scholarly journals 4375 Developing team science for practical applications of artificial intelligence in health systems to improve value and outcomes: A case study in reducing avoidable emergency department use

2020 ◽  
Vol 4 (s1) ◽  
pp. 117-117
Author(s):  
Vladimir G. Manuel ◽  
Eran Halperin ◽  
Jeffrey Chiang ◽  
Kodi Taraszka ◽  
Laura Kim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Emergency Department ◽  
Health Systems ◽  
Prediction Models ◽  
Interdisciplinary Team ◽  
Learning System ◽  
Emergency Department Use ◽  
System Perspective ◽  
System Leaders ◽  
Computer Scientists

OBJECTIVES/GOALS: Health care systems are complex, dynamic, and varied. Advances in artificial intelligence (AI) are enabling healthcare systems to use their own data to elicit patterns and design suitable interventions. To realize this potential, computer scientists and clinicians need an effective, practical, and replicable approach to collaboration METHODS/STUDY POPULATION: In this study, computer scientists partnered with clinicians to investigate predictors of avoidable emergency department use. The team sought an approach to computational medicine that could increase the relevance and impact of prediction to solve pressing problems in the health system. The team adopted an emergent architecture that engaged system leaders, computer scientists, data scientists, health services researchers, and practicing clinicians with deep ambulatory and inpatient knowledge to form the initial questions that shaped the prediction model; to understand nuances of coding and recording in source data and the implications for models; and to generate insights for promising points of intervention. The team recorded decisions and challenges as it progressed to analyze its function. RESULTS/ANTICIPATED RESULTS: Most avoidance models focus on a narrow time period around target events, or on high cost patients and events. This interdisciplinary team used their insights into the health system’s workflows and patient population to adopt a longitudinal approach to their prediction models. They used AI to build models of behavior in the system and consider prevention points across clinical units, time, and place. The holistic, systemwide focus enabled the team to generate insights that the system leaders and subsequently specific clinical units could apply to improve value and outcomes. A facilitated team process using learning system and cooperative network principles allowed a large and modular interdisciplinary team to build a transparent AI modeling process that yielded actionable insights into hypercomplex workflows. DISCUSSION/SIGNIFICANCE OF IMPACT: An architecture for involving diverse stakeholders in computational medicine projects can increase the relevance and impact of AI for solving care delivery problems in complex health systems. Translational science and computational medicine programs can foster this type of engagement and encourage a whole system perspective.

scholarly journals Educational Stakeholders’ Independent Evaluation of an Artificial Intelligence-Enabled Adaptive Learning System Using Bayesian Network Predictive Simulations

2019 ◽  
Vol 9 (2) ◽  
pp. 110 ◽  
Author(s):  
Meng-Leong HOW ◽  
Wei Loong David HUNG
Keyword(s):  
Artificial Intelligence ◽  
Adaptive Learning ◽  
Large Scale ◽  
Small Sample Size ◽  
Small Sample ◽  
Learning System ◽  
Learning Needs ◽  
Educational Stakeholders ◽  
Independent Evaluation ◽  
Computer Scientists

Artificial intelligence-enabled adaptive learning systems (AI-ALS) are increasingly being deployed in education to enhance the learning needs of students. However, educational stakeholders are required by policy-makers to conduct an independent evaluation of the AI-ALS using a small sample size in a pilot study, before that AI-ALS can be approved for large-scale deployment. Beyond simply believing in the information provided by the AI-ALS supplier, there arises a need for educational stakeholders to independently understand the motif of the pedagogical characteristics that underlie the AI-ALS. Laudable efforts were made by researchers to engender frameworks for the evaluation of AI-ALS. Nevertheless, those highly technical techniques often require advanced mathematical knowledge or computer programming skills. There remains a dearth in the extant literature for a more intuitive way for educational stakeholders—rather than computer scientists—to carry out the independent evaluation of an AI-ALS to understand how it could provide opportunities to educe the problem-solving abilities of the students so that they can successfully learn the subject matter. This paper proffers an approach for educational stakeholders to employ Bayesian networks to simulate predictive hypothetical scenarios with controllable parameters to better inform them about the suitability of the AI-ALS for the students.

