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Shang Y, Tian Y, Lyu K, Zhou T, Zhang P, Chen J, Li J. Electronic Health Record-Oriented Knowledge Graph System for Collaborative Clinical Decision Support Using Multicenter Fragmented Medical Data: Design and Application Study. J Med Internet Res 2024; 26:e54263. [PMID: 38968598 DOI: 10.2196/54263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 02/02/2024] [Accepted: 05/16/2024] [Indexed: 07/07/2024] Open
Abstract
BACKGROUND The medical knowledge graph provides explainable decision support, helping clinicians with prompt diagnosis and treatment suggestions. However, in real-world clinical practice, patients visit different hospitals seeking various medical services, resulting in fragmented patient data across hospitals. With data security issues, data fragmentation limits the application of knowledge graphs because single-hospital data cannot provide complete evidence for generating precise decision support and comprehensive explanations. It is important to study new methods for knowledge graph systems to integrate into multicenter, information-sensitive medical environments, using fragmented patient records for decision support while maintaining data privacy and security. OBJECTIVE This study aims to propose an electronic health record (EHR)-oriented knowledge graph system for collaborative reasoning with multicenter fragmented patient medical data, all the while preserving data privacy. METHODS The study introduced an EHR knowledge graph framework and a novel collaborative reasoning process for utilizing multicenter fragmented information. The system was deployed in each hospital and used a unified semantic structure and Observational Medical Outcomes Partnership (OMOP) vocabulary to standardize the local EHR data set. The system transforms local EHR data into semantic formats and performs semantic reasoning to generate intermediate reasoning findings. The generated intermediate findings used hypernym concepts to isolate original medical data. The intermediate findings and hash-encrypted patient identities were synchronized through a blockchain network. The multicenter intermediate findings were collaborated for final reasoning and clinical decision support without gathering original EHR data. RESULTS The system underwent evaluation through an application study involving the utilization of multicenter fragmented EHR data to alert non-nephrology clinicians about overlooked patients with chronic kidney disease (CKD). The study covered 1185 patients in nonnephrology departments from 3 hospitals. The patients visited at least two of the hospitals. Of these, 124 patients were identified as meeting CKD diagnosis criteria through collaborative reasoning using multicenter EHR data, whereas the data from individual hospitals alone could not facilitate the identification of CKD in these patients. The assessment by clinicians indicated that 78/91 (86%) patients were CKD positive. CONCLUSIONS The proposed system was able to effectively utilize multicenter fragmented EHR data for clinical application. The application study showed the clinical benefits of the system with prompt and comprehensive decision support.
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Affiliation(s)
- Yong Shang
- Research Center for Data Hub and Security, Zhejiang Laboratory, Hangzhou, China
| | - Yu Tian
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Kewei Lyu
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Tianshu Zhou
- Research Center for Data Hub and Security, Zhejiang Laboratory, Hangzhou, China
| | - Ping Zhang
- Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianghua Chen
- Kidney Disease Center, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingsong Li
- Research Center for Data Hub and Security, Zhejiang Laboratory, Hangzhou, China
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Lacson R, Yu Y, Kuo TT, Ohno-Machado L. Biomedical blockchain with practical implementations and quantitative evaluations: a systematic review. J Am Med Inform Assoc 2024; 31:1423-1435. [PMID: 38726710 PMCID: PMC11105130 DOI: 10.1093/jamia/ocae084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/26/2024] [Accepted: 04/16/2024] [Indexed: 05/22/2024] Open
Abstract
OBJECTIVE Blockchain has emerged as a potential data-sharing structure in healthcare because of its decentralization, immutability, and traceability. However, its use in the biomedical domain is yet to be investigated comprehensively, especially from the aspects of implementation and evaluation, by existing blockchain literature reviews. To address this, our review assesses blockchain applications implemented in practice and evaluated with quantitative metrics. MATERIALS AND METHODS This systematic review adapts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to review biomedical blockchain papers published by August 2023 from 3 databases. Blockchain application, implementation, and evaluation metrics were collected and summarized. RESULTS Following screening, 11 articles were included in this review. Articles spanned a range of biomedical applications including COVID-19 medical data sharing, decentralized internet of things (IoT) data storage, clinical trial management, biomedical certificate storage, electronic health record (EHR) data sharing, and distributed predictive model generation. Only one article demonstrated blockchain deployment at a medical facility. DISCUSSION Ethereum was the most common blockchain platform. All but one implementation was developed with private network permissions. Also, 8 articles contained storage speed metrics and 6 contained query speed metrics. However, inconsistencies in presented metrics and the small number of articles included limit technological comparisons with each other. CONCLUSION While blockchain demonstrates feasibility for adoption in healthcare, it is not as popular as currently existing technologies for biomedical data management. Addressing implementation and evaluation factors will better showcase blockchain's practical benefits, enabling blockchain to have a significant impact on the health sector.
