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Perivolaris A, Adams-McGavin C, Madan Y, Kishibe T, Antoniou T, Mamdani M, Jung JJ. Quality of interaction between clinicians and artificial intelligence systems. A systematic review. Future Healthc J 2024; 11:100172. [PMID: 39281326 PMCID: PMC11399614 DOI: 10.1016/j.fhj.2024.100172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 07/15/2024] [Accepted: 08/04/2024] [Indexed: 09/18/2024]
Abstract
Introduction Artificial intelligence (AI) has the potential to improve healthcare quality when thoughtfully integrated into clinical practice. Current evaluations of AI solutions tend to focus solely on model performance. There is a critical knowledge gap in the assessment of AI-clinician interactions. We systematically reviewed existing literature to identify interaction traits that can be used to assess the quality of AI-clinician interactions. Methods We performed a systematic review of published studies to June 2022 that reported elements of interactions that impacted the relationship between clinicians and AI-enabled clinical decision support systems. Due to study heterogeneity, we conducted a narrative synthesis of the different interaction traits identified from this review. Two study authors categorised the AI-clinician interaction traits based on their shared constructs independently. After the independent categorisation, both authors engaged in a discussion to finalise the categories. Results From 34 included studies, we identified 210 interaction traits. The most common interaction traits included usefulness, ease of use, trust, satisfaction, willingness to use and usability. After removing duplicate or redundant traits, 90 unique interaction traits were identified. Unique interaction traits were then classified into seven categories: usability and user experience, system performance, clinician trust and acceptance, impact on patient care, communication, ethical and professional concerns, and clinician engagement and workflow. Discussion We identified seven categories of interaction traits between clinicians and AI systems. The proposed categories may serve as a foundation for a framework assessing the quality of AI-clinician interactions.
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Affiliation(s)
- Argyrios Perivolaris
- Institute of Medical Sciences, University of Toronto, Canada
- St. Michaels Hospital, Unity Health Toronto, Canada
| | - Chris Adams-McGavin
- Department of Surgery, Temetry Faculty of Medicine, University of Toronto, Canada
| | - Yasmine Madan
- Department of Health Sciences, McMaster University, Canada
| | | | - Tony Antoniou
- Department of Family and Community Medicine, St. Michael's Hospital, Canada
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Canada
- Department of Family and Community Medicine, University of Toronto, Canada
| | - Muhammad Mamdani
- St. Michaels Hospital, Unity Health Toronto, Canada
- Leslie Dan Faculty of Pharmacy, Temerty Faculty of Medicine, University of Toronto, Canada
- Dalla Lana School of Public Health, University of Toronto, Canada
| | - James J Jung
- Institute of Medical Sciences, University of Toronto, Canada
- St. Michaels Hospital, Unity Health Toronto, Canada
- Department of Surgery, Temetry Faculty of Medicine, University of Toronto, Canada
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Evans RP, Bryant LD, Russell G, Absolom K. Trust and acceptability of data-driven clinical recommendations in everyday practice: A scoping review. Int J Med Inform 2024; 183:105342. [PMID: 38266426 DOI: 10.1016/j.ijmedinf.2024.105342] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 12/08/2023] [Accepted: 01/14/2024] [Indexed: 01/26/2024]
Abstract
BACKGROUND Increasing attention is being given to the analysis of large health datasets to derive new clinical decision support systems (CDSS). However, few data-driven CDSS are being adopted into clinical practice. Trust in these tools is believed to be fundamental for acceptance and uptake but to date little attention has been given to defining or evaluating trust in clinical settings. OBJECTIVES A scoping review was conducted to explore how and where acceptability and trustworthiness of data-driven CDSS have been assessed from the health professional's perspective. METHODS Medline, Embase, PsycInfo, Web of Science, Scopus, ACM Digital, IEEE Xplore and Google Scholar were searched in March 2022 using terms expanded from: "data-driven" AND "clinical decision support" AND "acceptability". Included studies focused on healthcare practitioner-facing data-driven CDSS, relating directly to clinical care. They included trust or a proxy as an outcome, or in the discussion. The preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) is followed in the reporting of this review. RESULTS 3291 papers were screened, with 85 primary research studies eligible for inclusion. Studies covered a diverse range of clinical specialisms and intended contexts, but hypothetical systems (24) outnumbered those in clinical use (18). Twenty-five studies measured trust, via a wide variety of quantitative, qualitative and mixed methods. A further 24 discussed themes of trust without it being explicitly evaluated, and from these, themes of transparency, explainability, and supporting evidence were identified as factors influencing healthcare practitioner trust in data-driven CDSS. CONCLUSION There is a growing body of research on data-driven CDSS, but few studies have explored stakeholder perceptions in depth, with limited focused research on trustworthiness. Further research on healthcare practitioner acceptance, including requirements for transparency and explainability, should inform clinical implementation.
