International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Automated Data Quality Frameworks for Healthcare Data Lakes

Author(s) Narasimha Chaitanya Samineni
Country United States
Abstract Healthcare data lakes serve as unified repositories that integrate electronic health records (EHR), claims data, laboratory systems, imaging metadata, device-generated patient streams, and financial information into scalable analytical environments. As these data ecosystems expand in volume and heterogeneity, ensuring data quality becomes increasingly difficult. Manual data quality checks are insufficient for detecting schema drift, missing values, inaccurate clinical codes, ingestion errors, or time-dependent inconsistencies. Automated Data Quality (ADQ) frameworks introduce scalable mechanisms to evaluate, score, and enforce data quality rules across ingestion, transformation, and consumption layers. This research article proposes a comprehensive ADQ framework for healthcare data lakes, integrates rule-based and machine-learning-driven validation, introduces anomaly detection techniques, and formalizes a governance-aligned scoring model. The study contributes an architectural blueprint, large data quality metrics tables, validation guidelines, and implementation recommendations for health systems, payers, and analytics platforms. The framework emphasizes automation, metadata-driven design, regulatory alignment, and real-time operability to meet the evolving data needs of digital health ecosystems. [1][3][6][8][10]
Keywords Healthcare Data Lakes, Data Quality, Automated Data Quality Frameworks, Anomaly Detection, Metadata Profiling, Clinical Rules, Healthcare Analytics, Data Governance.
Field Engineering
Published In Volume 5, Issue 6, November-December 2023
Published On 2023-11-08
DOI https://doi.org/10.36948/ijfmr.2023.v05i06.66459
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E-ISSN 2582-2160
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CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

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