In the context of an ethnically diverse, aging society, attention is increasingly turning to health-related quality of life measurement to evaluate healthcare and treatment options for chronic diseases. When evaluating and treating symptoms and concerns such as fatigue, pain, or physical function, reliable and accurate assessment is a priority. Modern psychometric methods have enabled us to move from long, static tests that provide inefficient and often inaccurate assessment of individual patients, to computerized adaptive tests (CATs) that can precisely measure individuals on health domains of interest. These modern methods, collectively referred to as item response theory (IRT), can produce calibrated "item banks" from larger pools of questions. From these banks, CATs can be conducted on individuals to produce their scores on selected domains. Item banks allow for comparison of patients across different question sets because the patient's score is expressed on a common scale. Other advantages of using item banks include flexibility in terms of the degree of precision desired; interval measurement properties under most circumstances; realistic capability for accurate individual assessment over time (using CAT); and measurement equivalence across different patient populations. This work summarizes the process used in the creation and evaluation of item banks and reviews their potential contributions and limitations regarding outcome assessment and patient care, particularly when they are applied across people of different cultural backgrounds.