Abstract
Monolithic Electronic Health Record (EHR) architectures remain fundamentally misaligned with high-resolution, specialty-specific clinical workflows, contributing to significant physician burnout and challangies in secondary use of data. This study introduces the Enterprise Health Twin (EHT) framework, a paradigm grounded in the Digital Twin concept, with the goal to transform static clinical ledgers into dynamic decision-support systems. The EHT establishes continuous bidirectional feedback (feed-forward ingestion and feed-backward decision support) between the physical entities of clinical workflows and the digital counterparts of interfaces reflecting the evolving states of the workflow, for each individual patient in the EHT. We conceptualized, prototyped, and operationalized the EHT framework for epilepsy care for seven years, in production, at the point of care. Deployed at the Epilepsy Center of the University of Texas Health Science Center at Houston, a ∼400% increase productivity has been achieved in specialized surgical evaluation throughput without additional staffing. The EHT achieved de novo capture of ontology-anchored structured data at the point of care, mitigating cognitive load while growing the volume of high-resolution, annotated longitudinal datasets for clinical research, and driving an increase of care velocity in a learning healthcare system.
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ABSTRACT
The NIH BRAIN Initiative Cell Atlas Network (BICAN) is a collaborative effort among neuroscientists, computational biologists, and data scientists to create reference brain cell atlases to be used by the research community as a molecular and anatomical foundational framework for the study of human brain function and disorders. Coordinated sharing and management of the scarce human brain tissue, across lifespans and among the eleven participating BICAN centers and laboratories with anatomical precision, is a formidable undertaking. Creating and operating the necessary data production pipelines with de novo metadata standards prospectively established to adhere to the highest possible levels of Findable, Accessible, Interoperable, and Reusable (FAIR) data principles, present unparalleled challenges that place BICAN into uncharted territories. A digital twin paradigm has been formulated and operationalized to manage advanced single-cell molecular techniques and complexity of data production workflows that are used in the BICAN consortium. The Neuroanatomy-anchored Information Management Platform (NIMP) implements the digital twin paradigm from conception to design, to production. NIMP is an agile, extensible, catalytic infrastructure for integrative and collaborative BICAN consortium-scale FAIR data generation. Digital-twin thinking ensures that NIMP provides tissue-to-bytes, end-to-end integration of data production pipelines spanning brain banks, laboratories, sequencing centers, and data archives. NIMP’s digital twin characteristics are manifested in the coupling of real-world workflows occurring in the BICAN laboratories with their digital counterparts moving progressively through verifiable provenance lineage paths. The implementation of NIMP’ digital twin features are grounded on advanced informatics strategies that include codification of anatomical contexts; standardized tissue request and sharing workflows, library preparation, sequencing, and single-cell omics data generation and deposition. These workflows and processes are governed by standardized Common Data Elements (CDEs); tracked by blockchain-inspired resource-identifiers (ID); ingested and edited using role-based access control (RBAC); and accessed by query-embedded dashboarding and atlas-enabled interactive resource exploration with dynamically rendered Sankey diagrams. In three years, NIMP has managed data and metadata for 762 human donors, 8,449 brain slabs, and 21,781 sequencing libraries across 11 laboratories, resulting in an estimated total of over 18 million cells for Basal Ganglia alone. The NIMP digital twin paradigm not only serves BICAN; it provides a blueprint for future-proof data coordination at consortium scale.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Corrected a typo in abstract: NIPM changed to NIMP.
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