This case study summarises Barts Health NHS Trust’s development of a comprehensive maternity dashboard to address ethnic inequities in maternal and neonatal health outcomes, particularly for their diverse population in London.
Context and background
Barts Health NHS Trust participated in NHS Race and Health Observatory’s Learning Action Network, a large-scale national improvement programme across 9 NHS Trusts aimed at addressing ethnic inequities in maternal and neonatal health. Barts Health provide maternity services to a very diverse population in London, with Asian women making up 54% of the maternity population, and specifically Bengali women presenting 27.4% of all births at the Trust.
One of the key challenges the Barts faced was understanding outcomes and experience of care using a nuanced, intersectional lens. Previously, the Trust had in place a maternity dashboard that used national reported localised datasets on maternity and neonatal outcomes with simple RAG (red, amber, green) ratings to identify improvement areas. However, these national datasets were not localised to the needs of the Trusts and did not provide the granularity needed for frontline teams to implement service improvements tailored to the needs of local communities.
Addressing the issues
Building on the latest digital tools available at the Trust, the Barts team aimed to develop a comprehensive maternity dashboard in Qlik Sense, a business intelligence platform for data visualization and analysis, using the Trust’s electronic patient record (EPR) data. The development of the dashboard was driven by the Trust’s digital midwives who were keen to embrace new technologies at the organisation and work with the digital teams to integrate them into their daily workflows.
Although the midwives were already tracking a wide array of maternity-related metrics, these data points were not being systematically analysed alongside all the information collected within the EPR. To understand the types of data that the dashboard needed to include, the Business Intelligence and Data Reporting teams held regular workshops with the digital midwives from the outset to ensure these metrics were clearly defined. These detailed discussions also helped shape the back-end data model by identifying what data was collected, when, and how it was recorded by frontline teams in the EPR. By investing time into building a robust data model, they helped to ensure that any future changes to the metrics could be implemented without changing the front-end of the dashboard. Before data was displayed on the dashboard, final meetings with the digital midwives were held to review and approve the data model and validate the accuracy of the logic and calculations behind the metrics.
To encourage staff to use the dashboard routinely, it was important that it was designed to be accessible, accurate, and updated in real time . A range of functionalities was embedded to empower maternity teams in exploring data dynamically. The “explorer” functionality in Qlik Sense allowed users to manipulate data presentation, slicing key indicators based on individual metrics of interest. Additionally, the dashboard enabled users to overlay ethnicity, deprivation status, and other patient characteristics onto broader maternity service statistics.
To ensure the dashboard was effectively adopted and utilised, various initiatives were put in place to support staff. Digital midwives played a key role in embedding the tool within routine meetings, helping colleagues understand how to use the dashboard to get the insight needed for service improvements. A series of lunch-and-learn sessions introduced staff to the dashboard’s functionalities, beginning with six introductory workshops, followed by advanced training. This hands-on approach encouraged maternity teams to recognise the dashboard’s potential in improving care delivery.
The dashboard has two views:
- the full Dashboard that is available to all users across the Trust where person-identifiable data is anonymised, ensuring broad engagement with the platform, and
- (view of person-identifiable information for staff involved in direct service delivery. The latter view was managed through an application process reviewed and approved by the digital midwives.
The dashboard also has a built-in feedback mechanism, allowing users to submit suggestions for enhancements and report any issues, thus fostering continuous refinement of the tool.
Outcomes
The new maternity dashboard has transformed how quality improvement is approached at Barts Health and has allowed for more targeted, equitable service planning. It has further empowered staff by giving them access to real-time data, enabling them to plan and deliver services with a clearer understanding of the needs of diverse populations. It also provided the evidence needed to elevate maternity equity discussions to board level, embedding these priorities into the Trust’s strategic direction.
For example, the dashboard is being used by the maternity teams to reduce the number of babies born before 27 weeks in a hospital site that does not have neonatal intensive care. Its functionalities enabled staff to understand the ethnicity, deprivation, and languages spoken of mothers who previously gave birth before 27 weeks. Through these granular insights, it was found that it affected women who do not speak English and were from one postcode area. This has led to collaboration with the Integrated Care Board team to identify concrete actions to introduce interpreters in this geographical area where language barriers affect maternal care. Finally, the dashboard enables continuous data quality checks which have supported frontline teams and the digital midwives to improve the quality of the data collected, such as provision of necessary refresher training on data entry into the EPR system
Together, these outcomes reflect a shift toward more inclusive, data-informed, and community-centred maternity care at Barts Health, laying the groundwork for long-term improvements in both clinical outcomes and patient experience.
Key learnings and recommendations
The development of the dashboard highlighted the critical role of data in driving maternity care improvements. By enabling analysis based on ethnicity and other demographics, the tool supports more equitable service delivery. The “explore” functionality allows teams to cut the data in bespoke ways, enhancing flexibility in data interpretation.
Balancing the resources and time needed to develop the dashboard with daily responsibilities required careful time management, emphasising the need to set realistic expectations. Data cleansing and development of the data model took approximately 6-8 months, and a further six months to design and deliver the dashboard. Thoroughly mapping out every data point was time-intensive, but time well spent as it helped increase the robustness and accuracy of the data model that sat behind the dashboard. Securing data sign-off remained a challenge due to evolving national definitions, reinforcing the importance of continuous validation.
Engagement of end-users and frontline staff throughout the dashboard co-design process was vital to ensuring that it was used in practice. Regular signoffs of the metrics by the digital midwives during the development process helped ensure that the dashboard was accurate and met the needs of frontline teams when undertaking equity-focused service improvement.
The Barts Health team are now exploring the way in which to further develop their dashboard. They are now gathering additional user requirements to use artificial intelligence predictive analytics to make impactful improvements to patient experience and address health inequities. For example, they hope to use these advanced analytics tools to predict foetal sizes based on historical pregnancies and outcomes, as well as sociodemographic characteristics, so that staff can proactively consider suitable interventions in advance to prevent negative neonatal health outcomes.
Additional information
TBA
Relevant links
Contact: bartshealth.weinform@nhs.net