These slides are from a presentation for the Nuffield Department of Primary Care Health Sciences. This covers reasons for capturing outcomes, challenges with commissioning and how our system of managing risks address these problems.

1. CIC
Darren Wright

2. Inside Outcomes Purpose
• Improve Commissioning
• Use data to create social value
•Use data to describe the areas we live in
•Using communities to design services
• Relating what happens to policy

3. Challenges With Commissioning
• The difference between commissioning and procurement
• Co-designing services is more expensive
• What if the public don’t agree?
• Where do you get your data from?

4. What do we commission against?
• QOF Data
• Open Exeter Data
•National Data Sets (Atlas - life expectancy, infant mortality)
•Local data sets?
•Mosaic?
• Community data on service design?

5. There are two types of outcome
1. Systemic outcomes – e.g. A&E attendance, GP appointments
2. Individual outcomes – measurable improvement in health and
social situation

7. Challenges with Measuring Outcomes
• Causality – Did you cause that outcome?
• Complexity – Especially in preventative services
• Why are you doing it?
• Contract specific outcomes can change a service
• Place vs service

8. Issues with data collected by services
• Compliance in collection
• Accuracy in collection
• Consistency in collection
• No closing the feedback loop
• Transactional in nature

9. All organisations collect four types of data
Demographics – Data that identifies individuals
Activity – What has happened to an individual
Outcome – What benefits, or disbenefits, have been received
Satisfaction – How happy the individual is

10. Name Definition Examples Strengths Weaknesses
Demographic data The identifying
factors for
individuals
 Gender
 Age
 Ethnicity
 Can help to measure how
representative of a community
a service is
 Can be used as a comparator
for outcome data
 On its own it is not very useful
data
 Ease of collection can result in
excess collection
 Data protection issues
Activity data A measurement of
the inputs provided
by a service
 Number of people that have
used a service
 Number of referrals (in and
out)
 Number of sessions carried
out
 Easy to measure
 An important element in
calculating your costs
 More of a measure of how
busy a service is rather than
how effective
 Not a measure of quality
Outcome data A measurement of
the change in an
individual
 Clients that have given up
smoking
 Clients that have lost weight
 Clients accessing entitled
range of benefits.
 Much more focus on the
person receiving the service
 A measure of the quality of the
service you provide
 Can be used to compare with
other services
 Can be hard to measure
 Requires measurement at two
points
Satisfaction data Perception of the
intervention
 Client satisfaction surveys  Satisfaction is important in
assessing if people will return
to a service
 Can be used as a basis adding
a personal element to
reporting
 Inherently subjective
 Not comparable inside an
organisation let alone with
other organisations
 People liking a service doesn’t
mean it is a good service

11. Using Risk Maps – Managing Risk, Reducing Inequalities, Demonstrating Impact
• Performance management
• Consistent measurement
• Aggregated data for collective impact
• Auditable outcomes aligned to national frameworks
• Measures by risk and protective factor
• Evidence based
• Quality assured

12. Since 2012 there has been a push from Government to build
outcome frameworks
Services should be commissioned against outcome frameworks
Outcome frameworks often overlap
Outcome frameworks often contradict
Outcome frameworks

13. Data Collection is
Consistent and Aligned
with National Outcome
Frameworks

14. Data Collection is Structured to Match the Life Course
Starting
Well Data
Dictionary
Developing
Well Data
Dictionary
Working
Well Data
Dictionary
Living Well
Data
Dictionary
Ageing Well
Data
Dictionary
Diabetes
Data
Dictionary
But also service specific
Mental
Health Data
Dictionary
Supported
Housing Data
Dictionary
End of Life
Data
Dictionary
Domestic
Abuse Data
Dictionary
We have identified 93 common risks and issues.
Each has been defined and is monitored for any change in policy.
We are adding to this list all of the time.

15. Living Well Data Dictionary
Personal Circumstances:
• Domestic Abuse
• Homeless
• Temporary Accommodation
• Unsuitable Accommodation
• Vulnerable Adult
• Financial Hardship
• Social Isolation - Loneliness
• Environment - Noise
• Environment - Outdoor Spaces
Behaviour:
• Very Low Fruit & Vegetable Intake
• Low Fruit and Vegetable Intake
• Significant Fried and Processed Food
Intake
• Excessive Sugar
• Nutrition - Iron
• Physical Activity - Moderately
• Physical Activity - Inactive
• Alcohol Misuse
• Smoking
• Substance Misuse
Status:
• Weight - Overweight
• Weight – Obese
• Mental Health – Low Reported
Wellbeing
• Mental Health - Stress and Anxiety
• Sexual Health - Unwanted Pregnancy
• Sexual Health – Sexually Transmitted
Infections
• Pre - Diabetes: Non - Diabetic
• Screening - Increased Blood Pressure
• Screening - High Blood Pressure

16. Exampleple
Tracey:
● In debt
● Socially isolated
● Lives in a hostel
● Been to see GP 7 times in 3 months
● Stressed and anxious
● Attended A & E on two occasions with alcohol related
issues
● Smokes
● Misuses alcohol
● Poor diet
● No exercise

18. Household
income is
>60% of UK
average
Reduce
households
where
neither
parent is in
work
Healthy Child
Programme
The family can
afford food
and clothing
items
Social Justice Outcomes
Framework
Department of Health
Department of Work and
Pensions
Financial Hardship
After
required fuel
costs the
family
remains
above the
poverty line
Improving
Outcomes
Supporting
Transparency
Reduce the
proportion of
those on
work-related
benefits
The number
of working
age adults
engaged in
work related
activity

21. Our Main Challenges
• Outcomes are not a priority
• Limited headspace to change
•Services focus on what they’re good at
•Measuring activity is easy
•Transactional data puts the focus on the service rather than community
• Who owns the outcomes?
•Risk aversion

22. Next Steps
•Developing an open standard
•Integration with other systems
•Promoting what can be measured
• Seeing data as an end in itself

23. www.insideoutcomes.co.uk
@InsideOutcomes