Evaluating patient experience in maternity services using a Bayesian belief network model

Area of Science:

• Health Services Research
• Maternal Health
• Patient Experience Studies

Background:

• Pregnancy and childbirth present unique challenges and risks, particularly with inadequate care.
• Maternity patient experience is shaped by various interconnected factors.
• Improving maternity care quality is essential for positive maternal and infant outcomes.

Purpose of the Study:

• To investigate factors influencing maternity patient experience.
• To explore complex interactions among these influencing factors.
• To identify key drivers of maternity care satisfaction.

Main Methods:

• Utilized data from the 2021 National Health Services (NHS) maternity patient survey in England.
• Employed a Bayesian Belief Network (BBN) for modeling factor interactions.
• Applied structural learning models (Bayesian Search, Peter-Clark, Greedy Thick Thinning) and sensitivity analysis.

Main Results:

• Identified eight key domains influencing maternity care experiences: start of care, antenatal check-ups, care during pregnancy, labor and birth, staff care, hospital care, infant feeding, and postnatal care.
• Highlighted the significant interdependencies among these domains.
• Quantified interactions and identified the most influential factors affecting patient outcomes.

Conclusions:

• Recognizing the interconnectedness of maternity care domains is vital for enhancing patient experience.
• Findings provide actionable insights for healthcare managers to develop targeted strategies.
• This research contributes to improving the quality of maternity care and overall maternal and infant well-being.