Version: 1.0.0 | Published: 2 Sep 2026 | Updated: 0 days ago
Summary
Documentation
Associated Media:
Description:
Primary Care Observation data is the structured record of coded clinical observations. It forms the backbone of a patient’s longitudinal primary care record and is one of the richest sources of clinical detail available from GP systems.
GP Observation data is one of the most clinically rich datasets in UK primary care and is essential for understanding patient health trajectories and improving care quality.
The data set includes:
1. Core clinical observations including Diagnoses and problem entries, Test results, Procedure entries, Family history, Template based clinical data, Qualifiers (e.g., severity), Episodicity (new, ongoing, resolved).
2. Measurement and numeric data such as Blood pressure, BMI, peak flow, blood tests, etc. OR Numeric values with ranges and units OR Flags for abnormal results.
3. Metadata and context including:
- Effective date of the observation
- Consultation context (e.g., face to face, telephone)
- Organisation and patient identifiers
- Sensitivity/confidentiality flags
- Data lineage timestamps
It excludes Allergies, Immunisations and Referrals.
Researchers would find the below most important or relevant when using Primary Care Observations:
1. Longitudinal clinical trajectories: due to observations including diagnoses, measurements, and test results over time, researchers can study:
- Disease progression
- Impact of interventions
- Chronic condition management
- Early warning signs for deterioration
2. Population health insights
Observation data supports Prevalence studies, Risk stratification, Identification of multimorbidity patterns, Monitoring of long term conditions (e.g., diabetes, hypertension).
3. Quality and safety improvement - Researchers can analyse:
- Abnormal test result follow up
- Recording completeness
- Variation in coding practices
- Timeliness of clinical reviews
4. Inequalities and access
Demographic linked observations allow:
- Analysis of variation in diagnosis rates
- Differences in monitoring frequency
- Identification of underserved groups
5. Predictive modelling and AI
Rich numeric and coded data make observations ideal for:
- Risk prediction models
- Early detection algorithms
- Personalised care analytics
6. Linking to other datasets
Observation data can be linked to GP Appointments, Prescribing datasets, Hospital data (SUS). This enables whole pathway research across primary and secondary care.
Using GP Observation data, researchers can:
- Improve chronic disease management through better monitoring insights
- Identify gaps in care such as missed follow ups or abnormal results
- Strengthen early intervention by analysing risk markers
- Reduce inequalities by understanding variation in diagnosis and monitoring
- Support personalised care with detailed longitudinal clinical profiles.
In Pipeline:
Available
Coverage
Spatial:
- United Kingdom
- England
- London
Typical Age Range Min:
0
Typical Age Range Max:
150
Material Type:
None/not available
Follow Up:
> 10 Years
Pathway:
This dataset contains all Primary Care Observations for London patients. Each
patient will be identified using an unique patient key, this can be used to link
to other London SDE datasets that will help track the patient pathway.
Provenance
Origin
Purpose:
- Administrative
- Care
- Other
Dataset Type:
- Health and disease
- Treatments/Interventions
Dataset Sub-Type:
- Others
- Others
Source:
EPR
Collection Source:
Primary care - Clinic
Image Contrast:
No
Temporal
Publishing Frequency:
Monthly
Distribution Release Date:
01 September 2026
Start Date:
01 April 2000
Time Lag:
1-2 weeks
Accessibility
Access
Access Rights:
London Secure Data Environment Enquiry Form
Access Service Category:
TRE/SDE
Access Service:
Researchers will have access to a secure workspace via an airlocked Azure
Virtual Desktop with a specific username and password, MFA (multi-factor
authentication) and OAUTH (open authentication). Researchers will get specific
access to a relevant subset of the datasets that are present in the SDE
catalogue for their project and will be able to carry out their research within
the safe haven. There are restrictions applied which prevent the researchers
from taking data out of the safe haven. Once the research is completed the
London SDE admin team will need to be contacted for any requests to egress
summary analysis out of the safe haven which will not breach secure data
environment disclosure control standards.
Access Request Cost:
Access costs will be determined on a project-by-project basis and will depend on
the specific data requirements, platform setup, and any additional services
needed to deliver the project successfully.
Delivery Lead Time:
1-2 months
Data Controller:
Participating London health care organisations act as Joint Data Controllers
within the London SDE. These organisations include for example GP Practices and
Acute Providers from across London.
Data Processor:
The data processor for the London Secure Data Environment (SDE) is primarily
managed by OneLondon, a partnership of London's five integrated care systems
(ICSs) and three health innovation networks. NHS North East London ICB hosts
OneLondon and operates the London Data Service (LDS) as a data processor on
behalf of those organisations, whilst Imperial College Healthcare NHS Trust
hosts the London Analytics Platform (LAP), with Imperial NHS Trust acting as the
data processor for data processing activities undertaken within the platform.
Jurisdiction:
United Kingdom of Great Britain and Northern Ireland
Usage
Data Use Limitation:
No restriction
Data Use Requirements:
- Collaboration required
- Institution-specific restrictions
- Project-specific restrictions
- Time limit on use
- User-specific restriction
Resource Creator:
London SDE
Format and Standards
Vocabulary Encoding Scheme:
- NHS NATIONAL CODES
- LOCAL
- SNOMED CT
Conforms To:
- NHS DATA DICTIONARY
- LOCAL
Language:
English
Format:
Text
Enrichment and Linkage
Investigations:
Linkable Datasets
PID
Title
URL
Each patient will be identified by an unique patient key that can be used to link to all other datasets available within London SDE Platform
Observations
Statistical Population
Population Description
Population Size
Measured Property
Observation Date
Events
7462119139
Count
02 September 2026
Persons
Distinct Patient Count
11032000
Count
02 September 2026
Origin
Name:
NWL Data Catalogue