Cognitive Emulation
| 4 Min Read

Analyzing the Clinical Impact of COVID-19 Part II


By Iodine Data Science Team on May 8, 2020 

This is Part II of Iodine’s series on the clinical and financial impact of COVID-19 and what healthcare finance leaders need to be aware of as they plan for short- and long-term resiliency. To read Part I on the mortality impacts of COVID-19, click here

Iodine Software is a healthcare AI company that has pioneered a new machine learning approach—Cognitive Emulation—to help healthcare finance leaders build resilient organizations. To date, Iodine has partnered with over 600 hospitals in the United States to create a large and diverse clinical data set that can provide insights into the COVID-19 pandemic. 

Iodine’s Cognitive Emulation approach analyzes the full clinical record for each patient much the way a clinician would, but on a massive scale. Coupled with proprietary technology that forecasts DRGs for every patient still in the hospital, Iodine is able to look in real time at trends emerging in this data set without having to rely on final coded data that is typically only available post-discharge, after a considerable delay. 

This information is meant to help healthcare providers nationwide more accurately forecast their resource needs (including staff, ICU beds, ventilators and other critical care equipment) and understand the demographics most vulnerable to COVID-19, as well as support healthcare finance leaders in determining the right strategies to ensure financial resilience both in the near- and long-term.

Iodine reviewed more than 60,000 COVID-19 cases from the 600+ hospitals in its current data set—spanning cases from the entire country including both infection hot spots and emerging areas of concern. Within this data set, 50.1% of patients were male and 49.9% were female.

Length of Stay
Inpatients accounted for 56% of the 60,000 total COVID-19 admissions in Iodine’s data set. These 34,000 cases were analyzed by length of stay (LOS). The first figure (Percentage of Inpatients by Length of Stay) shows that 19.2% of people have an average length of stay between 3-5 days. The second figure (Inpatient Average Length of Stay by Age Group) shows the average length of stay by age group. On average, a COVID-19 inpatient was admitted for a length of stay of 7.6 days.

Ventilator Demand
The mortality rate of inpatients who were on a ventilator at some point during the inpatient stay is more than 5x higher than an inpatient who was never on a ventilator during their hospital stay (38.6% mortality rate for those on a ventilator compared to 7.3% mortality rate for those not on a ventilator).

Note: All mortality stats are in reference to admitted inpatients with COVID-19.

To learn more about Iodine’s Cognitive Emulation approach, please contact: