There Is A 90% Probability That Your Son Is Pregnant: Predicting the Future of Predictive Analytics in Healthcare
During this webinar you’ll learn the following:
- What to expect from predictive analytics as it relates to human behavior
- A general overview of predictive analytics models, and the contexts in which those various models should and should not be used
- The scenarios in which predictive models in healthcare are effective and when they are not, given that 80% of population health outcomes are determined by socio-economic factors, not healthcare delivery
- The relationship between predictive analytic accuracy and topics of data management such as data quality, data volume and patient outcomes data
- The use of predictive analytics to identify patients who are on a trajectory for poor, as well as good, outcomes
- How current predictive analytics strategies are overlooking the cost of intervention and “Return on Engagement”, ROE— the cost per unit of healthcare improvement for patient populations
- The cultural, philosophical, and legal conundrums that predictive analytics will create for healthcare, notably healthcare rationing
In this webinar, which is geared for managers and executives, Dale Sanders provides a new version of a very popular lecture he presented at this year’s Health Analytics Summit in Salt Lake City.
The success of predictive analytics will not be defined by the simple risk stratification of patient populations for care management teams. Success will depend on the costs of intervention to reduce the risks that are identified by predictive analytics, which boils down to this two-part question: Now that we can predict a patient’s risk for a bad healthcare outcome, “What’s the probability of influencing this patient’s behavior towards a better outcome?” And, “How much effort and cost will be required for that influence?”
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