ArticleBMC health services research2026
The replica dilemma: older patients' views on virtual models made from their health data-a qualitative study.
Article in BMC health services research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundVirtual models (digital twins, AI-simulated cohorts) built from patient data are transforming medical research, including applications in Transcatheter Edge-to-Edge Repair, yet the ethical perspectives of older patients, who are disproportionately represented in these datasets, remain critically unexplored. This study investigated how older adults with chronic conditions understand the use of their data for virtual model construction, what concerns emerge regarding digital replicas, and what rights they believe should accompany such data use.
methodsA descriptive qualitative study employing in-depth, semi-structured interviews with 22 chronically ill patients aged ≥ 65, using a standardized plain-language explanation of virtual models, purposively sampled for diversity in diagnosis and digital literacy. Data were analyzed inductively using reflexive thematic analysis.
resultsFive major themes emerged: (1) Conceptual opacity, limited understanding of virtual models, often conflated with clinical data use; (2) The "uncanny self", existential discomfort with an independently manipulable digital replica; (3) Consent inadequacy, recognition that past agreements cannot authorize unforeseen virtual applications; (4) The "trapped self", frustration over irreversible data integration nullifying withdrawal rights; (5) Rights reclamation, demands for notification, ongoing assent, digital deletion, use restrictions, and transparency.
conclusionOlder patients perceive virtual model construction as a fundamental ethical shift beyond traditional data sharing that current one-time consent frameworks cannot address. Findings provide a patient-derived rights framework to guide health policy and governance reform for the integration of AI-driven tools, including large language models, into health services involving this population.
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