Tag: NHS

  • Building sustainable change across London’s health and care system with the Race Equity Maturity Index (REMI): Dr. Amanda Simon

    Building sustainable change across London’s health and care system with the Race Equity Maturity Index (REMI): Dr. Amanda Simon

    Dr. Amanda Simon, Researcher at the Race Equality Foundation, reflects on her experience of creating the Race Equity Maturity Index (REMI).

    The Race Equity Maturity Index (REMI) is a pioneering self-assessment tool co-produced with diverse communities and stakeholders from London’s health and care system. REMI enables organisations to track, measure, and strengthen their commitment to race equity, complementing existing equality, diversity, and inclusion initiatives.

    The uniqueness of REMI

    REMI stands apart by focusing on key areas of race equity, such as leadership, recruitment, policy development, decision-making, and community engagement.

    One common challenge in organisational assessments is managing workloads and avoiding duplication with other tools. REMI addresses this by consolidating existing efforts, offering a streamlined, embedded approach that allows for sustainable and integrated change.

    With REMI, organisations can set realistic goals aligned with their capacity and build a solid foundation for strategic planning.

    But the benefits extend beyond moral imperatives. Research shows that organisations prioritising race equity gain:

    • Increased profitability and productivity
    • A more engaged and connected workforce
    • Greater innovation
    • Enhanced service user experiences and outcomes

    From inception to design

    At the start of this journey, the task of creating a fit-for-purpose tool to combat systemic racism felt overwhelming. My initial focus was on designing a basic framework informed by theoretical research and literature on successful maturity indices.

    However, as the tool evolved, so did my confidence in its potential. Input from health and care professionals was integral, with practitioners affirming REMI’s practicality, ease of use, and capacity to drive race equity goals.

    The power of co-production

    REMI’s development reflects the rich diversity of London’s population and its health and care workforce. Our co-production group included equality managers, service users, and senior officials from across the sector.

    Their questions, critiques, and suggestions were invaluable, shaping every stage of REMI. 

    For example, feedback informed:

    • The naming and conceptualisation of REMI stages
    • Adjustments to content based on organisational cultures and race equity priorities
    • Refinements to language, ensuring accessibility in health and care contexts

    This iterative process was akin to designing a building based on client specifications—nurturing insights to create a practical, impactful tool.

    Overcoming challenges

    As a researcher, I understand that there are always challenges along the way. During this process the key issues have been the nature and implementation demands of the REMI. 

    Self-assessment

    Organisations’ skepticism about self-assessment highlighted concerns about honesty and a culture of “box-ticking” driven by performance pressures. However, the opposite can also occur where practitioners can be more critical in their own assessments.

    REMI counters this through a rigorous six-stage process supported by guidance notes and peer support for smaller organisations. These resources empower teams to cross-check evidence and share best practices.

    Voluntary participation

    While REMI is non-compulsory, this has not hindered enthusiasm. Organisations across various levels value REMI’s comprehensive approach to achieving race equity goals.

    Let the REMI guide your organisation

    The journey of developing REMI has demonstrated its ability to hold its own among other tools in the race equity space.

    It has been a privilege to lead this initiative alongside dedicated contributors.

    If your organisation strives for excellence for all service users and staff, let REMI guide you toward meaningful change.

    Try out the REMI for yourself here.

  • LARCH Learning Event ‘Advancing anti–racism in health and care: introduction to the Race Equity Maturity Index (REMI)’

    LARCH Learning Event ‘Advancing anti–racism in health and care: introduction to the Race Equity Maturity Index (REMI)’

    It is well evidenced that racism has a detrimental effect on individuals physical and mental health. This experience is further compounded where systemic or institutional racism occurs resulting in discrimination through societal systems, practices, and policies which produce and perpetuate inequities for racial minorities. How can we mobilise anti-racist practice to reduce racial health inequalities in the health and care sector?

    Race Equity Maturity Index

    The Race Equity Maturity Index is a tool developed by London Anti-Racism Collaboration for Health (LARCH) to support organisations in progressing race equity by enabling the tracking and improvement of race maturity levels. The index supports organisations in the actions they take to embed anti-racist practice. The REMI therefore complements the current equality, diversity and inclusion assessments and initiatives that health and care organisations are committed to.

    Session details

    In November 2024, representatives from across the health and care sector joined us for a two-hour learning and engagement event to learn how to assess and manage change to address racial inequities within and beyond your organisation.

    This interactive event included:
    ● A discussion of the organisational benefits of race equity practice
    ● An outline of how the co-produced index was developed
    ● An overview of the Race Equity Maturity Index (REMI) stages of implementation
    ● An outline of the support available to organisations whilst implementing the REMI
    ● A Q&A panel with cross sector representation

    View the session recording:

  • How better ethnicity data can tackle racial health inequalities in the NHS

    How better ethnicity data can tackle racial health inequalities in the NHS

    Modern medical practice is built on the foundation of using measurements and data to inform clinical decision making. From basic tests of blood pressure or heart rate through to more recent innovations such as genomic analysis or continuous glucose monitoring, a world without data-informed care seems almost unimaginable.

