Beyond Human Audiences in Healthcare: A Conceptual Framework for Machine-Facing Branding and Algorithmic Legibility in AI-mediated Patient Choice

Abstract

Trinanjan Gupta

Patients increasingly use digital systems to locate hospitals, compare health services, assess telemedicine providers, and interpret information about health care. As generative and agentic artificial intelligence becomes part of this process, a computational intermediary may retrieve, summarise, rank, or recommend healthcare organisations before a patient encounters them directly. A provider may therefore be clinically credible and meaningful to patients yet remain absent from an AI-generated consideration set, or be represented inaccurately, because its identity, services, evidence, accreditation, limitations, or access conditions are difficult for the system to interpret. This conceptual paper reframes Machine-Facing Branding for healthcare and develops the Healthcare Algorithmic Legibility Framework (HALF). The framework was constructed through a purposive, theory-building synthesis of signalling theory, source credibility, organisational legitimacy, brand equity, trust in automation, patient decision-making, health recommender systems, and guidance on ethical artificial intelligence for health. It proposes eight antecedents of healthcare algorithmic legibility: semantic clarity of services, structured provider knowledge, entity identity and disambiguation, cross-source consistency, clinical and institutional authority, verifiable trust signals, transparency and disclosure, and technical and retrieval accessibility.

These conditions are linked to agent interpretation quality, recommendation confidence, AI-mediated provider consideration, calibrated patient trust, informed preference, and provider equity. Representational fidelity is introduced as a patient-safety safeguard requiring machine-generated descriptions to remain accurate, evidence-grounded, current, and appropriately limited. Twenty propositions, nine moderating conditions, indicative measures, and a five-stage validation programme are presented. The framework focuses on how patients find and choose healthcare providers, rather than on diagnosing medical conditions or recommending treatments. It positions healthcare communication as a dual-audience responsibility: organisations must remain understandable and trustworthy to people while ensuring that authorised digital systems can interpret their services safely and accurately. The model is offered for empirical scrutiny, not as a validated clinical tool.

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