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Affiliation(s)
- Roger Lacson
- Department of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, CT 06510, United States
| | - Yufei Yu
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, United States
- Department of Biomedical Informatics, University of California San Diego Health, La Jolla, CA 92093, United States
| | - Tsung-Ting Kuo
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, United States
- Department of Biomedical Informatics, University of California San Diego Health, La Jolla, CA 92093, United States
| | - Lucila Ohno-Machado
- Department of Biomedical Informatics & Data Science, Yale School of Medicine, New Haven, CT 06510, United States
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92093, United States
- Department of Biomedical Informatics, University of California San Diego Health, La Jolla, CA 92093, United States
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Spadafora L, Comandini GL, Giordano S, Polimeni A, Perone F, Sabouret P, Leonetti M, Cacciatore S, Cacia M, Betti M, Bernardi M, Zimatore FR, Russo F, Iervolino A, Aulino G, Moscardelli A. Blockchain technology in Cardiovascular Medicine: a glance to the future? Results from a social media survey and future perspectives. Minerva Cardiol Angiol 2024; 72:1-10. [PMID: 37971710 DOI: 10.23736/s2724-5683.23.06457-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
The leverage of digital facilities in medicine for disease diagnosis, monitoring, and medical history recording has become increasingly pivotal. However, the advancement of these technologies poses a significant challenge regarding data privacy, given the highly sensitive nature of medical information. In this context, the application of Blockchain technology, a digital system where information is stored in blocks and each block is linked to the one before, has the potential to enhance existing technologies through its exceptional security and transparency. This paradigm is of particular importance in cardiovascular medicine, where the prevalence of chronic conditions leads to the need for secure remote monitoring, secure data storage and secure medical history updating. Indeed, digital support for chronic cardiovascular pathologies is getting more and more crucial. This paper lays its rationale in three primary aims: 1) to scrutinize the existing literature for tangible applications of blockchain technology in the field of cardiology; 2) to report results from a survey aimed at gauging the reception of blockchain technology within the cardiovascular community, conducted on social media; 3) to conceptualize a web application tailored specifically to cardiovascular care based on blockchain technology. We believe that Blockchain technology may contribute to a breakthrough in healthcare digitalization, especially in the field of cardiology; in this context, we hope that the present work may be inspiring for physicians and healthcare stakeholders.
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Affiliation(s)
- Luigi Spadafora
- Department of Clinical, Internal Medicine, Anesthesiology and Cardiovascular Sciences, Sapienza University, Rome, Italy -
| | - Gian L Comandini
- Department of Engineering, Guglielmo Marconi University, Rome, Italy
- Department of Economics and Law, University of Macerata, Macerata, Italy
| | - Salvatore Giordano
- Division of Cardiology, Department of Medical and Surgical Sciences, Magna Græcia University, Catanzaro, Italy
| | - Alberto Polimeni
- Division of Cardiology, Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Cosenza, Italy
| | - Francesco Perone
- Cardiac Rehabilitation Unit, Villa delle Magnolie Rehabilitation Clinic, Castel Morrone, Caserta, Italy
| | - Pierre Sabouret
- Heart Institute and Action Group, Pitié-Salpétrière, Sorbonne University, Paris, France
- National College of French Cardiologists, Paris, France
| | | | - Stefano Cacciatore
- Department of Geriatrics, Orthopedics and Rheumatology, Sacred Heart Catholic University, Rome, Italy
| | - Michele Cacia
- Cardiology Unit, A.O.U. Renato Dulbecco, Catanzaro, Italy
- Department of Experimental and Clinical Medicine, Magna Græcia University, Catanzaro, Italy
| | - Matteo Betti
- Cardiovascular Section, Department of Clinical Sciences and Community Health, University of Milan, Milan, Italy
- Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Marco Bernardi
- Department of Clinical, Internal Medicine, Anesthesiology and Cardiovascular Sciences, Sapienza University, Rome, Italy
| | | | | | - Adelaide Iervolino
- Department of Clinical Medicine and Surgery, Federico II University Hospital, Naples, Italy
| | - Giovanni Aulino
- Section of Legal Medicine, Department of Health Surveillance and Bioethics, IRCCS A. Gemelli University Polyclinic Foundation, Sacred Heart Catholic University, Rome, Italy
