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Outcomes Seminar 25/10/17


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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.

Published in: Health & Medicine
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Outcomes Seminar 25/10/17

  1. 1. CIC Darren Wright
  2. 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. 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. 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. 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
  6. 6. 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
  7. 7. Issues with data collected by services • Compliance in collection • Accuracy in collection • Consistency in collection • No closing the feedback loop • Transactional in nature
  8. 8. 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
  9. 9. 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
  10. 10. 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
  11. 11. 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
  12. 12. Data Collection is Consistent and Aligned with National Outcome Frameworks
  13. 13. 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.
  14. 14. 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
  15. 15. 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
  16. 16. 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
  17. 17. 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
  18. 18. Next Steps •Developing an open standard •Integration with other systems •Promoting what can be measured • Seeing data as an end in itself
  19. 19. @InsideOutcomes