This document discusses how quality improvement projects can be aligned with population health needs using health needs assessments. It outlines how CLAHRCs were developed to close evidence-practice gaps. The CLAHRC approach combines research, improvement, and collaboration to deliver projects. Quality improvement drivers include clinical processes, patient needs, policies, and public health. A case study on improving access to mental health services in Ealing and Westminster is presented. Barriers to using public health data and approaches to overcome them are also discussed.

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. 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. 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. NIHR Innovation Pathway

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. Delivery of projects

7. Drivers for quality improvement
Some 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. 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. 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. Geodemographic

11. Health status Data
• Includes births, deaths, incidence of cancers and
other diseases
Recorded prevalence of Stroke and TIA
(% registered patients)

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. Case Study: Improving access to
Mental Health and Wellbeing
Services in Ealing and
Westminster

14. Project Aims
NHS Ealing
• Increase GP referral rates of BME patients to the
Ealing IAPT Mental Health and Wellbeing Service
Central Northwest London NHS Trust
• Increase self referral from older patients over the
age of 65

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. Deprivation- Ealing
• Ealing has pockets of severe deprivation across
the west of the borough as well as affluent areas

17. Deprivation- Westminster
• Westminster is mostly affluent borough with
pockets of severe deprivation in the northwest

18. Social Classification: Ealing

19. Social Classification: Westminster

20. Age structure in Ealing
• Ealing has relatively more people aged 20-39
and a fewer elderly people compared to the UK
average

21. Age structure in Westminster
• Westminster has relatively more people aged 20-
39 and a fewer younger people compared to the
London average

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. Health Status: QOF DEP02 in Southall
0-10
11-20
21+
Denominator values
for DEP02- number
of new diagnosis of
depression
DEP02: In those patients with a new diagnosis of depression, recorded between the
preceding 1 April and 31 March, the percentage of patients who have had an assessment of
severity at the outset of treatment using an assessment tool validated for use in primary care

24. Health service data: Admissions

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. 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. 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. 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