Cognitive Emulation
| 4 Min Read

Analyzing the Clinical Impact of COVID-19 Part I


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.

This post begins a series on the clinical and financial impacts from COVID-19. The following analysis will focus on an initial set of observations looking at mortality and admission status. 

Admission Status

Inpatients accounted for 56% of the 60,000 total COVID-19 admissions in Iodine’s data set. These 34,000 cases were analyzed by age and gender, as shown in the figure below. 

Mortality Rate by Age Group

Iodine assessed the relative likelihood of mortality by age group of admitted inpatients with COVID-19, as shown in the graph below. The mean mortality rate was 16.3% across all inpatients, while the mean mortality rate for all inpatients on ventilators was 38.6%.  Additionally, individuals aged 90+ had a mortality rate that was nearly 2x the mean mortality rate for all COVID-19 patients in an inpatient hospital setting.

The graph below assesses the relative likelihood of mortality by age group and gender of admitted inpatients with COVID-19. Across all age groups, males had a higher rate of mortality than females.

Mortality Rate by Comorbidity 

Certain pre-existing conditions placed COVID-19 patients at higher risk for severe illness. Two conditions, diabetes and morbid obesity (BMI of 40 or greater), were analyzed in greater detail. Overall, the mortality rate for COVID-19 patients with either of these conditions was 18.5%, compared to the overall rate of 16.3% across all age groups.

The figure below shows that, as age increased, the relative likelihood of mortality due to obesity and diabetes decreased, suggesting that other factors were more likely to drive mortality for older COVID-19 patients.

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

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