A national data-driven approach to population segmentation has been developed to support Population Health Management (PHM) outlined in the NHS Long Term Plan. This Reference Guide provides the background, definitions and Segmentation Dataset output delivered as part of this initiative.
For further details on the subsegment (condition) definitions click here.
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A national data-driven approach to population segmentation has been developed to support Population Health Management (PHM) outlined in the NHS Long Term Plan.
The segmentation approach used is an adaptation of the internationally recognised ‘Bridges to Health’ (B2H) segmentation model – a life course model that groups people into 8 segments. From the healthy / generally well population to populations at the end phases of life.
Data from the National Commissioning Data Repository (NCDR) for the entire population have been transformed into a person-centred segmentation dataset (or data model) that can be used by data analysts to derive segment-specific insights. This has been developed within NHS England’s data environment.
Originally developed in 2019 by Data, Analysis and Intelligence Service (DAIS) in NHSE, Public Health England (PHE), Outcomes Based Healthcare (OBH) and Arden & GEM CSU.
Further information on the National Bridges to Health Segmentation Dataset can be found in the Population and Person Insights (PaPI) Workspace on Future NHS.
Population segmentation can be used as part of the broader PHM strategy to improve care and outcomes in many ways…
Segmentation categorises populations according to their health and care needs, priorities, and circumstances. The ‘Bridges to Health’ (B2H) model is a fundamentally person-focused approach, with the principal goal of ‘pursuing the health of each population segment’.
To optimise health outcomes, patient experience, efficiency, and care costs, care delivery systems should respond to the needs of different population segments in different ways.
Each segment and sub-segment is defined clinically and translated to a data definition (clinical codes and logic) which is used to create condition registers for each subsegment.
Source: Outcomes Based Healthcare© 2017. OBH’s approach to segmentation is based on the ‘Bridges to Health’ model (Lynn et al. 2007).
The visual below shows how each Segment in the Bridges to Health segmentation model is defined by a set of Subsegments/Conditions within the Segmentation Dataset, and how the Healthy / Generally Well segment is defined as people who do not meet the criteria of any other core Segment.
Each subsegment has been defined using clinical codes and logic that are evidence-based, and derived from analysis of international and national best practice, guidelines and standards. OBH have spent 10 years building and maintaining this database and codebase.
Multiple datasets from the National Commissioning Data Repository (NCDR) are fed into the Segmentation Engine.
This is a very vast amount of ‘uncleaned’ data, from multiple setting-specific health and care providers, and is typically event- and/or diagnosis-based.
The Engine is an analytical data pipeline which ‘cleans’ the data, links the data together, and transforms it so that the output Segmentation Dataset presents data on a person-level basis, spanning different care settings.
Mapping tables translate the data into segments/subsegments, using clinical codes and logic based on OBH clinical evidence, national and international consensus.
The data transformation process run by the Engine produces a single dataset structured as a Data Model, designed to be as compact as possible, and quick and easy to query. The Segmentation Dataset is a set of tables that establish, for each person which segments and subsegments they are in, in any given month, as well as other demographic and geographical information relevant to the person.
The Segmentation Dataset is derived from a number of national operational and care planning pseudonymised patient-level data sources available in the National Commissioning Data Repository in NHS England. The following table summarises the data sources used and the number of years of data that has been longitudinally accrued.
The resulting Segmentation Dataset for local populations provides the backbone to any Population Health Management work. The Segmentation Dataset has been designed as a dimensional data model – a standard design approach for a database structure that is optimised for data analytics. This type of model is easy to understand and intuitive for analysts to use.
A dynamic, longitudinal model to analyse trends
Condition registers at any required historical ‘snapshot’
Assigning each person to a single segment, or to multiple segments
In any month, between April 2016 and September 2022, the following features are available in the Dataset for each person.
The Segmentation Dataset itself can be used to directly generate a number of insights, including:
Further value in the Segmentation Dataset is derived when it’s linked to other datasets to understand activity, outcomes or cost, as examples, by segment or subsegment. Analysts can join the Dataset at record level to the most relevant datasets for the purpose they need, to generate insights for each use case. For example:
These charts shows the difference between condition prevalence figures from the Segmentation Dataset and QOF data.
Data & method notes:
These charts shows the difference between condition prevalence figures from the NHSE Segmentation Dataset and a local linked Segmentation Dataset that includes primary care data for a single ICB.
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