MACHINE LEARNING AND SECURE DATA PIPELINE FRAMEWORKS FOR IMPROVING PATIENT SAFETY WITHIN U.S. ELECTRONIC HEALTH RECORD SYSTEMS

Abstract

This study examined how secure data pipelines operated as quantitative determinants of machine learning (ML) reliability and patient safety outcomes in Electronic Health Record (EHR) environments among U.S. healthcare providers. A retrospective, multi-site quantitative design was applied to de-identified EHR encounter streams, linking provider-level pipeline maturity (confidentiality, integrity, availability, and data quality) with ML safety-prediction performance and EHR-derived safety endpoints. The methodology and findings were grounded in an overall review of spanning secure healthcare data pipelines, EHR-driven ML safety prediction, cybersecurity incident impacts, fairness in clinical ML, and EHR safety endpoint operationalization; this number should match the total reported across the methods and findings sections of the article. The analytic cohort comprised 12 providers contributing 184,732 adult encounters, with a median of 14,980 encounters per provider. Pipeline maturity demonstrated cross-site differentiation (mean = 72.8, SD = 9.4), accompanied by measurable variability in quality and security indicators: missingness averaged 6.4% (SD = 3.1), timestamp misalignment averaged 3.8 per 1,000 events (SD = 1.9), unit harmonization errors averaged 5.6 per 10,000 labs (SD = 2.4), audit-log completeness averaged 91.5% (SD = 4.7), encryption coverage averaged 94.2% (SD = 3.9), and downtime averaged 2.7 hours per quarter (SD = 1.4). ML models for safety prediction showed stable reliability across endpoints, with mean discrimination AUROC = 0.84 (SD = 0.03), calibration slope = 0.97 (SD = 0.06), false-alarm burden = 14.9 alerts per 100 (SD = 3.5), lead-time advantage for deterioration alerts = 3.6 hours (SD = 1.1), and cross-provider transportability loss ΔAUROC = 0.04 (SD = 0.02). Safety outcomes occurred at clinically meaningful rates: preventable adverse drug events = 1.9%, abnormal-result follow-up delays = 7.6%, deterioration/failure-to-rescue events = 2.4%, and hospital-acquired harms = 3.1%. Multilevel regression indicated that higher pipeline maturity predicted lower composite harm incidence (β = −0.21, p < .001), while ML reliability independently reduced harms (β = −0.18, p < .001). Mediation analysis showed a significant indirect pathway through ML reliability (indirect effect = −0.09, p = .002) alongside a remaining direct maturity effect (β = −0.11, p = .007). Moderation tests indicated stronger maturity-to-reliability effects under higher interoperability (interaction β = 0.14, p = .019). Overall, the results demonstrated a statistically linked infrastructure–analytics pathway through which secure pipelines enhanced ML reliability and corresponded to lower patient-harm burdens in EHR-driven care.