Addressing health inequalities using health data: Regional experience and international opportunities

Prof Liz Sapey, Director, Health Data Research UK Data Hub, PIONEER, University of Birmingham talks about the crucial role of health data in addressing the health inequalities and highlights that advances in digital maturity of healthcare organisations can offer an opportunity to improve health by understanding and addressing health inequalities

The World Health Organisation defines health inequalities as “systematic differences in the health status of different population groups” and goes on to state that “these inequities have significant social and economic costs both to individuals and societies”[1]. UK health charity The Kings Fund notes that health inequalities include differences in access to health care, the quality of care that people receive, and opportunities that they have to lead healthy lives (including social determinants such as housing provision and air quality)[2].

Identifying health inequalities requires a comparison of health status and care provision, and most often inequalities are analysed by socio-economic factors, geography and specific characteristics of groups of people (such as sex, ethnicity and disability).

Crucially, most definitions of health inequalities define them as “avoidable”, suggesting it is within our power, as a global society, to tackle them.

Advances in digital maturity of healthcare organisations can offer an opportunity to improve health by understanding and addressing health inequalities. The primary use of Electronic health systems (EHS) is to direct the healthcare for the individual patient, with advantages over paper records including an easily accessible, longitudinal record of health, interventions and outcomes.

It is the secondary use of health data which perhaps provides the greatest potential to reduce health inequalities. This use includes health service planning, improving the quality of health services, and research and innovation.

Across a population, health data can be used to determine what the current level of service is being provided and taken up, to map this to population need, and to identify gaps in current health service delivery. Benchmarking against agreed priority outcomes across organisations, regions, and countries can identify outliers, enabling systems to learn from best practice and understand and address services which perform below accepted standards.

Curating large amounts of health data enables researchers to assess the efficacy of interventions and the impact of health services and health policy. Natural experiments using real world data can describe outcomes from deployed health services, and randomised controlled studies for new services or treatment approaches are greatly facilitated by EHS.

These have the potential to identify populations which gain most, or least, from new interventions, assessing the impact on those usually underserved by healthcare provision and innovation.

My own field of practice is Acute Internal Medicine, the provision of care to patients hospitalised with a medical illness within the first 48 hours of admission to hospital. It is an area of huge demand. The recent waves of the COVID pandemic have demonstrated the global impact of overwhelmed acute medical services. In the UK nearly 90 per cent of hospital beds are filled with acutely unwell medical patients at any given time, impacting on our ability to deliver planned, elective care for patients. Health inequalities are present, with those from lower socioeconomic groups more likely to rely on acute medical services compared with more affluent groups.

The timely embedding of a COVID-specific electronic clerking sheet into the EHS, with blood bundles to stratify risk and prescribing prompts, enabled the safe reconfiguration of health services to meet the first and subsequent waves of infections with SARS-CoV2 in the West Midlands[3] region of the UK.

The curation and analysis of regional admission data at scale enabled the first study which identified the increased risk of death of people from South Asian communities with COVID-19, even once socio-economic deprivation was accounted for[4]. This led to changing risk stratification tools for people from these communities, to mitigate their increased risk of poor outcomes. During the second wave of the pandemic, central health services suggested a COVID-virtual ward, to monitor people in the community through phone calls and oximetry readings. An assessment of this service identified unintended consequences, removing clinical staff from frontline duties with no survival or readmission benefits[5].

Away from COVID, the provision of an EHS has reduced missed medication doses in hospital by 60 per cent through building and testing clinical dashboards which flag delays in administration[6]. Clinical decision support tools have improved the guideline compliant management of acute patients to >90 per cent for some conditions[7].  Prescribing prompts which combine patient data with medications have reduced medication prescribing errors from 25 per cent to 0 per cent for complex care regimens[8].

The benefits of using health data at scale, in near real time and across populations to address health inequalities are manifold. However, there are real challenges in curating health data, making it discoverable and ready to be used for service planning and innovation.

First, healthcare organisations must have an EHS. Within the UK there is significant heterogeneity in digital maturity across NHS organisations, ranging from global digital exemplars which have run EHS for decades, to paper-based systems. Digitising health across populations is a significant undertaking.

Second, healthcare organisations need access to the relevant expertise to curate data for analysis. Having the provision to analyse data and amend the EHS to reflect changing need has been a cornerstone in improving health services in the Midlands region in the UK.

Third, to gain an understanding of population health requires data to be interoperable, with the ability to combine and compare datasets across health care providers, regions and countries.

Most importantly, we need to take patients and the public with us. Health data is sensitive and private, yet most patients are willing for their anonymised health data to be accessed to improve health care for others[9]. Building resilient governance systems which facilitate safe access to health data, meet the privacy and security expectations of our population and include public voices in access decision pathways has enabled data sharing at pace and scale within the West Midlands[10] region of the UK.

Sharing this kind of learning across health providers can enable best practice to grow exponentially across the globe.  This will ensure more and more of our population to benefit from insights are derived from representative and diverse health data, and that health inequalities around the world are reduced.

Data Driven Healthcare Day at UK House was hosted by the UK’s Department for International Trade and West Midlands Growth Company in partnership with University of Birmingham. You can watch the discussions on demand at: www.ukhouse2022.co.uk

 

References:

[1] https://www.who.int/news-room/facts-in-pictures/detail/health-inequities-and-their-causes

[2] https://www.kingsfund.org.uk/publications/what-are-health-inequalities#what

[3] doi: 10.1016/j.hlpt.2021.100568

[4] doi: 10.1136/bmjresp-2020-000644

[5] doi: 10.52964/AMJA.0876

[6]  doi: 10.1093/intqhc/mzt044.

[7]  doi:10.1101 /2021.01 .11.21249606

[8]  doi:10.1177/2055207620965046

[9] doi: 10.1186/s40900-021-00281-2.

[10] doi: 10.1136/bmjhci-2020-100294.