Optimizing Data Cleaning and Preprocessing Techniques for Large-Scale Electronic Health Records (EHRs)

The exponential growth of Electronic Health Records (EHRs) has transformed the landscape of healthcare, enabling data-driven decision-making and personalized patient care. However, the effectiveness of EHR systems is often compromised by the presence of incomplete, inconsistent, or erroneous data. This study focuses on optimizing data cleaning and preprocessing techniques for large-scale EHRs, aiming to enhance data quality and facilitate meaningful analyses that can improve healthcare outcomes.Employing a systematic approach, this research explores various data cleaning methodologies, including deduplication, data validation, and normalization, along with preprocessing steps such as data transformation and integration. By leveraging machine learning algorithms and advanced statistical methods, the study assesses the efficacy of these techniques in addressing common data quality issues encountered in EHR datasets. A mixed-methods research design is employed, combining quantitative datasets with qualitative insights from healthcare professionals and data scientists to provide a comprehensive understanding of the challenges and solutions related to data cleaning in EHR contexts.Preliminary findings reveal that optimized data cleaning techniques significantly enhance the integrity and usability of EHR data. Additionally, the research identifies best practices for implementing data preprocessing strategies that streamline data integration processes and facilitate the development of predictive models for healthcare analytics. By creating a framework for effective data cleaning and preprocessing, this study aims to contribute to enhanced patient care, improved clinical workflows, and informed healthcare policy decisions.This research not only addresses the technical aspects of data management but also highlights the importance of fostering a culture of data stewardship within healthcare organizations. By ensuring high-quality data, professionals can leverage analytics to derive insights that advance patient care while promoting operational efficiencies. Ultimately, this study serves as a resource for healthcare organizations seeking to optimize their EHR systems and maximize the potential of their vast data repositories.