What is the challenge your project is going to address and how does it connect to the theme of 'How can we improve across system boundaries?​

Non-attendance of hospital appointments has significantly impacted service effectiveness, quality of care and estimated to cost over £1billion annually. This impact is even more detrimental at a time where the NHS is experiencing the highest waiting times on record post COVID19, staff shortages and industrial action.  Moreover, non-attendance strains finances and compromises patient care, exhausting staff who rearrange appointments and overbook clinics.

Underserved populations show heightened non-attendance or limited engagement with services, contributing to health inequalities.

MBRRACE reports have consistently highlighted health disparities, revealing that woman from minority ethnic backgrounds and those living in areas of high deprivation experience poorer outcomes in childbirth. These groups face increased risks of complications and mortality compared to their white counterparts. A system approach can proactively identify and provide support to these vulnerable women from their initial point of contact, ensuring continuous support throughout their maternity journey and a smooth transition to community services.

What does your project aim to achieve?

This project plans to build on learning extracted from PEEC phase1, where we developed prototype to proactively identify and support young people from adolescent and paediatric service at risk of clinic non-attendance. For further details please visit: https://q.health.org.uk/q-improvement-lab/reducing-waits-in-elective-care/q-lab-test-teams/university-college-london-hospitals-paediatric-department/

We now aim to apply the learning from PEEC1 and scale this approach to maternity services by:

1)      Implementing our enhanced non-attendance prediction model (developed in PEEC phase 1) in maternity services to support women known to be at high risk of deprivation, thereby improving perinatal outcomes.

2)      Collaborating with community partners to deepen our understanding of the link between women’s healthcare engagement, health inequality and poor perinatal outcomes.

3)      Utilizing these insights to develop tailored solutions for addressing health inequality within maternity services, enhancing outpatient care efficiency, and fostering cultural competence within the trust and broader healthcare system.

4)      Creating a framework for maternity services that integrates health equity into perinatal care pathways.

How will the project be delivered?

We are a multidisciplinary and diverse team.  We have a well setup patient and public involvement strategy and we work closely with our community partners.

The project core-team has representation from:

·       Maternal Medicine and Public-Health senior midwives

·       Improvement and Population health management teams

·       Lived experience partners

·       Local Community support group

Our outcome measures include:

1)  Clinic non-attendance rate and maternity adverse health outcomes

2)   Qualitative data will be collected via:

a) series of focus groups with women using maternity services (in the                                         antenatal and postnatal period) as well as community engagement                                               workshops (including MNVP, community women’s groups and local                                               hubs)

b) interviewing women not engaging with health care, with a focus on                                          minority ethnic particularly those of Black and Asians backgrounds.

3) Women reported experience measures, collected via surveys, coproduced with lived experience partners.

How is your project going to share learning?

We aim to share our work with Q community with regular updates on the project.

Identify learning opportunities to share within the trust and with wider system partners via publication, conference abstract and presentations.

We will share our story via UCLH magazine, and our framework and prediction model steps by step development procedure which can replicated in other organisations.