There is currently a lack of consistency and generalizability in the methods used to assess electronic health record (EHR) data quality as well as abstracted clinical or EHR data. Using the Boolean function, keywords were used to search and source for articles which were later screened based on their titles and objectives to select only relevant literature materials for this study. A total of nineteen (19) articles were finally obtained and used for this review. The findings from this review show that ensuring reliability in clinical data and clinical data abstraction requires a combination of strong conceptual frameworks, standardized technical processes, and structured human practices. For clinical data, reliability is strengthened through multi-dimensional quality frameworks, standardized pre-processing pipelines, and rigorous validation methods that address missingness, inconsistencies, and temporal complexity. For clinical data abstraction, reliability depends on trained personnel, clear abstraction manuals, continuous quality assurance, and the strategic use of automation supported by human oversight. Manual abstraction remains essential for complex or subjective information, while automated and hybrid approaches improve efficiency for structured data.