Hurricanes Prompt Urgent Focus on Documenting Social Determinants of Health: Part I

EDITOR’S NOTE: This is the first in a two-part series on the coding and documenting of social determinants of health (SdoH). It comes on the heels of the aftermath of Hurricanes Harvey and Irma and their impact on victims and survivors in the flood-ravaged cities of Texas and Florida.

Hurricanes Harvey and Irma provide good illustrations of how important it is to document and code the social determinants of health (SDoH). There are multiple predictive models using demographic (U.S. Census), social, and economic data. In healthcare, data is collected either from the financial databases (for which ICD-10 data is key) or from more labor-intensive methods such as chart review and other studies.

There is unrealized potential for real-time data collection within the clinical documentation integrity (CDI) and medical coding communities. In these days and weeks ahead, coinciding with the post-disaster recovery efforts in both Texas and Florida, it is very important that healthcare professionals capture this information for several reasons. These include the following:

2. This data is reflective of an untapped real-time ability to connect actual health issues with SDoH so that healthcare systems can prepare more for future disasters going forward.
3. Traditionally, only physical manifestations of disaster have been acknowledged. But there currently exists the opportunity to link the additional layers of SDoH. If the patient’s psychological effects are also identified in the chart, it can be of great use as well. For example, in the past if patient presented to the ER after sustaining a laceration as a result disaster, the link would be made; however, the link to the SDoH is less clear when patients present to the ER for non-cardiac chest pain or shortness of breath, which may be a result of anxiety. Statisticians may find meaningful inference from the SDoH. 

Now, during hurricane season and its aftermath, would be a very critical time in data capture that can help allocate healthcare resources more appropriately going forward.