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Scott IA, van der Vegt A, Lane P, McPhail S, Magrabi F. Achieving large-scale clinician adoption of AI-enabled decision support. BMJ Health Care Inform 2024; 31:e100971. [PMID: 38816209 PMCID: PMC11141172 DOI: 10.1136/bmjhci-2023-100971] [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: 11/19/2023] [Accepted: 05/15/2024] [Indexed: 06/01/2024] Open
Abstract
Computerised decision support (CDS) tools enabled by artificial intelligence (AI) seek to enhance accuracy and efficiency of clinician decision-making at the point of care. Statistical models developed using machine learning (ML) underpin most current tools. However, despite thousands of models and hundreds of regulator-approved tools internationally, large-scale uptake into routine clinical practice has proved elusive. While underdeveloped system readiness and investment in AI/ML within Australia and perhaps other countries are impediments, clinician ambivalence towards adopting these tools at scale could be a major inhibitor. We propose a set of principles and several strategic enablers for obtaining broad clinician acceptance of AI/ML-enabled CDS tools.
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Affiliation(s)
- Ian A Scott
- Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, Queensland, Australia
- Centre for Health Services Research, The University of Queensland Faculty of Medicine and Biomedical Sciences, Brisbane, Queensland, Australia
| | - Anton van der Vegt
- Digital Health Centre, The University of Queensland Faculty of Medicine and Biomedical Sciences, Herston, Queensland, Australia
| | - Paul Lane
- Safety, Quality and Innovation, The Prince Charles Hospital, Brisbane, Queensland, Australia
| | - Steven McPhail
- Australian Centre for Health Services Innovation, Queensland University of Technology Faculty of Health, Brisbane, Queensland, Australia
| | - Farah Magrabi
- Macquarie University, Sydney, New South Wales, Australia
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2
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Wenderott K, Krups J, Luetkens JA, Weigl M. Radiologists' perspectives on the workflow integration of an artificial intelligence-based computer-aided detection system: A qualitative study. APPLIED ERGONOMICS 2024; 117:104243. [PMID: 38306741 DOI: 10.1016/j.apergo.2024.104243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 12/18/2023] [Accepted: 01/23/2024] [Indexed: 02/04/2024]
Abstract
In healthcare, artificial intelligence (AI) is expected to improve work processes, yet most research focuses on the technical features of AI rather than its real-world clinical implementation. To evaluate the implementation process of an AI-based computer-aided detection system (AI-CAD) for prostate MRI readings, we interviewed German radiologists in a pre-post design. We embedded our findings in the Model of Workflow Integration and the Technology Acceptance Model to analyze workflow effects, facilitators, and barriers. The most prominent barriers were: (i) a time delay in the work process, (ii) additional work steps to be taken, and (iii) an unstable performance of the AI-CAD. Most frequently named facilitators were (i) good self-organization, and (ii) good usability of the software. Our results underline the importance of a holistic approach to AI implementation considering the sociotechnical work system and provide valuable insights into key factors of the successful adoption of AI technologies in work systems.
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Affiliation(s)
- Katharina Wenderott
- Institute for Patient Safety, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.
| | - Jim Krups
- Institute for Patient Safety, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Julian A Luetkens
- Department of Diagnostic and Interventional Radiology, University Hospital Bonn, Germany; Quantitative Imaging Lab Bonn (QILaB), University Hospital Bonn, Germany
| | - Matthias Weigl
- Institute for Patient Safety, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
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3
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Lekadir K. A deep learning solution to detect left ventricular structural abnormalities with chest X-rays: towards trustworthy AI in cardiology. Eur Heart J 2024:ehad775. [PMID: 38527415 DOI: 10.1093/eurheartj/ehad775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/27/2024] Open
Affiliation(s)
- Karim Lekadir
- University of Barcelona, Department of Mathematics and Computer Science, Artificial Intelligence in Medicine Lab (BCN-AIM), Barcelona, Spain
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4
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Sibbald M, Zwaan L, Yilmaz Y, Lal S. Incorporating artificial intelligence in medical diagnosis: A case for an invisible and (un)disruptive approach. J Eval Clin Pract 2024; 30:3-8. [PMID: 35761764 DOI: 10.1111/jep.13730] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 12/30/2022]
Abstract
As big data becomes more publicly accessible, artificial intelligence (AI) is increasingly available and applicable to problems around clinical decision-making. Yet the adoption of AI technology in healthcare lags well behind other industries. The gap between what technology could do, and what technology is actually being used for is rapidly widening. While many solutions are proposed to address this gap, clinician resistance to the adoption of AI remains high. To aid with change, we propose facilitating clinician decisions through technology by seamlessly weaving what we call 'invisible AI' into existing clinician workflows, rather than sequencing new steps into clinical processes. We explore evidence from the change management and human factors literature to conceptualize a new approach to AI implementation in health organizations. We discuss challenges and provide recommendations for organizations to employ this strategy.
