Predictive analytics helps identifying high-risk Hepatitis C patients

Predictive Analytics
According to a study published in recent issue of Hepatology, researchers have developed a predictive analytics algorithm that uses basic EHR data to flag patients at high risk of developing complications from the hepatitis C virus (HCV). As payers seek to control the costs of extremely expensive HCV treatments like Sovaldi, the use of clinical analytics may help to avoid needless spending.
More than three million Americans are living with HCV, the Office of the National Coordinator said recently. The virus presents a significant challenge when it comes to reducing the costs of chronic disease management. Approximately 1/3 HCV patients are at high risk of developing complications from the virus.The team applied machine learning techniques to treat clinical information such as lab results, age, body mass index, and details of the virus type to create a risk score for patients. The score is more reliable than earlier attempts because the algorithm uses more lab values than other models and analyzes how the values change over time.
The tool can be integrated into EHR to help providers deliver more aggressive treatment to patients who would see the most benefit. It can also help healthcare companies develop a chronic disease management plan by identifying ideal intervals for primary care visits and follow-up. This is especially important for HCV patients, who often fall away from the continuum of care.
By combining predictive analytics and care coordination aids that help providers ensure higher levels of chronic disease management and medication adherence for patients, the healthcare system may be able to reduce waste and offer more treatment options to those who have a higher chance of developing life-threatening complications.

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