Unplanned transfers of patients from general medical-surgical wards to the Intensive Care Unit (ICU) may occur due to unexpected patient deterioration. Such patients tend to have higher mortality rates and longer lengths of stay than direct admits to the ICU. A new predictive model, the EDIP2, was developed with the intent to identify patients at risk for deterioration, which in some cases could trigger a proactive transfer to the ICU. While it is conceivable that proactive transfers could improve individual patient outcomes, they could also lead to ICU congestion. In this work, we utilize a retrospective dataset from 21 Kaiser Permanente Northern California hospitals to estimate the potential benefit of proactive ICU transfers. In order to increase the robustness of our estimation results, we make a number of design choices to strengthen the instrumental variable and reduce model dependence. Using our empirical results to calibrate a simulation model, we find that proactively admitting the most severe patients could reduce mortality rates without increasing ICU congestion. However, being too aggressive with proactive transfers could degrade quality of care and, ultimately, patient outcomes. Thus, while we find evidence that proactive transfers could be effective, they would need to be used judiciously.
Hu, Wenqi, Carri Chan, Jose Zubizarreta, and Gabriel Escobar. "An Examination of Early Transfers to the ICU Based on a Physiologic Risk Score." Columbia Business School, 2016.
Each author name for a Columbia Business School faculty member is linked to a faculty research page, which lists additional publications by that faculty member.
Each topic is linked to an index of publications on that topic.