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Themes and Background

This workshop focuses on the various form of ‘data work’ that emerges in the in the wake of the development of information infrastructures in healthcare (IIH).

Within the healthcare sector, digitization of healthcare has developed from occurring predominantly through small, stand-alone systems for documentation and coordination of healthcare work or clinical databases for research to include, in many settings, large-scale healthcare infrastructures (most notably integrated Electronic Patient Records, or “EPRs”) that cut across professions, wards, hospitals and regions (Bansler & Kensing, 2010; Monteiro et Al., 2013; Ellingsen and Røed, 2010).

Further, the healthcare sector seeks to reach out to the public through initiatives such as ‘patient reported outcome measures’ (PROM) in order to generate data on citizen’s health after or during prolonged treatment. Also, consumer health informatics has provided laypeople and patients with tools to collect, manage and share bodily parameters, whose measurement once required medical equipment and expertise. The envisioned potential of these data to inform healthcare research and measure quality and outcome of treatment and care has generated much interest and efforts to link and integrate data from the civic, public sphere with clinical data.

In sum, the work of being a patient and the work of healthcare professionals increasingly means generating, understanding, and using data.

Themes of the workshop

The workshop will address the following themes, although the list should not be seen as exclusionary:

  • The new work of healthcare data: What are the new competences, tasks, and functions that the emergence of data-driven healthcare entails? How are existing occupations and professions changing in the wake of the push for data-driven healthcare? What are the emerging healthcare data occupations?
  • The new ‘data work’ of patients: What does it involve to be enrolled or engaged in the generation, distribution, understanding of data on one’s health, and have such data come back to you filtered and interpreted by other parties that base interventions for you on those data?
  • The politics of creating and using healthcare data: How do categories, classifications and algorithms shape what counts as data, and what do these schemes make visible and invisible?
  • Artefacts and infrastructures as knowledge production: Artefacts enter and shape the processes of knowledge production according to their own characteristics. Characters, tables, and databases each entail their own epistemological implications and shape knowledge forms. How does this look within the emerging healthcare information infrastructures?
  • Reflection, management and accountability: What instances of reflection, management and accountability are created with specific healthcare IT systems? What are the challenges, conflicts, and opportunities?
  • Systems design: How do the agendas of data for accountability and secondary uses influence and become integrated into systems design and development? Is this a simple add-on, or a dominant concern? What can the role of CSCW be? 

Background

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, and performance resource management as well as providing a new source and foundation for healthcare and medical research data. As an illustration, EPRs are increasingly expected to become ‘meaningful audit tools’ by general practitioners (Winthereik, van der Ploeg & Berg 2007). Healthcare IT for coordinating work in operation rooms and IT systems used to support hospital portering services are additional examples of instances where the production of aggregate data derived from IT allow for inquiry into the performance of activities that are also supported by the IT (Bardram & Hansen 2010; Bossen 2015). The widespread adoption of HIT and IIH and the increasing capacity to store and analyze data mean that increasing expectations are developing for the types and depth of biomedical and organizational research that can be using second order data from these systems. Hence, healthcare data are expected to support inquiries such as: 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?

The growth of digital 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 practice to seemingly objective, rational, and scientific logics on an institutional scale. Hence, there is a pressing need 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, accreditation, and state authorities increasingly demand that healthcare organizations 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 attempting to ensure 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.

Amidst these high stakes come large concerns about the situated practices of making, managing, and using data.  The creation, maintenance, aggregation, transport, and re-purposing of data does not happen without work effort to collect and transform data. ‘Raw Data is an Oxymoron’, a bad idea and should be cooked with care, as Bowker succinctly stated (2008: p183-4).

With the emergence of IIH 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 job functions such 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 are 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 stored in specifically ordered databases: What are the politics of what counts as quality, process and outcome measures?  We may further investigate the practices and logics in and through which data are 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 mobilised for reflection, management and representation of work for accountability purposes.

