Using Big Data Analytics for Patient Safety, Hospital Acquired Conditions Posted on : Aug 06 - 2018

Big data analytics can provide valuable insight into avoiding patient safety events and reducing the incidence of hospital acquired conditions.

Prediction and prevention are the two main goals for patient safety experts seeking to avoid adverse events and reduce the prevalence of hospital acquired conditions (HACs). 

While workflow strategies, staff training, and human factors play a critical role in helping hospitals get ahead of infections, falls, pressure ulcers, and medication errors, big data analytics tools are becoming increasingly important in the age of digital care.

The electronic health record – when implemented well – can become a valuable hub for information and a key tool for communication across care teams.

When predictive analytics and machine learning are applied to EHR data and input from bedside devices, healthcare providers can access powerful clinical decision support that may help to catch human errors and prevent costly adverse events.

In conjunction with improved guidelines, federal support, and pressure from financial penalties, big data analytics tools are helping to rapidly reduce the incidence of potentially deadly infections and other HACs.

Patient safety events decreased by 17 percent between 2010 and 2013, according to HHS, saving more than $12 billion and avoiding the unnecessary deaths of approximately 50,000 people.

How can healthcare organizations continue this positive trajectory by taking advantage of predictive analytics, clinical decision support tools, and other data-driven strategies, especially in the inpatient setting?

WHAT IS A HOSPITAL ACQUIRED CONDITION?

Hospital acquired conditions and healthcare associated/hospital acquired infections (HAIs) are reportable incidents that originate or occur in the healthcare setting.

The term has specific meaning for Medicare, which uses an MS-DRG reimbursement code to identify HACs and present on admission (POA) conditions and apportion reimbursement accordingly. View More