A decision-analytic model assists decision-makers in getting a clear understanding of cost-effectiveness analysis and the consequences of incremental costs. Characterization of uncertainty such as parameter and methodological uncertainty is important when designing and interpreting any decision-analytic model. A good decision-analytic model addresses various aspects of health economic evaluation.
Shared governance in nursing involves shared decision-making between the management and healthcare workforce. The shared governance model is based on the principles of partnership, accountability, and equity among the stakeholders in the healthcare industry. Shared governance has been associated with positive outcomes and quality patient outcomes. An analytic decision model provides a framework for cost analysis and describes the cost-benefit relationship prior to the implementation of the shared governance model.
Uncertainty in the shared governance model can be characterized using decision modeling. The decision-analytic modeling can also determine the cost-effectiveness of the shared governance model in nursing using different parameters such as patient outcomes and job satisfaction, scientific judgments, and averaging different alternate models before comparing them with shared governance.
The decision-analytic modeling can also evaluate the models that assess the prevalence of particular diseases among patients. The techniques of root cause analysis and Bayesian networks are applicable in medicine in assessing the risk in medicine. The Root cause analysis can be used to identify the causal factors that influence performance in medicine, including medical errors and industrial accidents. The Bayern networks are applicable in medicine in performing risk analysis. The shared governance model has positive outcomes, including improved patient outcomes and job satisfaction among patients, which can be assessed using an analytic decision model. The analytic decision model provides useful guidance for the evaluation of the cost-effectiveness of decision models in the health industry.