Text-mining the data in electronic health records can help identify patients with multiple sclerosis (MS) and track the key clinical traits in the course of their disease, according to a new study in the Journal of the American Medical Informatics Association (JAMIA).
With the advent of healthcare reform, it's more important than ever for healthcare providers to identify and target high-risk patients to prevent readmissions. The problem is determining which model best predicts those patients.
Using predictive modeling to classify patient populations based on individual risk and anticipated response to an intervention can help hospitals achieve the much-touted triple aim goals, concludes a Walgreens study published this week in Health Affairs.
Utah's Intermountain Medical Center developed a computerized algorithm to identify heart attack patients at risk for readmissions, presented at the American Heart Association Scientific Sessions 2012 in Los Angeles.
As hospitals face penalties for excess readmissions starting in October, they can predict which patients are most likely to bounce back to their facility by simply picking up the phone.
In a continuing fight against fraudulent claims, the Centers for Medicare & Medicaid Services is relying more heavily on predictive modeling, reported The Washington Post.
Blue Cross and Blue Shield of North Carolina (BCBSNC) has partnered with software company SAS to develop more personalized and better targeted health plans for members. The partnership, which the...
Hospitals are finding that predictive modeling programs are identifying at-risk patients successfully and keeping them from returning to their facilities unnecessarily, HealthLeaders Media reported.
Private insurers managing Medicare Advantage plans likely will be scrutinized more intensely as the Centers for Medicare & Medicaid Services seeks to rectify a "longstanding problem" of improper
The Centers for Medicare & Medicaid Services (CMS) has contracted with aerospace and defense technology company Northrop Grumman to create a predictive model that is expected to cut down on