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Verma AA, Trbovich P, Mamdani M, Shojania KG. Grand rounds in methodology: key considerations for implementing machine learning solutions in quality improvement initiatives. BMJ Qual Saf 2024; 33:121-131. [PMID: 38050138 DOI: 10.1136/bmjqs-2022-015713] [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: 04/10/2023] [Accepted: 11/04/2023] [Indexed: 12/06/2023]
Abstract
Machine learning (ML) solutions are increasingly entering healthcare. They are complex, sociotechnical systems that include data inputs, ML models, technical infrastructure and human interactions. They have promise for improving care across a wide range of clinical applications but if poorly implemented, they may disrupt clinical workflows, exacerbate inequities in care and harm patients. Many aspects of ML solutions are similar to other digital technologies, which have well-established approaches to implementation. However, ML applications present distinct implementation challenges, given that their predictions are often complex and difficult to understand, they can be influenced by biases in the data sets used to develop them, and their impacts on human behaviour are poorly understood. This manuscript summarises the current state of knowledge about implementing ML solutions in clinical care and offers practical guidance for implementation. We propose three overarching questions for potential users to consider when deploying ML solutions in clinical care: (1) Is a clinical or operational problem likely to be addressed by an ML solution? (2) How can an ML solution be evaluated to determine its readiness for deployment? (3) How can an ML solution be deployed and maintained optimally? The Quality Improvement community has an essential role to play in ensuring that ML solutions are translated into clinical practice safely, effectively, and ethically.
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Affiliation(s)
- Amol A Verma
- Unity Health Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
- Medicine, University of Toronto Faculty of Medicine, Toronto, Ontario, Canada
| | - Patricia Trbovich
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Centre for Quality Improvement and Patient Safety, Department of Medicine, University of Toronto, Toronto, ON, Canada
- North York General Hospital, Toronto, ON, Canada
| | - Muhammad Mamdani
- Unity Health Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Medicine, University of Toronto Faculty of Medicine, Toronto, Ontario, Canada
| | - Kaveh G Shojania
- Medicine, University of Toronto Faculty of Medicine, Toronto, Ontario, Canada
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada
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van der Vegt AH, Campbell V, Mitchell I, Malycha J, Simpson J, Flenady T, Flabouris A, Lane PJ, Mehta N, Kalke VR, Decoyna JA, Es’haghi N, Liu CH, Scott IA. Systematic review and longitudinal analysis of implementing Artificial Intelligence to predict clinical deterioration in adult hospitals: what is known and what remains uncertain. J Am Med Inform Assoc 2024; 31:509-524. [PMID: 37964688 PMCID: PMC10797271 DOI: 10.1093/jamia/ocad220] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/27/2023] [Accepted: 10/31/2023] [Indexed: 11/16/2023] Open
Abstract
OBJECTIVE To identify factors influencing implementation of machine learning algorithms (MLAs) that predict clinical deterioration in hospitalized adult patients and relate these to a validated implementation framework. MATERIALS AND METHODS A systematic review of studies of implemented or trialed real-time clinical deterioration prediction MLAs was undertaken, which identified: how MLA implementation was measured; impact of MLAs on clinical processes and patient outcomes; and barriers, enablers and uncertainties within the implementation process. Review findings were then mapped to the SALIENT end-to-end implementation framework to identify the implementation stages at which these factors applied. RESULTS Thirty-seven articles relating to 14 groups of MLAs were identified, each trialing or implementing a bespoke algorithm. One hundred and seven distinct implementation evaluation metrics were identified. Four groups reported decreased hospital mortality, 1 significantly. We identified 24 barriers, 40 enablers, and 14 uncertainties and mapped these to the 5 stages of the SALIENT implementation framework. DISCUSSION Algorithm performance across implementation stages decreased between in silico and trial stages. Silent plus pilot trial inclusion was associated with decreased mortality, as was the use of logistic regression algorithms that used less than 39 variables. Mitigation of alert fatigue via alert suppression and threshold configuration was commonly employed across groups. CONCLUSIONS : There is evidence that real-world implementation of clinical deterioration prediction MLAs may improve clinical outcomes. Various factors identified as influencing success or failure of implementation can be mapped to different stages of implementation, thereby providing useful and practical guidance for implementers.
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Affiliation(s)
- Anton H van der Vegt
- Centre for Health Services Research, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Victoria Campbell
- Intensive Care Unit, Sunshine Coast Hospital and Health Service, Birtynia, QLD 4575, Australia
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia
| | - Imogen Mitchell
- Office of Research and Education, Canberra Health Services, Canberra, ACT 2601, Australia
| | - James Malycha
- Department of Critical Care Medicine, The Queen Elizabeth Hospital, Woodville, SA 5011, Australia
| | - Joanna Simpson
- Eastern Health Intensive Care Services, Eastern Health, Box Hill, VIC 3128, Australia
| | - Tracy Flenady
- School of Nursing, Midwifery & Social Sciences, Central Queensland University, Rockhampton, QLD 4701, Australia
| | - Arthas Flabouris
- Intensive Care Department, Royal Adelaide Hospital, Adelaide, SA 5000, Australia
- Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia
| | - Paul J Lane
- Safety Quality & Innovation, The Prince Charles Hospital, Chermside, QLD 4032, Australia
| | - Naitik Mehta
- Patient Safety and Quality, Clinical Excellence Queensland, Brisbane, QLD 4001, Australia
| | - Vikrant R Kalke
- Patient Safety and Quality, Clinical Excellence Queensland, Brisbane, QLD 4001, Australia
| | - Jovie A Decoyna
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia
| | - Nicholas Es’haghi
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia
| | - Chun-Huei Liu
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia
| | - Ian A Scott
- Centre for Health Services Research, The University of Queensland, Brisbane, QLD 4102, Australia
- Department of Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, QLD 4102, Australia
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Aziz D, Maganti K, Yanamala N, Sengupta P. The Role of Artificial Intelligence in Echocardiography: A Clinical Update. Curr Cardiol Rep 2023; 25:1897-1907. [PMID: 38091196 DOI: 10.1007/s11886-023-02005-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/21/2023] [Indexed: 01/26/2024]
Abstract
PURPOSE OF REVIEW In echocardiography, there has been robust development of artificial intelligence (AI) tools for image recognition, automated measurements, image segmentation, and patient prognostication that has created a monumental shift in the study of AI and machine learning models. However, integrating these measurements into complex disease recognition and therapeutic interventions remains challenging. While the tools have been developed, there is a lack of evidence regarding implementing heterogeneous systems for guiding clinical decision-making and therapeutic action. RECENT FINDINGS Newer AI modalities have shown concrete positive data in terms of user-guided image acquisition and processing, precise determination of both basic and advanced quantitative echocardiographic features, and the potential to construct predictive models, all with the possibility of seamless integration into clinical decision support systems. AI in echocardiography is a powerful and ever-growing tool with the potential for revolutionary effects on the practice of cardiology. In this review article, we explore the growth of AI and its applications in echocardiography, along with clinical implications and the associated regulatory, legal, and ethical considerations.
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Affiliation(s)
- Daniel Aziz
- Department of Internal Medicine, Rutgers - Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Kameswari Maganti
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA
| | - Naveena Yanamala
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA
| | - Partho Sengupta
- Division of Cardiology, Rutgers - Robert Wood Johnson Medical School & University Hospital, 1 Robert Wood Johnson Place, New Brunswick, NJ, 08901, USA.
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