Analyzing human factors affecting severe maternal morbidity (SMM) using fuzzy Bayesian network (FBN)

Severe maternal morbidity (SMM) is one of the key indicators for assessing the quality of obstetric care and is frequency associated with human error. This study aimed to analyze the human factors contributing to SMM using fault tree analysis (FTA) and fuzzy Bayesian network (FBN).

The present study was conducted using morbidity file data obtained from Birjand and Gonabad universities supplemented with expert interviews. First, basic events were identified and the fault tree structure was validated. Error probabilities were estimated using three approaches: Fast Fourier Transform (FFT), and FBN with and without Common cause failures (CCFs). The L-NOR gate was used to reduce the complexity of conditional probability tables (CPT) and to capture dependencies among factors. Sensitivity analysis and the strength of influence of contributing factors were assessed.

The main contributors to SMM were the delay in initiating emergency resuscitation efforts, inadequate management of obstetric hemorrhage, and poor team coordination, which showed the highest strength of influence on SMM occurrence. The final SMM probability was 0.0196 in FFT, 0.0193 in FBN without CCFs, and 0.0167 in FBN with CCFs.

Integrating FTA and FBN methods, particularly with the L-NOR gate, overcomes limitations of traditional approaches and enables more accurate modeling of cause-and- effect relationships in complex systems. Strengthening team coordination, appropriate management of hemorrhage, and implementation and enforcement of standard protocols are among the suggested strategies to reduce SMM. These findings provide valuable insights for policy-making and strategies to improve obstetric care.