The recent Mayo Clinic announcement caught my attention for reasons that run deep. I am a breast cancer survivor who remembers how much depends on timely information, strong research, and clinical clarity. I am an executive who has spent years advising organizations on data, AI, and digital strategy. One of the ongoing challenges in working with health data is the non standard formats, data types, naming conventions, duplicates, along with privacy and security. Coupled with time, investment and ongoing support of integrated systems.
When Mayo introduced a platform built for shared data, research collaboration, and broader access, it aligned with questions I study closely. How do discoveries reach patients faster? How do institutions work together at scale? How do we build confidence in the tools that support care? How do these collaborations accelerate market leadership with global reach?
Relationships between health systems, non profits, universities and public agencies are not new. The difference here is a nuance, the structure of the relationships are what stands out in these collaborations. Health systems bring clinical depth and patient outcomes data. Research groups contribute study design, scientific rigor, and long-term inquiry. Technology partners supply the infrastructure that supports secure data access, shared analytics, and model development. Public agencies provide oversight, funding pathways, and national reach. Nonprofits add mission alignment and support for disease-specific breakthroughs. Each group carries a different piece of the work, and together they create environments that no single institution could maintain alone. These relationships influence how quickly discoveries move, how widely insights can be applied, and how consistently patients benefit across different regions and care settings. Early trust and transparency to the ecosystem and the public is an accelerator for this model.
Looking across the healthcare landscape, five trends stand out as meaningful for the year ahead.
1. Platform models are emerging as strategic infrastructure
Mayo Clinic’s platform work illustrates how clinical, imaging, and genomic data can live within one shared environment rather than across disconnected systems. Platform_Insights and Platform_Connect give partners a practical way to conduct research, evaluate AI, and support clinical decision-making without building separate infrastructures.
This structure matters for two reasons. It lowers barriers for institutions that want to participate in advanced research but lack in-house capabilities. It also strengthens the reliability of AI by grounding insights in larger and more diverse datasets. Patients gain access to tools informed by broader evidence, and providers gain confidence in the consistency of the results.
2. Oncology collaboratives are producing scale that individual institutions cannot match
Oncology continues to lead in multi-institution research. The Breast Cancer Research Foundation expanded its Drug Research Collaborative to give investigators and labs a shared research environment. Coordinated datasets and aligned protocols improve the pace and clarity of discovery.
A recent breast cancer study A Multimodal AI Model May Improve Recurrence Risk Stratification in Early Breast Cancer reinforced the value of this structure. ECOG-ACRIN, Caris Life Sciences, BCRF, the National Cancer Institute, and the Breast Cancer Research Stamp Fund worked together on a multimodal AI model for early-stage recurrence risk. As a survivor, I approached this research with personal interest. The partnership stood out for its transparency and structure. Public agencies, nonprofit organizations, and commercial partners contributed to one coordinated effort. This type of collaboration supports stronger evidence and provides a path toward clinical use.
For patients, shared data leads to earlier insights and more accurate risk assessment. For clinicians, it reduces variability and strengthens decision support.
3. International networks are expanding privacy-preserving research at scale
Several countries are investing in national research environments that allow institutions to participate in AI development without transferring raw data. Kakao Healthcare and Google Cloud support a federated network of hospitals in South Korea. The UK’s HDR UK and the European Health Data Space provide secure frameworks where universities, regulators, and care organizations contribute to joint studies.
These networks create practical benefits. Institutions maintain control of their data while contributing to large cross-regional studies. Researchers gain access to broader insight. Patients gain from tools informed by a wider range of clinical and demographic backgrounds.
4. Biopharma is building data alliances that strengthen discovery
Pharmaceutical and biotech organizations are forming alliances grounded in shared datasets and coordinated research ambitions. Bristol Myers Squibb and Takeda connect structural-biology datasets to support advanced protein modeling. Pfizer and Tempus align biomarker and real-world datasets to support precision oncology. Roche and Foundation Medicine work together to strengthen genomic understanding for diagnostic and therapeutic decision-making.
Cleveland Clinic and IBM created the Discovery Accelerator to align clinical data with computational biology and quantum-ready analytics. These alliances shorten the time from research to trial design and give scientists the ability to test ideas with clarity. Patients ultimately benefit from research grounded in stronger evidence and broader clinical context.
5. Trusted data connectivity is forming a new layer of healthcare infrastructure
A new category of platforms supports secure data collaboration across the healthcare ecosystem. Datavant connects clinical, payer, lab, and public health datasets through privacy-preserving methods. Sherpa.ai and Owkin support federated learning that allows models to train across institutions without moving sensitive information.
These tools give health systems an entry point into research partnerships without requiring large technical builds. They also introduce clearer governance practices that support trust across stakeholders. Providers gain access to research environments that once sat beyond their reach. Patients gain from insights built on diverse datasets that respect privacy.
Why these trends matter for care and innovation
Healthcare leaders face questions that influence strategy, operations, and the experience of care. Progress relies on choosing partners with intention, managing data responsibly, and taking part in environments built for joint research and shared insight.
We are looking for relatable applications of AI. Healthcare is an area we can all root for, as ourselves, our families or our neighbors are all patients. I am encouraged for the new year as these collaborations expand and accelerate. For patients, these collaborations support earlier detection, clearer risk assessment, and stronger guidance during treatment decisions. For providers, collaboration reduces the burden of creating advanced analytics and AI programs alone. For industry leaders, coordinated research supports evidence that stands up to real clinical use.
Institutions across the country are gathering around shared platforms, structured governance, and research environments designed for joint work. Research gains strength when many teams participate. AI gains reliability when trained across broader populations. Healthcare innovation is leaning toward networks built for scale, trust, and shared purpose. Organizations preparing for this model now will influence how AI supports care and clinical outcomes in the years ahead.
Editor’s note: The author has no financial relationships with any of the companies named in this article.
Photo: 9amstock, Getty Images
Marva Bailer is CEO of Qualaix, an industry expert featured in national news who covers digital innovation, AI, cybersecurity, and the evolving business of health. Her perspective connects emerging technology with real conditions in care and research settings. It reflects decades in leadership, work across data rich environments, and her patient experience as a breast cancer survivor.
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