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Affiliation(s)
- Ruth P Evans
- University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK.
| | | | - Gregor Russell
- Bradford District Care Trust, Bradford, New Mill, Victoria Rd, BD18 3LD, UK.
| | - Kate Absolom
- University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK.
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Okeibunor JC, Jaca A, Iwu-Jaja CJ, Idemili-Aronu N, Ba H, Zantsi ZP, Ndlambe AM, Mavundza E, Muneene D, Wiysonge CS, Makubalo L. The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review. Front Public Health 2023; 11:1102185. [PMID: 37469694 PMCID: PMC10352788 DOI: 10.3389/fpubh.2023.1102185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 06/19/2023] [Indexed: 07/21/2023] Open
Abstract
Background Artificial intelligence (AI) is a broad outlet of computer science aimed at constructing machines capable of simulating and performing tasks usually done by human beings. The aim of this scoping review is to map existing evidence on the use of AI in the delivery of medical care. Methods We searched PubMed and Scopus in March 2022, screened identified records for eligibility, assessed full texts of potentially eligible publications, and extracted data from included studies in duplicate, resolving differences through discussion, arbitration, and consensus. We then conducted a narrative synthesis of extracted data. Results Several AI methods have been used to detect, diagnose, classify, manage, treat, and monitor the prognosis of various health issues. These AI models have been used in various health conditions, including communicable diseases, non-communicable diseases, and mental health. Conclusions Presently available evidence shows that AI models, predominantly deep learning, and machine learning, can significantly advance medical care delivery regarding the detection, diagnosis, management, and monitoring the prognosis of different illnesses.
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Affiliation(s)
| | - Anelisa Jaca
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | | | - Ngozi Idemili-Aronu
- Department of Sociology/Anthropology, University of Nigeria, Nsukka, Nigeria
| | - Housseynou Ba
- World Health Organization Regional Office for Africa, Brazzaville, Republic of Congo
| | - Zukiswa Pamela Zantsi
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | - Asiphe Mavis Ndlambe
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
| | - Edison Mavundza
- World Health Organization Regional Office for Africa, Brazzaville, Republic of Congo
| | | | - Charles Shey Wiysonge
- Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa
- HIV and Other Infectious Diseases Research Unit, South African Medical Research Council, Durban, South Africa
| | - Lindiwe Makubalo
- World Health Organization Regional Office for Africa, Brazzaville, Republic of Congo
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Casano N, Santini SJ, Vittorini P, Sinatti G, Carducci P, Mastroianni CM, Ciardi MR, Pasculli P, Petrucci E, Marinangeli F, Balsano C. Application of machine learning approach in emergency department to support clinical decision making for SARS-CoV-2 infected patients. J Integr Bioinform 2023; 20:jib-2022-0047. [PMID: 36877860 PMCID: PMC10561065 DOI: 10.1515/jib-2022-0047] [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: 09/20/2022] [Revised: 01/20/2023] [Accepted: 02/08/2023] [Indexed: 03/08/2023] Open
Abstract
To support physicians in clinical decision process on patients affected by Coronavirus Disease 2019 (COVID-19) in areas with a low vaccination rate, we devised and evaluated the performances of several machine learning (ML) classifiers fed with readily available clinical and laboratory data. Our observational retrospective study collected data from a cohort of 779 COVID-19 patients presenting to three hospitals of the Lazio-Abruzzo area (Italy). Based on a different selection of clinical and respiratory (ROX index and PaO2/FiO2 ratio) variables, we devised an AI-driven tool to predict safe discharge from ED, disease severity and mortality during hospitalization. To predict safe discharge our best classifier is an RF integrated with ROX index that reached AUC of 0.96. To predict disease severity the best classifier was an RF integrated with ROX index that reached an AUC of 0.91. For mortality prediction the best classifier was an RF integrated with ROX index, that reached an AUC of 0.91. The results obtained thanks to our algorithms are consistent with the scientific literature an accomplish significant performances to forecast safe discharge from ED and severe clinical course of COVID-19.
