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Improving Health and Care Services Through Design

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We can improve the way that we commission and create services if we alter our model. Using Design Thinking we can create services that meet user needs but are also cheaper.

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Improving Health and Care Services Through Design

  1. 1. Darren Wright Improving Outcomes Through Design
  2. 2. Using data, from individuals, to describe communities and demonstrate outcomes 1. Using Design Thinking we can create better services that meet the needs of people 2. Data has a social value beyond proving contracts are working 3. Using data we can describe the communities we live in and co-produce services 4. Demonstrating outcomes in terms of national policy allows data to be aggregated for social benefit
  3. 3. We all love the idea of measuring outcomes rather than activity
  4. 4. Challenges with Measuring Outcomes • Attribution – 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
  5. 5. There is no shortage of Outcome Frameworks
  6. 6. Being Busy vs Being Effective
  7. 7. Built environment – 10% Clinical care – 20% Healthy behaviours – 30% Socio-economic factors – 40% Contributors to Overall Health Outcomes Source: Robert Wood Johnson Foundation and University of Wisconsin Population Health Institute
  8. 8. 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
  9. 9. A focus on outcomes should fundamentally change the way we commission and design services
  10. 10. By focussing on measuring the system we miss the opportunity of human-centric design
  11. 11. 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?
  12. 12. 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?
  13. 13. Traditional Commissioning Model This often the point where we start getting involved Build service model Test model with patient/user groups Decide on some metrics Go out to contract Measure metrics
  14. 14. What is the purpose of collecting data? Is it purely to measure transactions?
  15. 15. Wisdom Knowledge Information Data The Wisdom Hierarchy of Data Value
  16. 16. Alternative Service Design Model Set out problem to be solved Go out to contract Define user needs Identify data to meet user needs Design service Adapt service Test service against user needs Prove the problem is being solved
  17. 17. Tracey is 24. Tracey grew up in Chelmsley Wood until she was 21. She lived at home until she fell out with her family and came to stay with friends in Birmingham. After a couple of months it was obvious there wasn’t enough room in the one bedroom flat she was staying in and without anywhere else to go she ended up in a hostel in Birmingham. It’s not a great part of Birmingham and Tracey doesn’t feel safe walking around there. Tracey can’t stay in the hostel during the day so spends a lot her days in friend’s flats. During the day her, and her friends, watch TV and usually drink quite a lot of vodka. She has completed over 2000 levels of Candy Crush Traceyple
  18. 18. 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 alcohol related issues ● Smokes ● Misuses alcohol ● Poor diet ● No exercise Traceyple Socio-economic factors Clinical factorsHealthy behaviour
  19. 19. The danger of the microscope Defining people by the services they use, and the data we choose to collect on those services, leads to data reductionism
  20. 20. Not understanding the person can lead to Solutionism:- The providing of a solution or solutions to a customer or client (sometimes before a problem has been identified)
  21. 21. Challenges with Measuring Outcomes Attribution – Did you cause that outcome to happen? Place vs service
  22. 22. Integration around the individual GP A&E Smoking Cessation Alcohol Services Job Centre Money advice Gym Housing Health Trainer Collective Outcomes
  23. 23. 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.
  24. 24. 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
  25. 25. 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
  26. 26. Data has value Continuous service improvement Wider social value
  27. 27. Continuous service improvement Value Identify data Gather data Analyse data Identify service improvement Implement service improvement Wisdom Knowledge Information Data
  28. 28. Social Issues Can be Aggregated and Mapped
  29. 29. Our tips to designing services Start with the person Think about data that is relevant to the person Be broad in the data you collect Make data collection as simple as possible Iterate regularly If the data says the service isn’t working then change the service
  30. 30. www.insideoutcomes.co.uk @InsideOutcomes

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