Abstract

Machine learning (ML) is increasingly embedded in healthcare as an operational capability, not only as a set of clinical prediction models. This manuscript synthesizes evidence across three interdependent areas where ML reshapes value and risk: (1) security and privacy of patient data and critical healthcare infrastructure, (2) healthcare supply chains that determine the availability of drugs, devices, and vaccines, and (3) patient engagement technologies—especially conversational agents—that influence access, adherence, satisfaction, and trust. Building on healthcare analytics work such as OncoViz USA for population insight (Hasan et al., 2021) [14] and recent studies on infrastructure protection, supply resilience, and chatbots (Hasan et al., 2022 [15]; Rasel et al., 2022; Khan et al., 2024) [23], we develop an integrative Secure–Supply–Engage (SSE) framework. The review highlights recurring technical risks—membership inference, adversarial manipulation, and distribution shifts—and organizational risks—misaligned incentives and weak governance (Finlayson et al., 2019; Shokri et al., 2017) [10]. Across domains, effective practice converges on four principles: privacy-by-design (e.g., differential privacy and federated learning), integrity monitoring across model and data pipelines, resilience metrics for supply decisions, and patient-centered evaluation of digital engagement tools (Dwork, 2006; Kaissis et al., 2020; Rieke et al., 2020; Tudor Car et al., 2020) [9,22]. We translate these principles into actionable design requirements, a threat-to-control matrix, and reporting templates that connect clinical performance to operational reliability and equity. By treating ML as a sociotechnical system rather than a standalone algorithm, the SSE framework clarifies how security, supply continuity, and engagement must be co-designed to make ML clinically useful, operationally reliable, and socially legitimate.