1. Detecting early signs of patient deterioration in the ICU and the general ward
Predictive insights can be particularly valuable in the ICU, where a patient’s life may depend on timely intervention when their condition is about to deteriorate. In many countries including the US, ICUs were already overstrained prior to the COVID-19 pandemic as a result of aging populations, increasing use of complex surgical procedures, and a shortage of intensive care specialists. Since the outbreak of the coronavirus, the number of patients requiring acute care in the ICU has surged, further fueling the need for technology to aid caregivers in rapid decision-making.
As the vital signs of patients are continuously monitored and analyzed, predictive algorithms can help to identify patients with the highest probability of requiring an intervention in the next 60 minutes. This allows caregivers to proactively intervene at an early stage, based on subtle signs of deterioration in the patient’s condition. Similarly, predictive analytics can estimate the probability that patients risk death or readmission within 48 hours if they were discharged from the ICU, helping the caregiver decide which patients can be discharged.
Such predictive algorithms are now also deployed in tele-ICU settings, where patients are monitored remotely by intensivists and critical care nurses that are in constant contact with bedside clinical teams.
In addition, predictive analytics can help to spot early warning signs of adverse events in a hospital’s general ward, where deterioration of patients often goes unnoticed for prolonged periods of time. Automated early warning scoring allows caregivers to trigger an appropriate and early response from Rapid Response Teams at the point of care. Using this approach, one hospital reported a reduction in adverse events by 35%, and a cardiac arrest reduction of more than 86%.
With the further adoption of wearable biosensors, it could become even easier for care providers to detect early signs of patient deterioration as patients move through different acuity settings in the hospital. Such biosensors adhere discreetly to the patient’s chest to collect, store, measure and transmit respiratory rate and heart rate every minute – the top two predictors of deterioration – as well as contextual parameters such as posture, activity level and ambulation. Because wearable biosensors enable remote monitoring without care providers having to carry out physical spot checks, they are proving to be particularly useful in the clinical surveillance of patients with COVID-19.