The Critical Factors Affecting the Deployment and Scaling of Healthcare AI: Viewpoint from an Experienced Medical Center

Huang, Chien‐Cheng; Wang, Jhi-Joung; Kuo, Kuang-Ming; Chen, Chia‐Jung

doi:10.3390/healthcare9060685

Cited by 12 publications

(12 citation statements)

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“…Moreover, we call for more AI models for various diseases in the ED to be developed to maximize the coverage of monitored patients. In addition, discussing the success of medical AI at the the environmental and organizational level is a very worthy research topic, including the topics of policy trend driving, value-added and benefit assessment, harmony between stakeholders, and the consensus of the organization [ 11 , 23 ].…”

Section: Discussionmentioning

confidence: 99%

“…Chi Mei’s AI computing infrastructure (yellow area in Figure 1 ) consists of three cores, namely, medical big data, an AI development platform (including GPU), and an AI web service platform [ 11 ]. The original data source (data warehouse) is mainly the online hospital information system (HIS) database (historical EMR).…”

Section: Methodsmentioning

confidence: 99%

“…However, past research claims that the current medical healthcare AI available is only used to assist in medical decision-making. Thus, our hospitals adopted a spontaneous use, without compulsion, and the AI predictive results were provided for reference [ 11 ]. As a result, the ED healthcare staff did not use the medical AI tools often.…”

Section: Introductionmentioning

confidence: 99%

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Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients

et al. 2022

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The emergency department (ED) is at the forefront of medical care, and the medical team needs to make outright judgments and treatment decisions under time constraints. Thus, knowing how to make personalized and precise predictions is a very challenging task. With the advancement of artificial intelligence (AI) technology, Chi Mei Medical Center (CMMC) adopted AI, the Internet of Things (IoT), and interaction technologies to establish diverse prognosis prediction models for eight diseases based on the ED electronic medical records of three branch hospitals. CMMC integrated these predictive models to form a digital AI dashboard, showing the risk status of all ED patients diagnosed with any of these eight diseases. This study first explored the methodology of CMMC’s AI development and proposed a four-tier AI dashboard architecture for ED implementation. The AI dashboard’s ease of use, usefulness, and acceptance was also strongly affirmed by the ED medical staff. The ED AI dashboard is an effective tool in the implementation of real-time risk monitoring of patients in the ED and could improve the quality of care as a part of best practice. Based on the results of this study, it is suggested that healthcare institutions thoughtfully consider tailoring their ED dashboard designs to adapt to their unique workflows and environments.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients

et al. 2022

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“…Smart medical service is to uses AI technology as a tool to provide accurate and systematic medical services based on big data (12). With the breakthrough of the neural network, deep learning, image recognition, other key technologies, and renewed AI medical service will realize the leap from perceptual intelligence and computational intelligence to cognitive intelligence (13)(14)(15). Given the above, this study publicizes healthcare to undergraduates through lectures and electives in medical colleges, so as to judge whether students hide or exaggerate their illness and master the knowledge of healthcare, which is convenient for health guidance.…”

Section: Introductionmentioning

confidence: 98%

AI-Based Publicity Strategies for Medical Colleges: A Case Study of Healthcare Analysis

Wang

2022

Front. Public Health

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The health status and cognition of undergraduates, especially the scientific concept of healthcare, are particularly important for the overall development of society and themselves. The survey shows that there is a significant lack of knowledge about healthcare among undergraduates in medical college, even among medical undergraduates, not to mention non-medical undergraduates. Therefore, it is a good way to publicize healthcare lectures or electives for undergraduates in medical college, which can strengthen undergraduates' cognition of healthcare and strengthen the concept of healthcare. In addition, undergraduates' emotional and mental state in healthcare lectures or electives can be analyzed to determine whether undergraduates have hidden illnesses and how well they understand the healthcare content. In this study, at first, a mental state recognition method of undergraduates in medical college based on data mining technology is proposed. Then, the vision-based expression and posture are used for expanding the channels of emotion recognition, and a dual-channel emotion recognition model based on artificial intelligence (AI) during healthcare lectures or electives in a medical college is proposed. Finally, the simulation is driven by TensorFlow with respect to mental state recognition of undergraduates in medical college and emotion recognition. The simulation results show that the recognition accuracy of mental state recognition of undergraduates in a medical college is more than 92%, and the rejection rate and misrecognition rate are very low, and false match rate and false non-match rate of mental state recognition is significantly better than the other three benchmarks. The emotion recognition of the dual-channel emotion recognition method is over 96%, which effectively integrates the emotional information expressed by facial expressions and postures.

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“…Artificial intelligence (AI) has seen widespread application in the ED for outcome prediction in various diseases in recent years. [10][11][12][13] Compared to conventional CDRs, AI-based predictions are often more precise and user-friendly, particularly in the fast-paced environment of the ED. [10][11][12][13] Despite AI being explored for improving diagnosis, treatment, and severity prediction in AP for the past two decades, 14…”

Section: Introductionmentioning

confidence: 99%

Real‐time artificial intelligence predicts adverse outcomes in acute pancreatitis in the emergency department: Comparison with clinical decision rule

Chang,

Chen,

et al. 2023

Academic Emergency Medicine

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ObjectiveArtificial intelligence (AI) prediction is increasingly used for decision making in healthcare, but its application for adverse outcomes in emergency department (ED) patients with acute pancreatitis (AP) is not well understood. This study aimed to clarify this aspect.MethodsData from 8,274 ED patients with AP in three hospitals from 2009 to 2018 were analyzed. Demographic data, comorbidities, laboratory results, and adverse outcomes were included. Six algorithms were evaluated, and the one with the highest area under the curve (AUC) was implemented into the hospital information system (HIS) for real‐time prediction. Predictive accuracy was compared between the AI model and Bedside Index for Severity in Acute Pancreatitis (BISAP).ResultsThe mean age was 56.1±16.7 years, with 67.7% being male. The AI model was successfully implemented in the HIS, with Light Gradient Boosting Machine (LightGBM) showing the highest AUC for sepsis (AUC: 0.961) and ICU admission (AUC: 0.973), and eXtreme Gradient Boosting (XGBoost) showing the highest AUC for mortality (AUC: 0.975). Compared to BISAP, the AI model had superior AUC for sepsis (BISAP: 0.785), ICU admission (BISAP: 0.778), and mortality (BISAP: 0.817).ConclusionThe first real‐time AI prediction model implemented in the HIS for predicting adverse outcomes in ED patients with AP shows favorable initial results. However, further external validation is needed to ensure its reliability and accuracy.

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The Critical Factors Affecting the Deployment and Scaling of Healthcare AI: Viewpoint from an Experienced Medical Center

Cited by 12 publications

References 10 publications

Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients

Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients

AI-Based Publicity Strategies for Medical Colleges: A Case Study of Healthcare Analysis

Real‐time artificial intelligence predicts adverse outcomes in acute pancreatitis in the emergency department: Comparison with clinical decision rule

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