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Trevena W, Zhong X, Lal A, Rovati L, Cubro E, Dong Y, Schulte P, Gajic O. Model-driven engineering for digital twins: a graph model-based patient simulation application. Front Physiol 2024; 15:1424931. [PMID: 39189027 PMCID: PMC11345177 DOI: 10.3389/fphys.2024.1424931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 07/19/2024] [Indexed: 08/28/2024] Open
Abstract
Introduction Digital twins of patients are virtual models that can create a digital patient replica to test clinical interventions in silico without exposing real patients to risk. With the increasing availability of electronic health records and sensor-derived patient data, digital twins offer significant potential for applications in the healthcare sector. Methods This article presents a scalable full-stack architecture for a patient simulation application driven by graph-based models. This patient simulation application enables medical practitioners and trainees to simulate the trajectory of critically ill patients with sepsis. Directed acyclic graphs are utilized to model the complex underlying causal pathways that focus on the physiological interactions and medication effects relevant to the first 6 h of critical illness. To realize the sepsis patient simulation at scale, we propose an application architecture with three core components, a cross-platform frontend application that clinicians and trainees use to run the simulation, a simulation engine hosted in the cloud on a serverless function that performs all of the computations, and a graph database that hosts the graph model utilized by the simulation engine to determine the progression of each simulation. Results A short case study is presented to demonstrate the viability of the proposed simulation architecture. Discussion The proposed patient simulation application could help train future generations of healthcare professionals and could be used to facilitate clinicians' bedside decision-making.
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Affiliation(s)
- William Trevena
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, United States
| | - Xiang Zhong
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, United States
| | - Amos Lal
- Mayo Clinic, Rochester, MN, United States
| | | | - Edin Cubro
- Mayo Clinic, Rochester, MN, United States
| | - Yue Dong
- Mayo Clinic, Rochester, MN, United States
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Huang Y, Dai H, Xu J, Wei R, Sun L, Guo Y, Guo J, Bian J. Evolution of digital twins in precision health applications: a scoping review study. RESEARCH SQUARE 2024:rs.3.rs-4612942. [PMID: 39149471 PMCID: PMC11326392 DOI: 10.21203/rs.3.rs-4612942/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
An increasing amount of research is incorporating the concept of Digital twin (DT) in biomedical and health care applications. This scoping review aims to summarize existing research and identify gaps in the development and use of DTs in the health care domain. The focus of this study lies on summarizing: the different types of DTs, the techniques employed in DT development, the DT applications in health care, and the data resources used for creating DTs. We identified fifty studies, which mainly focused on creating organ- (n=15) and patient-specific twins (n=30). The research predominantly centers on cardiology, endocrinology, orthopedics, and infectious diseases. Only a few studies used real-world datasets for developing their DTs. However, there remain unresolved questions and promising directions that require further exploration. This review provides valuable reference material and insights for researchers on DTs in health care and highlights gaps and unmet needs in this field.
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Affiliation(s)
- Yu Huang
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Hao Dai
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Jie Xu
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Ruoqi Wei
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Leyang Sun
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Yi Guo
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Jingchuan Guo
- Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
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Rovati L, Gary PJ, Cubro E, Dong Y, Kilickaya O, Schulte PJ, Zhong X, Wörster M, Kelm DJ, Gajic O, Niven AS, Lal A. Development and usability testing of a patient digital twin for critical care education: a mixed methods study. Front Med (Lausanne) 2024; 10:1336897. [PMID: 38274456 PMCID: PMC10808677 DOI: 10.3389/fmed.2023.1336897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 12/26/2023] [Indexed: 01/27/2024] Open
Abstract
Background Digital twins are computerized patient replicas that allow clinical interventions testing in silico to minimize preventable patient harm. Our group has developed a novel application software utilizing a digital twin patient model based on electronic health record (EHR) variables to simulate clinical trajectories during the initial 6 h of critical illness. This study aimed to assess the usability, workload, and acceptance of the digital twin application as an educational tool in critical care. Methods A mixed methods study was conducted during seven user testing sessions of the digital twin application with thirty-five first-year internal medicine residents. Qualitative data were collected using a think-aloud and semi-structured interview format, while quantitative measurements included the System Usability Scale (SUS), NASA Task Load Index (NASA-TLX), and a short survey. Results Median SUS scores and NASA-TLX were 70 (IQR 62.5-82.5) and 29.2 (IQR 22.5-34.2), consistent with good software usability and low to moderate workload, respectively. Residents expressed interest in using the digital twin application for ICU rotations and identified five themes for software improvement: clinical fidelity, interface organization, learning experience, serious gaming, and implementation strategies. Conclusion A digital twin application based on EHR clinical variables showed good usability and high acceptance for critical care education.
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Affiliation(s)
- Lucrezia Rovati
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
- School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Phillip J. Gary
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | - Edin Cubro
- Department of Information Technology, Mayo Clinic, Rochester, MN, United States
| | - Yue Dong
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, United States
| | - Oguz Kilickaya
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | - Phillip J. Schulte
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, United States
| | - Xiang Zhong
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, United States
| | - Malin Wörster
- Center for Anesthesiology and Intensive Care Medicine, Department of Anesthesiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Diana J. Kelm
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | - Ognjen Gajic
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | - Alexander S. Niven
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | - Amos Lal
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
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Calcaterra V, Pagani V, Zuccotti G. Maternal and fetal health in the digital twin era. Front Pediatr 2023; 11:1251427. [PMID: 37900683 PMCID: PMC10601630 DOI: 10.3389/fped.2023.1251427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 10/03/2023] [Indexed: 10/31/2023] Open
Affiliation(s)
- Valeria Calcaterra
- Department of Internal Medicine and Therapeutics, University of Pavia, Pavia, Italy
- Pediatric Department, Buzzi Children’s Hospital, Milano, Italy
| | - Valter Pagani
- Grant & Research Department-LJA-2021, Asomi College of Sciences, Marsa, Malta
| | - Gianvincenzo Zuccotti
- Pediatric Department, Buzzi Children’s Hospital, Milano, Italy
- Department of Biomedical and Clinical Science, University of Milano, Milano, Italy
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