SPARRA is an algorithm that predicts patient risk of emergency hospital admission. It uses factors like age, gender, admissions history, diagnoses and more. It began in 2006 focusing on those 65+ and expanded to all ages in 2008. ISD generates quarterly risk scores for over 700,000 patients that health boards use. Current development focuses on expanding data sources like primary care to identify more at-risk patients. The Long Term Conditions Collaborative uses SPARRA to help identify high-risk patients and test initiatives to improve anticipatory care planning and complex case management.

1. SPARRA
Peter Martin (ISD)
Mandy Andrew
(Long Term Conditions Collaborative)

2. SPARRA
• What does it do?
– Risk factors / Patient Examples
• History of Development
• How is it being used?
– The Long Term Conditions Collaborative
• Current & Future Development

3. SPARRA
Scottish
Patients
At
Risk of
Readmission and
Admission
SPARRA is an algorithm for predicting a patient’s risk
of emergency inpatient admission in a particular year

4. SPARRA – Current Risk factors
Age
Gender
Deprivation Level of Residence
Number of previous emergency admissions
Time since last emergency admission Inpatient/Day
Case history in 3
Total bed days accumulated in the 3 years
years prior to the
Principal diagnosis (last emergency admission) risk year
Co-morbidity – number of diagnostic groups
Number of Elective admissions
Emergency Admission rate (standardised) of patient’s GP practice
Historic Period
2007 2008 2009 2010
Predictor Outcome
variables year

5. Example: individual with very high
predicted probability of admission
• Predicted probability of admission 86%
• Male aged 67
• Less than one month since most recent admission
• 6 previous emergency admissions
• Glasgow – most deprived decile
• Most recent admission diagnosis: COPD

6. Example: individual with very low
probability of admission
• Probability of admission 8%
• Male aged 67
• 2 years since most recent admission
• 1 previous emergency admissions
• Lothian – 2nd least deprived decile
• Most recent admission diagnosis: Injury

7. Development History
2006
• Initial Focus on those aged 65+
• Base-data
– Sources from National Inpatient/Day Case Data (SMR01)
– Patients with >=1 emergency admission 2001-2003 (200K+)
– Risk of admission 2004 – outcome was known
– Deaths before end of 2003 excluded
• Algorithm developed using multiple logistic regression
2008
Extension to those under 65
• Modelling work repeated on an ‘all ages’ cohort (700K+)
• Identifies 2 x high risk (50%) patients
• Adopted within the SPARRA service January 2009
SPARRA MH – risk of psychiatric inpatient admission

8. SPARRA the ISD service
• Risk Scores generated quarterly for all relevant patients
– >700K (previously 200K)
• Data relating to their ‘at risk’ population distributed to Health
Boards, CHPs & practices
– Chosen risk thresholds (often >50%)
– Patient-level data for medium to high risk patients
ID information
Risks scores & factor values
LTCs evident from inpatient/day case history
Admissions related to substance misuse
(alcohol/drugs)

9. SPARRA – current coverage
Very Acute
ency
merg ns sector
High
E
SPARRA issio
adm
High risk
coverage
Medium risk
Lower risk
4

10. SPARRA – Current Development Strategy
• Priority is more comprehensive case-finding tool
“Enhance SPARRA by expanding the cohort for whom a risk can be
estimated beyond those with a recent history of hospital admission”
Scottish Government – National LTC Action Plan
• Need to look at other data sources that
–will extend the cohort
–contain risk factors that will improve discriminatory power
– are likely to be available in most localities
A& E
Hospit al Communit y
NHS24 Social Care Prescribing
Admissions Syst ems
Ambulance
Primary Care
(General Pract ice)

11. SPARRA – Current Projects
• Using external data sources e.g.
– Data held by Primary Care Clinical Informatics Unit, Aberdeen
University on 40 practices and linked with national hospital
admission data
• Maximising/simplifying use of hospital admission data
– Admissions related to alcohol or drug misuse
– Admissions related to falls
– LTCs
• Streamlining our data generation/distribution process
– Making using of ISD’s warehousing developments
– Monthly updates
• SPARRA MH
– Evaluating long-term role
– Overlaps with ‘acute’ SPARRA cohort

12. Long Term Conditions Collaborative
‘Delivering sustainable improvements in
person centred services for people with
long term conditions’
Improvement and Support Team
Scottish Government

13. Policy Context
• Long Term Conditions Action Plan – June 2009
– Person Centred Care & Mutuality
– 7 Change Actions
• Linked to Long Term Conditions Collaborative
High Impact Changes
• Integrating Policy Streams

14. LTC Collaborative Workstreams
Level 3
Complex Highly
Case/Care complex
Management
Specialist
Level 2
(Condition)
Management High risk
Level 1
Self
Management 70-80% of
pop

15. Complex Care Workstream
Risk Prediction
(SPARRA)
• Learning Events
• Buddying
• Resources
• Whole Systems Improvement
Anticipatory Care
Care Plans Management

16. Current Developments & Practice
SPARRA Tests of Change
• East
• Using external data sources
– Workforce (NHS Forth Valley)
• Maximising/simplifying use of • West
hospital admission data
– Care Management Model
• Streamlining our data (NHS Lanarkshire)
generation/distribution process • North
– A3s and Anticipatory Care
• SPARRA Mental Health
Planning (NHS Grampian)
• SPARRA development group – Integrated Care Model (NHS
Tayside)