Therapeutic value in the time of digital brainwaves.

This article examines the value of medical technology through the case of electroencephalograms (EEGs), devices used to visualize brain activity and diagnose seizures. Drawing on ethnographic fieldwork, the article shows that EEGs are valued differently by patients and medical practitioners. While practitioners value EEGs for their clinical utility, i.e., ability to inform clinical decisions, patients value EEGs even in the absence of clinical utility. Indeed, patients derive long-lasting therapeutic effects from this diagnostic technology. These findings intervene in the utilitarian calculus of therapeutic value-a mode of reasoning that equates value with clinical utility-commonly deployed in biomedicine and engineering and call for a recognition of alternative notions such as the therapeutic value of being witnessed and cared for by medical experts via EEGs and other technologies that require time to work. Expansive notions of therapeutic value are imperative for including marginalized patients-especially low-income, disabled, and women patients-in debates on automation and the future of healthcare. Studying how multiple stakeholders value a medical technology provides insight into valuation, objectification, expertise, and other concerns central to science and technology studies.

Advancing maturity modeling for precision oncology.

Introduction: This study aimed to map the maturity of precision oncology as an example of a Learning Health System by understanding the current state of practice, tools and informatics, and barriers and facilitators of maturity. Methods: We conducted semi-structured interviews with 34 professionals (e.g., clinicians, pathologists, and program managers) involved in Molecular Tumor Boards (MTBs). Interviewees were recruited through outreach at 3 large academic medical centers (AMCs) (n = 16) and a Next Generation Sequencing (NGS) company (n = 18). Interviewees were asked about their roles and relationships with MTBs, processes and tools used, and institutional practices. The interviews were then coded and analyzed to understand the variation in maturity across the evolving field of precision oncology. Results: The findings provide insight into the present level of maturity in the precision oncology field, including the state of tooling and informatics within the same domain, the effects of the critical environment on overall maturity, and prospective approaches to enhance maturity of the field. We found that maturity is relatively low, but continuing to evolve, across these dimensions due to the resource-intensive and complex sociotechnical infrastructure required to advance maturity of the field and to fully close learning loops. Conclusion: Our findings advance the field by defining and contextualizing the current state of maturity and potential future strategies for advancing precision oncology, providing a framework to examine how learning health systems mature, and furthering the development of maturity models with new evidence.