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Introduction to Aligning Quality Improvement to Population Health


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Introduction to Aligning Quality Improvement to Population Health

  1. 1. Aligning quality improvement to the health needs of the population Ruth Barnes, Director of Public Health, NHS Ealing/ NIHR CLAHRC for Northwest London Stuart Green, Public Health Information Officer/Research Fellow, NIHR CLAHRC for Northwest London
  2. 2. Outline• Why CLAHRCs were developed• What is the Collaboration for Leadership in Applied Health Research and Care for Northwest London• CLAHRC’s contribution to improving health• Setting priorities in Quality Improvement• Types of routine data used in HNAs• Case study: Mental Health• Barriers to engaging public health data• Approaches to overcoming the barriers
  3. 3. Background to CLAHRC• Closing the second translational gap - Cooksey Report• Evidence Based-Medicine should be supported by Evidence Based Implementation – High Level Group for Clinical Effectiveness• 9 CLAHRCs awarded – different approaches• Partnerships between NHS and University
  4. 4. NIHR Innovation Pathway
  5. 5. The CLAHRC approach• Developing synergy between research, improvement and service delivery through evidence based implementation• Combining: – Research Methodologies – Improvement Methodologies – Collaborative Framework• Not disease specific – generic, transferable model through project based approach
  6. 6. Delivery of projects
  7. 7. Drivers for quality improvementSome of the drivers identified include:• Clinical care processes• Patient and stakeholder needs• Organisational need to develop services• Policy/evidence identified at local or national level• Cost effectiveness and efficiency measures• Public health/population health
  8. 8. Ensuring a population health approach• Using a health needs assessment approach allows QI projects to align to a number of domains: • Health needs and priorities for whole populations • Inclusion of well-being, prevention and equity • Local commissioning support• HNA approach allows us to align quality improvement projects to population need and a framework for evaluating the impact of quality improvement projects on population health
  9. 9. Needs assessment: Components• Nature of population• Burden of disease• Evidence of effective interventions• Cost effectiveness and affordability• Supply and availability of services• Demand, acceptability, patients’ views• Comparative, corporate, epidemiological approaches
  10. 10. Geodemographic
  11. 11. Health status Data• Includes births, deaths, incidence of cancers and other diseases Recorded prevalence of Stroke and TIA (% registered patients)
  12. 12. Health service data• Use of services by patients from activity data such as HES and GP data Alcohol related admission rates (per 100,000)
  13. 13. Case Study: Improving access to Mental Health and Wellbeing Services in Ealing and Westminster
  14. 14. Project AimsNHS Ealing• Increase GP referral rates of BME patients to the Ealing IAPT Mental Health and Wellbeing ServiceCentral Northwest London NHS Trust• Increase self referral from older patients over the age of 65
  15. 15. A HNA Approach: Gap analysis• Projects did not access deprivation or social classification data such as IMD and MOSAIC• Projects have not identified proxy measures to assess need, e.g. admissions data• Practice level data was not utilised to strategically identify practice engagement
  16. 16. Deprivation- Ealing• Ealing has pockets of severe deprivation across the west of the borough as well as affluent areas
  17. 17. Deprivation- Westminster• Westminster is mostly affluent borough with pockets of severe deprivation in the northwest
  18. 18. Social Classification: Ealing
  19. 19. Social Classification: Westminster
  20. 20. Age structure in Ealing• Ealing has relatively more people aged 20-39 and a fewer elderly people compared to the UK average
  21. 21. Age structure in Westminster• Westminster has relatively more people aged 20- 39 and a fewer younger people compared to the London average
  22. 22. Ethnicity distribution in Ealing• North and South Southall have a high proportion of people from BME communities, which account for up to 90% of the populations in some wards
  23. 23. Health Status: QOF DEP02 in Southall 0-10 11-20 21+ Denominator values for DEP02- number of new diagnosis of depressionDEP02: In those patients with a new diagnosis of depression, recorded between thepreceding 1 April and 31 March, the percentage of patients who have had an assessment ofseverity at the outset of treatment using an assessment tool validated for use in primary care
  24. 24. Health service data: Admissions
  25. 25. Barriers to engaging Public Health Data Data collection and collation skills • Sourcing appropriate data • Choice of relevant data from the vast amount available Analytical skills • Basic analysis of data • Presentation of data for a range of audiences • Interpretation of data
  26. 26. Framework to evaluate outcomes• Understanding of models of health• Interpretation of the evidence base• Use of qualitative data to complement quantitative data• Analysis of cost effectiveness, e.g. through a programme budgeting approach
  27. 27. Approaches to overcome barriers• Mapping data sources• Generation of disease/condition or client group specific data profiles for relevant localities• Analytical public health appraisals of data• Outcome framework to evaluate quality improvement projects contribution to population health and identify where quality improvement can be best employed.
  28. 28. Objectives of MPH Project• Demonstrate the utility of PH skills• Understand QI methodology and its implementation• Develop skills specific to personal learning objectives• Develop evaluation skills