Adoption of a Data-Driven Bayesian Belief Network Investigating Organizational Factors that Influence Patient Safety

Abstract

Medical errors pose high risks to patients. Several organizational factors may impact the high rate of medical errors in complex and dynamic healthcare systems. However, limited research is available regarding probabilistic interdependencies between the organizational factors and patient safety errors. To explore this, we adopt a data-driven Bayesian Belief Network (BBN) model to represent a class of probabilistic models, using the hospital-level aggregate survey data from U.K. hospitals. Leveraging the use of probabilistic dependence models and visual features in the BBN model, the results shed new light on relationships existing among eight organizational factors and patient safety errors. With the high prediction capability, the data-driven approach results suggest that "health and well-being" and "bullying and harassment in the work environment" are the two leading factors influencing the number of reported errors and near misses affecting patient safety. This study provides significant insights to understand organizational factors' role and their relative importance in supporting decision-making and safety improvements.

Keywords: Bayesian Network; healthcare operations; medical errors; patient safety; risk.

Fig 1

Network structure developed using the PC algorithm.

Fig 2

Network structure developed using the BS algorithm.

Fig 3

Network structure developed using the GTT algorithm.

Fig 4

(a) Backward propagation of beliefs once the low state of patient safety errors is established. (b) Probability distribution of factors associated with patent safety errors. (c) Backward propagation of beliefs once the high state of patient safety errors is established.

Fig 4

(a) Backward propagation of beliefs once the low state of patient safety errors is established. (b) Probability distribution of factors associated with patent safety errors. (c) Backward propagation of beliefs once the high state of patient safety errors is established.

Fig 4

(a) Backward propagation of beliefs once the low state of patient safety errors is established. (b) Probability distribution of factors associated with patent safety errors. (c) Backward propagation of beliefs once the high state of patient safety errors is established.

Fig 5

(a) Back propagation impact assessment given the patient safety errors in “s1” state. (b) Back propagation impact assessment given the patient safety errors in “s3” state.

Fig 6

(a) Impact of individual factors on patient safety errors relative to their “s1” state. (b) Impact of individual factors on patient safety errors relative to their “s3” state.