Tag Archives: claims data

Trusting predictive analytics

Most likely, the last time you purchased something online you were offered “recommendations” or saw what “others also liked.” The technology behind these common prompts uses predictive analytics. Your online merchant is leveraging data about your shopping and online searching behavior to predict their desired outcome — that you might buy something else! As we […]
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Making the most of health care claims data in value-based care

In my last blog post, I wrote about the need for using clinical data to create a comprehensive data set for analytics. The other side of that equation, however, is using claims data effectively. Providers already can more easily leverage claims data than clinical because of stronger reporting tools and standards, but there are some […]
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Clinical data a must for managing patient risk

In the new health care economy, we counsel providers seeking to strengthen their position should utilize a comprehensive data set. But too often, health care providers are limited in the data they use. Medicare claims data are their traditional source of data, and it’s often used as their main source of health care intelligence. It’s […]
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Sentara Medical Group puts improved predictive modeling into action

In a recent post, we discussed usability factors in predictive analytics. Today’s final post in the predictive analytics series will discuss an example of a provider that has used prediction to inform its population health management program. Predicting hospitalizations helps Sentara practice proactive care. Sentara Medical Group has 380 primary and specialty care physicians in […]
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Use big data and analytics to slow health care costs and compete on outcomes

By: The Optum Risk & Quality Solutions Team Health care is awash in data, so one might think that providers and payers would be on the forefront of the big data movement. While it’s true that claims data has been used for various purposes, including epidemiological research and predictive modeling, it doesn’t have the richness […]
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Today’s predictive analytics should provide timely, actionable intelligence

In a previous post on predictive analytics, we discussed the variables that determined predictive accuracy. Today’s topic is usability of predictive results. Old news is only good for wrapping fish. The point of prediction in health care is to head off bad outcomes before they happen. So for predictive usability, data must be timely. In […]
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Better predictive modeling requires bigger, more varied, higher quality data sets

In a previous blog about predictive analytics, we discussed how comprehensive health care data is necessary for a high degree of prediction. In this post, we’ll discuss the variables that increase predictive accuracy. The larger the better. As the sample size of a predictive model grows, the model’s uncertainty level and degree of bias decreases. […]
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Improved predictive analytics better identify high-risk patients

Health care providers have struggled in the past to accurately identify their full cohort of high-risk patients. Doctors can have an accurate sense of whether patients they see will become high-risk, but what about the patients within their population that don’t present? Too often, doctors only become aware of such patients’ conditions after an emergency […]
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Podcast: Applying analytics to optimize care coordination programs

In the ever-evolving world of health care, clinical analytics are a key capability clinics and hospitals need to have as they shift from providing care to managing health. Analytics help provider organizations find opportunities among their attributed populations that are actionable, predictable and preventable. In this edition of the Health Care Dialogue podcast, Kristin Landry […]
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Four steps to population health management: Step four— Expand chronic disease management to the full attributed population

In our last blog post, we talked about high-acuity patients, such as those with chronic conditions, who are at high risk for admissions and readmissions. These patients, who drive a disproportionate amount of health care costs, need to be closely monitored post-discharge and targeted for intervention to keep them on the road to recovery. Using […]
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