AI Failures Trace Back to Oncology Data Management Issues

SEATTLE - Nov. 28, 2025 - PRLog -- Artificial intelligence is often promoted as a breakthrough in AI cancer treatment, yet most oncology SaaS platforms fail to reach clinical-grade performance. According to new research from Jelvix, the problem lies not in the algorithms but in the fragile pipelines behind them. To address this gap, the company has released its Oncology Data Management Playbook, offering digital health providers a practical roadmap for integration resilience.

Fragile Foundations in Oncology Data Management

The research highlights that oncology medical software underperforms because of brittle interoperability layers. Despite significant investments in AI in oncology, data is often processed in static, siloed formats that undermine scalability and reliability.

Key findings from Jelvix's research show that:

-85% of oncology SaaS platforms still process genomic and pathology data in static PDFs, flat files, or portals.
-Only ~20% support native embedding into leading EHRs such as Epic and Cerner.
-70% report AI accuracy problems tied to incomplete or unclean datasets.
-50% remain unprepared for multi-regional compliance, including GDPR and MDR.

These shortcomings reveal why many companies investing in clinical research data management services fail to achieve results—the underlying issue is the absence of robust healthcare interoperability solutions.

Jelvix offers the Oncology Data Management Playbook (https://jelvix.com/blog/oncology-data-management-playbook) as an open asset, in line with the company's mission to support healthcare software development through knowledge sharing.

AI in Oncology: Inside the Oncology Data Playbook

The playbook provides a research-driven framework that addresses recurring failure points: static ingestion pipelines, late EHR integration, fragmented patient-reported outcomes, insufficient compliance handling, and how to improve interoperability in healthcare (https://jelvix.com/blog/how-to-improve-interoperability-i...). It provides CIOs, CTOs, and digital health startups a step-by-step approach to establish scalable oncology data management pipelines that can power AI-driven oncology workflows.

"In oncology, AI doesn't fail because of bad algorithms. It fails because the data foundations are fragile. Our Oncology Data Playbook gives digital health platforms the building blocks to succeed at scale," said Oleksandr Andrieiev, CEO at Jelvix.

By aligning oncology medical software with modern AI software development practices, the playbook demonstrates how resilient integrations can unlock the next stage of AI in oncology.

Global Implications for Healthcare Software Development

The importance of integration resilience extends beyond oncology SaaS. With global adoption of digital health solutions accelerating, weak pipelines risk slowing innovation across EHR vendors, trial-matching applications, and clinical decision support tools. Jelvix offers its playbook as an open asset—accepted as part of the company's overall mission to support healthcare software development through knowledge sharing.

By reframing oncology data management as a technological challenge rather than purely a clinical one, the playbook underscores the critical role of interoperability in scaling AI cancer treatment worldwide.