Author:Linda Hogle (University Wisconsin-Madison)
Paper short abstract:
Accountable Care Organizations (US) are responsible both for improved health outcomes and lower costs. This paper demonstrates how Big Data tools, including predictive analytics, are being used to stratify populations to achieve both aims, reexamining STS analytical frames of classification.
Paper long abstract:
The term population health management has become popularized as a way to describe activities to create clinical and financial opportunities to improve health outcomes and patient engagement, while also reducing costs. This way of considering public health is consonant with the recent rise in so-called Accountable Care Organizations in the U.S., in which provider-led organizations are held accountable for both improved patient outcomes and lower costs. This shifts risk from payers to ACO providers, incentivizing them to identify "high-risk," "risking-risk" and "non-adherent" patients who are likely to be (or will likely become) high cost patients. Big data tools--specifically, predictive analytics—are increasingly employed to stratify populations into such cost groups, which can then be managed with targeted interventions. This paper analyzes the implications of the new ways of creating populations using associative data that transgress conventional ways of classifying groups. Analyzing such processes of social sorting through the lens of STS analytical perspectives of classification and standardization provides a window through which to understand contemporary phenomena in public health.
From person to population and back: exploring accountability in public health