Version: 1.0.0 | Published: 2 Sep 2026 | Updated: 0 days ago
Summary
Documentation
Associated Media:
Description:
Primary Care Encounters represent the structured record of every interaction a patient has with the GP practice - clinical or administrative. They provide the backbone of the consultation timeline and are essential for understanding patient flow / journeys, workload, and care pathways / care quality in primary care.
The data set includes:
1. Consultation details
- Encounter type (clinical, administrative, telephone, online, home visit)
- Consultation mode (face to face, telephone, video, online message)
- Consultation category (e.g., acute, chronic disease review, medication review)
- Duration (where recorded)
2. Clinician and team information
- Responsible clinician (GP, nurse, pharmacist, paramedic, PA)
- SDS role (staff type)
- Practice and organisation identifiers
3. Clinical content linked to the encounter
Encounters act as containers for:
- Observations (diagnoses, symptoms, measurements, test results)
- Medication orders
- Medication statements
- Referrals
- Care plans
- Templates completed during the consultation
4. Metadata includes:
- Date/time of encounter
- Location (practice, home visit, remote)
- Flags for sensitivity/confidentiality
5. Coverage
- All encounters recorded across GP practices
- Longitudinal records spanning many years
- Full consultation history for each patient
What GP Encounters excludes:
- They do not include hospital encounters unless manually entered
- They do not include unbooked administrative work unless coded as an encounter
Researchers would find the below most important or relevant when using GP Encounters:
1. Understanding patient pathways
Encounters show:
- How often patients interact with primary care
- The sequence of consultations leading to diagnoses or referrals
- Continuity of care (same GP vs multiple clinicians)
2. Workload and workforce analysis
Researchers can study:
- GP vs nurse vs pharmacist consultation patterns
- Impact of multidisciplinary teams
- Variation in consultation duration and mode
3. Access and demand
Encounter timestamps reveal:
- Peak demand periods
- Urgent vs routine consultation patterns
- Seasonal pressures (e.g., winter surges)
4. Quality and safety
Linked clinical content allows analysis of:
- Follow up after abnormal results
- Timeliness of reviews
- Missed opportunities for intervention
- Variation in coding completeness
5. Inequalities in care
Demographic linkage enables:
- Studying variation in consultation frequency
- Differences in mode of care (remote vs face to face)
- Identifying underserved groups
6. Linking across datasets
Encounters can be linked to Observations, Medication orders, Medication statements, Appointments, Prescribing and dispensing data, Hospital datasets (SUS).
This enables full pathway research from appointment → encounter → clinical content → outcome.
Using Primary Care Encounters, researchers can:
- Improve continuity of care by analysing clinician patient relationships
- Strengthen early intervention through consultation linked clinical patterns
- Reduce inequalities by identifying variation in access and mode
- Enhance workforce planning with detailed consultation activity
- Improve chronic disease management by analysing encounter frequency and content
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 Encounters 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
2435169934
Count
02 September 2026
Persons
Distinct Patient Count
10996485
Count
02 September 2026
Origin
Name:
London SDE