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Affiliation(s)
- Matt Sibbald
- Department of Medicine, McMaster Education Research Innovation and Theory (MERIT) Program, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Laura Zwaan
- Erasmus Medical Center, Institute of Medical Education Research Rotterdam (iMERR), Rotterdam, The Netherlands
| | - Yusuf Yilmaz
- McMaster Education Research Innovation and Theory (MERIT) Program, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
- Continuing Professional Development Office, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
- Department of Medical Education, Faculty of Medicine, Ege University, Izmir, Turkey
| | - Sarrah Lal
- Department of Medicine, Division of Innovation and Education, McMaster University, Hamilton, ON, Canada
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Hua D, Petrina N, Young N, Cho JG, Poon SK. Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A scoping review. Artif Intell Med 2024; 147:102698. [PMID: 38184343 DOI: 10.1016/j.artmed.2023.102698] [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: 03/01/2023] [Revised: 09/29/2023] [Accepted: 10/29/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND Artificial intelligence (AI) technology has the potential to transform medical practice within the medical imaging industry and materially improve productivity and patient outcomes. However, low acceptability of AI as a digital healthcare intervention among medical professionals threatens to undermine user uptake levels, hinder meaningful and optimal value-added engagement, and ultimately prevent these promising benefits from being realised. Understanding the factors underpinning AI acceptability will be vital for medical institutions to pinpoint areas of deficiency and improvement within their AI implementation strategies. This scoping review aims to survey the literature to provide a comprehensive summary of the key factors influencing AI acceptability among healthcare professionals in medical imaging domains and the different approaches which have been taken to investigate them. METHODS A systematic literature search was performed across five academic databases including Medline, Cochrane Library, Web of Science, Compendex, and Scopus from January 2013 to September 2023. This was done in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Overall, 31 articles were deemed appropriate for inclusion in the scoping review. RESULTS The literature has converged towards three overarching categories of factors underpinning AI acceptability including: user factors involving trust, system understanding, AI literacy, and technology receptiveness; system usage factors entailing value proposition, self-efficacy, burden, and workflow integration; and socio-organisational-cultural factors encompassing social influence, organisational readiness, ethicality, and perceived threat to professional identity. Yet, numerous studies have overlooked a meaningful subset of these factors that are integral to the use of medical AI systems such as the impact on clinical workflow practices, trust based on perceived risk and safety, and compatibility with the norms of medical professions. This is attributable to reliance on theoretical frameworks or ad-hoc approaches which do not explicitly account for healthcare-specific factors, the novelties of AI as software as a medical device (SaMD), and the nuances of human-AI interaction from the perspective of medical professionals rather than lay consumer or business end users. CONCLUSION This is the first scoping review to survey the health informatics literature around the key factors influencing the acceptability of AI as a digital healthcare intervention in medical imaging contexts. The factors identified in this review suggest that existing theoretical frameworks used to study AI acceptability need to be modified to better capture the nuances of AI deployment in healthcare contexts where the user is a healthcare professional influenced by expert knowledge and disciplinary norms. Increasing AI acceptability among medical professionals will critically require designing human-centred AI systems which go beyond high algorithmic performance to consider accessibility to users with varying degrees of AI literacy, clinical workflow practices, the institutional and deployment context, and the cultural, ethical, and safety norms of healthcare professions. As investment into AI for healthcare increases, it would be valuable to conduct a systematic review and meta-analysis of the causal contribution of these factors to achieving high levels of AI acceptability among medical professionals.
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Affiliation(s)
- David Hua
- School of Computer Science, The University of Sydney, Australia; Sydney Law School, The University of Sydney, Australia
| | - Neysa Petrina
- School of Computer Science, The University of Sydney, Australia
| | - Noel Young
- Sydney Medical School, The University of Sydney, Australia; Lumus Imaging, Australia
| | - Jin-Gun Cho
- Sydney Medical School, The University of Sydney, Australia; Western Sydney Local Health District, Australia; Lumus Imaging, Australia
| | - Simon K Poon
- School of Computer Science, The University of Sydney, Australia; Western Sydney Local Health District, Australia.
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Wenderott K, Krups J, Luetkens JA, Gambashidze N, Weigl M. Prospective effects of an artificial intelligence-based computer-aided detection system for prostate imaging on routine workflow and radiologists' outcomes. Eur J Radiol 2024; 170:111252. [PMID: 38096741 DOI: 10.1016/j.ejrad.2023.111252] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 11/15/2023] [Accepted: 12/04/2023] [Indexed: 01/16/2024]
Abstract
OBJECTIVES Artificial intelligence (AI) is expected to alleviate the negative consequences of rising case numbers for radiologists. Currently, systematic evaluations of the impact of AI solutions in real-world radiological practice are missing. Our study addresses this gap by investigating the impact of the clinical implementation of an AI-based computer-aided detection system (CAD) for prostate MRI reading on clinicians' workflow, workflow throughput times, workload, and stress. MATERIALS AND METHODS CAD was newly implemented into radiology workflow and accompanied by a prospective pre-post study design. We assessed prostate MRI case readings using standardized work observations and questionnaires. The observation period was three months each in a single department. Workflow throughput times, PI-RADS score, CAD usage and radiologists' self-reported workload and stress were recorded. Linear mixed models were employed for effect identification. RESULTS In data analyses, 91 observed case readings (pre: 50, post: 41) were included. Variation of routine workflow was observed following CAD implementation. A non-significant increase in overall workflow throughput time was associated with CAD implementation (mean 16.99 ± 6.21 vs 18.77 ± 9.69 min, p = .51), along with an increase in diagnostic reading time for high suspicion cases (mean 15.73 ± 4.99 vs 23.07 ± 8.75 min, p = .02). Changes in radiologists' self-reported workload or stress were not found. CONCLUSION Implementation of an AI-based detection aid was associated with lower standardization and no effects over time on radiologists' workload or stress. Expectations of AI decreasing the workload of radiologists were not confirmed by our real-world study. PRE-REGISTRATION German register for clinical trials https://drks.de/; DRKS00027391.
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Affiliation(s)
| | - Jim Krups
- Institute for Patient Safety, University Hospital Bonn, Germany
| | - Julian A Luetkens
- Department of Radiology and Quantitative Imaging Lab Bonn (QILaB), University Hospital Bonn, Germany
| | | | - Matthias Weigl
- Institute for Patient Safety, University Hospital Bonn, Germany
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Farič N, Hinder S, Williams R, Ramaesh R, Bernabeu MO, van Beek E, Cresswell K. Early experiences of integrating an artificial intelligence-based diagnostic decision support system into radiology settings: a qualitative study. J Am Med Inform Assoc 2023; 31:24-34. [PMID: 37748456 PMCID: PMC10746311 DOI: 10.1093/jamia/ocad191] [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: 05/18/2023] [Revised: 08/23/2023] [Accepted: 09/13/2023] [Indexed: 09/27/2023] Open
Abstract
OBJECTIVES Artificial intelligence (AI)-based clinical decision support systems to aid diagnosis are increasingly being developed and implemented but with limited understanding of how such systems integrate with existing clinical work and organizational practices. We explored the early experiences of stakeholders using an AI-based imaging software tool Veye Lung Nodules (VLN) aiding the detection, classification, and measurement of pulmonary nodules in computed tomography scans of the chest. MATERIALS AND METHODS We performed semistructured interviews and observations across early adopter deployment sites with clinicians, strategic decision-makers, suppliers, patients with long-term chest conditions, and academics with expertise in the use of diagnostic AI in radiology settings. We coded the data using the Technology, People, Organizations, and Macroenvironmental factors framework. RESULTS We conducted 39 interviews. Clinicians reported VLN to be easy to use with little disruption to the workflow. There were differences in patterns of use between experts and novice users with experts critically evaluating system recommendations and actively compensating for system limitations to achieve more reliable performance. Patients also viewed the tool positively. There were contextual variations in tool performance and use between different hospital sites and different use cases. Implementation challenges included integration with existing information systems, data protection, and perceived issues surrounding wider and sustained adoption, including procurement costs. DISCUSSION Tool performance was variable, affected by integration into workflows and divisions of labor and knowledge, as well as technical configuration and infrastructure. CONCLUSION The socio-organizational factors affecting performance of diagnostic AI are under-researched and require attention and further research.
