Paper short abstract:

Through ethnographic research on obstetrical care, I describe a change in scale from performance measurement of hospitals to individual clinicians, and attendant dilemmas related to data quality management and tradeoffs between professional discretion and accountability.

Paper long abstract:

This paper examines the implementation and consequences of data science in a specific domain: evaluation and regulation of healthcare delivery. Recent iterations of data-driven management expand the dimensions along which organizations are evaluated and utilize a growing array of non-financial measures to audit performance (i.e. adherence to best practices). Abstract values such as "quality" and "effectiveness" are operationalized through design and implementation of certain performance measurements—it is not just what outcomes that demonstrate the quality of service provision, but the particular practices engaged during service delivery.

Recent years have seen the growth of a controversial new form of data-driven accountability in healthcare: application of performance measurements to the work of individual clinicians. Fine-grained performance measurements of individual providers were once far too resource intensive to undertake, but expanded digital capacities have made provider-level analyses feasible. Such measurements are being deployed as part of larger efforts to move from "volume-based" to "value- based" or "pay for performance" payment models.

Evaluating individual providers, and deploying pay for performance at the individual (rather than the organizational) level is a controversial idea. Critics argue that the measurements reflect a tiny sliver of any clinician's "quality," and that such algorithmic management schemes will lead professionals to focus on only a small number of measured activities. Despite these and other concerns, such measurements are on the horizon. I will discuss early ethnographic findings on implementation of provider-level cesarean section measurements, describing tensions between professional discretion and accountability and rising stakes of data quality in healthcare.