MACHINE LEARNING AND SECURE DATA PIPELINES FOR ENHANCING PATIENT SAFETY IN ELECTRONIC HEALTH RECORD (EHR) AMONG U.S. HEALTHCARE PROVIDERS

Abstract

This quantitative study investigates the integrated role of machine learning (ML) performance, secure data pipelines, governance maturity, and interoperability infrastructures in improving patient safety outcomes within Electronic Health Record (EHR) environments among U.S. healthcare providers. Drawing upon data from 22 institutions encompassing over 1.26 million de-identified patient records, the research sought to determine the extent to which algorithmic accuracy and data governance collectively predict measurable safety improvements. The study employed a multi-variable framework featuring descriptive statistics, correlation analysis, confirmatory factor analysis (CFA), and multiple linear regression modeling. Patient safety was measured using standardized Agency for Healthcare Research and Quality (AHRQ) indicators, while predictors included ML accuracy metrics (AUC-ROC, F1-score), Secure Data Pipeline Index (SDPI), Governance Maturity Score (GMS), and Interoperability Index (I²). Results indicated a strong explanatory power for the overall regression model (R² = 0.694; Adjusted R² = 0.673; F = 38.45; p < .001), confirming that the combined predictors accounted for nearly 70% of the variance in patient safety scores. ML predictive accuracy demonstrated the strongest individual contribution (β = 0.46, p < .001), followed by the Secure Data Pipeline Index (β = 0.32, p < .01), Governance Maturity (β = 0.27, p < .05), and Interoperability (β = 0.28, p < .01). Reliability analysis yielded Cronbach’s α values above 0.80 for all constructs, confirming internal consistency, while CFA results supported strong construct validity (CFI = 0.948, RMSEA = 0.054). These findings suggest that technological precision, data security, and governance oversight must co-evolve to achieve sustainable patient safety gains. The study concludes that healthcare institutions integrating ML analytics with secure, interoperable, and well-governed infrastructures experience superior safety performance, reinforcing the need for a socio-technical model of digital health reliability. Implications extend to policymakers and administrators seeking to align data-driven innovation with regulatory compliance, ethical governance, and long-term clinical resilience.