References: 1. Bansler, J. P., & Kensing, F. (2010). Information infrastructures for health care: Connecting practices across institutional and professional boundaries. Computer Supported Cooperative Work, 19(6), 519-520. 2. Bardram, J. E., & Hansen, T. R. (2010). Why the plan doesn't hold: a study of situated planning, articulation and coordination work in a surgical ward. In Proc. ACM CSCW (pp. 331-340). ACM. 3. Bossen, C, Jensen, LG & Witt, F. (2012). Medical secretaries’ care of records: the cooperative work of a non-clinical group. in Proc. ACM CSCW. pp. 821-830. 4. Bossen, C. (2015). Techno-anthropological sensibilities in health informatics: opportunities and challenges. Stud Health Technol Inform, 215, 168-179. 5. Bowers, J., Button, G., & Sharrock, W. (1995). Workflow from within and without: technology and cooperative work on the print industry shopfloor. In Proc. ECSCW’95 (pp. 51-66). Springer. 6. Bowker, G.C. (2008). Memory Practices in the Sciences. Cambridge: The MIT Press. 7. Christensen, B., & Ellingsen, G. (2014). User-Controlled standardization of health care practices, Proc ECIS, Tel Aviv, 2014 8. Dourish, P. (2001). Process descriptions as organizational accounting devices: the dual use of workflow technologies. Proc. GROUP 2001, ACM Press, 52–60. 9. Ellingsen, G., & Røed, K. (2010). The Role of Integration in Health-Based Information Infrastructures. Computer Supported Cooperative Work (CSCW), 19(6), 557–584. 10. Eriksen, S. (2002). Designing for accountability. NordiCHI 02, 177-186. 11. Gitelman, L. (2013). Raw Data Is an Oxymoron. Boston: MIT Press. 12. Monteiro, E., Pollock, N., Hanseth, O., & Williams, R. (2013). From artefacts to infrastructures. Computer Supported Cooperative Work, 22(4-6), 575-607. 13. Pine, K. H., & Mazmanian, M. (2014). Institutional logics of the EMR and the problem of 'perfect' but inaccurate accounts. CSCW 14, 283-294. 14. Pine, K., & Liboiron, M. 2015. The Politics of Measurement and Action. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15). ACM. 15. Winthereik, B.R., van der Ploeg, I., & Berg, M. (2011). The electronic patient record as a meaningful audit tool: accountability and autonomy in general practitioner work. Science, Technology, & Human Values, 32(1), 6-25.

References:

1.     Bansler, J. P., & Kensing, F. (2010). Information infrastructures for health care: Connecting practices across institutional and professional boundaries. Computer Supported Cooperative Work, 19(6), 519-520.

2.     Bardram, J. E., & Hansen, T. R. (2010). Why the plan doesn't hold: a study of situated planning, articulation and coordination work in a surgical ward. In Proc. ACM CSCW (pp. 331-340). ACM.

3.     Bossen, C, Jensen, LG & Witt, F. (2012). Medical secretaries’ care of records: the cooperative work of a non-clinical group. in Proc. ACM CSCW. pp. 821-830.

4.     Bossen, C. (2015). Techno-anthropological sensibilities in health informatics: opportunities and challenges. Stud Health Technol Inform, 215, 168-179.

5.     Bowers, J., Button, G., & Sharrock, W. (1995). Workflow from within and without: technology and cooperative work on the print industry shopfloor. In Proc. ECSCW’95 (pp. 51-66). Springer.

6.     Bowker, G.C. (2008). Memory Practices in the Sciences. Cambridge: The MIT Press.

7.     Christensen, B., & Ellingsen, G. (2014). User-Controlled standardization of health care practices, Proc ECIS, Tel Aviv, 2014

8.     Dourish, P. (2001). Process descriptions as organizational accounting devices: the dual use of workflow technologies. Proc. GROUP 2001, ACM Press, 52–60.

9.     Ellingsen, G., & Røed, K. (2010). The Role of Integration in Health-Based Information Infrastructures. Computer Supported Cooperative Work (CSCW), 19(6), 557–584.

10.  Eriksen, S. (2002). Designing for accountability. NordiCHI 02, 177-186.

11.  Gitelman, L. (2013). Raw Data Is an Oxymoron. Boston: MIT Press.

12.  Monteiro, E., Pollock, N., Hanseth, O., & Williams, R. (2013). From artefacts to infrastructures. Computer Supported Cooperative Work, 22(4-6), 575-607.

13.  Pine, K. H., & Mazmanian, M. (2014). Institutional logics of the EMR and the problem of 'perfect' but inaccurate accounts. CSCW 14, 283-294.

14.  Pine, K., & Liboiron, M. 2015. The Politics of Measurement and Action. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI '15). ACM.

 

15.  Winthereik, B.R., van der Ploeg, I., & Berg, M. (2011). The electronic patient record as a meaningful audit tool: accountability and autonomy in general practitioner work. Science, Technology, & Human Values, 32(1), 6-25.