OBJECTIVES: Health Digital Twins (HDT) are virtual representations of patients that serve as computational models for testing diagnostic and prognostic algorithms. HDTs are generally multidimensional and represent a patient population of a determined health ecosystem. We sought to assess their hypothetical implementation across the real-world pathway of care.

METHODS: We conducted a targeted literature search using the criteria: Health Digital Twin. Articles that mentioned clinical applications were selected for analysis and data extraction considering: disease / medical specialty, diagnostics (according to ICD-10), chart data (i.e. vital signs and lab test), demographics (ethnicity & age), wearables data, privacy and ethics considerations. We also analyzed their impact on quality of care, patient satisfaction and healthcare resources use (HRU). We present a narrative description of these findings.

RESULTS: From 203 articles mentioning the search terms, 17 met the criteria and underwent full-text analysis. Medical specialties included: cardiology: 4; neurology: 4; oncology: 2; infectious diseases: 2; precision medicine, surgery, critical care, gerontology, nutrition: 1 each. From these specialties, diagnostics most cited were: Multiple Sclerosis, Breast Cancer, and Colorectal Cancer. Type of data included: clinical: 16; demographic: 13; OMICs (i.e. genomic sequencing): 7; wearables data: 9. Three articles addressed quality of care (i.e. ubiquitous patient support); Two considered patient satisfaction, and four mentioned any HRU (reduction of patient visits to healthcare centers). Privacy and ethics were noted in two and seven articles, respectively.

CONCLUSIONS: The clinical application of health digital twins is still immature. Further research that includes comprehensive risk assessment on safety, quality and algorithm accuracy of these virtual models are required to guarantee their expansion in the real-world patient care pathway.