OBJECTIVES: NOVELTY is a global 3-year observational study of 14,800 patients with asthma and/or COPD, to describe patient characteristics, disease burden, treatment patterns, and identify phenotypes/endotypes associated with differential outcomes. An electronic medical records (EMR) feasibility study was performed to optimize NOVELTY design; data from Australia, China, and Japan are presented. METHODS: EMRs from patients with ≥1 year of data from most recent diagnosis were identified from databases over a 1-year period using diagnostic codes. In China, centralized EMR databases were unavailable, so EMR data for a sample of patients were extracted from different hospital systems, in those hospitals prepared to share EMR dataDisease severity was classified by clinical guideline-based algorithms. Coverage and completeness EMR variables were assessed relative to the desired NOVELTY protocol. RESULTS: EMRs identified 6,666-35,730 patients with asthma and 1,925-35,248 with COPD in the various countries; 5% (Australia) and 13% (Japan) had both diagnoses. Disease severity could not be classified for 57% (asthma) and 63% (COPD) of patients in Japan, 22% (asthma) and 15% (COPD) in Australia, and ≤26% (asthma) and ≤22% (COPD) in China, due to treatment groupings that are not defined in algorithms or missing treatment type and dosage data. Australian EMRs were generally representative of the country-wide population; Japanese EMRs were heavily weighted towards older patients (>55 years) in both genders (data unavailable for China). In China and Japan, EMRs routinely captured demographic characteristics, medical history, laboratory tests, and treatment; additional variables captured in Australian EMRs encompassed physiological data, exacerbation risk factors, and partial information on imaging and lung function. CONCLUSIONS: EMRs contain information on asthma and/or COPD diagnoses and disease severity, providing an insight into potential NOVELTY study populations. This study highlighted that treatment patterns do not always fit clinical guideline-based algorithms, and identified variables not routinely collected or recorded by physicians.