Rate of electronic health record adoption in South Korea: A nation-wide survey

Kim, Young-Gun; Jung, Kyoungwon; Park, Young‐Taek; Shin, Dahye; Cho, Soo‐Yeon; Yoon, Dukyong; Park, Rae Woong

doi:10.1016/j.ijmedinf.2017.02.009

Cited by 60 publications

(42 citation statements)

References 18 publications

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“…The rates of EMR system adoption in hospitals and clinics were 96.5% and 95.7%, respectively. The overall adoption rates were much higher than those found in studies conducted in the following year [ 8 ]. The study found that the adoption rates in tertiary hospitals and general hospitals were 97.3% and 91.4%, respectively.…”

Section: Discussionmentioning

confidence: 55%

Current Status of Electronic Medical Record Systems in Hospitals and Clinics in Korea

Park

Han

2017

Healthc Inform Res

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ObjectivesMany healthcare organizations and professionals have had interests in healthcare information and communication technology (ICT). The objective of this study was to investigate the current status of overall healthcare ICT, especially focusing on Electronic Medical Record (EMR) systems in Korea.MethodsThis study used a part of the nationwide survey collected for the OECD benchmarking ICT study. The Health Insurance Review and Assessment Service conducted the survey from November 19, 2013 to January 10, 2014. This study followed the methodological guidelines of the OECD. A total of 2,093 hospitals and clinics, including long-term care hospitals, participated in the survey. Among them, 554 hospitals and 906 clinics were included in this study for the generalization of the results.ResultsThe adoption rates of EMR systems were 96.3% in hospitals and 95.7% in clinics. Most of the hospitals and clinics had high rates of healthcare information exchange (HIE) within the organization; however, there were extremely low HIE rates among external organizations. Most of the hospitals and clinics had EMR systems with clinical-decision-supporting functionalities. Ninety-six percent of the EMR systems of the hospitals and 89.2% of the clinic systems had checking functions, such as alerts or reminders, on contraindications of drug-drug and drug-age interaction.ConclusionsKorea has maintained a high healthcare ICT status compared to countries in the European Union. The EMR systems of hospitals and clinics in Korea had sophisticated functionalities; however, their HIE status was extremely low, which indicates the need for healthcare ICT standardization.

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Section: Discussionmentioning

confidence: 55%

Current Status of Electronic Medical Record Systems in Hospitals and Clinics in Korea

Park

Han

2017

Healthc Inform Res

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“…1 Recent surveys indicate that 80.5% of United States hospitals and 58.1% of South Korean hospitals report at least basic EMR usage, and EMR adoption is expected to rise in the coming years. [2][3][4][5] EMR systems contain a wide variety of data, including demographics, vitals, labs, imaging studies, medications, diagnoses, and more. The breadth of EMR data is also expected to increase with the integration of new types of information such as patient-reported outcome scores.…”

Section: Introductionmentioning

confidence: 99%

Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

et al. 2019

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Developments in machine learning in recent years have precipitated a surge in research on the applications of artificial intelligence within medicine. Machine learning algorithms are beginning to impact medicine broadly, and the field of spine surgery is no exception. Electronic medical records are a key source of medical data that can be leveraged for the creation of clinically valuable machine learning algorithms. This review examines the current state of machine learning using electronic medical records as it applies to spine surgery. Studies across the electronic medical record data domains of imaging, text, and structured data are reviewed. Discussed applications include clinical prognostication, preoperative planning, diagnostics, and dynamic clinical assistance, among others. The limitations and future challenges for machine learning research using electronic medical records are also discussed.

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“…Nearly all hospitals in Korea also use the computerized physician order entry (CPOE) system. However, the EMR adoption rate is only 58.1%, and the fully comprehensive EMR adoption rate has dropped to 11.6% [7]. This implies a lack of digitalized data for healthcare AI research in Korea.…”

Section: Introductionmentioning

confidence: 99%

Status and Direction of Healthcare Data in Korea for Artificial Intelligence

Park

Shin

2017

Hanyang Med Rev

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Predicting temporal patterns across various domains poses significant challenges due to their nuanced and often nonlinear trajectories. To address this challenge, prediction frameworks have been continuously refined, employing data-driven statistical methods, mathematical models, and machine learning. Recently, as one of the challenging systems, shared transport systems such as public bicycles have gained prominence due to urban constraints and environmental concerns. Predicting rental and return patterns at bicycle stations remains a formidable task due to the system's openness and imbalanced usage patterns across stations. In this study, we propose a deep learning framework to predict rental and return patterns by leveraging cartogram approaches. The cartogram approach facilitates the prediction of demand for newly installed stations with no training data as well as long-period prediction, which has not been achieved before. We apply this method to public bicycle rentaland-return data in Seoul, South Korea, employing a spatial-temporal convolutional graph attention network. Our improved architecture incorporates batch attention and modified node feature updates for better prediction accuracy across different time scales. We demonstrate the effectiveness of our framework in predicting temporal patterns and its potential applications.

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Rate of electronic health record adoption in South Korea: A nation-wide survey

Cited by 60 publications

References 18 publications

Current Status of Electronic Medical Record Systems in Hospitals and Clinics in Korea

Current Status of Electronic Medical Record Systems in Hospitals and Clinics in Korea

Applications of Machine Learning Using Electronic Medical Records in Spine Surgery

Status and Direction of Healthcare Data in Korea for Artificial Intelligence

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