In crucial sectors like healthcare, education, and housing, policymakers are turning to the tools ofmarket designto incentivize public and private actors to more efficiently and effectively produce the public good. Although market design has been a key policymaking tool for decades, datafication is increasingly central to this technocratic tinkering. This article explores a project of datafied market redesign in the U.S. healthcare industry, demonstrating that emerging federal health data regulations are designed to enable the state to more precisely quantify, and thereby incentivize, the production of “valuable” care. This case study demonstrates how both the public good and crucial data infrastructures are constrained through their enactment within market-based modes of governance. As this data-solutionism for extractive markets becomes a more prevalent mode of governance—particularly in areas like climate change—we must find alternative mechanisms for collectively defining the public good, and for achieving corporate accountability beyond financial incentive structures.
more »
« less
This content will become publicly available on July 31, 2026
Computing Care: Governing U.S. Healthcare Through Markets and Data
The U.S. healthcare system is in crisis, marked by soaring costs, inefficiencies, and stark disparities in health outcomes. Or at least, this is how U.S. policy makers have predominantly narrated and justified healthcare policy interventions since the 1970s. This dissertation examines how, starting in the 2010s, U.S. policymakers and politicians have turned towards data infrastructures – including new protocols and standards for exchanging clinical, billing, and administrative health data – as the newest site for reforming healthcare markets. Drawing on 24 months of multi-sited ethnographic research in the U.S. healthcare industry, I trace how federal regulations, particularly the 21st Century Cures Act (2016), position data interoperability, or standardized data sharing, as a means to realign a fragmented, profit-driven healthcare market with the efficient production of population health outcomes. I describe this mode of governance, which merges market-solutionism and techno-solutionism, as “computing care.” Through a close examination of “value-based care” policies, social determinants of health data, and the automation of prior authorization, I show how computing care helps to depoliticize the failures of for-profit healthcare markets to produce equitable, accessible, affordable care. The failures of healthcare markets are instead narrated by policymakers as technical problems - of insufficient information and sub-optimal market design. I argue that intensified computation and continually evolving capacities to collect, analyze, and store data help reproduce this depoliticized, market-solutionist mode of governance. Through this project, I interrogate the banal violence of computing care as a mode of governance and point towards the possibility of alternative modes of governance beyond market- and techno-solutionism. I also reflect specifically on the potential role of data infrastructures in advancing alternative modes of care governance. This project contributes to the fields of critical data studies, science and technology studies, and feminist political economy, highlighting the reconfigurations of data and governance necessary to achieve an equitable and just future of healthcare.
more »
« less
- Award ID(s):
- 1901171
- PAR ID:
- 10650525
- Publisher / Repository:
- University of Michigan
- Date Published:
- Subject(s) / Keyword(s):
- data governance ethnography data infrastructures U.S. healthcare science and technology studies interoperability Information and Library Science Social Sciences
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Reddy, S.; Winter, J.S.; Padmanabhan, S. (Ed.)AI applications are poised to transform health care, revolutionizing benefits for individuals, communities, and health-care systems. As the articles in this special issue aptly illustrate, AI innovations in healthcare are maturing from early success in medical imaging and robotic process automation, promising a broad range of new applications. This is evidenced by the rapid deployment of AI to address critical challenges related to the COVID-19 pandemic, including disease diagnosis and monitoring, drug discovery, and vaccine development. At the heart of these innovations is the health data required for deep learning applications. Rapid accumulation of data, along with improved data quality, data sharing, and standardization, enable development of deep learning algorithms in many healthcare applications. One of the great challenges for healthcare AI is effective governance of these data—ensuring thoughtful aggregation and appropriate access to fuel innovation and improve patient outcomes and healthcare system efficiency while protecting the privacy and security of data subjects. Yet the literature on data governance has rarely looked beyond important pragmatic issues related to privacy and security. Less consideration has been given to unexpected or undesirable outcomes of healthcare in AI, such as clinician deskilling, algorithmic bias, the “regulatory vacuum”, and lack of public engagement. Amidst growing calls for ethical governance of algorithms, Reddy et al. developed a governance model for AI in healthcare delivery, focusing on principles of fairness, accountability, and transparency (FAT), and trustworthiness, and calling for wider discussion. Winter and Davidson emphasize the need to identify underlying values of healthcare data and use, noting the many competing interests and goals for use of health data—such as healthcare system efficiency and reform, patient and community health, intellectual property development, and monetization. Beyond the important considerations of privacy and security, governance must consider who will benefit from healthcare AI, and who will not. Whose values drive health AI innovation and use? How can we ensure that innovations are not limited to the wealthiest individuals or nations? As large technology companies begin to partner with health care systems, and as personally generated health data (PGHD) (e.g., fitness trackers, continuous glucose monitors, health information searches on the Internet) proliferate, who has oversight of these complex technical systems, which are essentially a black box? To tackle these complex and important issues, it is important to acknowledge that we have entered a new technical, organizational, and policy environment due to linked data, big data analytics, and AI. Data governance is no longer the responsibility of a single organization. Rather, multiple networked entities play a role and responsibilities may be blurred. This also raises many concerns related to data localization and jurisdiction—who is responsible for data governance? In this emerging environment, data may no longer be effectively governed through traditional policy models or instruments.more » « less