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Liang X, Zhao J, Chen Y, Bandara E, Shetty S. Architectural Design of a Blockchain-Enabled, Federated Learning Platform for Algorithmic Fairness in Predictive Health Care: Design Science Study. J Med Internet Res 2023; 25:e46547. [PMID: 37902833 PMCID: PMC10644196 DOI: 10.2196/46547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 07/06/2023] [Accepted: 08/21/2023] [Indexed: 10/31/2023] Open
Abstract
BACKGROUND Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients' privacy at each site. OBJECTIVE This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead costs. METHODS We improved existing federated learning platforms by integrating blockchain through an iterative design approach. We used the design science research method, which involves 2 design cycles (federated learning for bias mitigation and decentralized architecture). The design involves a bias-mitigation process within the blockchain-empowered federated learning framework based on a novel architecture. Under this architecture, multiple medical institutions can jointly train predictive models using their privacy-protected data effectively and efficiently and ultimately achieve fairness in decision-making in the health care domain. RESULTS We designed and implemented our solution using the Aplos smart contract, microservices, Rahasak blockchain, and Apache Cassandra-based distributed storage. By conducting 20,000 local model training iterations and 1000 federated model training iterations across 5 simulated medical centers as peers in the Rahasak blockchain network, we demonstrated how our solution with an improved fairness mechanism can enhance the accuracy of predictive diagnosis. CONCLUSIONS Our study identified the technical challenges of prediction biases faced by existing predictive models in the health care domain. To overcome these challenges, we presented an innovative design solution using federated learning and blockchain, along with the adoption of a unique distributed architecture for a fairness-aware system. We have illustrated how this design can address privacy, security, prediction accuracy, and scalability challenges, ultimately improving fairness and equity in the predictive health care domain.
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Affiliation(s)
- Xueping Liang
- Department of Information Systems and Business Analytics, Florida International University, Miami, FL, United States
| | - Juan Zhao
- American Heart Association, Dallas, TX, United States
| | - Yan Chen
- Department of Information Systems and Business Analytics, Florida International University, Miami, FL, United States
| | - Eranga Bandara
- Virginia Modeling, Analysis and Simulation Center, Old Dominion University, Suffolk, VA, United States
| | - Sachin Shetty
- Virginia Modeling, Analysis and Simulation Center, Old Dominion University, Suffolk, VA, United States
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5
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Wu TC, Ho CTB. Blockchain Revolutionizing in Emergency Medicine: A Scoping Review of Patient Journey through the ED. Healthcare (Basel) 2023; 11:2497. [PMID: 37761695 PMCID: PMC10530815 DOI: 10.3390/healthcare11182497] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/29/2023] [Accepted: 09/06/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Blockchain technology has revolutionized the healthcare sector, including emergency medicine, by integrating AI, machine learning, and big data, thereby transforming traditional healthcare practices. The increasing utilization and accumulation of personal health data also raises concerns about security and privacy, particularly within emergency medical settings. METHOD Our review focused on articles published in databases such as Web of Science, PubMed, and Medline, discussing the revolutionary impact of blockchain technology within the context of the patient journey through the ED. RESULTS A total of 33 publications met our inclusion criteria. The findings emphasize that blockchain technology primarily finds its applications in data sharing and documentation. The pre-hospital and post-discharge applications stand out as distinctive features compared to other disciplines. Among various platforms, Ethereum and Hyperledger Fabric emerge as the most frequently utilized options, while Proof of Work (PoW) and Proof of Authority (PoA) stand out as the most commonly employed consensus algorithms in this emergency care domain. The ED journey map and two scenarios are presented, exemplifying the most distinctive applications of emergency medicine, and illustrating the potential of blockchain. Challenges such as interoperability, scalability, security, access control, and cost could potentially arise in emergency medical contexts, depending on the specific scenarios. CONCLUSION Our study examines the ongoing research on blockchain technology, highlighting its current influence and potential future advancements in optimizing emergency medical services. This approach empowers frontline medical professionals to validate their practices and recognize the transformative potential of blockchain in emergency medical care, ultimately benefiting both patients and healthcare providers.