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Affiliation(s)
- Nicolò Casano
- School of Emergency Medicine, Interdisciplinary BioMedical group on Artificial Intelligence, IBMAI, Department MeSVA, University of L’Aquila, L’Aquila, Italy
| | - Silvano Junior Santini
- School of Emergency Medicine, Interdisciplinary BioMedical group on Artificial Intelligence, IBMAI, Department MeSVA, University of L’Aquila, L’Aquila, Italy
- Francesco Balsano Foundation, Via Giovanni Battista Martini 6, 00198, Rome, Italy
| | - Pierpaolo Vittorini
- School of Emergency Medicine, Interdisciplinary BioMedical group on Artificial Intelligence, IBMAI, Department MeSVA, University of L’Aquila, L’Aquila, Italy
| | - Gaia Sinatti
- School of Emergency Medicine, Interdisciplinary BioMedical group on Artificial Intelligence, IBMAI, Department MeSVA, University of L’Aquila, L’Aquila, Italy
- Francesco Balsano Foundation, Via Giovanni Battista Martini 6, 00198, Rome, Italy
| | - Paolo Carducci
- School of Emergency Medicine, Interdisciplinary BioMedical group on Artificial Intelligence, IBMAI, Department MeSVA, University of L’Aquila, L’Aquila, Italy
| | - Claudio Maria Mastroianni
- Department of Public Health and Infectious Diseases, “Sapienza” University of Rome, Policlinico Umberto I Hospital, Rome, Italy
| | - Maria Rosa Ciardi
- Department of Public Health and Infectious Diseases, “Sapienza” University of Rome, Policlinico Umberto I Hospital, Rome, Italy
| | - Patrizia Pasculli
- Department of Public Health and Infectious Diseases, “Sapienza” University of Rome, Policlinico Umberto I Hospital, Rome, Italy
| | - Emiliano Petrucci
- Department of Anesthesiology, Intensive Care and Pain Treatment, University of L’Aquila, L’Aquila, Italy
| | - Franco Marinangeli
- Department of Anesthesiology, Intensive Care and Pain Treatment, University of L’Aquila, L’Aquila, Italy
| | - Clara Balsano
- School of Emergency Medicine, Interdisciplinary BioMedical group on Artificial Intelligence, IBMAI, Department MeSVA, University of L’Aquila, L’Aquila, Italy
- Francesco Balsano Foundation, Via Giovanni Battista Martini 6, 00198, Rome, Italy
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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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Peng H, Hu C, Deng W, Huang L, Zhang Y, Luo B, Wang X, Long X, Huang X. Incubation period, clinical and lung CT features for early prediction of COVID-19 deterioration: development and internal verification of a risk model. BMC Pulm Med 2022; 22:188. [PMID: 35549897 PMCID: PMC9095818 DOI: 10.1186/s12890-022-01986-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
Background Most severe, critical, or mortal COVID-19 cases often had a relatively stable period before their status worsened. We developed a deterioration risk model of COVID-19 (DRM-COVID-19) to predict exacerbation risk and optimize disease management on admission. Method We conducted a multicenter retrospective cohort study with 239 confirmed symptomatic COVID-19 patients. A combination of the least absolute shrinkage and selection operator (LASSO), change-in-estimate (CIE) screened out independent risk factors for the multivariate logistic regression model (DRM-COVID-19) from 44 variables, including epidemiological, demographic, clinical, and lung CT features. The compound study endpoint was progression to severe, critical, or mortal status. Additionally, the model's performance was evaluated for