The healthcare industry has massive amounts of data available in health records, clinical trials, and billings and claims processing systems; and yet, the industry still struggles to unlock value in this data to drive better patient outcomes and comply with healthcare regulations.
Learn how automated machine learning is transforming healthcare.
Automated machine learning is helping transform the billions of data points collected in electronic health records, clinical trials, and billings and claims processing into predictions that drive down costs, improve operations, and ultimately, save lives.
Transform operations through machine learning
Whether through cost minimization or revenue maximization, all facets of the healthcare industry — payers, providers, and pharmas — have the opportunity to greatly transform and improve their operations through machine learning. Here are some of the ways that the healthcare industry is already applying automated machine learning:
By using risk stratification to prioritize the management of at-risk patients and prevent adverse outcomes, payers also have the opportunity to not only improve quality of care, but also dramatically reduce costs for all involved. Machine learning can help them create a system that better utilizes limited resources to process claims and handle fraud, minimizing overall costs.
The high rate of hospital readmissions is one of the biggest cost burdens on the healthcare industry. Another way for providers to minimize costs is through improving operational efficiencies. Models that provide insights on how to staff efficiently or manage medical inventory create improved efficiency that substantially reduces cost burdens.
ELIZE Solutions helps pharma companies by automatically building highly accurate models that optimize the design of clinical trials, shorten the approval process, and greatly reduce pharma innovation costs. The pharma industry is rife with inefficiencies that could be solved through machine learning.