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Kamel Rahimi A, Pienaar O, Ghadimi M, Canfell OJ, Pole JD, Shrapnel S, van der Vegt AH, Sullivan C. Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers. J Med Internet Res 2024; 26:e49655. [PMID: 39094106 DOI: 10.2196/49655] [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: 08/11/2023] [Revised: 02/08/2024] [Accepted: 05/22/2024] [Indexed: 08/04/2024] Open
Abstract
BACKGROUND Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets. Integrating these algorithms with real-time EMR data is rare. There is a poor understanding of the current enablers and barriers to empower this shift from data set-based use to real-time implementation of AI in health systems. Exploring these factors holds promise for uncovering actionable insights toward the successful integration of AI into clinical workflows. OBJECTIVE The first objective was to conduct a systematic literature review to identify the evidence of enablers and barriers regarding the real-world implementation of AI in hospital settings. The second objective was to map the identified enablers and barriers to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS. METHODS The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were adhered to. PubMed, Scopus, Web of Science, and IEEE Xplore were searched for studies published between January 2010 and January 2022. Articles with case studies and guidelines on the implementation of AI analytics in hospital settings using EMR data were included. We excluded studies conducted in primary and community care settings. Quality assessment of the identified papers was conducted using the Mixed Methods Appraisal Tool and ADAPTE frameworks. We coded evidence from the included studies that related to enablers of and barriers to AI implementation. The findings were mapped to the 3-horizon framework to provide a road map for hospitals to integrate AI analytics. RESULTS Of the 1247 studies screened, 26 (2.09%) met the inclusion criteria. In total, 65% (17/26) of the studies implemented AI analytics for enhancing the care of hospitalized patients, whereas the remaining 35% (9/26) provided implementation guidelines. Of the final 26 papers, the quality of 21 (81%) was assessed as poor. A total of 28 enablers was identified; 8 (29%) were new in this study. A total of 18 barriers was identified; 5 (28%) were newly found. Most of these newly identified factors were related to information and technology. Actionable recommendations for the implementation of AI toward achieving an LHS were provided by mapping the findings to a 3-horizon framework. CONCLUSIONS Significant issues exist in implementing AI in health care. Shifting from validating data sets to working with live data is challenging. This review incorporated the identified enablers and barriers into a 3-horizon framework, offering actionable recommendations for implementing AI analytics to achieve an LHS. The findings of this study can assist hospitals in steering their strategic planning toward successful adoption of AI.
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Affiliation(s)
- Amir Kamel Rahimi
- Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- Digital Health Cooperative Research Centre, Australian Government, Sydney, Australia
| | - Oliver Pienaar
- The School of Mathematics and Physics, The University of Queensland, Brisbane, Australia
| | - Moji Ghadimi
- The School of Mathematics and Physics, The University of Queensland, Brisbane, Australia
| | - Oliver J Canfell
- Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- Digital Health Cooperative Research Centre, Australian Government, Sydney, Australia
- Business School, The University of Queensland, Brisbane, Australia
- Department of Nutritional Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom
| | - Jason D Pole
- Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- Dalla Lana School of Public Health, The University of Toronto, Toronto, ON, Canada
- ICES, Toronto, ON, Canada
| | - Sally Shrapnel
- Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- The School of Mathematics and Physics, The University of Queensland, Brisbane, Australia
| | - Anton H van der Vegt
- Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Clair Sullivan
- Queensland Digital Health Centre, Faculty of Medicine, The University of Queensland, Brisbane, Australia
- Metro North Hospital and Health Service, Department of Health, Queensland Government, Brisbane, Australia