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Affiliation(s)
- Tzu-Chi Wu
- Institute of Technology Management, National Chung-Hsing University, Taichung 40227, Taiwan;
- Department of Emergency Medicine, Show Chwan Memorial Hospital, Changhua 500009, Taiwan
| | - Chien-Ta Bruce Ho
- Institute of Technology Management, National Chung-Hsing University, Taichung 40227, Taiwan;
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López-Ojeda W, Hurley RA. The Medical Metaverse, Part 1: Introduction, Definitions, and New Horizons for Neuropsychiatry. J Neuropsychiatry Clin Neurosci 2023; 35:A4-3. [PMID: 36633472 DOI: 10.1176/appi.neuropsych.20220187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Wilfredo López-Ojeda
- Veterans Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center (MIRECC) and the Research and Academic Affairs Service Line, W. G. Hefner Veterans Affairs Medical Center, Salisbury, N.C. (López-Ojeda, Hurley); Departments of Psychiatry and Behavioral Medicine (López-Ojeda, Hurley) and Department of Radiology (Hurley), Wake Forest School of Medicine, Winston-Salem, N.C.; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston (Hurley)
| | - Robin A Hurley
- Veterans Affairs Mid-Atlantic Mental Illness Research, Education and Clinical Center (MIRECC) and the Research and Academic Affairs Service Line, W. G. Hefner Veterans Affairs Medical Center, Salisbury, N.C. (López-Ojeda, Hurley); Departments of Psychiatry and Behavioral Medicine (López-Ojeda, Hurley) and Department of Radiology (Hurley), Wake Forest School of Medicine, Winston-Salem, N.C.; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston (Hurley)
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7
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Zhang C, Feng S, He R, Fang Y, Zhang S. Gastroenterology in the Metaverse: The dawn of a new era? Front Med (Lausanne) 2022; 9:904566. [PMID: 36035392 PMCID: PMC9403067 DOI: 10.3389/fmed.2022.904566] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 07/26/2022] [Indexed: 12/03/2022] Open
Abstract
2021 is known as the first Year of the Metaverse, and around the world, internet giants are eager to devote themselves to it. In this review, we will introduce the concept, current development, and application of the Metaverse and the use of the current basic technologies in the medical field, such as virtual reality and telemedicine. We also probe into the new model of gastroenterology in the future era of the Metaverse.
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Affiliation(s)
- Chi Zhang
- The First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Shuyan Feng
- The First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Ruonan He
- The First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yi Fang
- The First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - Shuo Zhang
- The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
- *Correspondence: Shuo Zhang
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8
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A Conceptual Framework for Blockchain Enhanced Information Modeling for Healing and Therapeutic Design. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19138218. [PMID: 35805875 PMCID: PMC9266876 DOI: 10.3390/ijerph19138218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 07/02/2022] [Accepted: 07/03/2022] [Indexed: 01/27/2023]
Abstract
In the face of the health challenges caused by the COVID-19 pandemic, healing and therapeutic design (HTD) as interventions can help with improving people’s health. It is considered to have great potential to promote health in the forms of art, architecture, landscape, space, and environment. However, there are insufficient design approaches to address the challenges during the HTD process. An increased number of studies have shown that emerging information modeling (IM) such as building information modeling (BIM), landscape information modeling (LIM), and city information modeling (CIM) coupled with blockchain (BC) functionalities have the potential to enhance designers’ HTD by considering important design elements, namely design variables, design knowledge, and design decision. It can also address challenges during the design process, such as design changes, conflicts in design requirements, the lack of design evaluation tools and frameworks, and incomplete design information. Therefore, this paper aims to develop a conceptual BC enhanced IM for HTD (BC-HTD) framework that addresses the challenges in the HTD and promotes health and well-being. The structure of BC-HTD framework is twofold: (1) a conceptual high-level framework comprising three levels: user; system; and information, (2) a conceptual low-level framework of detailed content at the system level, which has been constructed using a mixed quantitative and qualitative method of literature analysis, and validated via a pre-interview questionnaire survey and follow-up interviews with industry experts and academics. This paper analyzes the process of BC enhanced HTD and the knowledge management of HTD to aid design decisions in managing design information. This paper is the first attempt to apply the advantages of BC enabled IM to enhance the HTD process. The results of this study can foster and propel new research pathways and knowledge on the value of design in the form of non-fungible token (NFT) based on the extended advantages of BC in the field of design, which can fully mobilize the healing and therapeutic behaviors of designers and the advantage potential of HTD to promote health, and realize the vision of Health Metaverse in the context of sustainable development.
