It is now becoming common for critically ill patients to be cared for in locations that were previously anesthesia recovery areas or general wards. However, in the face of the pandemic, a significantly higher ICU surge capacity has been needed and critically ill patients may need to receive care outside of a traditional ICU, depending on available resources. In general, most American ICUs are equipped to withstand an approximate 20% surge in demand. Predictive models of decompensation may be a key element in ensuring appropriate and timely interaction with patients while still having adequate time to conform to the best personal protective equipment practices. Reducing the frequency and duration of times that staff enters the rooms of patients with Covid-19, is generally thought to reduce healthcare worker exposure.
Given the complexities associated with Covid-19 treatment, such models may enhance patient interaction, ICU staff capacity, and virtual ICU care. In the ICU, predictive markers of patient deterioration enable clinical decision support to facilitate resource utilization and enhance patient outcomes. As an overall response, predictive models have been proposed to support healthcare authorities in the early planning of resources, personnel, ICU, and hospital bed capacity. Rarely used in American healthcare, such models may offer an opportunity to change practice and enhance efficiency in the operation of clinical enterprises.
One area that may assist in the current environment is real-time prediction models.
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