Updated on: April 8, 2020

Understanding the Expectations for “Flattening the Curve”

Original story posted on: April 7, 2020

Examining the layers supporting the methodology behind social distancing.

The Institute of Health Metrics and Evaluation (IHME) is an independent population health research center at the University of Washington’s School of Medicine. The IHME was recruited at a state and federal level to provide modeling in determining if or when COVID-19 could overwhelm the national healthcare system’s ability to manage and treat patients.

On a national level, the IHME has done well thus far in predicting the daily COVID-19 deaths a week out. While predictions sound grim, the model, which provides insight over and above a simple warning, illustrates particulars for healthcare providers on what to anticipate, in order to decrease certain unknowns in a time when lack of predictability has become commonplace.

Statistical models tell us a bit about the timing of the approaching peak of coronavirus cases and deaths. Projections give detailed information to healthcare providers as to the number of beds needed to combat the virus, and the number of beds available on a state and national level, to include at a granular level the number of ICU beds and ventilators projected to be required versus the number of each on hand. These predictions are alarming, most significantly the number of deaths to be anticipated on a daily and weekly basis. 

What can we decipher from this data?

Undoubtedly, it can help healthcare professionals identify issues and determine good solutions to avoid certain unmanageable consequences. The information provides front-line workers with a benchmark that enables the ability to plan for what can be expected, as effectively as possible, following the predictive model. More pronounced than the detail in the predictive model is the margin of probability. When reviewing the model, there is a distinct shaded area, referred to as the area of uncertainty, and as we all know, the unknown does not lend itself to perfection. One can assume that the margin of uncertainty correlates to a world outside of healthcare, presumably much in the hands of individuals and policymakers. The more the public follows the recommended COVID-19 rules, the more the shaded area may diminish the degree of uncertainty, resulting in more accurate predictive models.   

New IHME COVID-19 estimation updates published April 5 noted that expanded data provided by various organizations, including state-level data on hospital utilization, is improving predictions, with more accurate models. With this new data, the improved models compared with earlier released predictions are indicating the same peak date for daily COVID-19 deaths in the U.S.; however, some states are now showing a movement to earlier timeframes of COVID-19 peak deaths. These improved models still do not take into account areas of uncertainty, revealing the variability that remains with analysis. 

Though there is science behind the predictive models, much is simply impossible to forecast regarding the true “flattening of the curve.” Ultimately, results will depend on all factors of the equation combined – individuals’ choices in their daily routines, public health policies, community mandates, and the reporting of accurate data. All considered, there are real indications that we are moving in a positive direction. 

For national and state information, please visit  https://covid19.healthdata.org/projections

Disclaimer: Every reasonable effort was made to ensure the accuracy of this information at the time it was published. However, due to the nature of industry changes over time we cannot guarantee its validity after the year it was published.
Susan Gatehouse, RHIT, CCS,CPC, AHIMA-Approved ICD-10-CM/PCS Trainer

Susan Gatehouse is the founder and chief executive officer of Axea Solutions. An industry expert in revenue cycle management, Gatehouse established Axea Solutions in 1998, and currently partners with healthcare organizations across the nation, to craft solutions for unique challenges in the dynamic world of healthcare reimbursement and data management.

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