Assessment of the organizational factors in incident management practices in healthcare: A tree augmented Naive Bayes model.

Despite the exponential transformation occurring in the healthcare industry, operational failures pose significant challenges in the delivery of safe and efficient care. Incident management plays a crucial role in mitigating these challenges; however, it encounters limitations due to organizational factors within complex and dynamic healthcare systems. Further, there are limited studies examining the interdependencies and relative importance of these factors in the context of incident management practices. To address this gap, this study utilized aggregate-level hospital data to explore the influence of organizational factors on incident management practices. Employing a Bayesian Belief Network (BBN) structural learning algorithm, Tree Augmented Naive (TAN), this study assessed the probabilistic relationships, represented graphically, between organizational factors and incident management. Significantly, the model highlighted the critical roles of morale and staff engagement in influencing incident management practices within organizations. This study enhances our understanding of the importance of organizational factors in incident management, providing valuable insights for healthcare managers to effectively prioritize and allocate resources for continuous quality improvement efforts.

Capacity factor analysis for evaluating water and sanitation infrastructure choices for developing communities.

40% of the world's population lacks access to adequate supplies of water and sanitation services to sustain human health. In fact, more than 780 million people lack access to safe water supplies and about 2.5 billion people lack access to basic sanitation. Appropriate technology for water supply and sanitation (Watsan) systems is critical for sustained access to these services. Current approaches for the selection of Watsan technologies in developing communities have a high failure rate. It is estimated that 30%-60% of Watsan installed infrastructures in developing countries are not operating. Inappropriate technology is a common explanation for the high rate of failure of Watsan infrastructure, particularly in lower-income communities (Palaniappan et al., 2008). This paper presents the capacity factor analysis (CFA) model, for the assessment of a community's capacity to manage and sustain access to water supply and sanitation services. The CFA model is used for the assessment of a community's capacity to operate, and maintain a municipal sanitation service (MSS) such as, drinking water supply, wastewater and sewage treatment, and management of solid waste. The assessment of the community's capacity is based on seven capacity factors that have been identified as playing a key role in the sustainability of municipal sanitation services in developing communities (Louis, 2002). These capacity factors and their constituents are defined for each municipal sanitation service. Benchmarks and international standards for the constituents of the CFs are used to assess the capacity factors. The assessment of the community's capacity factors leads to determine the overall community capacity level (CCL) to manage a MSS. The CCL can then be used to assist the community in the selection of appropriate Watsan technologies for their MSS needs. The selection is done from Watsan technologies that require a capacity level to operate them that matches the assessed CCL of the community.