    Sadly, the same cannot be said for how we diagnose and treat problems at a population health level. The reality of addressing serious public health issues such as the pervasive racial health inequalities in our health and care system is that there are significant gaps in the data that is available. 

    Imagine a surgeon beginning their incisions without the benefit of imaging, or an oncologist unable to monitor the bloodwork of their patient to see if a medicine is having an effect on their cancer. Too often, the fog of missing, incomplete or poor-quality data is what those tasked with solving racial health inequalities are faced with.

    Data tells a story. The facts and figures it generates provide critical insights into demographics and medical information, enabling us to analyse, assess, and understand experiences, access, and disparities across communities. In healthcare, the potential of data to improve services across the NHS is undeniable. Particularly in addressing health inequalities, data plays a crucial role in identifying knowledge gaps. Through rigorous analysis, we can gain a deeper understanding of the challenges faced by specific communities, allowing for the development of tailored interventions to address inequalities in access, experiences, and outcomes.

    NHS England has prioritised complete and timely datasets as part of its five key strategies to reduce healthcare inequalities. The goal is to better understand the challenges faced by marginalised communities, including Black, Asian, and minoritised ethnic populations, and to develop targeted interventions based on these insights. However, significant gaps remain in accessing the right insights across the NHS, hindering progress in closing the healthcare inequality gap.

    Data, Ethnicity, and Health Inequalities: London Anti-Racism Collaboration for Health 

    On October 21, the Health Innovation Network South London and Race Equality Foundation launched the first in a series of learning and engagement sessions as part of the London Anti-Racism Collaboration for Health (LARCH). LARCH, a Greater London Authority (GLA) funded initiative, aims to improve the health and wellbeing of London’s Black, Asian, and minoritised ethnic communities. The event highlighted the role of ethnicity data in driving anti-racist strategies and advancing health equity.

    The event featured a distinguished panel of experts, including:

    • Tracey Bignall, Director of Policy and Engagement, Race Equality Foundation
    • Brenda Hayanga, expert on ethnic inequalities in healthcare use and care equality for people with multiple long-term conditions, City University
    • Macius Kurowski, Royal Free London NHS Foundation Trust, and Manal Sadik, North Middlesex University Hospital, discussing data-led approaches to reducing race-related health inequalities
    • Mary Hill, NHS England, Head of Policy, Healthcare Inequalities Improvement, discussing data, ethnicity recording, and coding

    Key Barriers to Improvement

    Collecting ethnicity data is vital for understanding the unique health experiences of different ethnic groups. For instance, we know that 1 in 4 Black men will develop prostate cancer earlier in life, compared to 1 in 8 White men. Yet despite its importance, research reveals significant issues with the quality of data being collected.

    Across the board, there is inconsistency. Research from the Nuffield Trust highlights that up to 40% of patients were coded as ‘any other ethnic group,’ even when a more specific ethnic group code would have provided deeper insights. Furthermore, the research from the Race Equality Foundation on the recording of ethnicity in health settings found variation in ethnic categories used with most settings where the 2001 and 2011 censuses are used inconsistently. Many people also wish to be identified by more specific ethnic terms, like “Hong Kongese,” but the options provided are often too generic.

    Another challenge is the use of arbitrary codes like ‘not asked,’ ‘not stated,’ or ‘unknown,’ which do not contribute meaningfully to population insights. These categories might meet organisational reporting requirements but reflect the discomfort or lack of training among staff in asking about ethnicity.

    Barriers for Patients and Staff

    Research in partnership with the Wellcome Trust shows that while communities are generally willing to share their demographic information, many are unclear on how the data will be used or fear it may be used in discriminatory ways. This lack of trust is reinforced by findings in the Earning Trust: A Foundation for Health Equity report, which noted that despite growing diversity in the NHS workforce, a 2022 review found that only 14% of NHS board members came from Black and minority ethnic backgrounds, while these groups constitute at least 18% of the population of England and Wales. The findings demonstrate that leadership isn’t reflective of the communities they serve and therefore strategies to see improvements are lagging behind. 

    Staff uncertainty also hinders progress. The Race Equality Foundation found that employees are unsure about how the data they collect will be used, and there is little evidence that managers emphasise the importance of ethnicity data collection during supervision. Some staff also feel awkward asking for this information, fearing they are being invasive, and lack the confidence to explain why it is important.

    Recommendations moving forward

    A system-wide approach is urgently needed to improve ethnicity data collection, monitor inequalities, and train staff in data capture. Poor-quality ethnicity data is masking health inequalities, and addressing this requires concerted effort.

    Having more rigorous ethnicity data collection provides a more nuanced understanding of the issues facing minority populations and enables healthcare systems to focus their efforts on addressing the issues at hand. We need to be led by the data in order to solve health inequalities. 

    We can do this by driving: 

    1. Confidence: Build confidence in using data to understand experiences, assess progress, and reduce health inequalities.
    2. Guidance: Provide clear guidance to improve the understanding, collection, and recording of ethnicity data.
    3. Procedures: Ensure there are robust procedures in place to monitor the quality of ethnicity data.

    Between now and March 2025, LARCH will be having more learning and engagement events. Stay tuned for future events.