Traditional program evaluation approaches fall short when it comes to assessing complex interventions, but HCOs must be capable of evaluating whether or not their programs work if they are to succeed in healthcare’s new business model. Without a “gold standard” evaluation approach, how can HCOs measure the sustainability and impact of their interventions?
More than ever before, artificial intelligence/machine learning (AI/ML) models have the potential to improve healthcare and decision-making; and the stakes are high. Considering what’s at stake, can data scientists accept a predictive model deployment rate of only 1 in 10?
Empty feature stores shouldn’t be the obstacle that keeps HCOs from using ML to proactively address preventable, negative health outcomes and reduce costs. Purchasing or building a feature store is essential to scale and utilize ML implementations for predictive modeling.
Let’s take a look at each of the 5 steps in the Disparities Impact Statement and how you can go beyond checking the boxes to ensure ACO REACH program success.
Healthcare data scientists must confront a host of challenges that do not exist in other industries. The fact that many data scientists come to healthcare from non-healthcare backgrounds means they will not be familiar with the subtle-yet-vital details waiting for them.
Healthcare is rapidly evolving toward a value-based system, one centered on individuals, prevention, and the management of chronic disease.
Chronic Kidney Disease (CKD) is a complex, clinically dynamic, and progressive condition. Managing it successfully demands close surveillance and monitoring and that clinicians balance CKD’s clinical, medical, and psychological effects while simultaneously avoiding its excess of potential adverse events.
Selecting an appropriate definition of fairness is difficult for healthcare algorithms, as they are applied to myriad diverse problems. Read the paper to learn why we need different definitions of fairness and to understand the most ideal fairness metric for population health AI/ML.