising AI technologies fail to gain traction in real-world clinical settings. This presentation explores how systems thinking provides a critical framework for understanding why these failures occur, not due to technical shortcomings, but because of misalignment with the complex healthcare ecosystem.
Despite solving genuine clinical problems, AI-enabled devices often falter when introduced into environments shaped by entrenched workflows, regulatory constraints, and deeply ingrained patterns of clinical decision-making. These technologies must navigate a landscape where fitting seamlessly into existing trust paradigms, workflows, and institutional systems is not optional. It is essential for adoption. Systems thinking enables innovators to step back from the technology itself and examine the broader healthcare context. This perspective reveals how factors beyond the algorithm shape the conditions for success or failure.
In research exploring next-generation cardiac treatment systems, we found that the potential acceptance of AI features depends less on technical sophistication and more on the perception of how well they fit into established workflows and mental models. When algorithmic tools steer clinical actions, such as suggesting specific navigation paths for cardiac catheters, clinicians stated that they preferred to maintain manual control. They cited mistrust in the algorithm's accuracy and the additional oversight they anticipated to avoid mistakes. In contrast, AI that worked behind the scenes to simplify existing treatment validation steps by summarizing familiar quality control data points into a treatment confidence score, was viewed as highly valuable. It was seen as easy to trust, and even influential in technology selection. These patterns highlight a critical insight for this product: adoption accelerates when AI reinforces clinicians’ sense of control and integrates seamlessly into their existing routines.
By identifying leverage points within the system, such as workflow design, incremental implementation strategies, and the cultural framing of clinical authority, teams can make small, targeted changes that produce outsized effects. These leverage points are not always obvious, but when activated, they can shift the behavior of entire stakeholder groups, influence institutional norms, and reshape the conditions for adoption. These systems-level dynamics frequently outweigh technological capabilities, making early ecosystem analysis essential to avoid costly missteps.
We introduce a practical framework based on three zones of influence:
What You Can Control (device features)
What You Can Influence (user interactions)
What You Must Adapt To (regulations, infrastructure, and cultural paradigms)
This model helps teams anticipate points of friction, align stakeholder expectations, and design solutions that are both technically sound and responsive to the realities of healthcare delivery. It also provides a lens for evaluating readiness, not just of the technology, but of the environment it enters.
Attendees will leave with actionable strategies for designing AI-enabled devices that work not just in theory, but within the lived realities of healthcare delivery. They will gain tools to identify feedback loops, activate leverage points, and align innovation efforts with the complex, interdependent systems that define clinical practice.
Despite solving genuine clinical problems, AI-enabled devices often falter when introduced into environments shaped by entrenched workflows, regulatory constraints, and deeply ingrained patterns of clinical decision-making. These technologies must navigate a landscape where fitting seamlessly into existing trust paradigms, workflows, and institutional systems is not optional. It is essential for adoption. Systems thinking enables innovators to step back from the technology itself and examine the broader healthcare context. This perspective reveals how factors beyond the algorithm shape the conditions for success or failure.
In research exploring next-generation cardiac treatment systems, we found that the potential acceptance of AI features depends less on technical sophistication and more on the perception of how well they fit into established workflows and mental models. When algorithmic tools steer clinical actions, such as suggesting specific navigation paths for cardiac catheters, clinicians stated that they preferred to maintain manual control. They cited mistrust in the algorithm's accuracy and the additional oversight they anticipated to avoid mistakes. In contrast, AI that worked behind the scenes to simplify existing treatment validation steps by summarizing familiar quality control data points into a treatment confidence score, was viewed as highly valuable. It was seen as easy to trust, and even influential in technology selection. These patterns highlight a critical insight for this product: adoption accelerates when AI reinforces clinicians’ sense of control and integrates seamlessly into their existing routines.
By identifying leverage points within the system, such as workflow design, incremental implementation strategies, and the cultural framing of clinical authority, teams can make small, targeted changes that produce outsized effects. These leverage points are not always obvious, but when activated, they can shift the behavior of entire stakeholder groups, influence institutional norms, and reshape the conditions for adoption. These systems-level dynamics frequently outweigh technological capabilities, making early ecosystem analysis essential to avoid costly missteps.
We introduce a practical framework based on three zones of influence:
What You Can Control (device features)
What You Can Influence (user interactions)
What You Must Adapt To (regulations, infrastructure, and cultural paradigms)
This model helps teams anticipate points of friction, align stakeholder expectations, and design solutions that are both technically sound and responsive to the realities of healthcare delivery. It also provides a lens for evaluating readiness, not just of the technology, but of the environment it enters.
Attendees will leave with actionable strategies for designing AI-enabled devices that work not just in theory, but within the lived realities of healthcare delivery. They will gain tools to identify feedback loops, activate leverage points, and align innovation efforts with the complex, interdependent systems that define clinical practice.
Event Type
Oral Presentations
TimeMonday, March 2310:30am - 11:00am EDT
LocationNassau
Digital Health