-
A Linguistic Analysis of News Coverage of E-Healthcare in China with a Heterogeneous Graphical ModelE-healthcare has been envisaged as a major component of the infrastructure of modern healthcare, and has been developing rapidly in China. For healthcare, news media can play an important role in raising public interest and utilization of a particular service and complicating (and, perhaps clouding) debate on public health policy issues. We conducted a linguistic analysis of news reports from January 2015 to June 2021 related to E-healthcare in mainland China, using a heterogeneous graphical modeling approach. This approach can simultaneously cluster the datasets and estimate the conditional dependence relationships of keywords. It was found that there were eight phases of media coverage. The focuses and main topics of media coverage were extracted based on the network hub and module detection. The temporal patterns of media reports were found to be mostly consistent with the policy trend. Specifically, in the policy embryonic period (2015–2016), two phases were obtained, industry management was the main topic, and policy and regulation were the focuses of media coverage. In the policy development period (2017–2019), four phases were discovered. All the four main topics, namely industry development, health care, financial market, and industry management, were present. In 2017 Q3–2017 Q4, the major focuses of media coverage included social security, healthcare and reform, and others. In 2018 Q1, industry regulation and finance became the focuses. In the policy outbreak period (2020–), two phases were discovered. Financial market and industry management were the main topics. Medical insurance and healthcare for the elderly became the focuses. This analysis can offer insights into how the media responds to public policy for E-healthcare, which can be valuable for the government, public health practitioners, health care industry investors, and others.more » « less
-
Emotion AI, or AI that claims to infer emotional states from various data sources, is increasingly deployed in myriad contexts, including mental healthcare. While emotion AI is celebrated for its potential to improve care and diagnosis, we know little about the perceptions of data subjects most directly impacted by its integration into mental healthcare. In this paper, we qualitatively analyzed U.S. adults' open-ended survey responses (n = 395) to examine their perceptions of emotion AI use in mental healthcare and its potential impacts on them as data subjects. We identify various perceived impacts of emotion AI use in mental healthcare concerning 1) mental healthcare provisions; 2) data subjects' voices; 3) monitoring data subjects for potential harm; and 4) involved parties' understandings and uses of mental health inferences. Participants' remarks highlight ways emotion AI could address existing challenges data subjects may face by 1) improving mental healthcare assessments, diagnoses, and treatments; 2) facilitating data subjects' mental health information disclosures; 3) identifying potential data subject self-harm or harm posed to others; and 4) increasing involved parties' understanding of mental health. However, participants also described their perceptions of potential negative impacts of emotion AI use on data subjects such as 1) increasing inaccurate and biased assessments, diagnoses, and treatments; 2) reducing or removing data subjects' voices and interactions with providers in mental healthcare processes; 3) inaccurately identifying potential data subject self-harm or harm posed to others with negative implications for wellbeing; and 4) involved parties misusing emotion AI inferences with consequences to (quality) mental healthcare access and data subjects' privacy. We discuss how our findings suggest that emotion AI use in mental healthcare is an insufficient techno-solution that may exacerbate various mental healthcare challenges with implications for potential distributive, procedural, and interactional injustices and potentially disparate impacts on marginalized groups.more » « less
-
This article examines the U.S. legislative and policy landscape and its historical and contemporary recognition of young people as caregivers and their importance to public health, both as care providers and as a category of special concern for overall wellbeing. Drawing on feminist geographies of health to situate a historical analysis, we aim to answer two key questions: First, what is the history of recognition of caregiving youth in key moments of federal action to address family caregiving needs? Second, how might we use this history to better understand and analyze the patchwork geography of caregiving youth recognition in the U.S. and other countries that similarly lack formal national policy recognition to improve and enhance public health? We use the term patchwork to describe how federal recognition of caregiving youth in broader debates about public health is uneven across both time and space, and contingent upon civil society, non-profit organizations, and researchers working in and with geographically bound communities. Our results illustrate how a focus on the relationships of recognition, both in the past and the present and at local and national scales, reveals a different perspective on caregiving youth in the U.S. with a much more complex history than previously identified. The article describes how relationships established in the absence of federal policy or legislation are sometimes directed towards building more formal recognition, and other times with the goal of changing practices in a specific location.more » « less
An official website of the United States government
