Status of AI-Enabled Clinical Decision Support Systems Implementations in China

Ji, Mengting; Chen, Xiaoyun; Genchev, Georgi Z.; Wei, Ming; Yu, Guangjun

doi:10.1055/s-0041-1736461

Cited by 12 publications

(7 citation statements)

References 34 publications

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“…22 Moreover, these systems are not considering the knowledge of experts. 23 Different experts will consider different symptoms, degrees of manifestation, and severity levels while diagnosing a patient. Hence, this work focuses on analyzing the agreement among experts from different places and then considering their knowledge for providing decision support to clinicians.…”

Section: Decision Support Systemmentioning

confidence: 99%

An Explainable Knowledge-Based System Using Subjective Preferences and Objective Data for Ranking Decision Alternatives

et al. 2022

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Background Allergy is a hypersensitive reaction that occurs when the allergen reacts with the immune system. The prevalence and severity of the allergies are uprising in South Asian countries. Allergy often occurs in combinations which becomes difficult for physicians to diagnose. Objectives This work aims to develop a decision-making model which aids physicians in diagnosing allergy comorbidities. The model intends to not only provide rational decisions, but also explainable knowledge about all alternatives. Methods The allergy data gathered from real-time sources contain a smaller number of samples for comorbidities. Decision-making model applies three sampling strategies, namely, ideal, single, and complete, to balance the data. Bayes theorem-based probabilistic approaches are used to extract knowledge from the balanced data. Preference weights for attributes with respect to alternatives are gathered from a group of domain-experts affiliated to different allergy testing centers. The weights are combined with objective knowledge to assign confidence values to alternatives. The system provides these values along with explanations to aid decision-makers in choosing an optimal decision. Results Metrics of explainability and user satisfaction are used to evaluate the effectiveness of the system in real-time diagnosis. Fleiss' Kappa statistic is 0.48, and hence the diagnosis of experts is said to be in moderate agreement. The decision-making model provides a maximum of 10 suitable and relevant pieces of evidence to explain a decision alternative. Clinicians have improved their diagnostic performance by 3% after using CDSS (77.93%) with a decrease in 20% of time taken. Conclusion The performance of less-experienced clinicians has improved with the support of an explainable decision-making model. The code for the framework with all intermediate results is available at https://github.com/kavya6697/Allergy-PT.git.

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Section: Decision Support Systemmentioning

confidence: 99%

An Explainable Knowledge-Based System Using Subjective Preferences and Objective Data for Ranking Decision Alternatives

et al. 2022

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“…In the third stage, from 2016 to 2022, particular interest in the latter period has been research on the impact of focused on barriers during implementation [32,33]. Research shows a shift from exploratory research to speci c clinical practice, The possible problems involved in the introduction and use of construction are discussed [32,34,35] [26]. Generally speaking, journals with a high frequency of publication of related literature provide researchers with publication guidelines for documents.…”

Section: 1general Information On Cdsss Research In Nursingmentioning

confidence: 99%

“…Research suggests a shift from clinical research to exploratory practice. The possible problems involved in the introduction and use of practice are discussed [32,34,35]. In terms of areas that have not yet been su ciently developed or are not yet present in the literature of the sample analyzed, and which should be the subject of future research work.…”

Section: Key Themes Evolution Research Hot Spots and Future Challenge...mentioning

confidence: 99%

Clinical Decision Support Systems Research in Nursing: A Visual Survey

Lan

Zhu

Liu

et al. 2022

Preprint

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Background：Artificial intelligence (AI) has a big impact on healthcare now and in the future. Nurses, representing the largest proportion of healthcare workers, are set to benefit greatly from this technology. AI-Enabled Clinical Decision Support Systems has received a great deal of attention recently. Bibliometric analysis can offer an objective, systematic, and comprehensive analysis of specific field with a vast background. However, no bibliometric analysis has investigated AI-Enabled Clinical Decision Support Systems research in Nursing.Objective:To determine the characteristics of articles about the global performance and development of AI-Enabled Clinical Decision Support Systems research in Nursing.Methods: In this study, the bibliometric approach was used to estimate the searched data on Clinical Decision Support Systems research in Nursing from 2009 to 2022, and we also utilized CiteSpace and VOSviewer software to build visualizing maps to assess the contribution of different journals, authors, et al, as well as to identify research hot spots and promising future trends in this research field.Result:From 2009 to 2022, a total of 2159 publications were retrieved. The number of publications and citations on AI-Enabled Clinical Decision Support Systems research in Nursing has increased obviously in the last years. However, They are understudied in the field of nursing and there is a compelling need to development more more high-quality research. Conclusion: AI-Enabled Nursing Decision Support System use in clinical practice is still in its early stages. These analyses and results hope to provide useful information and references for future research directions for researchers and nursing practitioners who use AI-Enabled Clinical Decision Support Systems.

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“…CDSSs may also aggregate data from multiple sources that are not synced properly, and users may develop risky manual workarounds [8]. On the contrary, bene ting from the emergence of the new arti cial intelligence (AI) technologies represented by deep learning and knowledge inference, AI-enabled CDSSs (AI-CDSSs) among the non-predetermined-rule-based CDSSs have developed rapidly and are widely used [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Factors influencing doctors’ acceptance of artificial intelligence-enabled clinical decision support systems in tertiary hospitals in China

Yang,

Man,

Yun

et al. 2023

Preprint

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Artificial intelligence-enabled clinical decision support systems (AI-CDSSs) can enhance decision-making ability to improve healthcare performance and efficiency by introducing new AI technologies such as deep neural networks and knowledge graphs. This study aimed to analyze the factors influencing doctors’ acceptance of AI-CDSSs in tertiary hospitals in China. We proposed a hypothesized model based on the unified theory of acceptance and use of technology (UTAUT) model, and developed a 25-item measurement scale. 187 doctors completed the web-survey, who’s from four tertiary hospitals in four provinces in China. We used partial least squares structural equation modeling (PLS-SEM) to analyze the data. The Cronbach α value was 0.932, the corrected item to total correlation values ranged from 0.467 to 0.744, and the average variance of extracted values ranged from 0.628 to 0.782. The standardized root mean square residual value was 0.055, the squared Euclidean distance and geodesic distance were 0.995 and 0.566, respectively. The model had explained 73.1% of doctors’ acceptance. Doctors’ acceptance of AI-CDSSs is strongly influenced by effort expectancy, is moderately influenced by trust in AI and is weakly influenced by social factors. Trust in AI as an important new factor is moderately influenced by social factors and is weakly influenced by effort expectancy and performance expectancy. Compared to the performance of decision-making, doctors are more interested in improving efficiency. Moreover, improving the ease of use, focusing on doctors' trust in AI technology, and enhancing the introduction of AI-CDSSs will encourage doctors to accept AI-CDSSs.

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Status of AI-Enabled Clinical Decision Support Systems Implementations in China

Cited by 12 publications

References 34 publications

An Explainable Knowledge-Based System Using Subjective Preferences and Objective Data for Ranking Decision Alternatives

An Explainable Knowledge-Based System Using Subjective Preferences and Objective Data for Ranking Decision Alternatives

Clinical Decision Support Systems Research in Nursing: A Visual Survey

Factors influencing doctors’ acceptance of artificial intelligence-enabled clinical decision support systems in tertiary hospitals in China

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