Institutions, infrastructures, and data friction – Reforming secondary use of health data in Finland

Aula, Ville

doi:10.1177/2053951719875980

Cited by 17 publications

(10 citation statements)

References 46 publications

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“…Several interwoven institutional, political, and economic forces are contributing to demands for data intensive resourcing in healthcare in which data sources, data analytics, health domains, and dataintensive applications are connected (Hogle, 2016). Healthcare organizations experience pressure to improve data infrastructures and to reconfigure healthcare organizations and healthcare work around data creation and use (Aula, 2019). As Hogle (2016) describes " .…”

Section: The Institutional Context Of Data Intensive Resourcing In Healthcarementioning

confidence: 99%

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

Pine

Bossen

2020

Big Data & Society

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Healthcare organizations and workers are under pressure to produce increasingly complete and accurate data for multiple data-intensive endeavors. However, little research has examined the emerging occupations arising to carry out the data work necessary to produce “improved” data sets, or the specific work activities of these emerging data occupations. We describe the work of Clinical Documentation Integrity Specialists (CDIS), an emerging occupation that focuses on improving clinical documentation to produce more detailed and accurate administrative datasets crucial for evolving data-intensive forms of healthcare accountability, management, and research. Using ethnographic methods, we describe the core of CDIS’ work as a translation practice in which the language, interests, and concerns of clinicians and clinical documentation are translated via real-time “nudging” and ongoing education of clinicians into the language, interests, and concerns of medical coders, structured administrative datasets, and the various stakeholders of these datasets. Further, we show how the institutional context of CDIS’ work shapes the occupational virtues that guide CDIS’ translation practice, including financial reimbursement, quality measures, clinical accuracy, and protecting clinician’s time. Despite the existence of these multiple virtues, financial reimbursement is the most prominent virtue guiding CDIS’ limited attention. Thus, overall clinical documentation is “improved” in specific, partial ways. This research provides one of the first studies of the emergent data work occupations arising in the wake of digitization and big data opportunities, and shows how local data settings shape large scale data in specific ways and thus may influence outcomes of analyses based on such data.

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Section: The Institutional Context Of Data Intensive Resourcing In Healthcarementioning

confidence: 99%

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

Pine

Bossen

2020

Big Data & Society

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“…This labour is also related to the collaborative and interdisciplinary character of data practices in the healthcare sector. Furthermore, the contributions made by Bonde et al (2019) and Aula (2019) have been extended by recognising that data practices are situated within not only the same organisation but also more broadly within the power dynamics of the service economy and are thus reflected in public–private partnerships.…”

Section: Discussionmentioning

confidence: 99%

“…Via these insights, this study aligns with current scholarship (D’Ignazio and Klein, 2020; Kennedy and Hill, 2018; Pinel et al, 2020; Puig de la Bellacasa, 2011; Taylor, 2020) that has advocated for the recognition of emotions and feelings-based approaches in datafication processes in healthcare for the purpose of challenging rational and neutral perspectives on data practices and data technologies. The recognition of emotions and emotional labour expands the existing scholarship on the tensions and ambiguities inherent in the secondary use of Finnish healthcare data (Aula, 2019; Grön, 2021) and the development of the health data economy (Tupasela et al, 2020). It also facilitates the recognition of the continuous work and meaning making inherent in datafication and shifts attention to its processes rather than its end-products, such as data analytics or data technologies.…”

Section: Discussionmentioning

confidence: 99%

Emotional labour in the collaborative data practices of repurposing healthcare data and building data technologies

Choroszewicz

2022

Big Data & Society

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This article focuses on emotions, conceptualised as emotional labour, evoked during data practices used to repurpose and enable healthcare data journeys for Finnish public healthcare. Combined approaches from critical data studies and the sociology of emotions were used to contribute to a better understanding of the mundane but often invisible work of the emotions of experts involved in data practices, such as facilitating data journeys and building data technologies. The article is based on a two-and-a-half-year ethnographic study conducted in a Finnish regional public healthcare and social service organisation. The study results were derived from the analysis of 39 interviews and fieldnotes produced by observing 170 h of various meetings, events and work activities performed by experts. The results were organised into three forms of observed experts’ emotional labour related to three phases of healthcare data journeys: (a) caring for data production and preparing data for travel, (b) managing excitement and frustration in data processing for continually building the data management system, and (c) reassuring users in making sense of obtained data analytics. The results contribute to a greater understanding of the emotions and emotional labour generated by healthcare data journeys and in relation to the volatile nature of healthcare data and the collaborative character of data practices. This work advocates for a better recognition of the emotional aspects of data practices and their implications on data-based knowledge and datafication processes in healthcare.

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“…The Finnish approach to data access is evident in its Act on the Secondary Use of Health and Social Data ( Ministry of Social Affairs and Health, 2019 ) which provides the basis for the national data permit authority FinData to facilitate access to and sharing of patient data. The country has adopted a national policy oriented towards big data and open data to transform the technical and governance infrastructure for AI and other computer science research ( Aula, 2019 ). In Finland, consent is not legally required for including personal data in national health registries, but data access is controlled through detailed policies and security procedures ( Vrijenhoek et al, 2021 ).…”

Section: Variation In European Union Data Governancementioning

confidence: 99%

You Can’t Have AI Both Ways: Balancing Health Data Privacy and Access Fairly

et al. 2022

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Artificial intelligence (AI) in healthcare promises to make healthcare safer, more accurate, and more cost-effective. Public and private actors have been investing significant amounts of resources into the field. However, to benefit from data-intensive medicine, particularly from AI technologies, one must first and foremost have access to data. It has been previously argued that the conventionally used “consent or anonymize approach” undermines data-intensive medicine, and worse, may ultimately harm patients. Yet, this is still a dominant approach in European countries and framed as an either-or choice. In this paper, we contrast the different data governance approaches in the EU and their advantages and disadvantages in the context of healthcare AI. We detail the ethical trade-offs inherent to data-intensive medicine, particularly the balancing of data privacy and data access, and the subsequent prioritization between AI and other effective health interventions. If countries wish to allocate resources to AI, they also need to make corresponding efforts to improve (secure) data access. We conclude that it is unethical to invest significant amounts of public funds into AI development whilst at the same time limiting data access through strict privacy measures, as this constitutes a waste of public resources. The “AI revolution” in healthcare can only realise its full potential if a fair, inclusive engagement process spells out the values underlying (trans) national data governance policies and their impact on AI development, and priorities are set accordingly.

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Institutions, infrastructures, and data friction – Reforming secondary use of health data in Finland

Cited by 17 publications

References 46 publications

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

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

Emotional labour in the collaborative data practices of repurposing healthcare data and building data technologies

You Can’t Have AI Both Ways: Balancing Health Data Privacy and Access Fairly

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