Factors influencing the adoption of computerized medical diagnosing system for tuberculosis

Panicker, Rani Oomman; Sabu, M. K.

doi:10.1007/s41870-019-00396-6

Cited by 6 publications

(10 citation statements)

References 41 publications

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“…This is a surprising result that seems to be inconsistent with previous studies of the UTAUT model in healthcare information systems [84,85]. In general, when doctors consider that the healthcare information system can promote their work, they will have higher behavioral intentions and increased use behaviors [33][34][35]. However, the viewpoint hides an important premise, i.e., doctors can get enough bene ts in terms of performance or e ciency to offset the costs and possible risks of using the system, for example, learning costs and error assistance information.…”

Section: Discussionmentioning

confidence: 69%

“…It has a high similarity with perceived usefulness in TAM and directly in uences use intention [32]. In the context of healthcare information systems, if doctors believe the new technology is more effective and bene cial for their clinical work, they will be motivated to accept it [29,33,34]. In doctors' acceptance of AI-CDSSs, performance expectancy refers to the degree to which doctors believe that using the CDSS for decision-making will improve medical performance [35].…”

Section: Hypotheses Based On the Utaut Modelmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 69%

Section: Hypotheses Based On the Utaut Modelmentioning

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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“…Many descriptions of the network graph parameters make Jaccard's definition of the similarity of network nodes have certain limitations. Therefore, Hub Promoted and Hub Depressed 25 considered the influence of the edge weight of connected nodes on the similarity, and proposed the definitions of formulas (2) and 3:…”

Section: Related Definitionsmentioning

confidence: 99%

“…It can only analyze the medication rules for a disease based on a large number of medical prescriptions for a certain disease, 1 and cannot take into account the use of drugs between related diseases. 2 Exploring the common use of medicines between different diseases helps experimental researchers to explore the core medicines prescribed, 3,4 and also provides clues and goals for clinical researchers to explore further. In this way, not only can we more objectively confirm and reproduce the existing disease medication laws, 5,6 but also discover new knowledge and clues, and make more contributions to the research of medical data.…”

Section: Introductionmentioning

confidence: 99%

A medical network clustering method with weighted graph structure

Wang

2020

Measurement and Control

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Today, most of the databases used for drug information mining are derived from the collection of many treatments under a single disease, and some special drug compatibility rules can be found from them. However, researchers’ exploration of medical data is not limited to this. The comparative analysis of drugs for different diseases has become a new research point. In this paper, the drug is used as a node, the relationship is the edge connecting the two nodes, the co-occurrence frequency of the drug is used as the weight of the edge to establish a network graph. We use the clustering algorithm of the weighted network graph center diffusion method combining the network topology and the edge weights to divide the network graph into communities. Then we proposed the Structural Clustering Algorithm on Weighted Networks (SCW), it helps to study the prescription of medical prescriptions and provides more scientific recommendations for auxiliary prescriptions. In the experiment, SCW is compared with the classic community discovery algorithm CPM, the network function modular analysis algorithm MCODE and the hierarchical network graph structure analysis algorithm BGLL. We analyze the results according to NMI, ARI and F-Measure. Finally, a case study of real data was conducted to ensure the correctness and effectiveness of the algorithm, and to obtain the potential drug combination in the medical prescription.

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“…These results are consistent with prior findings which also found positive and significant relationships between PBE and use intention. Technology acceptance model (TAM) or the diffusion of innovation (DoI) was used to examine the relationship between perceived usefulness or relative advantage and use intention [36][37][38][39].…”

mentioning

confidence: 99%

The moderating role of trust in government adoption e-service during Covid-19 pandemic: health belief model perspective

Sihotang

Andriqa

Alfahmi

et al. 2023

Int. j. inf. tecnol.

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The present paper discusses the influence of factors in the health belief model (HBM) on adopting government e-services during the Covid-19 pandemic in Indonesia. Furthermore, the present study demonstrates the moderating effect of trust in HBM. Therefore, we propose an interacting model between trust and HBM. A survey of 299 citizens in Indonesia was used to test the proposed model. By using a structural equation model (SEM), this study found that the HBM factors (perceived susceptibility, perceived benefit, perceived barriers, self-efficacy, cues to action, health concern) significantly affect the intention to adopt government e-services during the Covid-19 pandemic, except for the perceived severity factor. In addition, this study reveals the role of the trust variable, which significantly strengthens the effect of HBM on government e-service.

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Factors influencing the adoption of computerized medical diagnosing system for tuberculosis

Cited by 6 publications

References 41 publications

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

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

A medical network clustering method with weighted graph structure

The moderating role of trust in government adoption e-service during Covid-19 pandemic: health belief model perspective

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