Combining Health Data Uses to Ignite Health System Learning.

Area of Science:

• Health Informatics
• Public Health
• Health Systems Research

Background:

• Health systems often operate with siloed information pipelines for distinct functions like service commissioning, performance auditing, financial management, public health monitoring, and research.
• These separate pipelines frequently duplicate data extraction, processing, and analysis, despite utilizing common data sources.
• A lack of integrated expertise and shared contextual knowledge across these pipelines hinders optimal data utilization and health system learning.

Purpose of the Study:

• To characterize the essential components—linked data, methods, and expertise—required for health systems to evolve into learning health systems.
• To identify opportunities for consolidating data processing across different uses of common data sources to enhance information quality and reduce duplication.
• To pinpoint challenges associated with scaling up health data reuse to effectively support health system learning and adaptation.

Main Methods:

• Utilized e-health stakeholder consultations and workshops in Northern England (2011-2014) to identify challenges and opportunities.
• Refined concepts through ongoing feedback from collaborators, including patient and citizen representatives, within a regional health informatics research network since 2013.
• Employed a qualitative approach to synthesize insights from stakeholder engagement and expert feedback.

Main Results:

• Identified five distinct information pipelines in health systems (commissioning, auditing, finance, public health, research) that commonly duplicate data efforts.
• Highlighted how fragmented expertise and inaccessible contextual knowledge within separate pipelines lead to suboptimal analyses and hinder system learning.
• Challenged three core assumptions hindering population health intelligence: universality, time-invariance, and reducibility of evidence.

Conclusions:

• Conceptualized a population health and care intelligence system designed to foster health system learning.
• Proposed maturity tests for such systems, emphasizing 'data-action latency'—the time between data availability and actionable insights.
• Advocated for networked critical masses of data, methods, and expertise to minimize data-action latency and stimulate system-wide learning and improvement.