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Farhat H, Makhlouf A, Gangaram P, El Aifa K, Howland I, Babay Ep Rekik F, Abid C, Khenissi MC, Castle N, Al-Shaikh L, Khadhraoui M, Gargouri I, Laughton J, Alinier G. Predictive modelling of transport decisions and resources optimisation in pre-hospital setting using machine learning techniques. PLoS One 2024; 19:e0301472. [PMID: 38701064 PMCID: PMC11068197 DOI: 10.1371/journal.pone.0301472] [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: 10/12/2023] [Accepted: 03/11/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND The global evolution of pre-hospital care systems faces dynamic challenges, particularly in multinational settings. Machine learning (ML) techniques enable the exploration of deeply embedded data patterns for improved patient care and resource optimisation. This study's objective was to accurately predict cases that necessitated transportation versus those that did not, using ML techniques, thereby facilitating efficient resource allocation. METHODS ML algorithms were utilised to predict patient transport decisions in a Middle Eastern national pre-hospital emergency medical care provider. A comprehensive dataset comprising 93,712 emergency calls from the 999-call centre was analysed using R programming language. Demographic and clinical variables were incorporated to enhance predictive accuracy. Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost) algorithms were trained and validated. RESULTS All the trained algorithm models, particularly XGBoost (Accuracy = 83.1%), correctly predicted patients' transportation decisions. Further, they indicated statistically significant patterns that could be leveraged for targeted resource deployment. Moreover, the specificity rates were high; 97.96% in RF and 95.39% in XGBoost, minimising the incidence of incorrectly identified "Transported" cases (False Positive). CONCLUSION The study identified the transformative potential of ML algorithms in enhancing the quality of pre-hospital care in Qatar. The high predictive accuracy of the employed models suggested actionable avenues for day and time-specific resource planning and patient triaging, thereby having potential to contribute to pre-hospital quality, safety, and value improvement. These findings pave the way for more nuanced, data-driven quality improvement interventions with significant implications for future operational strategies.
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Affiliation(s)
- Hassan Farhat
- Ambulance Service, Hamad Medical Corporation, Doha, Qatar
- Faculty of Sciences, University of Sfax, Sfax, Tunisia
- Faculty of Medicine “Ibn El Jazzar”, University of Sousse, Sousse, Tunisia
| | - Ahmed Makhlouf
- Ambulance Service, Hamad Medical Corporation, Doha, Qatar
- College of Engineering, Qatar University, Doha, Qatar
| | - Padarath Gangaram
- Faculty of Health Sciences, Durban University of Technology, Durban, South Africa
| | | | - Ian Howland
- Ambulance Service, Hamad Medical Corporation, Doha, Qatar
| | | | - Cyrine Abid
- Laboratory of Screening Cellular and Molecular Process, Centre of Biotechnology of Sfax, University of Sfax, Sfax, Tunisia
| | | | | | - Loua Al-Shaikh
- Ambulance Service, Hamad Medical Corporation, Doha, Qatar
| | - Moncef Khadhraoui
- Higher Institute of Biotechnology, University of Sfax, Sfax, Tunisia
| | - Imed Gargouri
- Faculty of Medicine, University of Sfax, Sfax, Tunisia
| | - James Laughton
- Ambulance Service, Hamad Medical Corporation, Doha, Qatar
| | - Guillaume Alinier
- Ambulance Service, Hamad Medical Corporation, Doha, Qatar
- University of Hertfordshire, Hatfield, United Kingdom
- Weill Cornell Medicine-Qatar, Doha, Qatar
- Northumbria University, Newcastle upon Tyne, United Kingdom
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Mickley JP, Grove AF, Rouzrokh P, Yang L, Larson AN, Sanchez-Sotello J, Maradit Kremers H, Wyles CC. A Stepwise Approach to Analyzing Musculoskeletal Imaging Data With Artificial Intelligence. Arthritis Care Res (Hoboken) 2024; 76:590-599. [PMID: 37849415 DOI: 10.1002/acr.25260] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/27/2023] [Accepted: 10/13/2023] [Indexed: 10/19/2023]
Abstract
The digitization of medical records and expanding electronic health records has created an era of "Big Data" with an abundance of available information ranging from clinical notes to imaging studies. In the field of rheumatology, medical imaging is used to guide both diagnosis and treatment of a wide variety of rheumatic conditions. Although there is an abundance of data to analyze, traditional methods of image analysis are human resource intensive. Fortunately, the growth of artificial intelligence (AI) may be a solution to handle large datasets. In particular, computer vision is a field within AI that analyzes images and extracts information. Computer vision has impressive capabilities and can be applied to rheumatologic conditions, necessitating a need to understand how computer vision works. In this article, we provide an overview of AI in rheumatology and conclude with a five step process to plan and conduct research in the field of computer vision. The five steps include (1) project definition, (2) data handling, (3) model development, (4) performance evaluation, and (5) deployment into clinical care.
