StreamCare: Real-Time Healthcare Analytics at Scale
DOI:
https://doi.org/10.5281/zenodo.20744283Keywords:
Real-Time Healthcare Analytics, Event-Driven Data Streaming, Healthcare Claims Adjudication, Real-Time Claims Processing, Healthcare Quality Measurement, Clinical and Administrative Decision Support, Data Provenance and Instrumentation, Micro-Batch Streaming Architectures, Healthcare Performance Indicators, Closed-Loop Quality Improvement, Scalable Health Data Platforms, Anomaly Detection in Healthcare KPIs, Data Governance in Healthcare Analytics, Low-Latency Health Information Systems, Population-Scale Quality Monitoring.Abstract
Healthcare analytics is increasingly expected to operate in real-time. The demand for real-time queries and feedback has propelled the move toward event-driven data streaming architectures, which have been adopted in many industries—commerce, finance, social networking—but have yet to be fully realized in healthcare. Although challenges remain in isolating, modeling, and sharing the right data, there are clear motivations for both clinical care—with the potential for detecting and acting on sudden changes in patient health—and administrative use cases, with the prospect of providing proactive operations dashboards that alert business analysts to deepening problems that they can address before they escalate into costly crises. This paper focuses on two specific use cases: the real-time processing of healthcare claims as they are submitted and a framework for measuring, monitoring, and improving healthcare quality in real-time and at scale. The first addresses data provenance and instrumentation needs for the timely execution of the rules governing claims submission and adjudication. The second defines the requirements—timeliness, accuracy, completeness, and so on—for quality measurement at scale based on New York State Department of Health and Centers for Medicare & Medicaid Services performance indicators and establishes a framework for providing real-time feedback on adherence to these requirements.
Although a number of high-level components of a comprehensive real-time analytics architecture have been previously proposed or described, decision support, data , governance, and provisioning are still in their formative stages. Building from previous work outlining an event-driven, micro-batch-enabled architecture for real-time data streaming and analytics at scale, the present discussion focuses on claims adjudication and quality measurement use cases. Other descriptive aspects complete the taxonomy of real-time analytics building blocks. Technical requirements are then explicitly addressed for a claims processing framework that analyzes a claims life by identifying the key events, states, and timing constraints that drive the process, and for a real-time quality measurement system that detects anomalies across key performance indicators and provides closed-loop feedback for quality improvement.
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