Improving discovery in maternity care: data-to-knowledge in maternal Learning Health Systems

Cockburn, Neil ORCID: 0000-0001-9284-6991 (2025). Improving discovery in maternity care: data-to-knowledge in maternal Learning Health Systems. University of Birmingham. Ph.D.

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Abstract

Background
Maternity services in the UK face significant pressure and scrutiny. Simultaneously, electronic health record adoption and new technologies create opportunities to use data collected by maternity services for improvement. However, poor quality, inaccessible, and unstructured data limits it’s use for quality improvement and research, particularly for complex problems such as dramatic differences in maternal mortality rates between social groups. Learning Health Systems (LHS) approaches are an approach to strengthening continuous improvement in services by capturing practice as data, using data to generate new knowledge, and implementing new knowledge into practice.

Objectives
This thesis aims to improve the data-to-knowledge part of the Learning Health Systems in maternity services.

Methods
This thesis presents a series of studies that first explore how data-to-knowledge in LHS could support maternity services, then deploys infrastructure in a maternity service to build towards a LHS capable of using the solutions explored in earlier chapters.

To explore the use of LHS, a systematic review of Clinical Decision Support Systems in maternity, methods development for automated analytics of electronic health records, and a cohort study of health inequalities in comorbidities at the start of pregnancy in primary care electronic health records were conducted.

A data platform was then deployed in a local maternity service, and two case studies demonstrate epidemiological analysis that could inform support maternity services in providing care.

Results
The systematic review showed that Clinical Decision Support Systems were effective in improving care, but careful attention to design and context are essential for success. Software for automated analysis is presented and evaluated in an ectopic pregnancy example. Systematic analysis of health inequalities shows systematic differences in diagnosis of health conditions amongst different social groups, with increased diagnoses in women from more deprived areas and reduced diagnoses in women from minoritised ethnic groups.

Deployment of the data platform enabled epidemiological studies using routinely collected care data. Two case studies showed that despite differences in access to induction between groups, there was no difference in outcomes achieved, and that eligibility for trial recruitment varied significantly by socioeconomic characteristics. This data was then used to target recruitment efforts to ensure representative inclusion in the trial.

Conclusions
This thesis demonstrates progress towards LHS and identifies future developments required to support maternity services. Careful attention to localised context is required to effectively use data to generate actionable knowledge. Long term investment of informatics capabilities is required to support maternity services and improve outcomes for women and babies across society.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):

Supervisor(s)	Email	ORCID
Chandan, Joht Singh	UNSPECIFIED	orcid.org/0000-0002-9561-5141
Nirantharakumar, Krishnarajah	UNSPECIFIED	orcid.org/0000-0002-6816-1279
Taylor, Beck	UNSPECIFIED	orcid.org/0000-0002-3559-7922
Parry-Smith, Will	UNSPECIFIED	orcid.org/0000-0002-0017-7266