Leveraging Technology and Data Analytics to Address Health Inequalities in Maternity Care

Recent findings in maternity and neonatal care highlight significant disparities in outcomes for socioeconomically deprived and ethnic minority populations, prompting the National Health Service (NHS) to prioritise initiatives to address these inequities.

This research explores how maternal demographic data (including socioeconomic deprivation index), clinical history, lifestyle factors, and care pathways can inform the design of more equitable and efficient healthcare interventions. This study draws on the NHS Maternity Services Data Set (MSDS) and its linked datasets (e.g., the Emergency Care Data Set (ECDS)) to evaluate operational strategies aimed at improving outcomes for at-risk populations. Using advanced data analytics, we assess interventions such as continuity of care and digital tools like SMS reminders with embedded cognitive nudges. The goal is to propose personalised care pathways that enhance system efficiency and reduce disparities in care delivery. This project is supported by the Sui Foundation.

EUROPE

The Challenge

Despite efforts to reduce healthcare disparities, socioeconomically deprived and ethnic minority populations continue to face worse outcomes in maternity and neonatal care. Reports like Mothers and Babies: Reducing Risk through Audits and Confidential Enquiries by MBRRACE-UK highlight that these groups experience higher risks and worse clinical outcomes. The problem is compounded by operational inefficiencies that prevent personalised care, which further exacerbates health inequities. Healthcare systems are often unable to respond effectively to the diverse needs of these vulnerable populations.

The challenge lies in not only improving the care provided but also ensuring that healthcare systems are designed to address these disparities. Currently a lack of operationally oriented research limits progress in designing and implementing effective solutions. Without interventions that are both targeted and efficient, health inequalities will persist, limiting access to necessary care and worsening health outcomes for those most in need.

The Interventions

This research takes an evidence-based approach to improving maternity care by leveraging patient-level data from MSDS and its linked datasets, which for the first time includes public records from the COVID-19 pandemic and spans an extended time horizon. The study will employ advanced analytical techniques, including causal inference, machine learning, and stochastic modelling, to classify patients into clinically relevant clusters and identify the key factors that drive disparities in outcomes for deprived and minority populations. The research will evaluate the effectiveness of operational interventions, such as continuity of care, and explore the impact of digital tools, including SMS reminders with cognitive nudges, on reducing late antenatal bookings, missed appointments, and improving patient engagement and health outcomes during pregnancy.

The Potential Impact

Leveraging recent, granular data to provide a holistic view of maternity services in England, the aims is to provide evidence-based, actionable insights for NHS, supporting targeted interventions at the local level. The focus of this research is on improving care delivery for socioeconomically deprived and ethnic minority populations, ultimately advancing health equity. Overall, this study contributes to the academic

literature on healthcare operations management, and aligns with the growing national effort to identify and address the drivers of health inequality in maternity care.