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Affiliation(s)
- Nuša Farič
- Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Sue Hinder
- Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Robin Williams
- Institute for the Study of Science, Technology and Innovation, University of Edinburgh, Edinburgh, United Kingdom
| | - Rishi Ramaesh
- Department of Radiology, Royal Infirmary Hospital, Edinburgh, United Kingdom
| | - Miguel O Bernabeu
- Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
- The Bayes Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - Edwin van Beek
- Centre for Cardiovascular Science, Edinburgh Imaging and Neuroscience, University of Edinburgh, Edinburgh, United Kingdom
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Cioffi GM, Pinilla-Echeverri N, Sheth T, Sibbald MG. Does artificial intelligence enhance physician interpretation of optical coherence tomography: insights from eye tracking. Front Cardiovasc Med 2023; 10:1283338. [PMID: 38144364 PMCID: PMC10739524 DOI: 10.3389/fcvm.2023.1283338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 11/20/2023] [Indexed: 12/26/2023] Open
Abstract
Background and objectives The adoption of optical coherence tomography (OCT) in percutaneous coronary intervention (PCI) is limited by need for real-time image interpretation expertise. Artificial intelligence (AI)-assisted Ultreon™ 2.0 software could address this barrier. We used eye tracking to understand how these software changes impact viewing efficiency and accuracy. Methods Eighteen interventional cardiologists and fellows at McMaster University, Canada, were included in the study and categorized as experienced or inexperienced based on lifetime OCT use. They were tasked with reviewing OCT images from both Ultreon™ 2.0 and AptiVue™ software platforms while their eye movements were recorded. Key metrics, such as time to first fixation on the area of interest, total task time, dwell time (time spent on the area of interest as a proportion of total task time), and interpretation accuracy, were evaluated using a mixed multivariate model. Results Physicians exhibited improved viewing efficiency with Ultreon™ 2.0, characterized by reduced time to first fixation (Ultreon™ 0.9 s vs. AptiVue™ 1.6 s, p = 0.007), reduced total task time (Ultreon™ 10.2 s vs. AptiVue™ 12.6 s, p = 0.006), and increased dwell time in the area of interest (Ultreon™ 58% vs. AptiVue™ 41%, p < 0.001). These effects were similar for experienced and inexperienced physicians. Accuracy of OCT image interpretation was preserved in both groups, with experienced physicians outperforming inexperienced physicians. Discussion Our study demonstrated that AI-enabled Ultreon™ 2.0 software can streamline the image interpretation process and improve viewing efficiency for both inexperienced and experienced physicians. Enhanced viewing efficiency implies reduced cognitive load potentially reducing the barriers for OCT adoption in PCI decision-making.
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Affiliation(s)
| | | | | | - Matthew Gary Sibbald
- Division of Cardiology, Hamilton General Hospital, Hamilton Health Sciences, McMaster University, Hamilton, ON, Canada
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Oh SS, Kuang I, Jeong H, Song JY, Ren B, Moon JY, Park EC, Kawachi I. Predicting Fetal Alcohol Spectrum Disorders Using Machine Learning Techniques: Multisite Retrospective Cohort Study. J Med Internet Res 2023; 25:e45041. [PMID: 37463016 PMCID: PMC10394506 DOI: 10.2196/45041] [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: 12/13/2022] [Revised: 05/22/2023] [Accepted: 06/18/2023] [Indexed: 07/21/2023] Open
Abstract
BACKGROUND Fetal alcohol syndrome (FAS) is a lifelong developmental disability that occurs among individuals with prenatal alcohol exposure (PAE). With improved prediction models, FAS can be diagnosed or treated early, if not completely prevented. OBJECTIVE In this study, we sought to compare different machine learning algorithms and their FAS predictive performance among women who consumed alcohol during pregnancy. We also aimed to identify which variables (eg, timing of exposure to alcohol during pregnancy and type of alcohol consumed) were most influential in generating an accurate model. METHODS Data from the collaborative initiative on fetal alcohol spectrum disorders from 2007 to 2017 were used to gather information about 595 women who consumed alcohol during pregnancy at 5 hospital sites around the United States. To obtain information about PAE, questionnaires or in-person interviews, as well as reviews of medical, legal, or social service records were used to gather information about alcohol consumption. Four different machine learning algorithms (logistic regression, XGBoost, light gradient-boosting machine, and CatBoost) were trained to predict the prevalence of FAS at birth, and model performance was measured by analyzing the area under the receiver operating characteristics curve (AUROC). Of the total cases, 80% were randomly selected for training, while 20% remained as test data sets for predicting FAS. Feature importance was also analyzed using Shapley values for the best-performing algorithm. RESULTS Overall, there were 20 cases of FAS within a total population of 595 individuals with PAE. Most of the drinking occurred in the first trimester only (n=491) or throughout all 3 trimesters (n=95); however, there were also reports of drinking in the first and second trimesters only (n=8), and 1 case of drinking in the third trimester only (n=1). The CatBoost method delivered the best performance in terms of AUROC (0.92) and area under the precision-recall curve (AUPRC 0.51), followed by the logistic regression method (AUROC 0.90; AUPRC 0.59), the light gradient-boosting machine (AUROC 0.89; AUPRC 0.52), and XGBoost (AUROC 0.86; AURPC 0.45). Shapley values in the CatBoost model revealed that 12 variables were considered important in FAS prediction, with drinking throughout all 3 trimesters of pregnancy, maternal age, race, and type of alcoholic beverage consumed (eg, beer, wine, or liquor) scoring highly in overall feature importance. For most predictive measures, the best performance was obtained by the CatBoost algorithm, with an AUROC of 0.92, precision of 0.50, specificity of 0.29, F1 score of 0.29, and accuracy of 0.96. CONCLUSIONS Machine learning algorithms were able to identify FAS risk with a prediction performance higher than that of previous models among pregnant drinkers. For small training sets, which are common with FAS, boosting mechanisms like CatBoost may help alleviate certain problems associated with data imbalances and difficulties in optimization or generalization.
