ON.JourneyView: Empowering Oncology Research & Care
ON.Journey integrates iKnowMed EHR data from The US Oncology Network (The Network) with claims in a single, standardized, research‑ready dataset designed for longitudinal oncology research.
Clinical intent and longitudinal care patterns are aligned from the outset, so teams can generate defensible evidence without separate linkage, downstream reconciliation or rebuilding cohorts as research questions evolve.
How broad is ON.Journey data across community oncology?
Evaluate outcomes and comorbidities using longitudinal clinical data, with claims augmenting completeness. Integrated EHR and claims support evidence generation without opaque linkage or reconciliation of disconnected sources.
Apply consistent analytic approaches across tumor types using standardized datasets designed to scale. Spend less time validating inputs and more time answering high‑impact research questions.
Assess treatment patterns, sequencing and outcomes using longitudinal cohorts that reflect routine community oncology care rather than curated trial populations
Understand how therapies are used outside clinical trials and extend analyses across indications or follow‑up periods without rebuilding cohorts
Support publications, regulatory exchange and external scrutiny with traceable, research‑grade data grounded in real‑world community practice
Establish a reusable evidence foundation that supports repeatable research across tumor areas as expectations for rigor and transparency increase
Which strategic research questions can ON.Journey help you answer?
Longitudinal patient journeys
Understand how patients move through lines of therapy over time using a unified view of clinical intent and real‑world utilization in a single dataset
Depth and breadth of research‑grade data
Analyze more than 150 oncology‑specific variables across clinical and utilization domains, curated and standardized to support defensible evidence generation
Real‑world treatment patterns
Evaluate how oncology therapies are used in practice by interpreting treatment decisions, outcomes and comorbidities, with claims‑based resource use providing additional context
Trial‑to‑real‑world comparison
Assess how real‑world use and outcomes compare with assumptions from clinical trials across broader, community‑based oncology populations
Guidelines and variation
Identify where real‑world treatment patterns diverge from guideline‑ or trial‑based expectations using consistent longitudinal definitions
Generalizability and external validity
Examine how well trial findings translate to routine community oncology care using data that reflect real‑world patient populations
How ON.Journey supports evidence generation over time
Establish a consistent evidence baseline using tumor‑specific datasets that reflect community oncology care. Standardized definitions and integrated clinical and claims‑based signals support early analyses without custom cohort builds.
Generate longitudinal evidence as therapies move into routine use by evaluating treatment patterns, utilization and outcomes together. Integrated data support analyses that extend beyond single studies or point‑in‑time snapshots.
Address new research questions, endpoints or indications using the same standardized datasets. Reuse supports comparability over time as evidence programs mature and scrutiny increases.
Establish a consistent evidence baseline using tumor‑specific datasets that reflect community oncology care. Standardized definitions and integrated clinical and claims‑based signals support early analyses without custom cohort builds.
Generate longitudinal evidence as therapies move into routine use by evaluating treatment patterns, utilization and outcomes together. Integrated data support analyses that extend beyond single studies or point‑in‑time snapshots.
Address new research questions, endpoints or indications using the same standardized datasets. Reuse supports comparability over time as evidence programs mature and scrutiny increases.
Clinical EHR data and claims are integrated natively in a single standardized model, aligning clinical context and utilization from the outset. This reduces reliance on external linkage and downstream reconciliation while supporting defensible longitudinal analyses.
The data are curated with clear provenance, standardized definitions and transparency into how data are captured and prepared. This supports evidence that can withstand scientific review as scrutiny increases.
Datasets are delivered by tumor type in a standardized structure, enabling analysis without bespoke cohort builds or separate claims linkage for each new study.
Data are derived from iKnowMed records, generated within The Network and integrated with claims to support a longitudinal view of routine community oncology care
The consistent data model enables follow‑on analyses across new endpoints, indications or time horizons without rebuilding cohorts, supporting comparability as evidence programs mature.
Talk with an Ontada expert about how ON.Journey can support your oncology research questions over time and whether a standardized, longitudinal data foundation fits your evidence program.