Short abstract:

This presentation shares insights from ongoing ethnographic research on the datafication of the healthcare industry. It argues that the real-time reporting of quality metrics empowers insurers to transfer more risk onto providers and exert greater control over the labor process of care.

Long abstract:

U.S. federal regulations are incrementally shifting healthcare business models away from paying for treatments and tests towards paying for health. However, paying for care outcomes requires intensive processes of datafication: new kinds of data tracking, analysis and reporting. Quality metrics, standardized measures of providers’ adherence to ‘best-practices’ (e.g. using a EMR, doing preventative screenings) and overall performance (e.g., readmissions, mortality rates) play a pivotal role in the shift towards “paying for care.” Increasingly, insurers provide financial incentives for provider organizations to demonstrate good performance on ‘quality metrics’ in an attempt to pay more directly for the health of an overall population, rather than paying for services.

Traditionally, quality metrics are summarized annually through labor-intensive sampling from electronic health records. Increasingly, standardized and granular health data is enabling automated, real-time reporting, transforming quality metrics from a “rear-view mirror” of provider practices to real-time surveillance mechanisms. This shift enables insurers to exert more direct control over providers' adherence to best practices. Drawing from ongoing ethnographic research into the datafication of the U.S. healthcare industry, this presentation explores how the shift towards real-time reporting of digital quality measures may empower insurance companies to exert more direct control over providers and provider organizations. I illustrate this as one example of how datafication is enabling healthcare insurers to transfer the risks of care increasingly onto healthcare providers. Through conversations with the other panelists, I am interested in comparing how this technique of risk-displacement-through-datafication compares to other industries and contexts.