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Affiliation(s)
- Sarah Soyeon Oh
- Department of Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Boston, MA, United States
- Institute of Health Services Research, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Irene Kuang
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Hyewon Jeong
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Jin-Yeop Song
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Boyu Ren
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Jong Youn Moon
- Artificial Intelligence and Big-Data Convergence Center, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea
| | - Eun-Cheol Park
- Institute of Health Services Research, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Ichiro Kawachi
- Department of Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Boston, MA, United States
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Scanzera AC, Beversluis C, Potharazu AV, Bai P, Leifer A, Cole E, Du DY, Musick H, Chan RVP. Planning an artificial intelligence diabetic retinopathy screening program: a human-centered design approach. Front Med (Lausanne) 2023; 10:1198228. [PMID: 37484841 PMCID: PMC10361413 DOI: 10.3389/fmed.2023.1198228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 06/16/2023] [Indexed: 07/25/2023] Open
Abstract
Diabetic retinopathy (DR) is a leading cause of vision loss in the United States and throughout the world. With early detection and treatment, sight-threatening sequelae from DR can be prevented. Although artificial intelligence (AI) based DR screening programs have been proven to be effective in identifying patients at high risk of vision loss, adoption of AI in clinical practice has been slow. We adapted the United Kingdom Design Council's Double-Diamond model to design a strategy for care delivery which integrates an AI-based screening program for DR into a primary care setting. Methods from human-centered design were used to develop a strategy for implementation informed by context-specific barriers and facilitators. The purpose of this community case study is to present findings from this work in progress, including a system of protocols, educational documents and workflows created using key stakeholder input.
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Affiliation(s)
- Angelica C. Scanzera
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, IL, United States
| | - Cameron Beversluis
- Institute for Healthcare Delivery Design, Office of Population Health Sciences, University of Illinois Chicago, Chicago, IL, United States
| | - Archit V. Potharazu
- Institute for Healthcare Delivery Design, Office of Population Health Sciences, University of Illinois Chicago, Chicago, IL, United States
| | - Patricia Bai
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, IL, United States
| | - Ariel Leifer
- Department of Family and Community Medicine, University of Illinois at Chicago, Chicago, IL, United States
| | - Emily Cole
- W.K. Kellogg Eye Center, University of Michigan, Ann Arbor, MI, United States
| | - David Yuzhou Du
- Segal Design Institute, Northwestern University, Evanston, IL, United States
| | - Hugh Musick
- Institute for Healthcare Delivery Design, Office of Population Health Sciences, University of Illinois Chicago, Chicago, IL, United States
| | - R. V. Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, IL, United States
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Robinson R, Liday C, Lee S, Williams IC, Wright M, An S, Nguyen E. Artificial Intelligence in Health Care-Understanding Patient Information Needs and Designing Comprehensible Transparency: Qualitative Study. JMIR AI 2023; 2:e46487. [PMID: 38333424 PMCID: PMC10851077 DOI: 10.2196/46487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/10/2023] [Accepted: 05/14/2023] [Indexed: 02/10/2024]
Abstract
Background Artificial intelligence (AI) is as a branch of computer science that uses advanced computational methods such as machine learning (ML), to calculate and/or predict health outcomes and address patient and provider health needs. While these technologies show great promise for improving healthcare, especially in diabetes management, there are usability and safety concerns for both patients and providers about the use of AI/ML in healthcare management. Objectives To support and ensure safe use of AI/ML technologies in healthcare, the team worked to better understand: 1) patient information and training needs, 2) the factors that influence patients' perceived value and trust in AI/ML healthcare applications; and 3) on how best to support safe and appropriate use of AI/ML enabled devices and applications among people living with diabetes. Methods To understand general patient perspectives and information needs related to the use of AI/ML in healthcare, we conducted a series of focus groups (n=9) and interviews (n=3) with patients (n=40) and interviews with providers (n=6) in Alaska, Idaho, and Virginia. Grounded Theory guided data gathering, synthesis, and analysis. Thematic content and constant comparison analysis were used to identify relevant themes and sub-themes. Inductive approaches were used to link data to key concepts including preferred patient-provider-interactions, patient perceptions of trust, accuracy, value, assurances, and information transparency. Results Key summary themes and recommendations focused on: 1) patient preferences for AI/ML enabled device and/or application information; 2) patient and provider AI/ML-related device and/or application training needs; 3) factors contributing to patient and provider trust in AI/ML enabled devices and/or application; and 4) AI/ML-related device and/or application functionality and safety considerations. A number of participant (patients and providers) recommendations to improve device functionality to guide information and labeling mandates (e.g., links to online video resources, and access to 24/7 live in-person or virtual emergency support). Other patient recommendations include: 1) access to practice devices; 2) connection to local supports and reputable community resources; 3) simplified display and alert limits. Conclusion Recommendations from both patients and providers could be used by Federal Oversight Agencies to improve utilization of AI/ML monitoring of technology use in diabetes, improving device safety and efficacy.
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Affiliation(s)
- Renee Robinson
- College of Pharmacy, Idaho State University, Anchorage, AK, US
| | - Cara Liday
- College of Pharmacy, Idaho State University, Pocatello, ID, US
| | - Sarah Lee
- College of Pharmacy, Idaho State University, Meridian, ID, US
| | - Ishan C Williams
- School of Nursing, University of Virginia, Charlottesville, VA, US
| | - Melanie Wright
- College of Pharmacy, Idaho State University, Meridian, ID, US
| | - Sungjoon An
- College of Pharmacy, Idaho State University, Meridian, ID, US
| | - Elaine Nguyen
- College of Pharmacy, Idaho State University, Meridian, ID, US
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12
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Liaw WR, Ramos Silva Y, Soltero EG, Krist A, Stotts AL. An Assessment of How Clinicians and Staff Members Use a Diabetes Artificial Intelligence Prediction Tool: Mixed Methods Study. JMIR AI 2023; 2:e45032. [PMID: 38875578 PMCID: PMC11041401 DOI: 10.2196/45032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 03/09/2023] [Accepted: 04/22/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND Nearly one-third of patients with diabetes are poorly controlled (hemoglobin A1c≥9%). Identifying at-risk individuals and providing them with effective treatment is an important strategy for preventing poor control. OBJECTIVE This study aims to assess how clinicians and staff members would use a clinical decision support tool based on artificial intelligence (AI) and identify factors that affect adoption. METHODS This was a mixed methods study that combined semistructured interviews and surveys to assess the perceived usefulness and ease of use, intent to use, and factors affecting tool adoption. We recruited clinicians and staff members from practices that manage diabetes. During the interviews, participants reviewed a sample electronic health record alert and were informed that the tool uses AI to identify those at high risk for poor control. Participants discussed how they would use the tool, whether it would contribute to care, and the factors affecting its implementation. In a survey, participants reported their demographics; rank-ordered factors influencing the adoption of the tool; and reported their perception of the tool's usefulness as well as their intent to use, ease of use, and organizational support for use. Qualitative data were analyzed using a thematic content analysis approach. We used descriptive statistics to report demographics and analyze the findings of the survey. RESULTS In total, 22 individuals participated in the study. Two-thirds (14/22, 63%) of respondents were physicians. Overall, 36% (8/22) of respondents worked in academic health centers, whereas 27% (6/22) of respondents worked in federally qualified health centers. The interviews identified several themes: this tool has the potential to be useful because it provides information that is not currently available and can make care more efficient and effective; clinicians and staff members were concerned about how the tool affects patient-oriented outcomes and clinical workflows; adoption of the tool is dependent on its validation, transparency, actionability, and design and could be increased with changes to the interface and usability; and implementation would require buy-in and need to be tailored to the demands and resources of clinics and communities. Survey findings supported these themes, as 77% (17/22) of participants somewhat, moderately, or strongly agreed that they would use the tool, whereas these figures were 82% (18/22) for usefulness, 82% (18/22) for ease of use, and 68% (15/22) for clinic support. The 2 highest ranked factors affecting adoption were whether the tool improves health and the accuracy of the tool. CONCLUSIONS Most participants found the tool to be easy to use and useful, although they had concerns about alert fatigue, bias, and transparency. These data will be used to enhance the design of an AI tool.
