The Mayo Clinic’s recent statement caught my attention, and the reasons for this are deep. I am a breast cancer survivor, and I remember how much timely information, strong research, and clinical clarity depended on me. I'm an executive who has been advising organizations on data, AI, and digital strategy for many years. Among the ongoing challenges in working with health data are non-standard formats, data types, naming conventions, duplication, and privacy and security. Plus time, investment and ongoing support for integrated systems.
When Mayo launched a platform built for sharing data, research collaboration and wider access, it aligned with issues I study closely. How can we bring discoveries to patients faster? How do agencies collaborate at scale? How do we build confidence in the tools that support care? How do these collaborations accelerate market leadership and global reach?
The relationship between health systems, nonprofits, universities, and public agencies is not new. The difference here is nuanced, and the structure of the relationship is what stands out most in these collaborations. Health systems bring clinical depth and patient outcomes data. The research team contributed study design, scientific rigor, and long-term inquiry. Technology partners provide the infrastructure that supports secure data access, shared analysis, and model development. Public agencies provide oversight, funding pathways and national influence. Nonprofits increase mission alignment and support for specific disease breakthroughs. Each group has a different job, and together they create an environment that no one agency can sustain alone. These relationships influence how quickly discoveries are made, how broadly insights are applied, and how sustained patient benefits are across geographies and care settings. Early trust and transparency in the ecosystem and the public are accelerators for this model.
Looking across the healthcare landscape, there are five trends that will be significant for the year ahead.
1. The platform model is becoming strategic infrastructure
Mayo Clinic’s platform work illustrates how clinical, imaging and genomic data can exist in a shared environment rather than across disconnected systems. Platform_Insights and Platform_Connect provide partners with a practical way to conduct research, evaluate artificial intelligence and support clinical decision-making without having to build separate infrastructure.
This structure is important for two reasons. It lowers barriers for institutions that want to participate in advanced research but lack in-house capabilities. It also enhances the reliability of AI by providing insights across larger and more diverse data sets. Patients have access to tools powered by a wider range of evidence, while providers are confident in the consistency of results.
2. Oncology collaboration creates a scale that no single institution can match.
Oncology continues to lead in multi-institutional research. The Breast Cancer Research Foundation has expanded its Drug Research Collaborative to provide a shared research environment for researchers and laboratories. Harmonized data sets and consistent protocols increase the speed and clarity of discovery.
A recent breast cancer study, “Multimodal artificial intelligence model improves recurrence risk stratification in early-stage breast cancer,” reinforces the value of this construct. ECOG-ACRIN, Caris Life Sciences, BCRF, the National Cancer Institute, and the Breast Cancer Research Stamp Fund jointly developed a multimodal AI model targeting early recurrence risk. I conducted this research with personal interest as a survivor. The partnership stands out for its transparency and structure. It’s a coordinated effort involving public agencies, nonprofits, and business partners. This type of collaboration supports stronger evidence and provides pathways to clinical use.
For patients, shared data can lead to earlier insights and more accurate risk assessments. For clinicians, it can reduce variability and enhance decision support.
3. International networks are expanding privacy protection research on a large scale
Some countries are investing in national research environments that enable institutions to participate in AI development without transferring raw data. Kakao Healthcare and Google Cloud support South Korea's consortium of hospitals. HDR UK and the European Health Data Space provide secure frameworks where universities, regulators and care organizations can contribute to joint research.
These networks create real benefits. Institutions maintain control of their data while contributing to large, cross-regional research. Researchers can gain broader insights. Patients can benefit from tools across a wider range of clinical and demographic contexts.
4. Biopharma is building data alliances to enhance discovery
Pharmaceutical and biotech organizations are forming alliances based on shared data sets and coordinated research goals. Bristol-Myers Squibb and Takeda connect structural biology data sets to support advanced protein modeling. Pfizer and Tempus combine biomarkers with real-world data sets to support precision oncology. Roche and Foundation Medicine work together to enhance genomic understanding for diagnostics and treatment decisions.
Cleveland Clinic and IBM created Discovery Accelerator to combine clinical data with computational biology and quantum-ready analytics. These alliances shorten the time from research to trial design and enable scientists to clearly test ideas. Patients ultimately benefit from research based on stronger evidence and a broader clinical context.
5. Trusted data connections are forming a new healthcare infrastructure layer
A new class of platforms enables secure data collaboration across the healthcare ecosystem. Datavant connects clinical, payer, laboratory and public health data sets with a privacy-preserving approach. Sherpa.ai and Owkin support federated learning, allowing models to be trained across institutions without moving sensitive information.
These tools provide health systems with an entry point into research partnerships without requiring a large-scale technology build. They also introduced clearer governance practices to support trust among stakeholders. Providers gain access to research environments that were once beyond their capabilities. Patients benefit from insights based on disparate data sets that respect privacy.
Why these trends matter for care and innovation
Healthcare leaders face issues that impact strategy, operations, and the care experience. Progress relies on consciously choosing partners, responsibly managing data, and engaging in environments built for joint research and shared insights.
We are looking for related applications of artificial intelligence. Healthcare is an area we can all support, as we ourselves, our family members, or our neighbors are all patients. I’m encouraged about the new year as these collaborations expand and accelerate. For patients, these collaborations support earlier detection, clearer risk assessment, and stronger guidance in treatment decision-making. For providers, collaboration relieves the burden of creating advanced analytics and artificial intelligence programs individually. For industry leaders, coordinated research supports evidence that stands up to actual clinical use.
Institutions across the country have come together around a shared platform, structured governance and a research environment designed for joint work. Research becomes more powerful when many teams are involved. When AI is trained on a wider population, it gains reliability. Healthcare innovation favors networks built for scale, trust and sharing. Organizations that prepare for this model now will impact how AI supports care and clinical outcomes for years to come.
Editor's note: The author has no financial relationship with any company mentioned in this article.
Photo: 9amstock, Getty Images
Marva Bailer, CEO of Qualaix, is an industry expert in the national news covering digital innovation, artificial intelligence, cybersecurity and the growing health business. Her perspective connects emerging technologies to real-world situations in care and research settings. It reflects her decades of leadership, work in data-rich environments, and her patient experience as a breast cancer survivor.
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