This study investigates what happens to pregnant women and their babies when labour is induced. Induction of labour involves treatments to start labour, often due to overdue pregnancy (past 42 weeks) or slowed baby growth. Recent advice recommends more inductions, as they seem to improve outcomes for women and babies. However, inductions increase hospital stays, adding pressure on maternity services.

We will analyse data from electronic health records (EHRs) of 20,000 pregnancies annually in West Midlands maternity services to compare outcomes in induced versus non-induced pregnancies. These EHRs, routinely collected by healthcare workers, include valuable data on pregnancy care, such as age, ethnicity, smoking, and weight.

By understanding the risks and benefits of induction, this study aims to improve care for pregnant women and support healthcare providers. Clearer guidance could reduce anxiety, avoid unnecessary treatments, and improve outcomes, enhancing the safety and experience of induction of labour.

The expected benefits of this project for patients and the NHS are significant. For patients, it could improve care by identifying the risks and benefits of induction of labour and providing clearer information about birth choices and outcomes. For the NHS, the findings could highlight capacity needs and bottlenecks, compare performance across trusts, and either provide reassurance or identify risks related to induction rates, helping guide service development during high-pressure times.

The study’s results could also support updated, evidence-based guidelines for managing induction of labour, promoting consistent, high-quality care across maternity services. By refining care pathways through data, the NHS can enhance maternal and fetal outcomes while maintaining a patient-centred approach. The public will benefit from improved safety, clarity, and efficiency in care, fostering greater trust in NHS maternity services.

This study investigates a risk calculator for postpartum haemorrhage (PPH), a potentially life-threatening condition where excessive bleeding occurs after birth. Drugs are routinely used during pregnancy to reduce PPH risk, with additional treatments available for higher-risk women. However, identifying those at risk is challenging, as many PPH cases occur in women without obvious risk factors.

A risk calculator developed in the USA aims to predict an individual’s PPH risk, but its effectiveness in the UK is unknown. Differences in diagnosis and care between the two countries may impact its accuracy. This study will test the calculator on UK data by analysing Electronic Health Records (EHRs) from West Midlands maternity services, which record around 20,000 pregnancies annually. These records, routinely collected by healthcare workers, include information on age, ethnicity, smoking, weight, and other risk factors.

By comparing pregnancies with and without PPH, this study will evaluate the calculator’s accuracy for predicting risk at the start of pregnancy. If effective, it could be used by women and healthcare staff to assess individual PPH risk. This study aims to improve care by refining PPH risk management, reducing risks, anxiety, and unnecessary treatments, ultimately enhancing safety and support for women and healthcare providers.

The expected benefits of this project for patients and the NHS are significant. For patients, it could improve care by identifying PPH risks and tailoring preventative treatments to those at higher risk. Women who need extra care would receive it, while others avoid unnecessary interventions, reducing stress during pregnancy.

For the NHS, the findings could prevent complications, improve outcomes, and reduce care needs, enabling more efficient resource allocation and potential cost savings. The results could also support evidence-based guidelines for managing PPH, ensuring consistent, high-quality care across maternity services.

By refining care pathways with data, the NHS can enhance maternal and fetal outcomes while maintaining a patient-centred approach. The public would benefit from greater safety, clarity, and efficiency in care, fostering trust in NHS maternity services.

Background:

Musculoskeletal conditions, including back, neck, joint, and muscle pain, are a leading cause of disability in the UK and globally. Over 20 million people in the UK live with these conditions, causing over 23 million lost working days annually. Despite most patients being managed in community and primary care (e.g., Physiotherapy and GP clinics), there is no publicly available information on care quality, consistency, or equity across these services. A data collection platform is needed to gather musculoskeletal health intelligence for multiple services in the West Midlands, improving care quality and highlighting best practices.

Aim: To evaluate the feasibility of using the West Midlands Secure Data Environment (West Midlands SDE) as a platform to house enhanced health intelligence data for musculoskeletal services, improving care in community and primary settings.

Design/Methods: The study will recruit at least four large community services in the West Midlands. Standardised data collection will include patient-reported outcomes and experiences, service-level data (e.g., waiting times, staffing), and electronic health records (e.g., diagnoses, treatments). Adults aged 18+ consulting for musculoskeletal conditions will be included. Data will be shared with West Midlands SDE under appropriate permissions.

Realisable benefits to patients and the NHS include:

Routine, large-scale surveillance in Community/Primary care MSK services for the first time.

Local interactive dashboards for quality improvement, contextualised for commissioners, service providers, clinicians, and patients, with infographics and bite-sized reports.

GIRFT-style benchmarked reporting, enabling like-for-like provider-level comparisons.

Sharing best practices and identifying local challenges to support regional quality improvement.

Identifying inequalities’ extent and drivers to enable targeted improvement initiatives.

Determining best-value, most effective care models, with data supporting research and informing national policy.

Infrastructure for real-world evaluation of initiatives to improve MSK care quality.

Collaborative health intelligence addressing key questions for stakeholders, including patients, clinicians, commissioners, and researchers.