The qualculative dimension of healthcare data interoperability

Abstract

Research on interoperability and information exchange between information technology systems touts the use of secondary data for a variety of purposes, including research, management, quality improvement, and accountability. However, many studies have pointed out that this is difficult to achieve in practice. Hence, this article aims to examine the causes for this by reporting an ethnographic study of the data work performed by medical records coders and birth certificate clerks working in a hospital system to uncover the practices of creating administrative data (e.g. secondary data). The article illustrates that clerks and coders use situated qualitative judgments of the accuracy and authority of different primary medical accounts. Coders and clerks also employ their understandings of the importance of different future uses of data as they make crucial decisions about how much discretion to exercise in producing accurate data and how much effort to put toward clarifying problematic medical data. These findings suggest that information technology systems designed for interoperability and secondary data also need to be designed in ways that support the qualculative practices of data workers in order to succeed, including making future uses of data clear to data workers and finding ways to minimize conflicting data before data workers encounter it.

Data work in healthcare: An Introduction

“…These include data-driven management, accountability, and governance based on performance measurements, and secondary clinical research. While data infrastructures are built to enable monitoring of populations and early prevention measures, 20 and to monitor the quality of services, 21,22 healthcare workers increasingly learn to maneuver playfully within these environments. 23 Thus, the papers collected here discuss the data work of different groups: doctors struggling to clarify data ambiguity and use predictive algorithms for personalized medicine; 24 coming to terms with data variability-varying data on the same phenomena; 25 clinicians' response to patients' increasing data literacy; 26 nurses' interpretation of patient-generated data as a means of inclusion in their own care; 27,28 and patients generating data about their health.…”

“…23 Thus, the papers collected here discuss the data work of different groups: doctors struggling to clarify data ambiguity and use predictive algorithms for personalized medicine; 24 coming to terms with data variability-varying data on the same phenomena; 25 clinicians' response to patients' increasing data literacy; 26 nurses' interpretation of patient-generated data as a means of inclusion in their own care; 27,28 and patients generating data about their health. 29,30 Finally, there is work to produce data itself, including skillfully assessing messy charts to create structured datasets, 22 to sanitizing and validating data, 31 and building data integrations between various information systems. 32…”

“…These include data-driven management, accountability, and governance based on performance measurements, and secondary clinical research. While data infrastructures are built to enable monitoring of populations and early prevention measures, 20 and to monitor the quality of services, 21,22 healthcare workers increasingly learn to maneuver playfully within these environments. 23…”

Good organizational reasons for better medical records: The data work of clinical documentation integrity specialists

“…For example, since medical records are the major source of data extraction, clinicians are facing new demands for documentation in patient records to be accurate and comprehensive (Kuhn et al, 2015), so that other data workers such as medical coders can extract highquality data for usages such as data-driven accountability. Subsequently, a new line of research has begun to examine the on-the-ground work required to produce, manage, analyze, and deploy data in healthcare carried out by healthcare workers, including both clinicians and non-clinicians (Bjørnstad and Ellingsen, 2019;Bonde et al, 2019;Grisot et al, 2019;Islind et al, 2019;Pine, 2019).…”

Patient data work: filtering and sensing patient‐reported outcomes

“…2019, Hult et al . 2019, Pine 2019, Wallenburg and Bal 2019). However, in the same collection of papers, Islind et al .…”