Tag Archives: predictive modeling

The journey to value: Taking the first value-based steps with population health management-Part 2

In the previous Journey to Value post, we discussed the usefulness of population health management (PHM) as a bridge between the volume and value worlds. This final post in the Journey to Value series discusses another program necessity as organizations journey to value: comprehensive data and sophisticated analytics. The fuel for the value-based care is […]
Read More »

The journey to value: Taking the first value-based steps with population health management-Part 1

In our previous Journey to Value post, we suggested four elements that organizations should consider as they as they determine their roadmap for value-based care. With a roadmap that accounts for market needs, financial impacts, provider network strategies and aligned incentives, organizations are ready to program the milestones around which they will convert to value-based […]
Read More »

Four areas where providers can dramatically improve care management

Value-based care is fundamentally altering the nature of health care in the United States. Care delivery is transitioning from an episodic, somewhat reactive model into a more proactive health management approach. More and more often, provider organizations are accepting a per-member-per-month budget to manage the health of a population, and their incentives revolve around maximizing […]
Read More »

Interest in predictive models tied to value-based care

There is a link between the transition to value-based care and an increased interest in analytics-based models that can successfully predict patient outcomes. As providers move to payment models that reward results over total number of procedures performed, they are accepting more financial liability. To meet that responsibility, providers must understand more about how their […]
Read More »

Peer into patients’ and populations’ futures with predictive analytics

Predicting the future. It’s the stuff of fantasy and metaphysics—seeing clearly what will happen and making bold decisions based on that future vision. But prediction is also part of our day-to-day lives. We rely on weather reports to predict temperature, precipitation and other environmental conditions. We count on credit card companies to predict how we […]
Read More »

Lehigh Valley Health Network to discuss clinical and organizational transformation at “Hospital of Tomorrow” event

The hospital of tomorrow will do much more than provide just-in-time care for patients. It will also provide just-in-time interventions that will keep populations healthy as well as navigate the sick through their illnesses so that they can avoid a hospital admission. Leaders from the Lehigh Valley Health Network (LVHN), an award-winning health system in […]
Read More »

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 […]
Read More »

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 […]
Read More »

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 […]
Read More »

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 »