Problem
One of the measures used to assess the quality of emergency services is the rate of returning patients within 72 hours of the initial visit. A data set of 200 variables related to a hospital's Emergency Department visit was used to develop a k-means clustering model with a sensitivity of 25% and specificity of 75%. Even though the hospital has employed attempts to operationalize such a model, they are currently failing. The model can precisely identify patients that would likely return to the Emergency Department, although there is not a current intervention to mitigate patient revisits.
Provide at least two potential explanations why intervention efforts have failed with this group of patients.
Provide a rationale for why you believe this modeling was successful or unsuccessful. What would your next steps be in mitigating the outcomes from this scenario?
Provide examples and delineate reasons that may elevate issues with models for each of the following scenarios: low sensitivity - low specificity, low sensitivity - high specificity, high sensitivity - low specificity, and high sensitivity - high specificity.