Hospital readmissions down thanks to big data

Hospital readmissions down thanks to big data

Readmission rates are a key performance indicator (KPI) for hospitals. They indicate the quality of care the practice provides as well as the tendency of the establishment to get overcrowded. Some legislation even reduces government payouts to hospitals with high readmission rates. Regardless of whether you want to avoid a stiff penalty or just improve your patient services, data analytics may help you lower your readmission rates. We’ve put together all the interesting facts and figures right here.

At its core, business intelligence (BI) software is all about data analytics. BI software is able to accept overwhelming amounts of data in short periods of time and use advanced analysis algorithms to search for trends in the data that even the most experienced statistician cannot find. Because it can provide deep insights in short periods of time, businesses across industries have utilized different BI software to gain competitive advantages and streamline their workflows. For instance, businesses in the healthcare industry use BI to manage their readmission rates.

For the context of this article, readmission refers to when a patient returns for more care within 30 days of their original hospital stay. Cases like these usually stem from conditions immediately following the initial visit, such as mismanagement of the original condition, improper self-medication, and not enough access to proper medical services and medications in their community.

BI can help reduce readmission rates in several ways. For instance, by using patient fields such as income level, English proficiency, housing conditions, and community resources instead of variables like previous number of purchases, order size, and order frequency, hospital administrators will have a better insight on the patient demographics. This knowledge will enable them to provide extra care to people who need it most and help them prevent expensive readmissions.

Furthermore, by combining socioeconomic data with electronic medical records (EMR) in a BI software environment, medical professionals can easily create individual profiles that will predict how likely a patient is going to require readmission, even before care is provided. For practices looking for methods to reduce readmissions by 3 percent or greater, predictive analytics allow doctors to ensure that certain types of patients can totally avoid readmissions with proper initial care.

Effective implementation of these solutions can definitely save hospitals a lot of money. In fact, one particular practice was able to save $72 million on medical services after reducing the incidence of readmissions by 6,000 patients annually while avoiding $4 million in Medicare penalties and boosting its reputation by leaps and bounds.

Big data isn’t only for big business. BI software can deliver your practice unprecedented levels of care and efficiency. Whether you want to lower readmission rates or ensure your EMR compliance, we have the knowledge and experience to get it done for you. Call us today to partner with a trusted IT expert.

Published with permission from TechAdvisory.org. Source.


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