Self-Learning System for Child Development Using Conversational AI and Natural Language Processing (NLP)

2021 ◽  
pp. 124-133
Author(s):  
Amit Mishra
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Child Development ◽  
Human Brain ◽  
Language Processing ◽  
Continuous Process ◽  
Learning System ◽  
Computer Scientists ◽  
Amount Of Knowledge ◽  
Self Learning

Education and learning are the most important aspects of the evolution of societies. They have been a favorite subject for philosophers and psychologists to work upon. Same questions are now being re-dealt by computer scientists in current scenarios. Although evolution is a continuous process, the pace of evolution is not a linear graph. Children acquire a huge amount of knowledge with very little input from teachers, friends, parents, and surroundings. Understanding how human brain works and more precisely, how the child brain actually functions is opening the path of researches in artificial intelligence (AI).

scholarly journals Emergency department use by oldest-old patients from 2005 to 2010 in a Swiss university hospital

2013 ◽  
Vol 13 (1) ◽  
Author(s):  
Sarah Vilpert ◽  
Hélène Jaccard Ruedin ◽  
Lionel Trueb ◽  
Stéfanie Monod-Zorzi ◽  
Bertrand Yersin ◽  
...  
Keyword(s):  
Emergency Department ◽  
Oldest Old ◽  
University Hospital ◽  
Emergency Department Use ◽  
Old Patients

scholarly journals Artificial intelligence for good health: a scoping review of the ethics literature

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Kathleen Murphy ◽  
Erica Di Ruggiero ◽  
Ross Upshur ◽  
Donald J. Willison ◽  
Neha Malhotra ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Global Health ◽  
Population Health ◽  
Health Systems ◽  
Scoping Review ◽  
Industrial Revolution ◽  
Ethical Issues ◽  
Grey Literature ◽  
Ethical Implications

Abstract Background Artificial intelligence (AI) has been described as the “fourth industrial revolution” with transformative and global implications, including in healthcare, public health, and global health. AI approaches hold promise for improving health systems worldwide, as well as individual and population health outcomes. While AI may have potential for advancing health equity within and between countries, we must consider the ethical implications of its deployment in order to mitigate its potential harms, particularly for the most vulnerable. This scoping review addresses the following question: What ethical issues have been identified in relation to AI in the field of health, including from a global health perspective? Methods Eight electronic databases were searched for peer reviewed and grey literature published before April 2018 using the concepts of health, ethics, and AI, and their related terms. Records were independently screened by two reviewers and were included if they reported on AI in relation to health and ethics and were written in the English language. Data was charted on a piloted data charting form, and a descriptive and thematic analysis was performed. Results Upon reviewing 12,722 articles, 103 met the predetermined inclusion criteria. The literature was primarily focused on the ethics of AI in health care, particularly on carer robots, diagnostics, and precision medicine, but was largely silent on ethics of AI in public and population health. The literature highlighted a number of common ethical concerns related to privacy, trust, accountability and responsibility, and bias. Largely missing from the literature was the ethics of AI in global health, particularly in the context of low- and middle-income countries (LMICs). Conclusions The ethical issues surrounding AI in the field of health are both vast and complex. While AI holds the potential to improve health and health systems, our analysis suggests that its introduction should be approached with cautious optimism. The dearth of literature on the ethics of AI within LMICs, as well as in public health, also points to a critical need for further research into the ethical implications of AI within both global and public health, to ensure that its development and implementation is ethical for everyone, everywhere.

scholarly journals Artificial intelligence-based education assists medical students’ interpretation of hip fracture

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Chi-Tung Cheng ◽  
Chih-Chi Chen ◽  
Chih-Yuan Fu ◽  
Chung-Hsien Chaou ◽  
Yu-Tung Wu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Education ◽  
Hip Fracture ◽  
Medical Students ◽  
Medical Image ◽  
Learning System ◽  
Significant Difference ◽  
Dynamic Collaboration ◽  
Increase In Accuracy ◽  
Image Learning

