Performance Review of Intelligent Guidance Robot at the Outpatient Clinic Setting

Alfred, C; Meng, Zuowei; Ding, Xiaorong

doi:10.7759/cureus.16840

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…Continuous improvements in artificial intelligence and ML algorithms offer great promise for hospitals, as well as the cities in which they locate, to become even smarter by applying new data analytics to convert information to intelligence (Ma et al. , 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Smart hospitals might apply DLSTM to forecast other crucial measures, such as medicine inventory management, hospital bed occupancy rate and medical equipment utilization. Continuous improvements in artificial intelligence and ML algorithms offer great promise for hospitals, as well as the cities in which they locate, to become even smarter by applying new data analytics to convert information to intelligence (Ma et al, 2021). With increased intelligence, hospital administrators could improve their decision-making, improve working conditions for medical staff and increase the quality of patient care.…”

Section: Discussionmentioning

confidence: 99%

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Making the hospital smart: using a deep long short-term memory model to predict hospital performance metrics

Jia

Zhu

et al. 2022

IMDS

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PurposeAbundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital data. Therefore, the current study aims to reports on an application of the DLSTM model to forecast multiple streams of healthcare data.Design/methodology/approachAs the most advanced machine learning (ML) method, static and dynamic DLSTM models aim to forecast time-series data, such as daily patient visits. With a comparative analysis conducted in a high-level, urban Chinese hospital, this study tests the proposed DLSTM model against several widely used time-series analyses as reference models.FindingsThe empirical results show that the static DLSTM approach outperforms seasonal autoregressive integrated moving averages (SARIMA), single and multiple RNN, deep gated recurrent units (DGRU), traditional long short-term memory (LSTM) and dynamic DLSTM, with smaller mean absolute, root mean square, mean absolute percentage and root mean square percentage errors (RMSPE). In particular, static DLSTM outperforms all other models for predicting daily patient visits, the number of daily medical examinations and prescriptions.Practical implicationsWith these results, hospitals can achieve more precise predictions of outpatient visits, medical examinations and prescriptions, which can inform hospitals' construction plans and increase the efficiency with which the hospitals manage relevant information.Originality/valueTo address a persistent gap in smart hospital and ML literature, this study offers evidence of the best forecasting models with a comparative analysis. The study extends predictive methods for forecasting patient visits, medical examinations and prescriptions and advances insights into smart hospitals by testing a state-of-the-art, deep learning neural network method.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Making the hospital smart: using a deep long short-term memory model to predict hospital performance metrics

Jia

Zhu

et al. 2022

IMDS

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“…This algorithm has good efficiency and results in solving the traveling salesman problem. Ma et al [17] have discussed the problem of parameter setting of ant colony algorithm in solving the travel salesman problem, and the results have certain reference value. The multi-pheromone dynamic updating ant colony algorithm is proposed, and the data of the travel service recommendation system is verified, with good performance.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based Multi-Object Adaptive Route Planning for Traveling Recommender Systems

Wang,

2023

IEEE Access

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In travel recommendation systems, the most important business demand is to make adaptive route planning and navigation decision based on computational intelligence. Existing researches mostly lacked the ability to adaptively suggest route planning schemes for users, according to contextual features. To deal with the problem, this paper proposes a deep reinforcement learning-based multi-object adaptive route planning method for travel recommendation systems. In particular, the technical framework utilizes both value iterative network (VIN) module and positioning module to construct a specific DRL algorithm for this purpose. It inputs the environment map of the agent into VIN module, obtains the value distribution corresponding to the map by means of convolution iteration, and extracts features by means of selfpositioning. Such a technical framework is expected to improve generalization ability of navigation strategies. Empirically, this work selects some popular scenic spots in a certain region as the research object for case analysis, and collects road network traffic data and tourist scenic spot parameter data through two public travel service platforms. The experimental results show that the suburban tourism route planning model based on deep reinforcement learning scene perception is reasonable and feasible, and can effectively improve tourist satisfaction.

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“…In clinical care, AI technology, especially AI robots, has a relatively wide range of applications throughout the entire process from patient admission to discharge, and even post-discharge home rehabilitation care. In busy hospital outpatient clinics, mobile intelligent guide robots play an important role in optimizing outpatient services, improving outpatient patient’s experience, and reducing the workload of nurses ( 8 ). In hospital intensive care unit, robots with different functions such as logistics and disinfection robots, rehabilitation assistance robots, and treatment assistance robots contribute to reduce the workload of nurses and provide personalized needs ( 9 ).…”

Section: Introductionmentioning

confidence: 99%

Knowledge and attitudes toward artificial intelligence in nursing among various categories of professionals in China: a cross-sectional study

Wang,

Fei,

Wei

et al. 2024

Front. Public Health

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ObjectivesThe application of artificial intelligence (AI) in healthcare is an important public health issue. However, few studies have investigated the perceptions and attitudes of healthcare professionals toward its applications in nursing. This study aimed to explore the knowledge, attitudes, and concerns of healthcare professionals, AI-related professionals, and others in China toward AI in nursing.MethodsWe conducted an online cross-sectional study on nursing students, nurses, other healthcare professionals, AI-related professionals, and others in China between March and April 2024. They were invited to complete a questionnaire containing 21 questions with four sections. The survey followed the principle of voluntary participation and was conducted anonymously. The participants could withdraw from the survey at any time during the study.ResultsThis study obtained 1,243 valid questionnaires. The participants came from 25 provinces and municipalities in seven regions of China. Regarding knowledge of AI in nursing, 57% of the participants knew only a little about AI, 4.7% did not know anything about AI, 64.7% knew only a little about AI in nursing, and 13.4% did not know anything about AI in nursing. For attitudes toward AI in nursing, participants were positive about AI in nursing, with more than 50% agreeing and strongly agreeing with each question on attitudes toward AI in nursing. Differences in the numbers of participants with various categories of professionals regarding knowledge and attitudes toward AI in nursing were statistically significant (p < 0.05). Regarding concerns and ethical issues about AI in nursing, every participant expressed concerns about AI in nursing, and 95.7% of participants believed that it is necessary to strengthen medical ethics toward AI in nursing.ConclusionNursing students and healthcare professionals lacked knowledge about AI or its application in nursing, but they had a positive attitude toward AI. It is necessary to strengthen medical ethics toward AI in nursing. The study’s findings could help develop new strategies benefiting healthcare.

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Performance Review of Intelligent Guidance Robot at the Outpatient Clinic Setting

Abstract: Ma et al. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Cited by 7 publications

References 8 publications

Making the hospital smart: using a deep long short-term memory model to predict hospital performance metrics

Making the hospital smart: using a deep long short-term memory model to predict hospital performance metrics

Deep Reinforcement Learning-Based Multi-Object Adaptive Route Planning for Traveling Recommender Systems

Knowledge and attitudes toward artificial intelligence in nursing among various categories of professionals in China: a cross-sectional study

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