OneOncology Releases AI Playbook for Community Clinics
The new guide helps oncology practices navigate artificial intelligence adoption and long-term infrastructure planning.
Updated on Oct. 19, 2026 in Artificial Intelligence
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OneOncology has introduced a new artificial intelligence playbook designed to assist community oncology practices with technology adoption. The resource provides guidance on contract writing and strategic planning without endorsing specific commercial products.
Why it matters
The playbook addresses significant technology gaps in pathology and imaging integration that currently hinder AI adoption in clinical settings. By focusing on systemic challenges rather than specific tools, it provides a flexible framework for diverse practices.
The organization utilizes iterative artificial intelligence models to extract data from pathology and imaging reports. These systems aim to support a 98% frequency of smooth workflow operations in clinical settings.
The players
OneOncology
OneOncology is a network of community oncology practices that provides clinical and operational support to physicians across the United States.
The details
Authored by a committee of physicians, the playbook ensures that terminology remains accessible and relevant to clinical practitioners. It prioritizes long-term infrastructure considerations over immediate product selection to help clinics prepare for a 3 to 5 year horizon.
Timeline
The guidance covers a strategic planning period of 3 to 5 years.
The Tech Race
The OneOncology playbook follows the pattern set by the ONC Health IT Certification Program by establishing industry-wide standards for digital tool implementation. This move mirrors the broader transition toward standardized digital infrastructure across the healthcare sector.
Community oncology practices can use this framework to streamline the integration of pathology and imaging reports into their daily digital workflows. This standardization is designed to reduce technical hurdles, potentially improving the reliability of patient document processing.
The takeaway
Clinics should prioritize scalable infrastructure over selecting specific software products to ensure long-term adaptability. Successful AI adoption in healthcare relies more on resolving foundational data integration gaps than on individual model capabilities.
Further reading
For more context on how medical systems are integrating new tools, visit our Artificial Intelligence section.
Source note: This article includes information reported by Ajmc.
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