Abstract Background With recent transformations in medical education, the integration of technology to improve medical students’ abilities has become feasible. Artificial intelligence (AI) has impacted several aspects of healthcare. However, few studies have focused on medical education. We performed an AI-assisted education study and confirmed that AI can accelerate trainees’ medical image learning. Materials We developed an AI-based medical image learning system to highlight hip fracture on a plain pelvic film. Thirty medical students were divided into a conventional (CL) group and an AI-assisted learning (AIL) group. In the CL group, the participants received a prelearning test and a postlearning test. In the AIL group, the participants received another test with AI-assisted education before the postlearning test. Then, we analyzed changes in diagnostic accuracy. Results The prelearning performance was comparable in both groups. In the CL group, postlearning accuracy (78.66 ± 14.53) was higher than prelearning accuracy (75.86 ± 11.36) with no significant difference (p = .264). The AIL group showed remarkable improvement. The WithAI score (88.87 ± 5.51) was significantly higher than the prelearning score (75.73 ± 10.58, p < 0.01). Moreover, the postlearning score (84.93 ± 14.53) was better than the prelearning score (p < 0.01). The increase in accuracy was significantly higher in the AIL group than in the CL group. Conclusion The study demonstrated the viability of AI for augmenting medical education. Integrating AI into medical education requires dynamic collaboration from research, clinical, and educational perspectives.

Design of English reading and learning management system in college education based on artificial intelligence

2021 ◽  
pp. 1-10
Author(s):  
Fen Zhang ◽  
Min She
Keyword(s):  
Artificial Intelligence ◽  
Model Performance ◽  
College Education ◽  
Personalized Learning ◽  
Learning System ◽  
Model Function ◽  
English Reading ◽  
Function Modules ◽  
Learning Platforms ◽  
Learning Management

English reading learning in college education is an efficient means of English learning. However, most of the current English reading learning platforms in colleges and universities only put different English books on the platform in electronic form for students to read, which leads to blindness of reading. Based on artificial intelligence algorithms, this paper builds model function modules according to the needs of English reading and learning management in college education and implements system functions based on artificial intelligence algorithms. Moreover, according to the above design principles of personalized learning model and the characteristics of personalized network learning, this paper designs a personalized learning system based on meaningful learning theory. In addition, this article verifies and analyzes the model performance. The research results show that the model proposed in this paper has a certain effect.

Endoscopic prediction of submucosal invasion in Barrett’s cancer with the use of Artificial Intelligence: A pilot Study

Endoscopy ◽  
2020 ◽  
Author(s):  
Alanna Ebigbo ◽  
Robert Mendel ◽  
Tobias Rückert ◽  
Laurin Schuster ◽  
Andreas Probst ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Pilot Study ◽  
White Light ◽  
Submucosal Invasion ◽  
Learning System ◽  
Endoscopic Images ◽  
Barrett’S Cancer ◽  
Significant Difference ◽  
Sensitivity Specificity

Background and aims: The accurate differentiation between T1a and T1b Barrett’s cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an Artificial Intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett’s cancer white-light images. Methods: Endoscopic images from three tertiary care centres in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross-validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) was evaluated with the AI-system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett’s cancer. Results: The sensitivity, specificity, F1 and accuracy of the AI-system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.73 and 0.71, respectively. There was no statistically significant difference between the performance of the AI-system and that of human experts with sensitivity, specificity, F1 and accuracy of 0.63, 0.78, 0.67 and 0.70 respectively. Conclusion: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett’s cancer. AI scored equal to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and in a real-life setting. Nevertheless, the correct prediction of submucosal invasion in Barret´s cancer remains challenging for both experts and AI.

Nonurgent Emergency Department Use During Pregnancy

2014 ◽  
Vol 123 ◽  
pp. 150S ◽  
Author(s):  
Kimberly A. Kilfoyle ◽  
Roxanne A. Vrees ◽  
Kristen A. Matteson ◽  
Maureen G. Phipps ◽  
Christina A. Raker
Keyword(s):  
Emergency Department ◽  
Emergency Department Use
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