A comparative quantitative study of utilizing artificial intelligence on electronic health records in the USA and China during 2008–2017

BACKGROUND With the accumulation of electronic health records data and the development of artificial intelligence, patients with cancer urgently need new evidence of more personalized clinical and demographic characteristics and more sophisticated treatment and prevention strategies. However, no research has systematically analyzed the application and significance of electronic health records and artificial intelligence in cancer care. OBJECTIVE In this study, we reviewed the literature on the application of AI based on EHR data from patients with cancer, hoping to provide reference for subsequent researchers, and help accelerate the application of EHR data and AI technology in the field of cancer, so as to help patients get more scientific and accurate treatment. METHODS Three databases were systematically searched to retrieve potentially relevant articles published from January 2009 to October 2020. A combination of terms related to "electronic health records", "artificial intelligence" and "cancer" was used to search for these publications. RESULTS Of the 1034 articles considered, 148 met the inclusion criteria. The review has shown that ensemble methods and deep learning were on the rise. It presented the representative literatures on the subfield of cancer diagnosis, treatment and care. In addition, the vast majority of studies in this area were based on private institutional databases, resulting in poor portability of the proposed methodology process. CONCLUSIONS The use of new methods and electronic health records data sharing and fusion were recommended for future research. With the help of specialists, artificial intelligence and the mining of massive electronic medical records could provide great opportunities for improving cancer management.

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Adoption of Electronic Health Care Records

Information Systems and Technologies for Enhancing Health and Social Care ◽

10.4018/978-1-4666-3667-5.ch010 ◽

2013 ◽

pp. 148-165

Author(s):

Jerald D. Hatton ◽

Thomas M. Schmidt ◽

Jonatan Jelen

Keyword(s):

Electronic Health Records ◽

New Technologies ◽

Phenomenological Study ◽

Cost Effective ◽

Research Quality ◽

Control Cost ◽

Health Records ◽

The Usa ◽

Electronic Health ◽

Support Of Research

Political, economic, and safety concerns have militated for the adoption of Electronic Health Records by physicians in the United States, but current rates of adoption have failed to penetrate the 50% level. A qualitative phenomenological study of practicing physicians reveals stumbling blocks to adoption. Maintaining a physician’s perceived sense of control of the process is key. Electronic Health Records (EHRs) are critical to the support of research, quality control, cost reduction, and implementation of new technologies and methods in healthcare. Progress in the USA towards adoption of standardized EHRs has been halting. The authors discuss the results of a phenomenological study of physicians and draw conclusions that will assist all stakeholders in building a more consistent, comprehensive, and cost-effective healthcare system. When attempting to persuade physicians to migrate to an EMR-based solution, a strong focus on the control that physicians will have should be emphasized. The transition to an EHR system is eased by clearly articulating early in the process the potential benefits and the degree of control physicians can have in the use of the applications.

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Introduction to Artificial Intelligence and Deep Learning with a Case Study in Analyzing Electronic Health Records for Drug Development

Real-World Evidence in Drug Development and Evaluation ◽

10.1201/9780429398674-8 ◽

2021 ◽

pp. 151-172

Author(s):

Xiaomao Li ◽

Qi Tang

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Electronic Health Records ◽

Drug Development ◽

Health Records ◽

Electronic Health

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Machine learning, natural language programming, and electronic health records: The next step in the artificial intelligence journey?

Journal of Allergy and Clinical Immunology ◽

10.1016/j.jaci.2018.02.025 ◽

2018 ◽

Vol 141 (6) ◽

pp. 2019-2021.e1 ◽

Cited By ~ 16

Author(s):

Neil Mehta ◽

Murthy V. Devarakonda

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Electronic Health Records ◽

Natural Language ◽

Health Records ◽

Natural Language Programming ◽

Electronic Health

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A Comparison of Explanations Given by Explainable Artificial Intelligence Methods on Analysing Electronic Health Records

2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI) ◽

10.1109/bhi50953.2021.9508618 ◽

2021 ◽

Author(s):

Jamie Duell ◽

Xiuyi Fan ◽

Bruce Burnett ◽

Gert Aarts ◽

Shang-Ming Zhou

Keyword(s):

Artificial Intelligence ◽

Electronic Health Records ◽

Health Records ◽

Artificial Intelligence Methods ◽

Explainable Artificial Intelligence ◽

Electronic Health

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An Explainable Artificial Intelligence Approach for Predicting Cardiovascular Outcomes using Electronic Health Records

10.1101/2021.07.21.21260474 ◽

2021 ◽

Author(s):

Sergiusz Wesolowski ◽

Gordon Howard Lemmon ◽

Edgar J Hernandez ◽

Alex Ryan Henrie ◽

Thomas A Miller ◽

...

Keyword(s):

Artificial Intelligence ◽

Electronic Health Records ◽

Large Scale ◽

Cardiovascular Health ◽

Cardiovascular Outcomes ◽

Health Records ◽

University Of Utah ◽

Explainable Artificial Intelligence ◽

Electronic Health ◽

Discovery Method

Understanding the conditionally-dependent clinical variables that drive cardiovascular health outcomes is a major challenge for precision medicine. Here, we deploy a recently developed massively scalable comorbidity discovery method called Poisson Binomial based Comorbidity discovery (PBC), to analyze Electronic Health Records (EHRs) from the University of Utah and Primary Children's Hospital (over 1.6 million patients and 77 million visits) for comorbid diagnoses, procedures, and medications. Using explainable Artificial Intelligence (AI) methodologies, we then tease apart the intertwined, conditionally-dependent impacts of comorbid conditions and demography upon cardiovascular health, focusing on the key areas of heart transplant, sinoatrial node dysfunction and various forms of congenital heart disease. The resulting multimorbidity networks make possible wide-ranging explorations of the comorbid and demographic landscapes surrounding these cardiovascular outcomes, and can be distributed as web-based tools for further community-based outcomes research. The ability to transform enormous collections of EHRs into compact, portable tools devoid of Protected Health Information solves many of the legal, technological, and data-scientific challenges associated with large-scale EHR analyzes.

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