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Affiliation(s)
- Winston R Liaw
- Department of Health Systems and Population Health Sciences, Tilman J Fertitta Family College of Medicine, University of Houston, Houston, TX, United States
| | | | - Erica G Soltero
- USDA/ARS Children's Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, United States
| | - Alex Krist
- Department of Family Medicine & Population Health, Virginia Commonwealth University School of Medicine, Richmond, VA, United States
| | - Angela L Stotts
- Department of Family & Community Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, United States
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Afshar M, Adelaine S, Resnik F, Mundt MP, Long J, Leaf M, Ampian T, Wills GJ, Schnapp B, Chao M, Brown R, Joyce C, Sharma B, Dligach D, Burnside ES, Mahoney J, Churpek MM, Patterson BW, Liao F. Deployment of Real-time Natural Language Processing and Deep Learning Clinical Decision Support in the Electronic Health Record: Pipeline Implementation for an Opioid Misuse Screener in Hospitalized Adults. JMIR Med Inform 2023; 11:e44977. [PMID: 37079367 PMCID: PMC10160938 DOI: 10.2196/44977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 02/01/2023] [Accepted: 03/26/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND The clinical narrative in electronic health records (EHRs) carries valuable information for predictive analytics; however, its free-text form is difficult to mine and analyze for clinical decision support (CDS). Large-scale clinical natural language processing (NLP) pipelines have focused on data warehouse applications for retrospective research efforts. There remains a paucity of evidence for implementing NLP pipelines at the bedside for health care delivery. OBJECTIVE We aimed to detail a hospital-wide, operational pipeline to implement a real-time NLP-driven CDS tool and describe a protocol for an implementation framework with a user-centered design of the CDS tool. METHODS The pipeline integrated a previously trained open-source convolutional neural network model for screening opioid misuse that leveraged EHR notes mapped to standardized medical vocabularies in the Unified Medical Language System. A sample of 100 adult encounters were reviewed by a physician informaticist for silent testing of the deep learning algorithm before deployment. An end user interview survey was developed to examine the user acceptability of a best practice alert (BPA) to provide the screening results with recommendations. The planned implementation also included a human-centered design with user feedback on the BPA, an implementation framework with cost-effectiveness, and a noninferiority patient outcome analysis plan. RESULTS The pipeline was a reproducible workflow with a shared pseudocode for a cloud service to ingest, process, and store clinical notes as Health Level 7 messages from a major EHR vendor in an elastic cloud computing environment. Feature engineering of the notes used an open-source NLP engine, and the features were fed into the deep learning algorithm, with the results returned as a BPA in the EHR. On-site silent testing of the deep learning algorithm demonstrated a sensitivity of 93% (95% CI 66%-99%) and specificity of 92% (95% CI 84%-96%), similar to published validation studies. Before deployment, approvals were received across hospital committees for inpatient operations. Five interviews were conducted; they informed the development of an educational flyer and further modified the BPA to exclude certain patients and allow the refusal of recommendations. The longest delay in pipeline development was because of cybersecurity approvals, especially because of the exchange of protected health information between the Microsoft (Microsoft Corp) and Epic (Epic Systems Corp) cloud vendors. In silent testing, the resultant pipeline provided a BPA to the bedside within minutes of a provider entering a note in the EHR. CONCLUSIONS The components of the real-time NLP pipeline were detailed with open-source tools and pseudocode for other health systems to benchmark. The deployment of medical artificial intelligence systems in routine clinical care presents an important yet unfulfilled opportunity, and our protocol aimed to close the gap in the implementation of artificial intelligence-driven CDS. TRIAL REGISTRATION ClinicalTrials.gov NCT05745480; https://www.clinicaltrials.gov/ct2/show/NCT05745480.
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Affiliation(s)
- Majid Afshar
- University of Wisconsin - Madison, Madison, WI, United States
| | | | - Felice Resnik
- University of Wisconsin - Madison, Madison, WI, United States
| | - Marlon P Mundt
- University of Wisconsin - Madison, Madison, WI, United States
| | - John Long
- University of Wisconsin - Madison, Madison, WI, United States
| | - Margaret Leaf
- University of Wisconsin - Madison, Madison, WI, United States
| | - Theodore Ampian
- University of Wisconsin - Madison, Madison, WI, United States
| | - Graham J Wills
- University of Wisconsin - Madison, Madison, WI, United States
| | | | - Michael Chao
- University of Wisconsin - Madison, Madison, WI, United States
| | - Randy Brown
- University of Wisconsin - Madison, Madison, WI, United States
| | - Cara Joyce
- Loyola University Chicago, Chicago, IL, United States
| | - Brihat Sharma
- University of Wisconsin - Madison, Madison, WI, United States
| | | | | | - Jane Mahoney
- University of Wisconsin - Madison, Madison, WI, United States
| | | | | | - Frank Liao
- University of Wisconsin - Madison, Madison, WI, United States
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14
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Wu C, Xu H, Bai D, Chen X, Gao J, Jiang X. Public perceptions on the application of artificial intelligence in healthcare: a qualitative meta-synthesis. BMJ Open 2023; 13:e066322. [PMID: 36599634 PMCID: PMC9815015 DOI: 10.1136/bmjopen-2022-066322] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
OBJECTIVES Medical artificial intelligence (AI) has been used widely applied in clinical field due to its convenience and innovation. However, several policy and regulatory issues such as credibility, sharing of responsibility and ethics have raised concerns in the use of AI. It is therefore necessary to understand the general public's views on medical AI. Here, a meta-synthesis was conducted to analyse and summarise the public's understanding of the application of AI in the healthcare field, to provide recommendations for future use and management of AI in medical practice. DESIGN This was a meta-synthesis of qualitative studies. METHOD A search was performed on the following databases to identify studies published in English and Chinese: MEDLINE, CINAHL, Web of science, Cochrane library, Embase, PsycINFO, CNKI, Wanfang and VIP. The search was conducted from database inception to 25 December 2021. The meta-aggregation approach of JBI was used to summarise findings from qualitative studies, focusing on the public's perception of the application of AI in healthcare. RESULTS Of the 5128 studies screened, 12 met the inclusion criteria, hence were incorporated into analysis. Three synthesised findings were used as the basis of our conclusions, including advantages of medical AI from the public's perspective, ethical and legal concerns about medical AI from the public's perspective, and public suggestions on the application of AI in medical field. CONCLUSION Results showed that the public acknowledges the unique advantages and convenience of medical AI. Meanwhile, several concerns about the application of medical AI were observed, most of which involve ethical and legal issues. The standard application and reasonable supervision of medical AI is key to ensuring its effective utilisation. Based on the public's perspective, this analysis provides insights and suggestions for health managers on how to implement and apply medical AI smoothly, while ensuring safety in healthcare practice. PROSPERO REGISTRATION NUMBER CRD42022315033.
