This document discusses pseudonymised GP data extraction in Northern Ireland for risk stratification. Four clinical priority areas were identified: frail elderly patients, respiratory patients, diabetic patients, and patients with a history of stroke or TIA. GP data is pseudonymised at source and extracted using MIQUEST software. The extracted data includes factors like emergency admissions, polypharmacy, high-risk drugs, comorbidities, and test results. This data is analyzed to produce stratified lists of high-risk patients for each practice to proactively manage. The process aims to improve care for complex patients while protecting patient privacy and allaying concerns related to care.data initiatives.

1. Pseudonymised GP data extraction
for risk stratification
CCIO Leaders Network Annual Conference
04 November 2014
Dr Brendan O’Brien
Consultant Clinical Informatics Specialist (CCIO)
Regional Health & Social Care Board, NI.

2. NI Context
Population: 1.8 million
Integrated Health & Social Care
Budget ~ £4.5 billion
Purchaser provider split
Commissioners: HSCB & PHA
Providers: 5 Acute Trusts, Regional Ambulance Service
351 GP practices

3. Clinical Priority Areas
Patients risk stratified according to complexity of
needs.
Four priority areas set by DHSSPS.
• Frail elderly patients
• Respiratory patients
• Diabetic patients
• Patients with a history of stroke / TIA

4. Approach
• Data pseudonymised at source (only the GP practice know the patients
identity)
• MIQUEST approach used for the data extraction
• Only coded information extracted
• HSCB writes the HQL code for the data extraction
• GP practices run the HQL queries
• HSCB conducts analysis on pseudonymised data set from GP practices
• Stratified list for each clinical area returned to practices
• Practice re-identify patients, add soft knowledge and proactively manage
those patients with greatest number of factors associated with unplanned
admissions
• HSCB will aggregate the extract for public health and commissioning uses

5. Agreement between HSCB & each Practice
Covers sharing of
anonymised and
pseudonymised
patient level data.

6. MIQUEST
• Free software for extracting and aggregating data
from all GP systems
• Incorporates strong security and confidentiality
safeguards
• Adopted as standard within the NHS for data
extraction
• Part of RFA99 (requirements for Accreditation)

7. LOCAL ENQUIRER QUERY
• Import query
• Run query
• Output response
• Use the data
GP Practice
REMOTE ENQUIRER QUERY
• Import the query
• Authorise the query
• Run query
• Authorise response
• Output response
• Use the data
HSCB or other enquirer

8. MIQUEST
CTV 3 Lots of quirks in
Individual systems

9. HQL Data Model

10. Main Factors
• Emergency admissions in past 12 months
• Polypharmacy
• High risk drugs: NSAIDs, diuretics, anticoagulants,
antiplatelets
• Number of comorbidities (20 registers)
• Test results
• Smoking status, alcohol intake
• ‘Soft knowledge’ added by GP Practice

11. Challenges
• No validated risk prediction algorithm for NI
population
• Care.data = increased stakeholder concern
• 4 GP Systems, 3 coding schemes
– Vision: Read Version 2
– EMIS PCS: Read Version 2
– EMIS LV: Read Version 2 + proprietary codes
– Crosscare (Merlok): Clinical Terms Version 3

12. Pseudonymisation At Source
We’re not
Brazil
CARE.DATA
We’re
Northern
Ireland.

13. Progress to date
• 1,124,040 patients records extracted so far
• 1,692 patients excluded from extract (0.15%)
– Due to controversy around care.data the decision was
made to exclude data on patients whose clinical record
contained a Read code opting them out of the Emergency
Care Summary (ECS) extract.
• 92% of GP practices signed up to the enhanced service
– Covering 93% of NI population
– EMIS LV and Merlok extractions this month

14. Polypharmacy
Highest number of unique
items in any single month
(July to December 2013)
Percentage of the patient
population included in the
extraction
0 items 36.16%
1-4 items 47.46%
5-9 items 12.94%
10+ items 3.44%

15. High Risk Drugs
Number of high risk drug
categories from which
patients have received
medication in period July
to December 2013
Percentage of the patient
population included in the
extraction
0 79.01%
1 16.98%
2 3.70%
3 0.30%
4 0.01%

16. Morbidity (number of conditions) by Age Band

17. Morbidities
Comorbidities

18. Thanks for listening
Email:
brendan.obrien@hscni.net
Twitter:
@drbrendanobrien