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Saeed HS, Fergie M, Mey K, West N, Bille M, Caye-Thomasen P, Nash R, Saeed SR, Stivaros SM, Black G, Bruce IA. Enlarged Vestibular Aqueduct and Associated Inner Ear Malformations: Hearing Loss Prognostic Factors and Data Modeling from an International Cohort. J Int Adv Otol 2023; 19:454-460. [PMID: 38088316 PMCID: PMC10765208 DOI: 10.5152/iao.2023.231044] [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: 01/11/2023] [Accepted: 08/29/2023] [Indexed: 12/18/2023] Open
Abstract
ACKGROUND There is a need to operationalize existing clinical data to support precision medicine in progressive hearing loss (HL). By utilizing enlarged vestibular aqueduct (EVA) and its associated inner ear abnormalities as an exemplar, we model data from a large international cohort, confirm prognostic factors for HL, and explore the potential to generate a prediction model to optimize current management paradigms. METHODS An international retrospective cohort study. Regression analyses were utilized to model frequency-specific HL and identify prognostic factors for baseline average HL severity and progression. Elastic-net regression and machine learning (ML) techniques were utilized to predict future average HL progression based upon routinely measurable clinical, genetic, and radiological data. RESULTS Higher frequencies of hearing were lost more severely. Prognostic factors for HL were the presence of incomplete partition type 2 (coefficient 12.95 dB, P=.011, 95% CI 3.0-22 dB) and presence of sac signal heterogeneity (P=.009, 95% CI 0.062-0.429) on magnetic resonance imaging. Elastic-net regression outperformed the ML algorithms (R2 0.32, mean absolute error 11.05 dB) with coefficients for baseline average hearing level and the presence of sac heterogeneity contributing the most to prediction outcomes. CONCLUSION Incomplete partition type 2 and endolymphatic sac signal heterogeneity phenotypes should be monitored closely for hearing deterioration and need for early audiological rehabilitation/cochlear implant. Preliminary prediction models have been generated using routinely collected health data in EVA. This study showcases how international collaborative research can use exemplar techniques to improve precision medicine in relatively rare disease entities.
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Affiliation(s)
- Haroon S. Saeed
- Royal Manchester Children’s Hospital, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Martin Fergie
- Division of Informatics, Imaging and Data Sciences, University of Manchester, University of Manchester, School of Biological Sciences, Manchester, UK
| | - Kristianna Mey
- Department of Otorhinolaryngology, Head and Neck Surgery, and Audiology, Copenhagen Hearing and Balance Center (CHBC), Copenhagen University Hospital, Copenhagen, Denmark
| | - Niels West
- Department of Otorhinolaryngology, Head and Neck Surgery, and Audiology, Copenhagen Hearing and Balance Center (CHBC), Copenhagen University Hospital, Copenhagen, Denmark
| | - Michael Bille
- Department of Otorhinolaryngology, Head and Neck Surgery, and Audiology, Copenhagen Hearing and Balance Center (CHBC), Copenhagen University Hospital, Copenhagen, Denmark
| | - Per Caye-Thomasen
- Department of Otorhinolaryngology, Head and Neck Surgery, and Audiology, Copenhagen Hearing and Balance Center (CHBC), Copenhagen University Hospital, Copenhagen, Denmark
- University of Copenhagen, Faculty of Health and Medical Sciences, Copenhagen, Denmark
| | - Robert Nash
- Department of Paediatric Otolaryngology, Great Ormond Street Hospital for Children, NHS Foundation Trust, London, UK
| | - Shakeel R. Saeed
- The Royal National ENT and Eastman Dental Hospitals, University College London Hospitals NHS Foundation Trust, London, UK
- University College London Ear Institute, London, UK
| | - Stavros M. Stivaros
- Department of Paediatric Radiology, Royal Manchester Children’s Hospital, Manchester, UK
| | - Graeme Black
- Manchester Centre for Genomic Medicine, Manchester Academic Health Sciences Centre, Manchester University NHS Foundation Trust, Manchester, UK
| | - Iain A. Bruce
- Royal Manchester Children’s Hospital, Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Department of Infection, Immunity and Respiratory Medicine, University of Manchester, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester, UK
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Allaume P, Rabilloud N, Turlin B, Bardou-Jacquet E, Loréal O, Calderaro J, Khene ZE, Acosta O, De Crevoisier R, Rioux-Leclercq N, Pecot T, Kammerer-Jacquet SF. Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review. Diagnostics (Basel) 2023; 13:diagnostics13101799. [PMID: 37238283 DOI: 10.3390/diagnostics13101799] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/04/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field. OBJECTIVE The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields. RESULTS 42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias. CONCLUSIONS DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool.
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Affiliation(s)
- Pierre Allaume
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Noémie Rabilloud
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
| | - Bruno Turlin
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
- Research Unit n°UMR1341 NuMeCan-Nutrition, Métabolismes et Cancer, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Edouard Bardou-Jacquet
- Research Unit n°UMR1341 NuMeCan-Nutrition, Métabolismes et Cancer, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
- Department of Liver Diseases CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
| | - Olivier Loréal
- Research Unit n°UMR1341 NuMeCan-Nutrition, Métabolismes et Cancer, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Julien Calderaro
- Assistance Publique-Hôpitaux de Paris, Department of Pathology Henri Mondor, 94000 Créteil, France
- INSERM U955, Team Pathophysiology and Therapy of Chronic Viral Hepatitis and Related Cancers, 94000 Créteil, France
| | - Zine-Eddine Khene
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
- Department of Urology, CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Oscar Acosta
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
| | - Renaud De Crevoisier
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
- Department of Radiotherapy, Centre Eugène Marquis, 35033 Rennes, France
| | - Nathalie Rioux-Leclercq
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
| | - Thierry Pecot
- Biosit Platform UAR 3480 CNRS US18 INSERM U955, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
| | - Solène-Florence Kammerer-Jacquet
- Department of Pathology CHU de Rennes, Rennes 1 University, Pontchaillou Hospital, 2 rue Henri Le Guilloux, CEDEX 09, 35033 Rennes, France
- Impact TEAM, Laboratoire Traitement du Signal et de l'Image (LTSI) INSERM, Rennes 1 University, Pontchaillou Hospital, 35033 Rennes, France
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Martínez-Sellés M, Marina-Breysse M. Current and Future Use of Artificial Intelligence in Electrocardiography. J Cardiovasc Dev Dis 2023; 10:jcdd10040175. [PMID: 37103054 PMCID: PMC10145690 DOI: 10.3390/jcdd10040175] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 04/14/2023] [Accepted: 04/16/2023] [Indexed: 04/28/2023] Open
Abstract
Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed.
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Affiliation(s)
- Manuel Martínez-Sellés
- Cardiology Department, Hospital General Universitario Gregorio Marañón, Calle Doctor Esquerdo, 46, 28007 Madrid, Spain
- Centro de Investigación Biomédica en Red-Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Facultad de Ciencias de la Salud, Universidad Europea, Villaviciosa de Odón, 28670 Madrid, Spain
- Facultad de Medicina, Universidad Complutense, 28040 Madrid, Spain
| | - Manuel Marina-Breysse
- Centro de Investigación Biomédica en Red-Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, 28029 Madrid, Spain
- IDOVEN Research, 28013 Madrid, Spain
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Myocardial Pathophysiology Area, 28029 Madrid, Spain
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Stoumpos AI, Kitsios F, Talias MA. Digital Transformation in Healthcare: Technology Acceptance and Its Applications. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3407. [PMID: 36834105 PMCID: PMC9963556 DOI: 10.3390/ijerph20043407] [Citation(s) in RCA: 54] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 02/08/2023] [Accepted: 02/10/2023] [Indexed: 05/27/2023]
Abstract
Technological innovation has become an integral aspect of our daily life, such as wearable and information technology, virtual reality and the Internet of Things which have contributed to transforming healthcare business and operations. Patients will now have a broader range and more mindful healthcare choices and experience a new era of healthcare with a patient-centric culture. Digital transformation determines personal and institutional health care. This paper aims to analyse the changes taking place in the field of healthcare due to digital transformation. For this purpose, a systematic bibliographic review is performed, utilising Scopus, Science Direct and PubMed databases from 2008 to 2021. Our methodology is based on the approach by Wester and Watson, which classify the related articles based on a concept-centric method and an ad hoc classification system which identify the categories used to describe areas of literature. The search was made during August 2022 and identified 5847 papers, of which 321 fulfilled the inclusion criteria for further process. Finally, by removing and adding additional studies, we ended with 287 articles grouped into five themes: information technology in health, the educational impact of e-health, the acceptance of e-health, telemedicine and security issues.
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Affiliation(s)
- Angelos I. Stoumpos
- Healthcare Management Postgraduate Program, Open University Cyprus, P.O. Box 12794, Nicosia 2252, Cyprus
| | - Fotis Kitsios
- Department of Applied Informatics, University of Macedonia, 156 Egnatia Street, GR54636 Thessaloniki, Greece
| | - Michael A. Talias
- Healthcare Management Postgraduate Program, Open University Cyprus, P.O. Box 12794, Nicosia 2252, Cyprus
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Basran PS, Appleby RB. The unmet potential of artificial intelligence in veterinary medicine. Am J Vet Res 2022; 83:385-392. [PMID: 35353711 DOI: 10.2460/ajvr.22.03.0038] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Veterinary medicine is a broad and growing discipline that includes topics such as companion animal health, population medicine and zoonotic diseases, and agriculture. In this article, we provide insight on how artificial intelligence works and how it is currently applied in veterinary medicine. We also discuss its potential in veterinary medicine. Given the rapid pace of research and commercial product developments in this area, the next several years will pose challenges to understanding, interpreting, and adopting this powerful and evolving technology. Artificial intelligence has the potential to enable veterinarians to perform tasks more efficiently while providing new insights for the management and treatment of disorders. It is our hope that this will translate to better quality of life for animals and those who care for them.
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Affiliation(s)
- Parminder S Basran
- Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY
| | - Ryan B Appleby
- Department of Clinical Studies, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
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Kellogg KC, Sadeh-Sharvit S. Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians. Front Psychiatry 2022; 13:990370. [PMID: 36147984 PMCID: PMC9485594 DOI: 10.3389/fpsyt.2022.990370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 08/19/2022] [Indexed: 11/13/2022] Open
Abstract
The integration of artificial intelligence (AI) technologies into mental health holds the promise of increasing patient access, engagement, and quality of care, and of improving clinician quality of work life. However, to date, studies of AI technologies in mental health have focused primarily on challenges that policymakers, clinical leaders, and data and computer scientists face, rather than on challenges that frontline mental health clinicians are likely to face as they attempt to integrate AI-based technologies into their everyday clinical practice. In this Perspective, we describe a framework for "pragmatic AI-augmentation" that addresses these issues by describing three categories of emerging AI-based mental health technologies which frontline clinicians can leverage in their clinical practice-automation, engagement, and clinical decision support technologies. We elaborate the potential benefits offered by these technologies, the likely day-to-day challenges they may raise for mental health clinicians, and some solutions that clinical leaders and technology developers can use to address these challenges, based on emerging experience with the integration of AI technologies into clinician daily practice in other healthcare disciplines.
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Affiliation(s)
- Katherine C Kellogg
- Department of Work and Organization Studies, MIT Sloan School of Management, Cambridge, MA, United States
| | - Shiri Sadeh-Sharvit
- Eleos Health, Cambridge, MA, United States.,Center for M2Health, Palo Alto University, Palo Alto, CA, United States
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