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Affiliation(s)
- Chenxi Wu
- West China School of Nursing/West China Hospital, Sichuan University, Chengdu, Sichuan, China
- School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Huiqiong Xu
- West China School of Nursing,Sichuan University/ Abdominal Oncology Ward, Cancer Center,West China Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Dingxi Bai
- School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Xinyu Chen
- School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Jing Gao
- School of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Xiaolian Jiang
- West China School of Nursing/West China Hospital, Sichuan University, Chengdu, Sichuan, China
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15
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Wenderott K, Gambashidze N, Weigl M. Integration of Artificial Intelligence Into Sociotechnical Work Systems-Effects of Artificial Intelligence Solutions in Medical Imaging on Clinical Efficiency: Protocol for a Systematic Literature Review. JMIR Res Protoc 2022; 11:e40485. [PMID: 36454624 PMCID: PMC9756121 DOI: 10.2196/40485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 09/16/2022] [Accepted: 10/20/2022] [Indexed: 11/07/2022] Open
Abstract
BACKGROUND When introducing artificial intelligence (AI) into clinical care, one of the main objectives is to improve workflow efficiency because AI-based solutions are expected to take over or support routine tasks. OBJECTIVE This study sought to synthesize the current knowledge base on how the use of AI technologies for medical imaging affects efficiency and what facilitators or barriers moderating the impact of AI implementation have been reported. METHODS In this systematic literature review, comprehensive literature searches will be performed in relevant electronic databases, including PubMed/MEDLINE, Embase, PsycINFO, Web of Science, IEEE Xplore, and CENTRAL. Studies in English and German published from 2000 onwards will be included. The following inclusion criteria will be applied: empirical studies targeting the workflow integration or adoption of AI-based software in medical imaging used for diagnostic purposes in a health care setting. The efficiency outcomes of interest include workflow adaptation, time to complete tasks, and workload. Two reviewers will independently screen all retrieved records, full-text articles, and extract data. The study's methodological quality will be appraised using suitable tools. The findings will be described qualitatively, and a meta-analysis will be performed, if possible. Furthermore, a narrative synthesis approach that focuses on work system factors affecting the integration of AI technologies reported in eligible studies will be adopted. RESULTS This review is anticipated to begin in September 2022 and will be completed in April 2023. CONCLUSIONS This systematic review and synthesis aims to summarize the existing knowledge on efficiency improvements in medical imaging through the integration of AI into clinical workflows. Moreover, it will extract the facilitators and barriers of the AI implementation process in clinical care settings. Therefore, our findings have implications for future clinical implementation processes of AI-based solutions, with a particular focus on diagnostic procedures. This review is additionally expected to identify research gaps regarding the focus on seamless workflow integration of novel technologies in clinical settings. TRIAL REGISTRATION PROSPERO CRD42022303439; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=303439. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/40485.
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Affiliation(s)
| | | | - Matthias Weigl
- Institute for Patient Safety, University Hospital Bonn, Bonn, Germany
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16
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Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review. NPJ Digit Med 2022; 5:162. [PMID: 36307479 PMCID: PMC9614192 DOI: 10.1038/s41746-022-00700-y] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 09/29/2022] [Indexed: 02/08/2023] Open
Abstract
In low- and middle-income countries (LMICs), AI has been promoted as a potential means of strengthening healthcare systems by a growing number of publications. We aimed to evaluate the scope and nature of AI technologies in the specific context of LMICs. In this systematic scoping review, we used a broad variety of AI and healthcare search terms. Our literature search included records published between 1st January 2009 and 30th September 2021 from the Scopus, EMBASE, MEDLINE, Global Health and APA PsycInfo databases, and grey literature from a Google Scholar search. We included studies that reported a quantitative and/or qualitative evaluation of a real-world application of AI in an LMIC health context. A total of 10 references evaluating the application of AI in an LMIC were included. Applications varied widely, including: clinical decision support systems, treatment planning and triage assistants and health chatbots. Only half of the papers reported which algorithms and datasets were used in order to train the AI. A number of challenges of using AI tools were reported, including issues with reliability, mixed impacts on workflows, poor user friendliness and lack of adeptness with local contexts. Many barriers exists that prevent the successful development and adoption of well-performing, context-specific AI tools, such as limited data availability, trust and evidence of cost-effectiveness in LMICs. Additional evaluations of the use of AI in healthcare in LMICs are needed in order to identify their effectiveness and reliability in real-world settings and to generate understanding for best practices for future implementations.
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17
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Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare. NPJ Digit Med 2022; 5:66. [PMID: 35641814 PMCID: PMC9156743 DOI: 10.1038/s41746-022-00611-y] [Citation(s) in RCA: 72] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 04/29/2022] [Indexed: 12/13/2022] Open
Abstract
Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to derive insights from clinical data and improve patient outcomes. However, these highly complex systems are sensitive to changes in the environment and liable to performance decay. Even after their successful integration into clinical practice, ML/AI algorithms should be continuously monitored and updated to ensure their long-term safety and effectiveness. To bring AI into maturity in clinical care, we advocate for the creation of hospital units responsible for quality assurance and improvement of these algorithms, which we refer to as “AI-QI” units. We discuss how tools that have long been used in hospital quality assurance and quality improvement can be adapted to monitor static ML algorithms. On the other hand, procedures for continual model updating are still nascent. We highlight key considerations when choosing between existing methods and opportunities for methodological innovation.