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Koebe P, Bohnet-Joschko S. The Impact of Digital Transformation on Inpatient Care: A Mixed Design Study (Preprint). JMIR Public Health Surveill 2022; 9:e40622. [PMID: 37083473 PMCID: PMC10163407 DOI: 10.2196/40622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 01/13/2023] [Accepted: 02/07/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND In the context of the digital transformation of all areas of society, health care providers are also under pressure to change. New technologies and a change in patients' self-perception and health awareness require rethinking the provision of health care services. New technologies and the extensive use of data can change provision processes, optimize them, or replace them with new services. The inpatient sector, which accounts for a particularly large share of health care spending, plays a major role in this regard. OBJECTIVE This study examined the influences of current trends in digitization on inpatient service delivery. METHODS We conducted a scoping review. This was applied to identify the international trends in digital transformation as they relate to hospitals. Future trends were considered from different perspectives. Using the defined inclusion criteria, international peer-reviewed articles published between 2016 and 2021 were selected. The extracted core trends were then contextualized for the German hospital sector with 12 experts. RESULTS We included 44 articles in the literature analysis. From these, 8 core trends could be deduced. A heuristic impact model of the trends was derived from the data obtained and the experts' assessments. This model provides a development corridor for the interaction of the trends with regard to technological intensity and supply quality. Trend accelerators and barriers were identified. CONCLUSIONS The impact analysis showed the dependencies of a successful digital transformation in the hospital sector. Although data interoperability is of particular importance for technological intensity, the changed self-image of patients was shown to be decisive with regard to the quality of care. We show that hospitals must find their role in new digitally driven ecosystems, adapt their business models to customer expectations, and use up-to-date information and communications technologies.
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Affiliation(s)
- Philipp Koebe
- Faculty of Management, Economics and Society, Witten/Herdecke University, Witten, Germany
| | - Sabine Bohnet-Joschko
- Faculty of Management, Economics and Society, Witten/Herdecke University, Witten, Germany
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10
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Xie Y, Lu L, Gao F, He SJ, Zhao HJ, Fang Y, Yang JM, An Y, Ye ZW, Dong Z. Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare. Curr Med Sci 2021; 41:1123-1133. [PMID: 34950987 PMCID: PMC8702375 DOI: 10.1007/s11596-021-2485-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 12/03/2021] [Indexed: 12/19/2022]
Abstract
Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the "Smart Healthcare" era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient's physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.
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Affiliation(s)
- Yi Xie
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Lin Lu
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Fei Gao
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Shuang-Jiang He
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Hui-Juan Zhao
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Ying Fang
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Jia-Ming Yang
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Ying An
- Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Wuhan Fourth Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430032, China
| | - Zhe-Wei Ye
- Department of Orthopedic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Laboratory of Intelligent Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Zhe Dong
- School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
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11
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Duan YY, Liu PR, Huo TT, Liu SX, Ye S, Ye ZW. Application and Development of Intelligent Medicine in Traditional Chinese Medicine. Curr Med Sci 2021; 41:1116-1122. [PMID: 34881423 PMCID: PMC8654490 DOI: 10.1007/s11596-021-2483-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Indexed: 01/16/2023]
Abstract
As modern science and technology constantly progresses, the fields of artificial intelligence, mixed reality technology, remote technology, etc. have rapidly developed. Meanwhile, these technologies have been gradually applied to the medical field, leading to the development of intelligent medicine. What’s more, intelligent medicine has greatly promoted the development of traditional Chinese medicine (TCM), causing huge changes in the diagnosis of TCM ailments, remote treatment, teaching, etc. Therefore, there are both opportunities and challenges for inheriting and developing TCM. Herein, the related research progress of intelligent medicine in the TCM in China and abroad over the years is analyzed, with the purpose of introducing the present application status of intelligent medicine in TCM and providing reference for the inheritance and development of TCM in a new era.
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Affiliation(s)
- Yu-Yu Duan
- Hubei University of Chinese Medicine, Wuhan, 430072, China
| | - Peng-Ran Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Tong-Tong Huo
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Song-Xiang Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Song Ye
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, 430060, China.
| | - Zhe-Wei Ye
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
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