As the name suggests, hospital-acquired conditions (HAC) is a medical condition or a complication that the patient acquires from his/her stay in the hospital. The condition or complication was not present prior to hospital admission. Some of the common HACs are ventilator-associated pneumonia, surgical site infection, urinary tract infection, falls and trauma, stage III and stage IV pressure ulcers, air embolism, etc.
With the rising risk and chronic healthcare management, CMS and hospitals are on the constant lookout for a permanent solution to prevent patient deterioration from hospital-acquired conditions.
Predictive analytics and healthcare
With the growing popularity of predictive analytics, some industries have already implemented it successfully and are reaping its benefits. The scope of predictive analytics in healthcare is immense. However, we need to keep in mind that we can leverage predictive analytics only when we can have actionableinsight into it.
For example, using a cognitive machine that can predict a ‘hospital acquired condition’ in a patient and offeran actionablesolution to reverse the impact in a patient’s health, his/her duration of stay in the hospital and can also impact the overall healthcare economy.
In short, millions of dollars can be saved on healthcare that is otherwise wasted through HAC by leveraging healthcare analytics.
How Cognitive Machines can reverse the condition?
Some hospitals have already implemented a Cognitive Machine that is capable of predicting and delivering insights about a patient’s future state of health. This tool not only predicts the likelihood of a patient developing HAC but also providesa roadmap to avoid it. This cognitive machine is 20X more capable than a human mind in assimilating and assessing information about a patient’s future health condition. By leveraging this cutting-edge tool, hospitals will surely be able to limit and reduce the number of HAC cases every year.
