Why AI, robotics, and quality improvement depend on structured data and synoptic reporting
Ken Dec, Chief Marketing Officer, mTuitive
The mTuitive team is just back from the ACS Clinical Congress 2025 in Chicago. The conference showcased an exciting future for surgery, one powered by artificial intelligence, robotic innovation, and data-driven quality improvement. Yet beneath the headline themes of AI algorithms and robotic platforms lies a critical foundation that often goes unmentioned: the data infrastructure that makes these advances possible.
While structured data, synoptic reporting, and clinical software weren't explicitly highlighted themes at this year's congress, they represent the essential backbone supporting every innovation discussed on stage. Here's why these "invisible" elements deserve recognition and investment.
The Data Challenge Behind AI Implementation
The conference's extensive AI programming explored diagnostic support, surgical planning, and outcome prediction. But here's what every AI discussion implicitly requires: high-quality, standardized, structured data.
Machine learning models are only as good as the data they're trained on. When surgical data exists in fragmented free-text notes, inconsistent terminology, and siloed systems, AI cannot deliver on its promise. The AI tools showcased at Clinical Congress depend entirely on:
Standardized data capture that ensures consistency across institutions
Structured fields that machines can parse and analyze
Interoperable systems that allow data to flow between platforms
Complete datasets with minimal missing or ambiguous information
Without robust data infrastructure, AI in surgery remains a theoretical exercise rather than a practical tool. The synoptic reporting frameworks developed for oncology offer a proven model: when data is captured in structured, standardized formats, it becomes exponentially more valuable for both individual patient care and population-level insights.
Quality Improvement Requires Measurable Data
The conference's emphasis on quality, safety, and evidence-based practice highlighted the importance of measuring outcomes and identifying best practices. The High-Impact Clinical Trials session showcased potentially practice-changing research—all of which depends on rigorous data collection and analysis.
Yet quality improvement initiatives consistently face a common obstacle: data buried in narrative notes that cannot be easily extracted, aggregated, or analyzed. Synoptic reporting addresses this challenge by:
Creating discrete, searchable data points from every case
Enabling real-time quality dashboards that track key metrics
Supporting registry participation without duplicative data entry
Facilitating benchmarking across institutions and time periods
Powering clinical decision support at the point of care
The surgical ergonomics initiatives, credentialing frameworks for new procedures, and outcome tracking discussed at Clinical Congress all require sophisticated data capture and reporting systems. Without structured data, these programs cannot scale or demonstrate their impact effectively.
Robotics and Technology Adoption Need Data Standards
As robotic surgery moves from experimental to mainstream—a major theme at Clinical Congress 2025—standardized data collection becomes critical for several reasons:
Credentialing and Privileging: Evaluating surgeon competency with new technologies requires objective, comparable metrics. Structured operative reports can capture procedure-specific details, complication rates, and learning curves in ways that free-text narratives cannot.
Technology Assessment: Healthcare systems making significant investments in robotic platforms need robust data to evaluate outcomes, efficiency gains, and return on investment. Synoptic reporting enables this comparative analysis.
Continuous Improvement: As techniques evolve and best practices emerge, structured data allows surgeons to identify what works, for whom, and under what circumstances. This feedback loop accelerates the refinement of robotic approaches.
The Software Integration Imperative
The conference highlighted the breadth of modern surgical practice: AI tools, robotic systems, imaging platforms, electronic health records, registry databases, and quality tracking systems. Each represents a distinct software ecosystem.
The challenge? These systems must communicate seamlessly to deliver on the promise of integrated, patient-centered care. This requires:
Interoperability standards like HL7 FHIR that allow data exchange
API-enabled platforms that can connect disparate systems
Standardized terminology (SNOMED, LOINC) for consistent meaning
Workflow integration that captures structured data without creating burden
When surgeons discuss AI-assisted diagnosis or robotic-enhanced procedures, they're implicitly discussing software integration challenges. The clinical value emerges not from isolated tools but from systems that work together—sharing data, surfacing insights, and supporting decision-making across the care continuum.
The Path Forward: Infrastructure Enables Innovation
The innovations celebrated at Clinical Congress 2025 paint an inspiring picture of surgery's future. Realizing that vision requires parallel investment in the less glamorous but equally essential data infrastructure:
For Individual Surgeons
Advocate for EHR systems with robust structured data capture
Support synoptic reporting initiatives in your specialty
Participate in registries that depend on quality data
Demand interoperability from software vendors
For Healthcare Systems
Invest in data standardization across surgical services
Implement synoptic reporting templates for high-volume procedures
Ensure systems integration allows data to flow seamlessly
Allocate resources for data quality initiatives
For The Profession
Develop and promote specialty-specific data standards
Create synoptic reporting templates for emerging procedures
Establish benchmarks that require comparable data
Recognize data infrastructure as foundational to quality care
Conclusion: The Foundation Matters
The AI breakthroughs, robotic innovations, and quality improvements discussed at ACS Clinical Congress 2025 represent surgery's exciting future. But that future rests on a foundation of structured data, standardized reporting, and integrated software systems.
As we celebrate the innovations, let's also recognize and invest in the infrastructure that makes them possible. The most sophisticated AI algorithm is useless without quality data. The most advanced robotic platform cannot demonstrate its value without standardized outcome tracking. The most ambitious quality initiative cannot succeed without measurable, comparable metrics.
The next generation of surgical excellence will be built not just on brilliant innovation, but on the disciplined infrastructure that turns data into knowledge, knowledge into insight, and insight into better patient care.
How is your institution addressing data standardization and structured reporting? What challenges are you facing in creating the infrastructure for AI and quality improvement?
Share your experiences in the comments.
mTuitive is revolutionizing reporting, data, and analytical software for digital pathology and surgical oncology. Their innovative synoptic reporting software allows for the aggregation of a patient's data with thousands of different reports, giving medical professionals new insights and understanding to elevate the standard of care and benefit the patient. By capturing all required data and ensuring standards compliance, hospitals and surgery centers can improve efficiency and accuracy. With a commitment to continued innovation, mTuitive is at the forefront of shaping the future of medicine, enabling the best minds in healthcare to make better decisions and provide the best possible outcomes for patients. Learn more at www.mtuitive.com.
