The Biomedical Research Hub: a federated platform for patient research data

NCI Cancer Research Data Commons: Core Standards and Services

The NCI Cancer Research Data Commons (CRDC) is a collection of data commons, analysis platforms, and tools that make existing cancer data more findable and accessible by the cancer research community. In practice, the two biggest hurdles to finding and using data for discovery are the wide variety of models and ontologies used to describe data, and the dispersed storage of that data. Here, we outline core CRDC services to aggregate descriptive information from multiple studies for findability via a single interface and to provide a single access method that spans multiple data commons. See related articles by Wang et al., p. 1388, Pot et al., p. 1396, and Kim et al., p. 1404.

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise

Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.

Economical Utilization of Health Information with Learning Healthcare System Data Commons

Finding, accessing, sharing, and analyzing patient data from a clinical setting for collaborative research has continually proven to be a challenge in healthcare organizations. The human and technological architecture required to perform these services exist at the largest academic institutions but are usually under-funded. At smaller, less academically focused healthcare organizations across the United States, where the majority of care is delivered, they are generally absent. Here we propose a solution called the Learning Healthcare System Data Commons where cost is usage-based and the most basic elements are designed to be extensible, allowing it to evolve with the changing landscape of healthcare. Herein we also discuss our reference implementation of this platform tailored specifically for operational sustainability and governance using the data generated in a hospital setting for research, quality, and educational purposes.