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

Abstract

Pregnancy and childbirth are commonly seen as positive experiences, but they can also pose distinct challenges and risks, especially when care is insufficient. This study investigates the factors influencing maternity patient experience by exploring the complex interactions among these factors. Using data from the 2021 maternity patient survey by the National Health Services (NHS) in England, we implemented a Bayesian Belief Network (BBN) to model these interactions. Three structural learning models were created, namely Bayesian Search (BS), Peter-Clark (PC), and Greedy Thick Thinning (GTT). Further, sensitivity analysis was conducted to quantify interactions among the influencing factors and identify the most influential factor affecting the outcome. The results underscore the importance of recognizing the interdependencies among the eight key domains of the survey, which collectively shape maternity care experiences. These factors include the start of care in pregnancy, antenatal check-ups, care during pregnancy, labour and birth, staff caring, care in the hospital, feeding the baby, and care after birth. These findings can guide healthcare managers and decision-makers in developing proactive strategies to mitigate factors impacting maternity patient experiences. Ultimately, this study contributes to the ongoing efforts to enhance the quality of maternity care and improve outcomes for mothers and their infants.