Discuss two reasons for implementing sensitivity analysis in the healthcare industry.
Discuss two difficulties that may arise when analyzing multiple goals.
Explain why a healthcare administrator might perform what-if analysis.
Discuss why a healthcare administrator might use goal seeking analysis.
Analyze the difference between decision tables and decision trees.
Explain how decision trees can be used in decision-making processes in a healthcare organization.
Prescriptive Analytics Determining the Impact of Change paper,
Sample Answer
Reasons for Implementing Sensitivity Analysis in the Healthcare Industry
Sensitivity analysis is a powerful tool in healthcare for understanding the robustness of decisions and the impact of uncertainty. Here are two key reasons for its implementation:
Assessing the Impact of Variable Costs and Reimbursements on Financial Viability: Healthcare organizations operate with complex financial models, influenced by fluctuating patient volumes, changing reimbursement rates from insurers (Medicare, Medicaid, private), drug costs, and staffing expenses. Sensitivity analysis allows administrators to model how changes in these key variables impact the organization's financial bottom line, profitability, or budget. For example, they can analyze how a 5% decrease in a particular insurance reimbursement rate or a 10% increase in a common drug's cost would affect the hospital's annual budget or the viability of a new service line. This helps in proactive financial planning, risk mitigation, and setting realistic financial targets.
Evaluating Clinical Treatment Pathways and Resource Allocation Under Uncertainty: In clinical decision-making, there are often uncertainties around patient outcomes, the efficacy of different treatments, and the availability of resources (e.g., bed capacity, specialized staff, equipment). Sensitivity analysis can be used to assess how variations in these factors affect the optimal treatment pathway or resource allocation strategy. For instance, a hospital might use it to understand how a higher-than-expected complication rate for a new surgical procedure or a surge in a specific type of illness (e.g., flu season) would impact bed utilization, staffing needs, and overall patient flow. This helps in making more resilient clinical and operational decisions that account for unforeseen circumstances.