The Work and Workers of Centering Patients in Quality Improvement Networks

Abstract

Quality Improvement Networks (QINs) are an emerging type of healthcare organization that operate at the network level and use a Learning Health Systems (LHS) approach: QINs connect health systems over large areas with the goal of improving patient health outcomes by learning from patient data. To do this work, QINs collect patient data, implement quality improvement (QI) initiatives to standardize care, and connect stakeholders to share resources. QINs do not provide direct care, but they do aim to improve patient care. Improving patient care through patient-centered means is the basis for an LHS approach. While methods for centering patients on the level of the health system are well established, on the network level at which QINs operate, they are not. My data comes from ethnographic observation of QINs and aims to identify how people within them understand the patients they seek to serve. The study was conducted over a period of one year during which I observed three QINs in a midwestern state. I attended a variety of QIN meetings including operational meetings, data manager meetings, and QIN conferences. I also conducted over 50 interviews with QIN stakeholders such as physicians, data abstractors, and patient advocates. Based on inductive qualitative analysis, I find that QINs use a combination of data abstraction, direct patient engagement, and a variety of indirect methods to understand patients. Much of this work to understand patients relies on a central, but understudied group of QIN staff: data workers. Data workers complete the foundational work of data abstraction though transcribing clinical data into centralized QIN databases. I find that data workers also conduct data analysis and implement QI initiatives while facing pressures ranging from clinical demands to outsourcing. As data work continues to expand to support LHSs at network-level scales, the multi-faceted world of the data worker that I present suggests that the contributions of these workers are important beyond their named role. Although QINs do not provide patient care, they do engage patient advocates directly to gain insight into their care and feedback on patient educational materials. QINs use a variety of methods to understand patients’ health outcomes and experiences. I describe how patient advocates become engaged in QINs, barriers to engagement, and activities of these patients. I also find that patients act as cheerleaders to motivate physicians to continue QI work. As efforts to build LHSs at ever larger scales continue to grow, the experiences I highlight show how patient engagement occurs at the network level. Finally, I describe the activities in which QINs engage to understand patients that do not involve direct patient contact. I find that QINs use indirect means such as data abstraction and analysis to understand patient health outcomes. Additionally, quality improvement-focused work and activities outside of the context of QINs provide invaluable insight into the QINs’ understanding of the logistical and emotional aspects of patient experience. As network-level organizations, QINs are situated far from the patients they ultimately serve. They therefore employ many strategies to center patients including data abstraction, direct patient engagement, and various indirect methods of data collection. While centering patients is a core value of the LHS approach, I elucidate the extent to which this value is actually adhered to at the network level as well as the work and workers involved in achieving this.