The social life of biomedical data: Capturing, obscuring, and envisioning care in the digital safety-net

We're implementing AI now, so why not ask us what to do? - How AI providers perceive and navigate the spread of diagnostic AI in complex healthcare systems

Despite high expectations of artificial intelligence (AI) in medical diagnostics, predictions of its extensive and rapid adoption have so far not been matched by reality. AI providers seeking to promote and perpetuate the use of this technology are faced with the complex reality of embedding AI-enabled diagnostics across variable implementation contexts. In this study, we draw upon a complexity science approach and qualitative methodology to understand how AI providers perceive and navigate the spread of AI in complex healthcare systems. Using semi-structured, one-to-one interviews, we collected qualitative data from 14 providers of AI-enabled diagnostics. We triangulated the data by complementing the interviews with multiple sources, including a focus group of physicians with experience using these technologies. The notion of embedding allowed us to connect local implementation efforts with systemic diffusion. Our study reveals that AI providers self-organise to increase their adaptability when navigating the variable conditions and unpredictability of complex healthcare contexts. In addition to the tensions perceived by AI providers within the sociocultural, technological, and institutional subsystems of healthcare, we illustrate the practices emerging among them to mitigate these tensions: stealth science, agility, and digital ambidexterity. Our study contributes to the growing body of literature on the spread of AI in healthcare by capturing the view of technology providers and adding a new theoretical perspective through the lens of complexity science.

Artificial intelligence-based computer aided detection (AI-CAD) in the fight against tuberculosis: Effects of moving health technologies in global health

Computer Aided Detection software based on Artificial Intelligence (AI-CAD), combined with chest X-rays have recently been promoted as an easy fix for a complex problem: ending TB by 2030. WHO has recommended the use of such imaging devices in 2021 and many partnerships have helped propose benchmark analysis and technology comparisons to facilitate their "market access". Our aim is to examine the socio-political and health issues that stem from using AI-CAD technology in a global health context conceptualized as a set of practice and ideas organizing global intervention "in the life of others". We also question how this technology, which is not yet fully implemented in routine use, may limit or amplify some inequalities in the care of tuberculosis. We describe AI-CAD through Actor-Network-Theory framework to understand the global assemblage and composite activities associated with detection through AI-CAD, and interrogate how the technology itself may consolidate a specific configuration of "global health". We explore the various dimensions of AI-CAD "health effects model": technology design, development, regulation, institutional competition, social interaction and health cultures. On a broader level, AI-CAD represents a new version of global health's accelerationist model centered on "moving and autonomous-presumed technologies". We finally present key aspects in our research which help discuss the theories mobilized: AI-CAD ambivalent insertion in global health, the social lives of its data: from efficacy to markets and AI-CAD human care and maintenance it requires. We reflect on the conditions that will affect AI-CAD use and its promises. In the end, the risk of new detection technologies such as AI-CAD is indeed that the fight against TB could be reduced to one that is purely technical and technological, with neglect to its social determinants and effects.

The social life of risk probabilities in medicine

In the closing decades of the 20th century, a method of calculating numerical probabilities based on populations-at-risk emerged in public health/epidemiology and then moved into clinical medicine. This new method had its own autonomous social life as it reorganised the fields of clinical perception and clinical practice. This paper documents that revolution in the epistemological basis of medicine by investigating, through primary sources, when and how the social life of a new method undermined the professional status of medicine and changed the doctor-patient relationship.

Discuss and remember: Clinician strategies for integrating social determinants of health in patient records and care

There is growing interest in standardizing data about social determinants of health (SDOH) in electronic health records (EHRs), yet little is known about how clinicians document SDOH in daily practice. This study investigates clinicians' strategies for working with SDOH data and the challenges confronting SDOH standardization. Drawing on ethnographic observation, interviews with patients and clinicians, and systematic review of patient EHRs-all at an urban teaching hospital in the US Midwest-we analyze three strategies clinicians deploy to integrate SDOH data into patient care. First, clinicians document SDOH using "signal phrases," keywords and short sentences that help them recall patients' social stories. Second, clinicians use other technology or face-to-face conversations to share about patients' SDOH with colleagues. Third, clinicians fold discussion of SDOH with patients into their personal relationships. While these local strategies facilitate personalized care and help clinicians minimize their computer workload, we also consider their limitations for efforts to coordinate care across institutions and attempts to identify SDOH in EHRs. These findings reveal ongoing tensions in projects of standardization in medicine, as well as the specific difficulty of standardizing data about SDOH. They have important clinical implications as they help explain how clinicians may attend to patients' SDOH in ways that are not legible in patient records. This paper is also relevant for policy at a time when mandates to include SDOH data in health records are expanding and strategies to standardize SDOH documentation are being developed.