Tag Archives: predictive modeling

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. […]
Read More »

Evaluating data’s impact on sepsis care

The previous post introduced Saint Thomas Health’s efforts to use data to reduce sepsis mortality within its patient population. This post will discuss some of the results of the organization’s efforts. Getting information into the hands of doctors and nurses treating potentially septic patients is key. As important is the collection and analysis of vast […]
Read More »

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 […]
Read More »

Combining high-tech and high-touch with care guides

The previous blog post focused on how Minneapolis-based HealthEast applied data and analytics from Optum One to get great care management results. This final blog in the three-part series will show how care management is making a difference in patient lives. One relatively new element to care coordination that HealthEast put into practice was certified […]
Read More »

The power of prediction: Sentara Medical Group puts predictive analytics into action

In my most recent post, I wrote about usability factors in predictive analytics. In today’s post—the final post in the predictive analytics series—I’ll share an example of a provider that put all the predictive pieces together to transform its population health management program. Sentara Medical Group uses predictive models to identify high-risk patients, particularly those […]
Read More »

The power of prediction: Predictive analytics need to provide timely and actionable intelligence

In my last post, I wrote about the variables that determined the accuracy of predictive models. Accuracy, however, is only half of the equation. The data also must be usable; that’s today’s topic. Timeliness is a critical aspect of usability in predictive analytics. For a provider to deploy predictive modeling in their organization, their own […]
Read More »

The power of prediction: Predictive accuracy depends on data set size, sources and quality

In my last blog post, I wrote about how predictive analytics needed comprehensive health care data to have a high degree of prediction. In today’s post, I’ll dig deeper into the variables the increase predictive accuracy.

The power of prediction: Predictive analytics help providers accurately identify high-risk patients

Readers of my blog posts have probably noticed an ongoing theme: Organizations taking the journey from volume to value need to apply advanced analytics to data to be able to manage risk and make the most out of value-based care. Over my next few blog posts, I’ll stick with that theme, with my focus on […]
Read More »

Data analytics optimize providers’ management of value-based contracts

Providers who manage value-based contracts are looking for ways to improve clinical outcomes and reduce costs. And they’re doing this by acquiring care management strategies and an ability to build better predictive risk models for high-risk populations. With a just small sliver of the U.S. population accounting for a bulk of health care costs, provider […]
Read More »