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Creating a learning system for Maternity Care

Dr Stamatina Iliodromiti

Dr Stamatina Iliodromiti

Level

Post-doctoral

Year awarded

2020

Host university

Centre for Women’s Health, Institute of Population Health Sciences
Queen Mary University London

Background

Childbirth in the United Kingdom (UK) is relatively safe; however, complications happen, and, in some units, complications happen more often than in others. It is crucial that all women, irrespective of where they live or their background, receive same quality of maternity care. To achieve this, we need a system for local units to monitor their performance, learn from their patients, change the way they deliver care, if their performance is not optimal and improve outcomes. This system is called “learning healthcare system”. The COVID-19 pandemic has changed the way that maternity care is delivered but also created opportunities for electronic information to be accessed faster. The aim of this project is to develop a system to assess how the pandemic affected maternity services, learn from the lessons and ultimately sustain the tool to monitor and innovate care long term.

Approach

I will use a combination of research methods to complete the study.

  • Access the pregnancy electronic records from each hospital.
  • Linkage the information about the mothers with information about the health of their babies.
  • Evaluate whether women who were pregnant or delivered before the pandemic had received better care compared with women during the pandemic.
  • Ask women and relevant public to define “better care”.
  • Create a “traffic light system” that will alert when a unit performs worse than others.
  • Investigate what factors are associated with a good performing unit and make recommendations to the least good ones. This way inequalities within maternity units will become smaller and childbirth will be safer across all units in the UK.

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Dr Stamatina Iliodromiti

Dr Stamatina Iliodromiti

Level

Post-doctoral

Year awarded

2020

Host university

Centre for Women’s Health, Institute of Population Health Sciences
Queen Mary University London
Matina is a Senior Lecturer at QMUL and Consultant Obstetrician in East London. Her research interests centre around utilising big cohorts or routine data to examine how perinatal exposures are associated with long term health outcomes and how access to routine data can improve quality of maternity care. View full bio