Breaking Down the Walls of Information Friction in Healthcare
Joshua Tamayo-Sarver, MD, PhD, FACEP, FAMIA
It was a typical Monday in the emergency department when a 12-year-old girl with asthma walked in, accompanied by her worried parents, complaining of fever, congestion, muscle aches, and difficulty breathing that had worsened over the past several hours. As an emergency physician, I quickly assessed her condition, ordered a chest X-ray to ensure there was no pneumonia, some oral dexamethasone, and a breathing treatment. In my mind, the next critical step was clear: the road test. After the breathing treatment, could she walk around without her oxygen levels dipping or showing signs of respiratory distress? This single piece of information would determine whether she could go home or needed admission. Yet, while I knew this was the pivotal factor, no one else in the department — nurses, charge staff, or even other physicians — had this insight because it was locked in my head. This disconnect is what I call information friction, and it’s pervasive in healthcare.
The Hidden Cost of Information Friction
Information friction isn’t about predicting the unpredictable — it’s about failing to share what’s already known. Across hospitals and health systems, crucial insights are often siloed within individuals rather than disseminated across teams or systems. This leads to inefficiencies, higher costs, and compromised patient care.
Take another example: an elderly woman admitted for an electrolyte imbalance. While registering her mother at the hospital, her daughter casually mentioned to the clerk that she wouldn’t be available to pick her up between 2 PM and 10 PM due to her kids’ sports schedules. This tidbit of information could have been invaluable for discharge planning — yet it remained with the clerk. The next day, the discharge was delayed because transportation couldn’t be arranged within that window. Someone knew the information; it just didn’t reach the right person at the right time.
This isn’t just about patient care — it extends to operational dynamics like nurse staffing, bed availability, and supply chain management. For example:
- Nurses may know who’s burnt out and unlikely to pick up extra shifts.
- Charge nurses may know which beds are technically ready but still occupied due to administrative delays.
- Supply managers might know which items are critically low but lack a system-wide alert mechanism.
In every case, someone knows something that could improve efficiency or care outcomes — but that knowledge doesn’t flow where it’s needed.
Why Generative AI Could Be a Game-Changer
Generative AI offers a promising solution to address information friction. Unlike traditional interfaces requiring point-and-click navigation or command-line inputs, generative AI provides a conversational interface that can synthesize disparate data points into actionable insights. Imagine an AI system that:
- Recognizes patterns in real-time conversations and documentation.
- Flags critical information like discharge timing conflicts or supply shortages.
- Suggests solutions based on aggregated data from multiple stakeholders.
For instance, in the case of our asthmatic patient, AI could prompt me to let it know what pivotal information I need and what my plan is to do with that information, then automatically notify staff that the road test outcome is pivotal for disposition planning. Similarly, for the elderly patient’s discharge, AI could alert admitting physicians about transportation constraints shared during registration.
This isn’t science fiction — it’s achievable with current technology. Generative AI can act as a bridge between human knowledge and system-wide action, reducing friction and enabling smarter decision-making.
Lessons Learned: Why We Haven’t Solved This Yet
Despite its potential, addressing information friction remains an underexplored frontier in healthcare innovation. Several barriers contribute to this inertia:
- Cultural Resistance: Healthcare professionals often operate in silos, relying on personal expertise rather than collaborative systems.
- Technological Gaps: Many existing tools are cumbersome and fail to integrate seamlessly with workflows.
- Data Overload: The sheer volume of information can overwhelm systems designed to process it linearly rather than contextually.
- Economic Constraints: Hospitals may hesitate to invest in AI solutions without clear ROI projections.
These challenges highlight why generative AI must be thoughtfully implemented — not as a replacement for human judgment but as an augmentation tool.
Actionable Steps for Healthcare Leaders
To tackle information friction effectively, healthcare leaders must take deliberate steps:
- Identify High-Friction Areas: Audit workflows to pinpoint where critical knowledge fails to transfer — whether it’s during patient handoffs or inventory management.
- Leverage AI Thoughtfully: Invest in generative AI tools that prioritize usability and integration over complexity.
- Promote Cultural Change: Foster collaboration by breaking down silos and encouraging transparent communication across departments.
- Measure Impact: Use metrics like reduced delays in discharge planning or improved staff satisfaction to evaluate success.
- Iterate Continuously: Treat technology adoption as an iterative process rather than a one-time implementation.
A Call to Action
The next time you walk into a hospital — whether as a provider or patient — ask yourself this: How much better could this experience be if everyone knew what they needed to know? The answer might surprise you — and inspire you to push for change.
Dr. Joshua Tamayo-Sarver, MD, PhD, FACEP, FAMIA, develops and deploys technology solutions in the healthcare ecosystem as a clinician, business leader, software engineer, statistician, and social justice researcher. As the Vice President of Innovation at Inflect Health and Vituity, his unique formula of skills has helped develop over 35 solutions and scale multiple new healthcare products, including the first AI occult sepsis tool with FDA breakthrough designation. Dr. Tamayo-Sarver oversees corporate venture, internal incubation, and advisory services for AI-driven healthcare solutions, blending consumerism and clinical quality to fit the delicate balance of patient desire, user experience and quality medical care. A Harvard graduate, he holds degrees in biochemistry, epidemiology, and biostatistics, as well as a medical degree from Case Western Reserve University. He is a Mentor in the Emergence Program at Stanford University.
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