The workshop would like address the following themes, although the list should not be seen as exclusionary.
Digitization of healthcare has developed from being small, stand-alone systems for documentation and coordination of healthcare work or clinical databases for research to become large-scale healthcare infrastructures (most notably integrated Electronic Patient Records) that cut across professions, wards, hospitals and regions (Bansler & Kensing, 2010; Monteiro et Al., 2013; Ellingsen and Røed, 2010).
The emergence of large-scale IIH has enabled the use of health data for a range of new purposes related to data-driven management, accountability, performance resource management as well as foundation for research data. As a illustration, Electronic patient records (EPRs) are increasingly expected to become ‘meaningful audit tools’ by general practitioners (Winthereik, van der Ploeg & Berg 2007), just as healthcare IT for coordinating operation rooms or hospital portering services through the production of aggregate data allow for inquiry into the performance of these activities (Bardram & Hansen 2010; Bossen 2015). Hence, healthcare data are expected to support inquiries such as the following: What drugs work best for which subgroup of patients with a certain diagnosis? How can operating rooms most optimally be staffed and used? Which surgeons are the quickest and least error-prone? More generally, how can IIH be used as a foundation for data-driven management and how can they ensure best practices?
Digitization of IIH and increasingly widespread availability of data tools such as SAP in healthcare organizations, as well as the proliferation of tools and consulting services that promise to make healthcare organizations “data-driven” are rapidly shifting the organization and management of healthcare practice. In the process, the socio-technical setup is reconfigured, from in situ, socially negotiated to seemingly objective, rational, and scientific logics on an institutional scale. Hence, it is pressing to explore how healthcare data and data-driven management contributes to this reconfiguration. How is the role of medical professions changing; how is the nature of the professional expertise changing, and what are the implications for the autonomy and discretion long enjoyed by clinicians?
Along similar lines, external actors such as the general public as well as funding and state authorities increasingly demand healthcare organizations to become more transparent and accountable by providing data through performances measures (Pine & Mazmanian 2014). This is spurred by a demand to see that healthcare organizations deliver services of high quality and according to the best healthcare standards (Christensen and Ellingsen, 2014), as well as being sure that funding and resources are used optimally. Healthcare organizations and, increasingly, individual clinicians are evaluated according to metrics that assess care delivery, such as: Are patients diagnosed with cancer treated within the stipulated time? Are levels of medication errors below the acceptable threshold? Are rates of central line infection higher than would be expected? Are surgical procedures such as cesarean section utilized appropriately, or are they over-utilized? Which ward or hospital is most cost and resource effective?
Performance measurements linked with such topics not only must be reported in greater volume and in more detail than in the past, but the results are tied to increasingly heavy sanctions. For example, a new model of healthcare reimbursement from both private insurance and public entities called “pay for performance” links payments for healthcare services to the performance of healthcare organizations on general measures of quality. The stakes are further raised by the fact that healthcare performance measurements are being made publicly available on websites, where individual consumers can look up data easily—while at present this is limited to organizations, it is expected in the future that individual clinicians may have their performance data published.
However, the creation, maintenance, aggregation, transport, and re-purposing of data does not happen without work effort and transformation of data. ‘Raw Data is an Oxymoron’, a bad idea and should be cooked with care, as Bowker succinctly stated (p183-4).
With the emergence of healthcare infrastructures and the increasing demand for data-driven management, accountability and increased performance, the importance and character of such ‘cooking’ work changes. Not only are managerial perspectives becoming more influential on health care practices, but professions have to learn new skills and include new tasks, just as new job functions as ‘healthcare data manager’ emerge (Pine & Liboiron 2015; Bossen 2012).Thus a new kind of cooperative work arises to support and enable the emerging infrastructures of accountability. External accountability raises complex and very timely questions for researchers, such as: how do we design and build sociotechnical infrastructure for accountability? What are the human and technological capacities needed to successfully engage in large-scale measurement? What new forms of data work are emerging, and how are they impacting the organization of healthcare? What are the potential unintended consequences for individuals and organizations—i.e. are low-resource organizations inadvertently penalized?
In addition to the attention to questions about new forms of data work, the design/use of infrastructure for healthcare data, and impacts for the organization of healthcare work, the workshop also addresses more profound questions about data. How data are created, shaped and acquire legitimacy is often closely intertwined with normative statements of what should become visible and granted importance. “Like events imagined and enunciated against the continuity of time, data are imagined and enunciated against the seamlessness of phenomena” (Gitelman 2013; p3) the categories and systems of classification embedded in the databases from which data is aggregated are inherently normative and hence political. Against the three precepts of data as being abstract, aggregative and mobilized graphically (Gitelman 2013), we want to look at the concrete work of how such abstractions are produced and ordered to become data that can be stores in specifically ordered databases: What are the politics of what counts as quality, process and outcome measures?. We may further investigate the ways and logics in and through which data is computed and transformed into aggregations: How does the formula or algorithm weigh quality, process and outcome measures into a ranking of healthcare work? In the new systems of accountability being constructed in IIH, what is being attended to and what remains invisible (i.e. the experiences of patients)? Finally, we may investigate the graphics through which aggregated data is mobised for reflection, management and presenting accountability.
References:
