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Moy S, Irannejad M, Manning SJ, Farahani M, Ahmed Y, Gao E, Prabhune R, Lorenz S, Mirza R, Klinger C. Patient Perspectives on the Use of Artificial Intelligence in Health Care: A Scoping Review. J Patient Cent Res Rev 2024; 11:51-62. [PMID: 38596349 PMCID: PMC11000703 DOI: 10.17294/2330-0698.2029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024] Open
Abstract
Purpose Artificial intelligence (AI) technology is being rapidly adopted into many different branches of medicine. Although research has started to highlight the impact of AI on health care, the focus on patient perspectives of AI is scarce. This scoping review aimed to explore the literature on adult patients' perspectives on the use of an array of AI technologies in the health care setting for design and deployment. Methods This scoping review followed Arksey and O'Malley's framework and Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Scoping Reviews (PRISMA-ScR). To evaluate patient perspectives, we conducted a comprehensive literature search using eight interdisciplinary electronic databases, including grey literature. Articles published from 2015 to 2022 that focused on patient views regarding AI technology in health care were included. Thematic analysis was performed on the extracted articles. Results Of the 10,571 imported studies, 37 articles were included and extracted. From the 33 peer-reviewed and 4 grey literature articles, the following themes on AI emerged: (i) Patient attitudes, (ii) Influences on patient attitudes, (iii) Considerations for design, and (iv) Considerations for use. Conclusions Patients are key stakeholders essential to the uptake of AI in health care. The findings indicate that patients' needs and expectations are not fully considered in the application of AI in health care. Therefore, there is a need for patient voices in the development of AI in health care.
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Affiliation(s)
- Sally Moy
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Mona Irannejad
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | | | - Mehrdad Farahani
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Yomna Ahmed
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Ellis Gao
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Radhika Prabhune
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Suzan Lorenz
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Raza Mirza
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Christopher Klinger
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- National Initiative for the Care of the Elderly, Toronto, Canada
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Mullie L, Afilalo J, Archambault P, Bouchakri R, Brown K, Buckeridge DL, Cavayas YA, Turgeon AF, Martineau D, Lamontagne F, Lebrasseur M, Lemieux R, Li J, Sauthier M, St-Onge P, Tang A, Witteman W, Chassé M. CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data. J Am Med Inform Assoc 2024; 31:651-665. [PMID: 38128123 PMCID: PMC10873779 DOI: 10.1093/jamia/ocad235] [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: 06/26/2023] [Revised: 10/28/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023] Open
Abstract
OBJECTIVES Distributed computations facilitate multi-institutional data analysis while avoiding the costs and complexity of data pooling. Existing approaches lack crucial features, such as built-in medical standards and terminologies, no-code data visualizations, explicit disclosure control mechanisms, and support for basic statistical computations, in addition to gradient-based optimization capabilities. MATERIALS AND METHODS We describe the development of the Collaborative Data Analysis (CODA) platform, and the design choices undertaken to address the key needs identified during our survey of stakeholders. We use a public dataset (MIMIC-IV) to demonstrate end-to-end multi-modal FL using CODA. We assessed the technical feasibility of deploying the CODA platform at 9 hospitals in Canada, describe implementation challenges, and evaluate its scalability on large patient populations. RESULTS The CODA platform was designed, developed, and deployed between January 2020 and January 2023. Software code, documentation, and technical documents were released under an open-source license. Multi-modal federated averaging is illustrated using the MIMIC-IV and MIMIC-CXR datasets. To date, 8 out of the 9 participating sites have successfully deployed the platform, with a total enrolment of >1M patients. Mapping data from legacy systems to FHIR was the biggest barrier to implementation. DISCUSSION AND CONCLUSION The CODA platform was developed and successfully deployed in a public healthcare setting in Canada, with heterogeneous information technology systems and capabilities. Ongoing efforts will use the platform to develop and prospectively validate models for risk assessment, proactive monitoring, and resource usage. Further work will also make tools available to facilitate migration from legacy formats to FHIR and DICOM.
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Affiliation(s)
- Louis Mullie
- Department of Medicine, Centre Hospitalier de l'Université de Montréal, Montréal, H2X 3E4, Canada
- Faculty of Medicine, Université de Montréal, Montréal, H3C 3J7, Canada
- Mila Quebec Artificial Intelligence Institute, Montréal, H2S 3H1, Canada
| | - Jonathan Afilalo
- Department of Medicine, Jewish General Hospital, Montréal, H3T 1E4, Canada
| | - Patrick Archambault
- Department of Emergency Medicine and Family Medicine, Université Laval, Québec, G1V 0A6, Canada
- Department of Anesthesiology and Critical Care Medicine, Université Laval, Québec, G1V 0A6, Canada
- Centre de Recherche Intégré pour un Système Apprenant en santé et Services Sociaux, Centre intégré de santé et de Services Sociaux de Chaudière-Appalaches, Lévis, G6V 3Z1, Canada
| | - Rima Bouchakri
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - Kip Brown
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - David L Buckeridge
- Mila Quebec Artificial Intelligence Institute, Montréal, H2S 3H1, Canada
- Department of Epidemiology and Biostatistics, School of Population and Global Health, McGill University Health Centre, Montréal, H3A 1G1, Canada
| | | | - Alexis F Turgeon
- Department of Anesthesiology and Critical Care Medicine, Université Laval, Québec, G1V 0A6, Canada
- Centre de recherche du CHU de Québec-Université Laval, Université Laval, Québec, G1V 4G2, Canada
| | - Denis Martineau
- Centre de recherche du CHU de Québec-Université Laval, Université Laval, Québec, G1V 4G2, Canada
| | - François Lamontagne
- Centre de recherche du CHUS, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, J1G 2E8, Canada
| | - Martine Lebrasseur
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - Renald Lemieux
- Centre de recherche du CHUS, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, J1G 2E8, Canada
| | - Jeffrey Li
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - Michaël Sauthier
- Faculty of Medicine, Université de Montréal, Montréal, H3C 3J7, Canada
- Department of Pediatrics, Université de Montréal and CHU Sainte-Justine Research Centre, Montréal, H3C 3J7, Canada
| | - Pascal St-Onge
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Université de Montréal, Montréal, H2X 0A9, Canada
| | - An Tang
- Faculty of Medicine, Université de Montréal, Montréal, H3C 3J7, Canada
- Department of Radiology, Centre Hospitalier de l’Université de Montréal, Montréal, H2X 3E4, Canada
| | - William Witteman
- Centre de Recherche Intégré pour un Système Apprenant en santé et Services Sociaux, Centre intégré de santé et de Services Sociaux de Chaudière-Appalaches, Lévis, G6V 3Z1, Canada
| | - Michaël Chassé
- Department of Medicine, Centre Hospitalier de l'Université de Montréal, Montréal, H2X 3E4, Canada
- Faculty of Medicine, Université de Montréal, Montréal, H3C 3J7, Canada
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Al Kuwaiti A, Nazer K, Al-Reedy A, Al-Shehri S, Al-Muhanna A, Subbarayalu AV, Al Muhanna D, Al-Muhanna FA. A Review of the Role of Artificial Intelligence in Healthcare. J Pers Med 2023; 13:951. [PMID: 37373940 PMCID: PMC10301994 DOI: 10.3390/jpm13060951] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 06/29/2023] Open
Abstract
Artificial intelligence (AI) applications have transformed healthcare. This study is based on a general literature review uncovering the role of AI in healthcare and focuses on the following key aspects: (i) medical imaging and diagnostics, (ii) virtual patient care, (iii) medical research and drug discovery, (iv) patient engagement and compliance, (v) rehabilitation, and (vi) other administrative applications. The impact of AI is observed in detecting clinical conditions in medical imaging and diagnostic services, controlling the outbreak of coronavirus disease 2019 (COVID-19) with early diagnosis, providing virtual patient care using AI-powered tools, managing electronic health records, augmenting patient engagement and compliance with the treatment plan, reducing the administrative workload of healthcare professionals (HCPs), discovering new drugs and vaccines, spotting medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. Nevertheless, this science pitch meets several technical, ethical, and social challenges, including privacy, safety, the right to decide and try, costs, information and consent, access, and efficacy, while integrating AI into healthcare. The governance of AI applications is crucial for patient safety and accountability and for raising HCPs' belief in enhancing acceptance and boosting significant health consequences. Effective governance is a prerequisite to precisely address regulatory, ethical, and trust issues while advancing the acceptance and implementation of AI. Since COVID-19 hit the global health system, the concept of AI has created a revolution in healthcare, and such an uprising could be another step forward to meet future healthcare needs.
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Affiliation(s)
- Ahmed Al Kuwaiti
- Department of Dental Education, College of Dentistry, Deanship of Quality and Academic Accreditation, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Khalid Nazer
- Department of Information and Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
- Health Information Department, King Fahad hospital of the University, Al-Khobar 31952, Saudi Arabia
| | - Abdullah Al-Reedy
- Department of Information and Technology, Family and Community Medicine Department, Family and Community Medicine Centre, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Shaher Al-Shehri
- Faculty of Medicine, Family and Community Medicine Department, Family and Community Medicine Centre, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Afnan Al-Muhanna
- Breast Imaging Division, Department of Radiology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
- Radiology Department, King Fahad hospital of the University, Al-Khobar 31952, Saudi Arabia
| | - Arun Vijay Subbarayalu
- Quality Studies and Research Unit, Vice Deanship of Quality, Deanship of Quality and Academic Accreditation, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Dhoha Al Muhanna
- NDirectorate of Quality and Patient Safety, Family and Community Medicine Center, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Fahad A. Al-Muhanna
- Nephrology Division, Department of Internal Medicine, Faculty of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
- Medicine Department, King Fahad hospital of the University, Al-Khobar 31952, Saudi Arabia
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Yamga E, Mullie L, Durand M, Cadrin-Chenevert A, Tang A, Montagnon E, Chartrand-Lefebvre C, Chassé M. Interpretable clinical phenotypes among patients hospitalized with COVID-19 using cluster analysis. Front Digit Health 2023; 5:1142822. [PMID: 37114183 PMCID: PMC10128042 DOI: 10.3389/fdgth.2023.1142822] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 03/13/2023] [Indexed: 04/29/2023] Open
Abstract
Background Multiple clinical phenotypes have been proposed for coronavirus disease (COVID-19), but few have used multimodal data. Using clinical and imaging data, we aimed to identify distinct clinical phenotypes in patients admitted with COVID-19 and to assess their clinical outcomes. Our secondary objective was to demonstrate the clinical applicability of this method by developing an interpretable model for phenotype assignment. Methods We analyzed data from 547 patients hospitalized with COVID-19 at a Canadian academic hospital. We processed the data by applying a factor analysis of mixed data (FAMD) and compared four clustering algorithms: k-means, partitioning around medoids (PAM), and divisive and agglomerative hierarchical clustering. We used imaging data and 34 clinical variables collected within the first 24 h of admission to train our algorithm. We conducted a survival analysis to compare the clinical outcomes across phenotypes. With the data split into training and validation sets (75/25 ratio), we developed a decision-tree-based model to facilitate the interpretation and assignment of the observed phenotypes. Results Agglomerative hierarchical clustering was the most robust algorithm. We identified three clinical phenotypes: 79 patients (14%) in Cluster 1, 275 patients (50%) in Cluster 2, and 203 (37%) in Cluster 3. Cluster 2 and Cluster 3 were both characterized by a low-risk respiratory and inflammatory profile but differed in terms of demographics. Compared with Cluster 3, Cluster 2 comprised older patients with more comorbidities. Cluster 1 represented the group with the most severe clinical presentation, as inferred by the highest rate of hypoxemia and the highest radiological burden. Intensive care unit (ICU) admission and mechanical ventilation risks were the highest in Cluster 1. Using only two to four decision rules, the classification and regression tree (CART) phenotype assignment model achieved an AUC of 84% (81.5-86.5%, 95 CI) on the validation set. Conclusions We conducted a multidimensional phenotypic analysis of adult inpatients with COVID-19 and identified three distinct phenotypes associated with different clinical outcomes. We also demonstrated the clinical usability of this approach, as phenotypes can be accurately assigned using a simple decision tree. Further research is still needed to properly incorporate these phenotypes in the management of patients with COVID-19.
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Affiliation(s)
- Eric Yamga
- Department of Medicine, Centre Hospitalier de l’Université de Montréal (CHUM), Montréal, QC, Canada
| | - Louis Mullie
- Department of Medicine, Centre Hospitalier de l’Université de Montréal (CHUM), Montréal, QC, Canada
| | - Madeleine Durand
- Department of Medicine, Centre Hospitalier de l’Université de Montréal (CHUM), Montréal, QC, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
| | | | - An Tang
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
- Department of Radiology and Nuclear Medicine, Centre Hospitalier de l’Université de Montréal (CHUM), Montréal, QC, Canada
| | - Emmanuel Montagnon
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
| | - Carl Chartrand-Lefebvre
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
- Department of Radiology and Nuclear Medicine, Centre Hospitalier de l’Université de Montréal (CHUM), Montréal, QC, Canada
| | - Michaël Chassé
- Department of Medicine, Centre Hospitalier de l’Université de Montréal (CHUM), Montréal, QC, Canada
- Centre de Recherche du Centre Hospitalier de l'Université de Montréal (CRCHUM), Montréal, QC, Canada
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Mann S, Berdahl CT, Baker L, Girosi F. Artificial intelligence applications used in the clinical response to COVID-19: A scoping review. PLOS DIGITAL HEALTH 2022; 1:e0000132. [PMID: 36812557 PMCID: PMC9931281 DOI: 10.1371/journal.pdig.0000132] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 09/20/2022] [Indexed: 11/05/2022]
Abstract
Research into using artificial intelligence (AI) in health care is growing and several observers predicted that AI would play a key role in the clinical response to the COVID-19. Many AI models have been proposed though previous reviews have identified only a few applications used in clinical practice. In this study, we aim to (1) identify and characterize AI applications used in the clinical response to COVID-19; (2) examine the timing, location, and extent of their use; (3) examine how they relate to pre-pandemic applications and the U.S. regulatory approval process; and (4) characterize the evidence that is available to support their use. We searched academic and grey literature sources to identify 66 AI applications that performed a wide range of diagnostic, prognostic, and triage functions in the clinical response to COVID-19. Many were deployed early in the pandemic and most were used in the U.S., other high-income countries, or China. While some applications were used to care for hundreds of thousands of patients, others were used to an unknown or limited extent. We found studies supporting the use of 39 applications, though few of these were independent evaluations and we found no clinical trials evaluating any application's impact on patient health. Due to limited evidence, it is impossible to determine the extent to which the clinical use of AI in the pandemic response has benefited patients overall. Further research is needed, particularly independent evaluations on AI application performance and health impacts in real-world care settings.
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Affiliation(s)
- Sean Mann
- RAND Corporation, Santa Monica, California, United States of America,* E-mail:
| | - Carl T. Berdahl
- RAND Corporation, Santa Monica, California, United States of America,Departments of Medicine and Emergency Medicine, Cedars-Sinai Medical Center, Los Angeles, California, United States of America
| | - Lawrence Baker
- RAND Corporation, Santa Monica, California, United States of America
| | - Federico Girosi
- RAND Corporation, Santa Monica, California, United States of America
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Luo MH, Huang DL, Luo JC, Su Y, Li JK, Tu GW, Luo Z. Data science in the intensive care unit. World J Crit Care Med 2022; 11:311-316. [PMID: 36160936 PMCID: PMC9483002 DOI: 10.5492/wjccm.v11.i5.311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/03/2022] [Accepted: 07/17/2022] [Indexed: 02/05/2023] Open
Abstract
In this editorial, we comment on the current development and deployment of data science in intensive care units (ICUs). Data in ICUs can be classified into qualitative and quantitative data with different technologies needed to translate and interpret them. Data science, in the form of artificial intelligence (AI), should find the right interaction between physicians, data and algorithm. For individual patients and physicians, sepsis and mechanical ventilation have been two important aspects where AI has been extensively studied. However, major risks of bias, lack of generalizability and poor clinical values remain. AI deployment in the ICUs should be emphasized more to facilitate AI development. For ICU management, AI has a huge potential in transforming resource allocation. The coronavirus disease 2019 pandemic has given opportunities to establish such systems which should be investigated further. Ethical concerns must be addressed when designing such AI.
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Affiliation(s)
- Ming-Hao Luo
- Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Dan-Lei Huang
- Shanghai Medical College, Fudan University, Shanghai 200032, China
| | - Jing-Chao Luo
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Ying Su
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Jia-Kun Li
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Guo-Wei Tu
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Zhe Luo
- Department of Critical Care Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
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Brunet F, Malas K, Pomey MP. Reconnecting health through innovation. Healthc Manage Forum 2022; 35:344-348. [PMID: 35960988 DOI: 10.1177/08404704221114249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Learning health systems identify appropriate data to improve their performance and population health. The pandemic has shown that a proper response depends on using data from patients' needs, scientific research, hospital capacity, digital innovations, and stakeholder knowledge. Academic health centres play a role in data collection, information synthesis, and decision making supported by digital innovations. The results obtained by an academic centre and network in Quebec have demonstrated the value of integrating these elements during the pandemic and beyond.
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Affiliation(s)
- Fabrice Brunet
- 25443Centre hospitalier de l'Université de Montréal, Montreal, Quebec, Canada.,HEC Montréal, Montreal, Quebec, Canada
| | - Kathy Malas
- 25443Centre hospitalier de l'Université de Montréal, Montreal, Quebec, Canada.,HEC Montréal, Montreal, Quebec, Canada
| | - Marie-Pascale Pomey
- 25443Centre hospitalier de l'Université de Montréal, Montreal, Quebec, Canada.,5622Université de Montréal, Montreal, Quebec, Canada
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