Healthcare professionals satisfaction and AI-based clinical decision support system in public sector hospitals during health crises: a cross-sectional study

Ahmad, Nisar Mohammad; Du, Shaofu; Ahmed, Fawad; Amin, Noor Ul; Yi, Xu

doi:10.1007/s10799-023-00407-w

Cited by 4 publications

(3 citation statements)

References 75 publications

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“…A substantial proportion of respondents expressed satisfaction with AI tools, particularly those with moderate to extensive experience in ENT practice [41]. These findings are consistent with previous research by Ahmad et al [42] and Nasseef et al [43], which demonstrated high levels of satisfaction among healthcare professionals with the use of AI in medical decision-making and patient care [42,43]. Additionally, respondents expressed optimism regarding the future role of AI in ENT practice, highlighting the potential for AI to enhance diagnostic capabilities, improve clinical outcomes, and streamline workflow efficiencies.…”

Section: Discussionsupporting

confidence: 88%

A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals

Alshehri,

Alahmari,

Alasiry

2024

JPM

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The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize medical diagnostics, particularly in specialized fields such as Ear, Nose, and Throat (ENT) medicine. However, the successful adoption of AI-assisted diagnostic tools in ENT practice depends on the understanding of various factors; these include influences on their effectiveness and acceptance among healthcare professionals. This cross-sectional study aimed to assess the usability and integration of AI tools in ENT practice, determine the clinical impact and accuracy of AI-assisted diagnostics in ENT, measure the trust and confidence of ENT professionals in AI tools, gauge the overall satisfaction and outlook on the future of AI in ENT diagnostics, and identify challenges, limitations, and areas for improvement in AI-assisted ENT diagnostics. A structured online questionnaire was distributed to 600 certified ENT professionals with at least one year of experience in the field. The questionnaire assessed participants’ familiarity with AI tools, usability, clinical impact, trust, satisfaction, and identified challenges. A total of 458 respondents completed the questionnaire, resulting in a response rate of 91.7%. The majority of respondents reported familiarity with AI tools (60.7%) and perceived them as generally usable and clinically impactful. However, challenges such as integration with existing systems, user-friendliness, accuracy, and cost were identified. Trust and satisfaction levels varied among participants, with concerns regarding data privacy and support. Geographic and practice setting differences influenced perceptions and experiences. The study highlights the diverse perceptions and experiences of ENT professionals regarding AI-assisted diagnostics. While there is general enthusiasm for these tools, challenges related to integration, usability, trust, and cost need to be addressed for their widespread adoption. These findings provide valuable insights for developers, policymakers, and healthcare providers aiming to enhance the role of AI in ENT practice.

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Section: Discussionsupporting

confidence: 88%

A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals

Alshehri,

Alahmari,

Alasiry

2024

JPM

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“…The semantic interoperability solution proposed by Gupta et.al is not based on fog computing. There is no cloud fog interoperability with the approach suggested by Rahmani et al [30], Ahmad et al [1] and Gupta et al [31]. A sample topology and comparison with existing work is shown in Fig.…”

Section: Simulation Time Analysismentioning

confidence: 99%

“…Fog computing is used in many sectors, including agriculture, clinical decision support systems [1], logistics, transportation, etc. The literature review on fog computing is summarized in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Semantic Interoperability in Healthcare Systems Using IFCIoT Architecture

2024

KSII TIIS

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Fast decision support systems and accurate diagnosis have become significant in the rapidly growing healthcare sector. As the number of disparate medical IoT devices connected to the human body rises, fast and interrelated healthcare data retrieval gets harder and harder. One of the most important requirements for the Healthcare Internet of Things (HIoT) is semantic interoperability. The state-of-the-art HIoT systems have problems with bandwidth and latency. An extension of cloud computing called fog computing not only solves the latency problem but also provides other benefits including resource mobility and on-demand scalability. The recommended approach helps to lower latency and network bandwidth consumption in a system that provides semantic interoperability in healthcare organizations. To evaluate the system's language processing performance, we simulated it in three different contexts. 1. Polysemy resolution system 2. System for hyponymy -hypernymy resolution with polysemy 3. System for resolving polysemy, hypernymy, hyponymy, meronymy, and holonymy. In comparison to the other two systems, the third system has lower latency and network usage. The proposed framework can reduce the computation overhead of heterogeneous healthcare data. The simulation results show that fog computing can reduce delay, network usage, and energy consumption.

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Physician Satisfaction With Clinical Decision Support Systems: Impact of Technology Identity and Computer Self-Efficacy

Deng,

Wang,

et al. 2024

IEEE Trans. Eng. Manage.

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Healthcare professionals satisfaction and AI-based clinical decision support system in public sector hospitals during health crises: a cross-sectional study

Cited by 4 publications

References 75 publications

A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals

A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals

Enhancement of Semantic Interoperability in Healthcare Systems Using IFCIoT Architecture

Physician Satisfaction With Clinical Decision Support Systems: Impact of Technology Identity and Computer Self-Efficacy

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