Medical societies hold one of the most valuable and underappreciated assets in healthcare today: specialty clinical data.
Through years of quality reporting, education, and clinical collaboration, many have developed longitudinal, clinically grounded registries that reflect real-world practice. As demand for real-world evidence grows and new analytical capabilities emerge, these datasets are becoming more valuable and increasingly sought after.
Medical societies now face a clear and consequential opportunity, to step forward and shape how specialty data is used or allow external contributors to define that future. The stakes in healthcare are uniquely high. Clinical data and analytics increasingly shape patient care, and when misapplied, can have direct implications on outcomes. The way clinical data is structured, interpreted, and applied is not just a technical issue, it is a matter of clinical accuracy, trust, and patient safety.
At the same time, the broader healthcare data landscape remains fragmented. Electronic health records have digitized care at scale, but the resulting data is often inconsistent across organizations1, lacks specialty-specific definitions, and is in turn difficult to use for longitudinal analysis. Many large-scale aggregation efforts prioritize size over clinical coherence, producing datasets that can be analyzed but are harder to translate into meaningful improvements in patient care.
As interest in applying AI and advanced analytics to healthcare data grows, this lack of cohesion becomes more consequential. Tools built on disjointed or inconsistently defined data can generate insights that are directionally interesting but clinically unreliable. When data is aggregated and used outside of specialty oversight, there is a greater likelihood of variation in interpretation, misaligned incentives, and limited accountability to the clinicians generating the data.
This is where specialty registries stand apart. Unlike generalized datasets, registries are built within the context of a specialty. They reflect shared definitions, clinically meaningful outcomes, and workflows that mirror real-world practice. They enable longitudinal tracking across sites of care and provide a level of consistency that is difficult to reconstruct after the fact. For years, these assets have primarily supported regulatory reporting and quality programs such as MIPS. But their potential extends far beyond compliance. That gap between what registries are today and what they could enable is becoming increasingly visible.
In this environment, medical societies are uniquely positioned to lead. They bring clinical authority grounded in deep expertise, along with longstanding trust from members and contributing practices. They have experience operating national-scale registries and establishing consensus-driven definitions and measures. Just as importantly, they have governance structures that can balance innovation with accountability, ensuring that new uses of data are introduced in ways that preserve clinical meaning, transparency, and trust over time.
However, realizing this opportunity requires a shift in how registries are positioned and operated. Most specialty registries today remain optimized for reporting rather than reuse. To move forward, societies must evolve from managing reporting programs to stewarding data ecosystems. This means defining clear governance models, establishing guardrails for appropriate use, and creating standardized approaches to data access, definition, and reuse. It also means investing in the tools and technologies that enable these capabilities, rather than allowing external platforms to dictate how specialty data is structured and applied.
The alternative is not neutral. As the value of healthcare data continues to increase2, technology and analytics firms are moving quickly to aggregate and operationalize it. Without deliberate leadership from medical societies, decisions about how specialty data is used may increasingly be shaped by commercial priorities rather than clinical ones. The choice societies now face is to help set the agenda or respond to one set by others.
Leading in this space does not mean slowing innovation. Rather, it means guiding it with intention. When governance, standards, and clinical context are established upfront, innovation can move faster and more effectively. AI and advanced analytics can operate within clearly defined boundaries, enhancing clinical expertise rather than attempting to replace or approximate it after the fact. Data can be collected once and reused across multiple applications, such as research, quality improvement, and clinical decision support, all while maintaining consistency and oversight.
Done well, this approach has meaningful downstream impact. It enables real-world research that better reflects clinical practice and allows quality initiatives to align more closely with specialty-defined priorities. It creates a foundation for collaboration across organizations without eroding clinician trust and ensures that new technologies are introduced through deliberate, clinically informed processes rather than reactive adoption. Most importantly, it supports a more sustainable and human-centered model of care.
In this model, clinicians remain at the core but are better supported by structured, meaningful data and insight. The burden of fragmented systems and duplicative reporting can be reduced, helping to alleviate burnout. Time and attention can shift back toward patients rather than screens, supported by tools that augment rather than obstruct care delivery.
Medical societies have a narrow but important window to define this future. By stepping forward now and establishing the guardrails and the infrastructure to support them, they can ensure that specialty data continues to serve clinicians and the patients it represents. Medical societies now have the opportunity to define the future of specialty data or work within frameworks established by others.
About Charles Sokolowski
Charles Sokolowski is a Clinical Data Specialist with expertise in healthcare quality reporting, clinical data analytics, and health data platforms. In his role, he works closely with clinical and technical teams to support quality measurement and performance initiatives.
References:
- Gurupur V, Hooshmand S, Fernandes Prabhu D, Trader E, Salvi S. Incompleteness of Electronic Health Records: An Impending Process Problem Within Healthcare. Healthcare. 2025;13(22):2900. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC12652376/
- American Medical Association. Trends in Healthcare Spending. Updated July 7, 2026. Available at: https://www.ama-assn.org/about/ama-research/trends-healthcare-spending
