Previously submitted to: JMIR Medical Informatics (no longer under consideration since Aug 18, 2026)
Date Submitted: Mar 24, 2026
Open Peer Review Period: Apr 17, 2026 - Jun 12, 2026
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An End-to-End DataOps Framework for Automated Predictive Analytics in Regulated Healthcare Environments: Architecture, Implementation, and Evaluation
ABSTRACT
Background:
Skilled nursing facilities (SNFs) operate under data-intensive regulatory environments requiring HIPAA-compliant, continuously deployable predictive analytics pipelines. Existing DataOps approaches address individual pipeline components in isolation but lack an integrated clinical informatics architecture tailored to the long-term care setting.
Objective:
To design, implement, and evaluate an end-to-end DataOps framework for automated predictive analytics in regulated SNF environments, integrating infrastructure-as-code, CI/CD automation, and automated MLOps into a unified architecture.
Methods:
We developed a five-layer, cloud-native DataOps framework unifying Azure Synapse Analytics, Terraform infrastructure-as-code, and GitHub Actions CI/CD. The framework was deployed across five SNF sites serving over 3,500 patients monthly and evaluated over a three-month pilot with a matched three-month pre-implementation baseline.
Results:
The framework reduced manual data engineering effort by 30%, improved 30-day readmission prediction ROC-AUC from 0.82 to 0.89, and was associated with a 12.2% reduction in 30-day unplanned readmissions (148 to 130 events). Dashboard latency was maintained below 15 minutes (mean: 11.4 min) and infrastructure provisioning was repeatable within 30 minutes.
Conclusions:
The proposed DataOps framework provides a reproducible, audit-ready clinical informatics architecture for SNF environments. The integration of CI/CD, infrastructure-as-code, and automated MLOps addresses a gap in the health informatics literature and offers a practical blueprint for operationalizing predictive analytics under regulatory constraints.
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