The practical ethics of repurposing health data: how to acknowledge invisible data work and the need for prioritization

"Goldmine" or "big mess"? An interview study on the challenges of designing, operating, and ensuring the durability of Clinical Data Warehouses in France and Belgium

OBJECTIVES

Clinical Data Warehouses (CDW) are the designated infrastructures to enable access and analysis of large quantities of electronic health record data. Building and managing such systems implies extensive "data work" and coordination between multiple stakeholders. Our study focuses on the challenges these stakeholders face when designing, operating, and ensuring the durability of CDWs for research.

MATERIALS AND METHODS

We conducted semistructured interviews with 21 professionals working with CDWs from France and Belgium. All interviews were recorded, transcribed verbatim, and coded inductively.

RESULTS

Prompted by the AI boom, healthcare institutions launched initiatives to repurpose data they were generating for care without a clear vision of how to generate value. Difficulties in operating CDWs arose quickly, strengthened by the multiplicity and diversity of stakeholders involved and grand discourses on the possibilities of CDWs, disjointed from their actual capabilities. Without proper management of the information flows, stakeholders struggled to build a shared vision. This was evident in our interviewees' contrasting appreciations of what mattered most to ensure data quality. Participants explained they struggled to manage knowledge inside and across institutions, generating knowledge loss, repeated mistakes, and impeding progress locally and nationally.

DISCUSSION AND CONCLUSION

Management issues strongly affect the deployment and operation of CDWs. This may stem from a simplistic linear vision of how this type of infrastructure operates. CDWs remain promising for research, and their design, implementation, and operation require careful management if they are to be successful. Building on innovation management, complex systems, and organizational learning knowledge will help.

Data work and practices in healthcare: A scoping review

CONTEXT

In healthcare, digitization has been widespread and profound, entailing a deluge of data. This has spurred ambitions for healthcare to become data-driven to improve efficiency and quality, and within medicine itself to improve diagnosing and treating diseases. The generation and processing of data requires human intervention and work, though this is often not acknowledged.

PURPOSE

The paper investigates who, where, by which means, and for which purposes data work is conducted which is crucial for healthcare managers and policy makers if ambitions to become data-driven are to succeed. To guide further research, it also provides an overview of existing research on data work and practices.

METHODS

We conducted a scoping review based on a search for papers including the terms healthcare or health care combined with at least one of the following terms: data work, data worker*, data practice*, data practitioner* in Scopus and Web of Science. 74 papers on data work or practices in healthcare were included.

ANALYSIS

The 74 papers were coded and analyzed regarding the following themes: the kind of data workers and practitioners, organizational settings, involved technologies, purposes, data work tasks, theories and concepts, and definitions of data work and practice.

RESULTS

Data work is pervasive in healthcare and conducted by various professions and people and in various contexts. The field researching data work and practices is emerging, with publications spread across multiple venues. and there is a need for more precise definitions of data work. Further, data work and practices are useful concepts that have enabled the exploration of those efforts and tasks in detail.

CONCLUSION

The research on data work and practices in healthcare is emerging and promising. We call for more research to consolidate the field and to better understand and support the work needed for healthcare to become data-driven.

Reconciling healthism and techno-solutionism: An observational study of a digital mental health trial

In a growing trend in digital psychiatry, algorithmic systems are used to determine correlations between data that is collected using wearable devices and self-reports of mood. They then offer recommendations for behaviour modification for improved mood. The present study consists of observations of the development of one of these systems. Descriptions of the trial emphasise the powerful role of the intrinsically motivated, responsible participant on one hand and the empowering machine learning (ML)-based technology on the other. This conceptualisation is shown to extend the neoliberal paradox of a freedom that, to be maintained, must be continually adjusted through discipline. Because of the paradoxical nature of this formulation, laboratory members disagree about the balance of agency between the objective machine learning system and the empowered participant. The guides who help participants interpret ML outputs and implement system recommendations are ascribed a replaceable role in formal accounts. Observations of this guidance practice make clear not only the important role played by guides but also how their work is relegated to the technological side of the broader formulation of the trial and further how this conceptualisation affects the way they conduct their work.

Mobilizing pilot-based evidence for the spread and sustainability of innovations in healthcare: The role of innovation intermediaries

An endemic challenge facing healthcare systems around the world is how to spread innovation more widely and sustainably. A common response to this challenge involves conducting pilot implementation studies to generate evidence of the innovation's benefits. However, despite the key role that such studies play in the local adoption of innovation, their contribution to the wider spread and sustainability of innovation is relatively under-researched and under-theorized. In this paper we examine this contribution through an empirical examination of the experiences of an innovation intermediary organization in the English NHS (National Health Service). We find that their work in mobilizing pilot-based evidence involves three main strands; configuring to context; transitioning evidence; and managing the transition. Through this analysis we contribute to theory by showing how the agency afforded by intermediary roles can support the effective transitioning of pilot-based evidence across different phases in the innovation journey, and across different occupational groups, and can thus help to create a positive feedback loop from localized early implementers of an innovation to later more widespread adoption and sustainability. Based on these findings, we develop insights on the reasons for the unnecessary repetition of pilots - so-called 'pilotitis'- and offer policy recommendations on how to enhance the role of pilots in the wider spread and sustainability of innovation.

Putting data to work for precision medicine

We discuss the One Stop Shop for Clinical Research (OSCAR) project that links clinical data, patient-reported outcomes, genomic data, and health registry data, employing a rigorous data privacy protection technology, to provide insights into treatment efficacy and safety and serve as a comparator for single-arm trials. This could inspire further initiatives to advance precision medicine.