Version: 1.0.0 | Published: 3 Sep 2026 | Updated: 0 days ago
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Associated Media:
Description:
Primary Care Schedule defines when, where, and with whom primary care activity can occur. This dataset is less about clinical content and more about capacity, availability, and operational workflow, making it crucial for understanding access and workload in general practice.
Primary Care Schedule data provides the backbone of planned GP capacity which is essential for improving access, efficiency, equity, and workforce planning in primary care.
The data set includes:
1. Session templates
- Named sessions (e.g., “Morning Surgery”, “Duty Clinic”, “Extended Access”)
- Start and end times
- Session type (clinical, administrative, home visits, triage)
- Recurrence patterns (daily, weekly, monthly)
2. Clinician availability
- Which clinicians are working each session
- SDS role (GP, nurse, pharmacist, PA, paramedic)
- Assigned duties (triage, urgent care, chronic disease reviews)
- Leave, training, and protected learning time
3. Appointment slot configuration
- Number of slots per session
- Slot duration
- Slot type (routine, urgent, telephone, video, home visit)
- Slot restrictions (e.g., GP only, nurse only, specific clinic)
4. Location and resource scheduling
- Rooms or clinical spaces assigned
- Remote vs in practice sessions
- Community clinic locations (e.g., care homes, outreach sites)
5. Metadata including Practice identifiers, Template creation and modification dates, Session ownership (clinician or team), Flags for embargoed or hidden slots.
6. Coverage
- All planned clinical and administrative sessions recorded
- Full rota and capacity planning for GP practices
- Longitudinal history of scheduling changes
What Primary Care Schedule data excludes:
- Actual appointment bookings (those are in Appointments)
- Clinical content (Observations, Medication Orders, Referrals)
- Outcomes or attendance status
- Hospital or community provider schedules unless manually added
Researchers would find the below most important or relevant when using Primary Care Schedule data:
1. Access and capacity modelling
Schedules reveal:
- How many appointment slots a practice intends to offer
- Variation in capacity across days, weeks, seasons
- Differences between practices, PCNs, and regions
- Impact of workforce shortages on available sessions
2. Workforce planning
Researchers can analyse:
- GP vs nurse vs pharmacist session distribution
- Duty doctor patterns
- Extended access and out of hours provision
- Multidisciplinary team utilisation
3. Digital transformation
Schedules show:
- Planned remote vs face to face capacity
- Adoption of video or online consultation slots
- Integration of triage first models
4. Inequalities in access
Linked demographic and appointment data allow:
- Studying whether certain groups struggle to access available slots
- Understanding variation in urgent vs routine capacity
- Identifying practices with systematically lower availability
5. Operational efficiency
Schedules help researchers identify:
- Under utilised sessions
- Overbooked or high pressure periods
- Mismatch between planned capacity and actual demand
- Effects of embargoed or hidden slots on access
6. Linking across datasets
Schedules can be linked to Appointments (actual use of planned capacity), Encounters (clinical activity delivered), Observations (clinical content), Medication Orders/Statements (treatment decisions).
This enables full pathway analysis from planned capacity → booked appointment → delivered care → outcome.
Using Primary Care Schedule data, researchers can:
- Improve access by analysing planned vs actual capacity
- Strengthen workforce planning with detailed rota information
- Reduce inequalities by identifying gaps in available slots
- Enhance operational efficiency through better session design
- Support service redesign (triage models, extended access, MDT clinics)
Coverage
Spatial:
United Kingdom,England,London
Typical Age Range:
0-150
Follow Up:
> 10 Years
Pathway:
This dataset contains all Primary Care Schedules 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
Purposes:
- Administrative
- Care
- Other
Sources:
EPR
Collection Situations:
Primary care - Clinic
Temporal
Accrual Periodicity:
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:
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.
Usage
Data Use Limitations:
No restriction
Data Use Requirements:
- Collaboration required
- Institution-specific restrictions
- Project-specific restrictions
- Time limit on use
- User-specific restriction
Resource Creators:
London SDE
Format and Standards
Vocabulary Encoding Schemes:
- NHS NATIONAL CODES
- LOCAL
- SNOMED CT
Conforms To:
- NHS DATA DICTIONARY
- LOCAL
Languages:
en
Formats:
Text
Enrichment and Linkage
Qualified Relations:
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
75916006
Count
02 September 2026
Persons
Distinct Patient Count
9623769
Count
02 September 2026