Version: 1.0.0 | Published: 3 Sep 2026 | Updated: 0 days ago
Summary
Documentation
Associated Media:
Description:
Primary Care Diagnostic Orders represent the structured record of tests, investigations, and diagnostic procedures ordered by a GP or primary care clinician. They show what diagnostic activity was requested, why, when, and with what clinical context. This dataset is essential for understanding diagnostic pathways, early detection, and monitoring quality in primary care.
Primary Care Diagnostic Orders provide a crucial view of how diagnostic activity is initiated, which is essential for improving early detection, monitoring, safety, and equity in primary care.
The data set includes:
1. Diagnostic tests and investigations requested
- Laboratory tests (HbA1c, renal function, lipids, thyroid, full blood count)
- Imaging (X ray, ultrasound, CT, MRI)
- Cardiac diagnostics (ECG, ambulatory BP, Holter monitoring)
- Respiratory diagnostics (spirometry, FeNO)
- Screening tests (e.g., cervical cytology)
- Community Diagnostic Centre requests (Supported by national diagnostic activity categories )
2. Clinical context
- Reason for request (coded or free text)
- Linked diagnosis or problem
- Relevant observations (symptoms, vital signs, previous results)
- Priority (routine, urgent, 2WW cancer pathway)
3. Metadata including Request date/time, Ordering clinician + SDS role, Practice identifiers, Status (requested, completed, cancelled), Whether generated via template or protocol.
4. Workflow and follow up
- Expected review date
- Flags for monitoring (e.g., repeat blood tests for chronic disease)
What Diagnostic Orders exclude:
- They do not confirm that the test/procedure was completed
- They do not include hospital initiated diagnostics unless manually entered
- They do not include medication orders or prescriptions
- They do not include dispensing information
Researchers would find the below most important or relevant when using Primary Care Diagnostic Orders:
1. Diagnostic pathways and early detection
Diagnostic orders reveal:
- How quickly tests are ordered after symptoms appear
- Delays in diagnostic pathways
- Variation in investigation patterns between clinicians or practices
2. Monitoring of long term conditions
Orders show:
- Frequency of monitoring (e.g., HbA1c for diabetes)
- Missed or delayed monitoring
- Variation in adherence to clinical guidelines
3. Safety and quality of care
Researchers can identify:
- High risk patients missing essential tests
- Incomplete follow up after abnormal results
- Over or under use of investigations
4. Inequalities in access to diagnostics
Linked demographics allow analysis of:
- Variation in test ordering by ethnicity, age, deprivation
- Differences in access to imaging or specialist diagnostics
- Disparities in chronic disease monitoring
5. Workforce and workflow optimisation
-Diagnostic orders help researchers understand:
- Which clinicians order which tests
- How multidisciplinary teams influence diagnostic activity
- Bottlenecks in test ordering and follow up
6. Linking across datasets
Diagnostic orders can be linked to Observations (results), Appointments (encounter context), Medication Orders (treatment decisions), Hospital datasets (SUS).
This enables full pathway research from symptom → diagnostic order → result → treatment → outcome.
Using Diagnostic Orders, researchers can:
- Improve early diagnosis by analysing delays and variation in test ordering
- Strengthen chronic disease management through monitoring patterns
- Reduce inequalities by identifying gaps in access to diagnostics
- Enhance patient safety by detecting missed follow ups
- Support personalised care with detailed diagnostic histories
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 Diagnostic Oders 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
27167353
Count
02 September 2026
Persons
Distinct Patient Count
2221721
Count
02 September 2026
Origin
Name:
London SDE