Version: 1.0.0 | Published: 2 Sep 2026 | Updated: 0 days ago
Summary
Documentation
Associated Media:
Description:
Primary Care Appointments provide a detailed, national view of GP appointment activity across practices using EMIS and TPP (SystmOne).
GP Appointments data provide the most detailed national picture of GP appointment activity which is essential for improving access, efficiency, equity, and patient experience in primary care.
The data set includes:
1. Appointment activity including:
- Total number of appointments booked
- Attendance status: attended, did not attend (DNA), unknown
- Appointment duration
- Time from booking to appointment
2. Appointment characteristics such as:
- Mode: face to face, telephone, video, online
- Health Care Professional (HCP) type (GP, nurse, pharmacist, etc.)
- SDS Role (Spine Directory Services role) - becoming the main published role type (gives an indication of staff type for the appointment)
- National category, service setting, and context type
3. Practice-level detail such as Data available down to individual GP practice, Comparisons with previous months, Data quality indicators and issues.
Researchers would find the below most important or relevant when using GP Appointment data:
1. Access and demand is highlighted by using activity volumes to reveal How many patients seek GP care, Seasonal pressures (e.g., winter surges), Variation between practices and regions.
2. Workforce and role utilisation by using SDS role and HCP type, this allows researchers to:
- Analyse workforce mix (GP vs nurse vs pharmacist)
- Understand how multidisciplinary teams affect access and outcomes.
3. Mode of care
Data on face to face vs remote appointments supports:
- Evaluation of digital transformation
- Understanding patient preferences
- Studying outcomes linked to consultation mode
4. Waiting times and continuity helps researchers by using Time from booking to appointment to Assess access pressures, Identify inequalities in wait times, Evaluate continuity of care and urgent vs routine demand.
5. Non attendance (DNAs) data analysis is crucial for Identifying groups at risk of missing appointments, Designing targeted interventions, Improving efficiency and reducing wasted clinical time.
6. Service setting and national categories help researchers Understand what types of care are delivered in general practice, Evaluate new service models (e.g., enhanced access hubs).
7. Data quality and completeness highlights Missing or inconsistent coding, Mapping progress for national categories, Practice level data quality issues.
Using GP Appointments, researchers can:
- Improve access by analysing wait times, demand, and appointment availability
- Reduce DNAs through targeted interventions
- Strengthen workforce planning using detailed role based activity
- Evaluate digital care by studying remote vs in person patterns
- Identify inequalities in access, mode, and outcomes
- Support policy and operational planning at practice, PCN, ICB, and national levels
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 Appointments 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
371415229
Count
02 September 2026
Persons
Distinct Patient Count
9623769
Count
02 September 2026
Origin
Name:
London SDE