Personalized HIV Treatment: Bringing Marginalized Patients to the Forefront With Situational Analysis
DOI:
https://doi.org/10.17169/fqs-24.2.4083Keywords:
personalized medicine, antiretroviral therapy, health care, artificial intelligence, machine learning, implicated actorsAbstract
Since the early 2000s, personalized medicine (PM) has been a much-hyped field of healthcare. HIV treatment optimization tools were one of the first successful examples of PM, and have since their development been used to find tailored and optimized treatment for HIV-positive people. In this paper on a case study of the social arena of personalized HIV therapy I show how social worlds worked on both shared and distinct goals within the arena. I highlight the simultaneous centering and marginalization to which people seeking HIV therapy were subjected discursively in the social worlds. I also demonstrate that the further patients were from practitioners' daily work, the more they were reduced to their blood samples, rather than being constructed as complex and human.
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Copyright (c) 2023 Renate Baumgartner
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