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EHRs and predictive analytics help prevent falls in nursing homes

July 2, 2015
by Richard R. Rogoski
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A new study published in the Journal of the American Medical Informatics Association shows that using data from electronic health records (EHRs) and predictive analytics algorithms can help prevent falls in the elderly.

Focusing on 5,129 nursing home residents in 13 nursing homes, the study used data from the CMS Minimum Data Set (MDS 3.0) and individual EHRs. The study found that combining data from MDS and EHR systems, which collect different types of information, improved the ability to identify those at the highest risk for falls compared to predictions made by using only MDS data (32.3% versus 28.6%).

However, researchers pointed out, data elements from the MDS are infrequently updated while data elements within an EHR tend to be richer and and more frequently updated, thereby giving clinicians a better picture of falls history and fall risk factors.  

Falls are the most common type of adverse event reported by nursing homes. A fall can cost more than $7,000 when direct costs, disability, reduced function and complications are taken into account, the study noted.


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