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Themes and Background

This workshop focuses on the various forms of ‘data work’ that emerge in the in the wake of the development of information infrastructures in healthcare (IIH) (Bjørn & Kensing, 2013), including EHR and various other health IT. The adoption of digital IIH often leads to new and increased data work for healthcare providers, patients, and administrators. 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 that cut across professions, wards, hospitals and regions (Bansler & Kensing, 2010; Monteiro et al., 2013; Ellingsen and Røed, 2010).

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 ‘ecosystem’ of data work is vast and includes clinicians, non-clinical healthcare workers, managers, administrators, patients, caregivers, and external organizations and workers (quality improvement organizations, researchers, IT companies, consultants, etc). This ecosystem includes work to generate primary data and work to manage, analyze, share, reuse, and deploy health and healthcare data.

Many of the professionals (both clinicians and non-clinical healthcare workers such as patient educators) doing this intensive work were not explicitly trained to do so, and may not see data work as part of their “job” but are required to do it nonetheless. Further, new occupations are arising explicitly in the wake of exploding needs for data work. Medical scribes, for example, enter clinicians’ documentation in EHR systems, and Clinical Documentation Improvement Specialists (CDIs) monitor clinicians’ charting in real time and query clinicians when significant charting errors occur.

Everyday citizens, including patients, caregivers, and even healthy individuals, are also engaging in increased data work. Through initiatives such as ‘patient reported outcome measures’ (PROM), individual citizens are now expected to generate data through various of self-tracking and monitoring technologies after or during prolonged treatment. Such consumer health informatic tools allow citizens to collect, manage and share bodily parameters -- the measurement once required medical equipment and expertise (Nafus & Neff, 2016).  The envisioned potential of these data to inform self-management of one’s health, healthcare research, healthcare management, and governance and accountability of healthcare providers has generated much interest and efforts to generate novel data (both personal health data and data resultant from clinical care), as well as link and integrate existing data streams from the civic, public sphere with clinical data.

Finally, the widespread adoption of IIH, increasing capacity to produce, store and analyze data (Møller, Bjørn et al. 2017), and widespread availability of data tools such as SAP mean that increasing expectations are developing for the types and depth of biomedical and organizational research that can be done using second order data. This managerial and administrative data work presents both novel data practices for these professionals, and also novel forms of management of healthcare organizations and workers. Healthcare organizations are increasingly required to gather and report data about their performance, gather and report data from patients, and “work to” influential measurement algorithms of healthcare quality. Healthcare organizations are struggling to keep up with emerging demands for performance measurement and reporting, and to shift re-organize their organizations to collect and manage data and to respond to the results of consequential quality measurements.

Thus. there is a need for studies of the multitude of data work taking place in healthcare by patients, clinicians, non-clinical workers, and administrators in order to answer inquiries such as: How do different foods impact a diabetics’ levels of blood sugars in real time? What drugs work best for which subgroup of patients with a certain diagnosis? How do different hospitals compare in terms of meeting best practices for cardiac care? How satisfied are patients with the care they receive? How can operating rooms most optimally be staffed and used? Which surgeons are the quickest and least error-prone? Which drug regimens provide the best outcomes for certain types of patients? How is the role of medical professions changing?

To generate the data required to answer these questions, people working throughout the healthcare delivery system are engaging in often quite intensive and time-consuming forms of data work including monitoring values, entering data, cleaning and re-using data, managing data, analyzing data, interpreting data results, communicating data, and deploying results.

Further, there is also a pressing need for research on the social implications of data work for these groups. How is the nature of the professional expertise changing, and what are the implications for the autonomy and discretion long enjoyed by clinicians? How do clinicians balance data work with direct patient care? Is data work increasing the burden of treatment of patients? Is it shifting the nature of the relationship between patients and clinicians? How is institutional pressure to be “data-driven” shifting the way that healthcare organizations are managed? 

The widespread availability of data about provider performance raises another key set of questions. 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? There is a need for research on how external stakeholders are using data about healthcare performance, and how consumer demands for transparency are changing healthcare practice.

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, Jensen, & Witt, 2012; Møller and Vikkelsø 2012) and patients take on new responsibility and potentially enjoy increased agency to evaluate providers’ service quality. Thus a new kind of cooperative work arises to support and enable the emerging infrastructures of healthcare data production and healthcare accountability.

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. Bjorn, P., & Kensing, F. (2013). Special issue on information infrastructures for healthcare: The global and local relation. International Journal of Medical Informatics, 82, 281-282.
  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. Bowker, G.C. (2008). Memory Practices in the Sciences. Cambridge: The MIT Press.
  5. Christensen, B., & Ellingsen, G. (2014). User-Controlled standardization of health care practices, Proc ECIS, Tel Aviv, 2014
  6. Ellingsen, G., & Røed, K. (2010). The Role of Integration in Health-Based Information Infrastructures. Computer Supported Cooperative Work (CSCW), 19(6), 557–584.
  7. Gitelman, L. (2013). Raw Data Is an Oxymoron. Boston: MIT Press.
  8. Monteiro, E., Pollock, N., Hanseth, O., & Williams, R. (2013). From artefacts to infrastructures. Computer Supported Cooperative Work, 22(4-6), 575-607.
  9. Møller, N.H. & Vikkelsø, S. (2012). The clinical work of secretaries: Exploring the intersection of administrative work and clinical work in the diagnosing process In Dugdale et al. (eds) From Research to Practice in the Design of Cooperative Systems, 33-47.
  10. Møller, N.H; Bjørn, P; Villumsen, J.C; Hancock, T.H; Aritake, S & Tani, T. (2017). Data tracking in search of workflows in proc. of the ACM conference on Computer-Supported Cooperative Work (CSCW 2017), 2153-2165.
  11. Neff, G., & Nafus, D. (2016). The Self-Tracking. MIT Press.
  12. Pine, K. H., & Mazmanian, M. (2014). Institutional logics of the EMR and the problem of 'perfect' but inaccurate accounts. CSCW 14, 283-294.
  13. 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.