Summary
Objectives: In this paper we aim to characterise the critical mass of linked data, methods and
expertise required for health systems to adapt to the needs of the populations they
serve – more recently known as learning health systems. The objectives are to: 1)
identify opportunities to combine separate uses of common data sources in order to
reduce duplication of data processing and improve information quality; 2) identify
challenges in scaling-up the reuse of health data sufficiently to support health system
learning.
Methods: The challenges and opportunities were identified through a series of e-health stakeholder
consultations and workshops in Northern England from 2011 to 2014. From 2013 the concepts
presented here have been refined through feedback to collaborators, including patient/citizen
representatives, in a regional health informatics research network (www.herc.ac.uk).
Results: Health systems typically have separate information pipelines for: 1) commissioning
services; 2) auditing service performance; 3) managing finances; 4) monitoring public
health; and 5) research. These pipelines share common data sources but usually duplicate
data extraction, aggregation, cleaning/preparation and analytics. Suboptimal analyses
may be performed due to a lack of expertise, which may exist elsewhere in the health
system but is fully committed to a different pipeline. Contextual knowledge that is
essential for proper data analysis and interpretation may be needed in one pipeline
but accessible only in another. The lack of capable health and care intelligence systems
for populations can be attributed to a legacy of three flawed assumptions: 1) universality:
the generalizability of evidence across populations; 2) time-invariance: the stability
of evidence over time; and 3) reducibility: the reduction of evidence into specialised
subsystems that may be recombined.
Conclusions: We conceptualize a population health and care intelligence system capable of supporting
health system learning and we put forward a set of maturity tests of progress toward
such a system. A factor common to each test is data-action latency; a mature system
spawns timely actions proportionate to the information that can be derived from the
data, and in doing so creates meaningful measurement about system learning. We illustrate,
using future scenarios, some major opportunities to improve health systems by exchanging
conventional intelligence pipelines for networked critical masses of data, methods
and expertise that minimise data-action latency and ignite system-learning.
Keywords
Health data reuse - secondary uses - meaningful use - learning health systems - adaptive
health systems - intelligence pipelines - health-care evidence - population health