discrimination, accuracy, calibration, and clinical utility, through internal validation using bootstrap resampling (1000 times). We used a nomogram and a network platform for model visualization. Results In the cohort study, 62 cases reached the compound endpoint, including 42 severe, 18 critical, and two mortal cases. DRM-COVID-19 included six factors: dyspnea [odds ratio (OR) 4.89;confidence interval (95% CI) 1.53–15.80], incubation period (OR 0.83; 95% CI 0.68–0.99), number of comorbidities (OR 1.76; 95% CI 1.03–3.05), D-dimer (OR 7.05; 95% CI, 1.35–45.7), C-reactive protein (OR 1.06; 95% CI 1.02–1.1), and semi-quantitative CT score (OR 1.50; 95% CI 1.27–1.82). The model showed good fitting (Hosmer–Lemeshow goodness, X2(8) = 7.0194, P = 0.53), high discrimination (the area under the receiver operating characteristic curve, AUROC, 0.971; 95% CI, 0.949–0.992), precision (Brier score = 0.051) as well as excellent calibration and clinical benefits. The precision-recall (PR) curve showed excellent classification performance of the model (AUCPR = 0.934). We prepared a nomogram and a freely available online prediction platform (https://deterioration-risk-model-of-covid-19.shinyapps.io/DRMapp/). Conclusion We developed a predictive model, which includes the including incubation period along with clinical and lung CT features. The model presented satisfactory prediction and discrimination performance for COVID-19 patients who might progress from mild or moderate to severe or critical on admission, improving the clinical prognosis and optimizing the medical resources. Supplementary Information The online version contains supplementary material available at 10.1186/s12890-022-01986-0.
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Affiliation(s)
- Hongbing Peng
- Department of Pulmonary and Critical Care Medicine, Loudi Central Hospital, No. 51, Changqing Middle Street, Loudi, 417000, People's Republic of China. .,Department of Pulmonary and Critical Care Medicine, Loudi Central Hospital, No. 51, Changqing Middle Street, Loudi, 417000, People's Republic of China.
| | - Chao Hu
- Department of Pulmonary and Critical Care Medicine, Xiangtan Central Hospital, No. 120, Road Heping, Distract Yuhu, Xiangtan, 411100, People's Republic of China
| | - Wusheng Deng
- Department of Pulmonary and Critical Care Medicine, Shaoyang Central Hospital, No. 36 Hongqi Road, Shaoyang, 422000, People's Republic of China
| | - Lingmei Huang
- Department of Pulmonary and Critical Care Medicine, The First People's Hospital of YueYang, No. 39 Dongmaoling Road, Yueyang, 414000, People's Republic of China
| | - Yushan Zhang
- Department of Pulmonary and Critical Care Medicine, Huaihua First People's Hospital, No. 144, Jinxi South Road, Huaihua, 418000, People's Republic of China
| | - Baowei Luo
- Department of Respiratory Medicine, Shuangfeng County People's Hospital, 238 Shuyuan Road, Shuangfeng County, 417007, People's Republic of China
| | - Xingxing Wang
- Department of Infection, Loudi Central Hospital, No. 51, Changqing Middle Street, Loudi, 417000, People's Republic of China
| | - Xiaodan Long
- Department of Urology Surgery, Loudi Central Hospital, No. 51, Changqing Middle Street, Loudi, 417000, People's Republic of China
| | - Xiaoying Huang
- Department of Pulmonary and Critical Care Medicine, Loudi Central Hospital, No. 51, Changqing Middle Street, Loudi, 417000, People's Republic of China
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