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18
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Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, Denniston AK, Faes L, Geerts B, Ibrahim M, Liu X, Mateen BA, Mathur P, McCradden MD, Morgan L, Ordish J, Rogers C, Saria S, Ting DSW, Watkinson P, Weber W, Wheatstone P, McCulloch P. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ 2022; 377:e070904. [PMID: 35584845 PMCID: PMC9116198 DOI: 10.1136/bmj-2022-070904] [Citation(s) in RCA: 60] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/26/2022] [Indexed: 01/04/2023]
Affiliation(s)
- Baptiste Vasey
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
- Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Myura Nagendran
- UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, UK
| | - Bruce Campbell
- University of Exeter Medical School, Exeter, UK
- Royal Devon and Exeter Hospital, Exeter, UK
| | - David A Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- British Heart Foundation Data Science Centre, London, UK
- Health Data Research UK, London, UK
- UCL Hospitals Biomedical Research Centre, London, UK
| | - Alastair K Denniston
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Livia Faes
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | | | - Mudathir Ibrahim
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Department of Surgery, Maimonides Medical Center, New York, NY, USA
| | - Xiaoxuan Liu
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Bilal A Mateen
- Institute of Health Informatics, University College London, London, UK
- Wellcome Trust, London, UK
- Alan Turing Institute, London, UK
| | - Piyush Mathur
- Department of General Anesthesiology, Anesthesiology Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Melissa D McCradden
- Hospital for Sick Children, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
| | | | - Johan Ordish
- The Medicines and Healthcare products Regulatory Agency, London, UK
| | | | - Suchi Saria
- Departments of Computer Science, Statistics, and Health Policy, and Division of Informatics, Johns Hopkins University, Baltimore, MD, USA
- Bayesian Health, New York, NY, USA
| | - Daniel S W Ting
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Peter Watkinson
- Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- NIHR Biomedical Research Centre Oxford, Oxford University Hospitals NHS Trust, Oxford, UK
| | | | | | - Peter McCulloch
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
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19
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Sibbald M, Abdulla B, Keuhl A, Norman G, Monteiro S, Sherbino J. Electronic diagnostic support in emergency physician triage: a qualitative study (Preprint). JMIR Hum Factors 2022; 9:e39234. [PMID: 36178728 PMCID: PMC9568817 DOI: 10.2196/39234] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 08/05/2022] [Accepted: 08/29/2022] [Indexed: 12/05/2022] Open
Abstract
Background Not thinking of a diagnosis is a leading cause of diagnostic error in the emergency department, resulting in delayed treatment, morbidity, and excess mortality. Electronic differential diagnostic support (EDS) results in small but significant reductions in diagnostic error. However, the uptake of EDS by clinicians is limited. Objective We sought to understand physician perceptions and barriers to the uptake of EDS within the emergency department triage process. Methods We conducted a qualitative study using a research associate to rapidly prototype an embedded EDS into the emergency department triage process. Physicians involved in the triage assessment of a busy emergency department were provided the output of an EDS based on the triage complaint by an embedded researcher to simulate an automated system that would draw from the electronic medical record. Physicians were interviewed immediately after their experience. Verbatim transcripts were analyzed by a team using open and axial coding, informed by direct content analysis. Results In all, 4 themes emerged from 14 interviews: (1) the quality of the EDS was inferred from the scope and prioritization of the diagnoses present in the EDS differential; (2) the trust of the EDS was linked to varied beliefs around the diagnostic process and potential for bias; (3) clinicians foresaw more benefit to EDS use for colleagues and trainees rather than themselves; and (4) clinicians felt strongly that EDS output should not be included in the patient record. Conclusions The adoption of an EDS into an emergency department triage process will require a system that provides diagnostic suggestions appropriate for the scope and context of the emergency department triage process, transparency of system design, and affordances for clinician beliefs about the diagnostic process and addresses clinician concern around including EDS output in the patient record.
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Affiliation(s)
- Matthew Sibbald
- McMaster Education Research, Innovation & Theory (MERIT) Program, Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Bashayer Abdulla
- McMaster Education Research, Innovation & Theory (MERIT) Program, Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Amy Keuhl
- McMaster Education Research, Innovation & Theory (MERIT) Program, Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Geoffrey Norman
- McMaster Education Research, Innovation & Theory (MERIT) Program, Department of Health Research Methods, Evidence & Impact, McMaster University, Hamilton, ON, Canada
| | - Sandra Monteiro
- McMaster Education Research, Innovation & Theory (MERIT) Program, Department of Medicine, McMaster University, Hamilton, ON, Canada
| | - Jonathan Sherbino
- McMaster Education Research, Innovation & Theory (MERIT) Program, Department of Medicine, McMaster University, Hamilton, ON, Canada
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20
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Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, Denniston AK, Faes L, Geerts B, Ibrahim M, Liu X, Mateen BA, Mathur P, McCradden MD, Morgan L, Ordish J, Rogers C, Saria S, Ting DSW, Watkinson P, Weber W, Wheatstone P, McCulloch P. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med 2022; 28:924-933. [PMID: 35585198 DOI: 10.1038/s41591-022-01772-9] [Citation(s) in RCA: 126] [Impact Index Per Article: 63.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 03/03/2022] [Indexed: 12/31/2022]
Abstract
A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system's actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings.
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Affiliation(s)
- Baptiste Vasey
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK.
- Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Myura Nagendran
- UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, UK
| | - Bruce Campbell
- University of Exeter Medical School, Exeter, UK
- Royal Devon and Exeter Hospital, Exeter, UK
| | - David A Clifton
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
- British Heart Foundation Data Science Centre, London, UK
- Health Data Research UK, London, UK
- UCL Hospitals Biomedical Research Centre, London, UK
| | - Alastair K Denniston
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Livia Faes
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Bart Geerts
- Healthplus.ai-R&D BV, Amsterdam, The Netherlands
| | - Mudathir Ibrahim
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
- Department of Surgery, Maimonides Medical Center, Brooklyn, NY, USA
| | - Xiaoxuan Liu
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Bilal A Mateen
- Institute of Health Informatics, University College London, London, UK
- The Wellcome Trust, London, UK
- The Alan Turing Institute, London, UK
| | - Piyush Mathur
- Department of General Anesthesiology, Anesthesiology Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Melissa D McCradden
- The Hospital for Sick Children, Toronto ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto ON, Canada
| | | | - Johan Ordish
- Medicines and Healthcare products Regulatory Agency, London, UK
| | | | - Suchi Saria
- Departments of Computer Science, Statistics, and Health Policy, and Division of Informatics, Johns Hopkins University, Baltimore, MD, USA
- Bayesian Health, New York, NY, USA
| | - Daniel S W Ting
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Peter Watkinson
- Critical Care Research Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- NIHR Biomedical Research Centre Oxford, Oxford University Hospitals NHS Trust, Oxford, UK
| | | | | | - Peter McCulloch
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
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21
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Choudhury A, Asan O, Medow JE. Effect of risk, expectancy, and trust on clinicians' intent to use an artificial intelligence system -- Blood Utilization Calculator. APPLIED ERGONOMICS 2022; 101:103708. [PMID: 35149301 DOI: 10.1016/j.apergo.2022.103708] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 01/27/2022] [Accepted: 01/31/2022] [Indexed: 06/14/2023]
Abstract
A gap exists between the capabilities of artificial intelligence (AI) technologies in healthcare and the extent to which clinicians are willing to adopt these systems. Our study addressed this gap by leveraging 'expectancy-value theory' and 'modified extended unified theory of acceptance and use of technology' to understand why clinicians may be willing or unwilling to adopt AI systems. The study looked at the 'expectancy,' 'trust,' and 'perceptions' of clinicians related to their intention of using an AI-based decision support system known as the Blood Utilization Calculator (BUC). The study used purposive sampling to recruit BUC users and administered a validated online survey from a large hospital system in the Midwest in 2021. The findings captured the significant effect of 'perceived risk' (negatively) and 'expectancy' (positively) on clinicians' 'trust' in BUC. 'Trust' was also found to mediate the relationship of 'perceived risk' and 'expectancy' with the 'intent to use BUC.' The study's findings established pathways for future research and have implications on factors influencing BUC use.
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Affiliation(s)
- Avishek Choudhury
- School of Systems and Enterprises, Stevens Institute of Technology, NJ, 07030, Hoboken, USA.
| | - Onur Asan
- School of Systems and Enterprises, Stevens Institute of Technology, NJ, 07030, Hoboken, USA.
| | - Joshua E Medow
- Department of Neurological Surgery, University of Wisconsin School of Medicine and Public Health, USA.
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22
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Tulk Jesso S, Kelliher A, Sanghavi H, Martin T, Henrickson Parker S. Inclusion of Clinicians in the Development and Evaluation of Clinical Artificial Intelligence Tools: A Systematic Literature Review. Front Psychol 2022; 13:830345. [PMID: 35465567 PMCID: PMC9022040 DOI: 10.3389/fpsyg.2022.830345] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 02/09/2022] [Indexed: 12/11/2022] Open
Abstract
The application of machine learning (ML) and artificial intelligence (AI) in healthcare domains has received much attention in recent years, yet significant questions remain about how these new tools integrate into frontline user workflow, and how their design will impact implementation. Lack of acceptance among clinicians is a major barrier to the translation of healthcare innovations into clinical practice. In this systematic review, we examine when and how clinicians are consulted about their needs and desires for clinical AI tools. Forty-five articles met criteria for inclusion, of which 24 were considered design studies. The design studies used a variety of methods to solicit and gather user feedback, with interviews, surveys, and user evaluations. Our findings show that tool designers consult clinicians at various but inconsistent points during the design process, and most typically at later stages in the design cycle (82%, 19/24 design studies). We also observed a smaller amount of studies adopting a human-centered approach and where clinician input was solicited throughout the design process (22%, 5/24). A third (15/45) of all studies reported on clinician trust in clinical AI algorithms and tools. The surveyed articles did not universally report validation against the “gold standard” of clinical expertise or provide detailed descriptions of the algorithms or computational methods used in their work. To realize the full potential of AI tools within healthcare settings, our review suggests there are opportunities to more thoroughly integrate frontline users’ needs and feedback in the design process.
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Affiliation(s)
- Stephanie Tulk Jesso
- Fralin Biomedical Research Institute, Virginia Tech, Roanoke, VA, United States.,Institute for Creativity, Arts, and Technology, Blacksburg, VA, United States
| | - Aisling Kelliher
- Department of Computer Science, College of Engineering, Virginia Tech, Blacksburg, VA, United States
| | | | - Thomas Martin
- Institute for Creativity, Arts, and Technology, Blacksburg, VA, United States.,Department of Electrical and Computer Engineering, College of Engineering, Virginia Tech, Blacksburg, VA, United States
| | - Sarah Henrickson Parker
- Fralin Biomedical Research Institute, Virginia Tech, Roanoke, VA, United States.,Department of Health Systems and Implementation Science, Virginia Tech Carilion School of Medicine, Roanoke, VA, United States
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23
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Beegle C, Hasani N, Maass-Moreno R, Saboury B, Siegel E. Artificial Intelligence and Positron Emission Tomography Imaging Workflow:: Technologists' Perspective. PET Clin 2021; 17:31-39. [PMID: 34809867 DOI: 10.1016/j.cpet.2021.09.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Artificial intelligence (AI) can enhance the efficiency of medical imaging quality control and clinical documentation, provide clinical decision support, and increase image acquisition and processing quality. A clear understanding of the basic tenets of these technologies and their impact will enable nuclear medicine technologists to train for performing advanced imaging tasks. AI-enabled medical devices' anticipated role and impact on routine nuclear medicine workflow (scheduling, quality control, check-in, radiotracer injection, waiting room, image planning, image acquisition, image post-processing) is reviewed in this article. With the assistance of AI, newly compiled patient imaging data can be customized to encompass personalized risk assessments of patients' disease burden, along with the development of individualized treatment plans. Nuclear medicine technologists will continue to play a crucial role on the medical team, collaborating with patients and radiologists to improve each patient's imaging experience and supervising the performance of integrated AI applications.
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Affiliation(s)
- Cheryl Beegle
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA
| | - Navid Hasani
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; University of Queensland Faculty of Medicine, Ochsner Clinical School, New Orleans, LA 70121, USA
| | - Roberto Maass-Moreno
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore Country, Baltimore, MD, USA; Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
| | - Eliot Siegel
- Department of Radiology and Nuclear Medicine, University of Maryland Medical Center, 655 W. Baltimore Street, Baltimore, MD 21201, USA.
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