This chapter examines the roles of noise and complexity in survey-based assessments like person-reported outcome measures (PROMs) and experience measures (PREMs). Noise, caused by variability in judgments, occurs during survey responses and can lead to inconsistent outcomes. It is categorized into level noise (differences among individuals), stable pattern noise (respondent tendencies), and occasion noise (temporary variations). Bias, though more studied, often causes systematic deviation and is typically less severe than noise. The chapter explores techniques to reduce noise and bias using decision hygiene principles, emphasizing accuracy, structured judgments, and independent evaluations. Complexity theory highlights healthcare as a dynamic, adaptive system where small changes can have large, unpredictable effects. The Non-Adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework is introduced, detailing challenges in adopting innovations across technological, organizational, and contextual dimensions. By managing noise and addressing complexity, healthcare providers can implement PROMs effectively, improve decision-making, and support sustainable innovations in patient care.