Call for Papers: “Data work in healthcare”
Special Issue of Health Informatics Journal
Abstract October 1st; Full paper November 1st
Working with data has become increasingly become part of many healthcare professionals job and of patient-citizens’ life, and data work requires more time, new competences and skills, and leads to new functions and roles.
A common phrase upon data and healthcare goes “Healthcare is data rich, yet information poor”. There is a huge amount of healthcare data, but it is inherently difficult to use for analyses of healthcare processes and outcomes be it regarding patients, treatments, resources, or efficiency. The challenge of turning data into information – that is making sense of data – has increased with the digitization of healthcare that has occurred within the last decade through, for example, electronic patient records (EHR) and more recently patient reported outcome measures (PROM). The amount of data available is grown hugely, and at the same time going digital has made the accumulation, visualization and analysis of data easier. More and more different kinds of data work have emerged, and this special issue aims to put into the focus the opportunities and challenges connection with the subsequent redistribution of work, time, resources, authority, and power that follow suit.
What is data work?
‘Data work’ has quite never been defined in the specialist literature, and remains a slippery notion. We see it as a coinage after the word ‘paper work’, of which it represents an abstraction, with respect to the medium of data representation; but also an extension, with respect to what people manage as data of their interest (besides accounting and resource management). As such, data work is not only “working on data”, typically producing new data in accounting for and recording a faithful representation of the work done and the involved phenomena at hand; but also that portion of work whose execution, articulation and appraisal deeply and intensively rely on data, i.e., “working by data”.
These two kinds of work are usually so deeply intertwined that distinguishing between them is useless and probably wrong: in the healthcare domain, the studies by Berg (Berg 1999), for instance, clearly show that clinicians record data on the patient record not only to accumulate data for archival reasons (and for many other secondary uses), but also to coordinate with each other, articulate the resources around a medical case, and take informed decision in a written, distributed communication with themselves and the other colleagues taking care of the same patient. In healthcare, data work regards the additional effort paid by caregivers in making the record a “working record” (Fitzpatrick 2004), that is a resource capable of keeping disparate competencies and roles bound together and connected around the same cases over time and space.
However, the concept of ‘data work’ can facilitate descriptions and analysis of activities and tasks connected with generating, cooking, transforming, representing, comprehending, ect. in order to bring forward the new skills and competences demanded by healthcare professionals and patient-citizens alike, as well as the shifts in resources, authority, and power that this enables and entails. As such, data work can serve as an analytical lens to make visible these kinds of efforts or work, much in the same way that Strauss proposed the concepts of ‘articulation work’ and ‘machine work’ to make visible the efforts of aligning and coordinating tasks and work, or the efforts of assembling, adjusting, and connecting machines and patients in healthcare (Strauss et al. 1985).
Digitization of healthcare and the generation of data
The emergence of large-scale information infrastructures in healthcare (IIHI 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. For example, EHRs are increasingly expected to become ‘meaningful audit tools’ by general practitioners (Winthereik et al. 2007). 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? How can IIHs be used as a foundation for data-driven management?
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, and 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 and 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) while using funding and resources most efficiently. Healthcare organizations and individual clinicians are evaluated according to metrics that assess care delivery, such as: Are patients diagnosed with cancer treated within the stipulated time? Which ward or hospital is most cost- and resource-effective?
Amidst these high stakes come 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 (Bowker 2005). With the emergence of IIH and the increasing demand for data-driven management, accountability and increased performance, the importance and character of working with, by and upon data increases.
Themes for the special issue
Topics relevant for this special issue include, but are not limited to, the following:
- 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?
- 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 and entail their own epistemological implications and shape knowledge forms.
- 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 is the role of health informatics research?
Important dates: Abstract October 1st; Full paper November 1st; 1st Notification January 15th; revised submissions March 15th; Final notification April 15th; Camera-ready papers May 1st.
Please check the website for guidelines upon formatting of your manuscript. Your manuscript should be between 3000 and 4000 words long (excl. references). Please also supply an abstract of 100-150 words, and up to five keywords, arranged in alphabetical order.
Mark your submission “Special issue on Data work in healthcare” in the manuscript header as well as in the submission letter.
Claus Bossen, Aarhus University (clausbossen at cc dot au dot dk)
Federico Cabitza, University of Milano-Bicocca (Federico dot Cabitza at gmail dot com)
Gunnar Ellingsen, Arctic University of Norway (gunnar dot Ellingsen at uit dot no)
Katie Pine, Arizona State University (kghammon at asu dot edu)
Enrico Piras, Bruno Kessler Foundation (piras at fbk dot eu)
Berg, Marc (1999). Accumulating and Coordinating: Occasions for Information Technologies in Medical Work. Journal of Computer Supported Cooperative Work., vol. 8, no. 4, pp. 373-401.
Bowker, Geoffrey C (2005). Memory practices in the sciences: Mit Press Cambridge, MA.
Christensen, B and Ellingsen, G. (2014): User-controlled standardisation of health care practices. Proceeding of ECIS 2014, Tel Aviv, Israel 9-11 June 2014, http://ecis2014.eu
Fitzpatrick, Geraldine (2004). Integrated care and the working record. Health Informatics Journal, vol. 10, no. 4, pp. 291-302.
Pine, Kathleen H., and Melissa Mazmanian (2014). Institutional logics of the EMR and the problem of ‘perfect’ but inaccurate accounts. Proceedings of the Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing, pp. 283-294.
Strauss, Anselm, Shizuko Fagerhaugh, Barbara Suczek, and Carolyn Wiener (1985). Social Organization of Medical Work. Chicago & London: University of Chicago Press.
Winthereik, Brit Ross, Irma van der Ploeg, and Marc Berg (2007). The Electronic Patient Record as a Meaningful Audit Tool: Accountability and Autonomy in General Practitioner Work. Science Technology & Human Values, vol. 32, no. 1, pp. 6-25.