Data Stewardship Barriers to Building Digital Twin Technology for Precision Medicine

Digital twins (DTs) are dynamic, virtual representations of individual patients that could support predictive diagnostics and personalized therapeutic optimization. Their development depends on patient-level data from real-world data (RWD) sources and electronic medical record (EMR) data, but major barriers in data-stewardship, interoperability, provenance, and governance persist. This review examines critical bottlenecks within the current healthcare ecosystem, with particular attention to fragmented EMRs, limited longitudinal data continuity, and missing, incomplete, or inaccurate information. We address data availability, stewardship, and provenance rather than model construction itself. We also explore ethical imperatives of mitigating representational bias, where over-representation of European ancestry amplifies existing health inequalities. We evaluate blockchain technology as a decentralized trust anchor to ensure provenance, automate consent, and incentivize longitudinal data stewardship. In parallel, we acknowledge that clinically useful DTs also depend on substantial advances in model specification, calibration, and validation, especially for biologically complex diseases. By synthesizing computational, regulatory, and ethical challenges, we provide a roadmap for developing robust, equitable, and interoperable ecosystems for precision medicine.