OBJECTIVES: Real world evidence (RWE)-based tools are important to fill data gaps and capture real world cost and treatment patterns in economic modeling.  The objective of this study was to assess the capabilities of US-based longitudinal, retrospective data assets to inform health economic models in diabetes and oncology. METHODS: To illustrate the availability of RWE data for modeling, several IMS data assets were compared in a landscape assessment, including Pharmetrics Plus (PMTX+), Oncology Electronic Medical Record (EMR), Ambulatory EMR, Charge Data Master (CDM), Pharmacy (LRx), Office Based Medical Claims Data (Dx), and Laboratory Data (Labs).  Diabetes and oncology were chosen to illustrate the range of needed inputs across commonly modeled diseases. Data availability was assessed in a matrix framework across core categories of model inputs including: treatment patterns, epidemiology, adverse events (AEs), patient health metrics (i.e., BMI), costs, resource use, and disease status. RESULTS: For oncology, inputs for treatment patterns (PMTX+, Oncology EMR), epidemiology (PMTX+, Oncology EMR, CDM), AEs (PMTX+, Oncology EMR, CDM), and resource use (PMTX+, Oncology EMR) are available in several data assets but information on patient health metrics and disease status may require leveraging the Oncology EMR database to capture sufficient detail. For diabetes, availability of data for populating models is more robust increasing information on treatment patterns (PMTX+, LRx linked to Dx), epidemiology (PMTX+, Ambulatory EMR, CDM), resource use (PMTX+, Labs, Dx), AEs (PMTX+, Ambulatory EMR, CDM, Dx), and patient health metrics (Ambulatory EMR, CDM).   While several databases report cost outcomes, the most relevant costs for modeling are found in PMTX+. CONCLUSIONS: Core concepts for economic modeling can be populated with RWE assets in the US though no single database is likely to cover all inputs. The choice of data should be informed by the research question, patient counts and the ability to link databases.