Managing Patient Data for Better Care

Data should not simply be collected. In order to be a useful tool, it needs to be organized, analyzed, and applied correctly. The healthcare industry is facing a significant challenge when it comes to making the best use of all the data it collects. Data is still collected from traditional sources, such as electronic health record systems, picture archiving and communication systems. However, new sources of data, including from Internet of Things (IoT) devices, personal fitness trackers and even social media are generating enormous amounts of data. By using machine learning and predictive analytics, the power of all that data can be harnessed to improve the quality of patient care.

What is Machine Learning and Predictive Analytics?

The amount of data that is collected every day is staggering. It would be impossible and extremely costly to have human employees apply predictive analytic models to try to uncover patterns or “trends” that are not glaringly apparent to the eye. Another consideration within the healthcare industry is patient privacy and the need to remain HIPAA compliant when dealing with patient data. By utilizing cost-effective machine learning with predictive analytics, massive amounts of data can be quickly analyzed to uncover commonalities that are causing trends to occur.

Putting Data to Work for Health Care

Large healthcare organizations have taken advantage of new technologies to gain insights from big data. In doing so, they have been able to transform their practices, improve patient care and determine what patients are at a higher risk of serious health complications. As the government continues to prioritize cost-reduction strategies, preventative health care that reduces hospital visits and keeps patients at home will become more prevalent. This, in turn, will increase the need for technologies that address the needs of home care specifically.