• The Journal of the Korea Contents Association
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• v.14 no.10
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• pp.10-19
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• 2014

In recent, hospital information systems are widely used to electronically record, manage and share the data collected in hospitals. Such systems have contributed greatly to improving the work efficiency in modern hospitals, however, the collected data concerning the patients should be appropriately processed and reused to provide the healthcare service providers with decision supports. Especially, this paper proposes the patient flow monitoring system for the operations management of the outpatient department for patients with chronic diseases, and discusses the related issues. The proposed system visualizes the standard process model extracted from the patient history data and various performance measures, and this enables the managers to evaluate and enhance the operations of the outpatient clinic. In this paper, the patient flow monitoring system is applied to the rheumatology clinic, and the prototype system optimized for I-pad is illustrated.

• Journal of Korean Clinical Nursing Research
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• v.15 no.1
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• pp.5-17
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• 2009

Purpose: This study was to develop a factor-type patient classification system for general nursing unit based on nursing needs (KPCS; Korean patient classification system for nurses). Method: We reviewed workload management system for nurses(WMSN) of Walter Reed Medical Center, Korean patient classification system for ICU, and nursing activities in nursing records and developed the first version of KPCS. The final version KPCS was evaluated via validity and reliability verifications based on panel discussions and data from 800 patient classifications. Content validity was performed by Delphi method and concurrent validity was verified by the correlation of two tools (r=.71). Construct validity was also tested by medical department (p<.001), patient type (p<.001), and nurse intuition (p<.001). These verifications were performed from April to October, 2008. Results: The KPCS has 75 items in classifying 50 nursing activities, and categorized into 12 different nursing area (measuring vital sign, monitoring, respiratory treatment, hygiene, diet, excretion, movement, examination, medication, treatment, special treatment, and education/emotional support). Conclusion: The findings of the study showed sound reliability and validity of KPCS based on nursing needs. Further study is mandated to refine the system and to develop index score to estimate the necessary number of nurses for adequate care.

• Journal of Radiation Protection and Research
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• v.5 no.1
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• pp.11-25
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• 1980

The increasing utilization of radioactive isotopes in industry, medicine and research has raised the question, 'How should hospitals deal with radiation injuries when they occur?' A system for initial management of radiation injuries has been developed by Radiation Management Corporation. Radiation injuries are classified and a treatment plan outlined for each at the emergency and short term medical care phase. This system includes clinical prognosis as well as a detailed plan for quick set up or a Radiation Emergency Area in any hospital. Procedures for patient admission, preparation of the facility, general decontamination, sample taking, and wound decontamination are included.

• Journal of Korean Neurosurgical Society
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• v.66 no.6
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• pp.632-641
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• 2023

Central nervous system tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive. AI, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.45-47
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• 1996

we developed an information management system that manages various types of medical information such as text, image, sound, and laboratory data. We also developed a multimedia description system, in which medical doctors can describe his findings and interpretations with text and speech. The descriptions include the references to the data items stored in the information management systems. The communication between the description system and the information management systems is carried out using OLE/COM mechanism. The information management system was implemented by using Microsoft Open Data Base Connectivity(ODBC).

• Journal of Digital Convergence
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• v.11 no.5
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• pp.347-354
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• 2013

In contrast to the increase in demand for high quality healthcare, there is limited medical human resources such as doctors and nurses so an excessive amount of workload is being forced to them. Therefore, a patient monitoring system using USN(Ubiquitous Sensor Network) is becoming a solution. This paper proposes a patient monitoring system applying USN in maternity hospital to reduce the workload of nurses. According to the efficiency evaluation test based on the model of two university hospitals(S, K University Hospital) and their doctor's diagnosis, the results showed that under the circumstances that one nurse is in charge of 12 patients(6 normal delivery patients and 6 cesarean delivery patients), a total of 1,260 minutes of workload was saved during hospitalization period(5 days). Also, we compared the workload of nurses with or without our proposed system, and the figures showed that in case of normal delivery patients, the workload of nurses decreased by 50 minutes per patient, whereas in case of cesarean delivery patients, the workload of nurses decreased by 130 minutes per patient.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.203-212
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• 2002

This study examined the predictive power of data mining algorithms by comparing the performance of logistic regression and decision tree algorithm, called CHAID (Chi-squared Automatic Interaction Detection), On the contrary to the previous studies, decision tree performed better than logistic regression. We have also developed a CDSS (Clinical Decision Support System) with three modules (doctor, nurse, and patient) based on data warehouse architecture. Data warehouse collects and integrates relevant information from various databases from hospital information system (HIS ). This system can help improve decision making capability of doctors and improve accessibility of educational material for patients.

• Korea Journal of Hospital Management
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• no.spc
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• pp.51-60
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• 2016

The forms of healthcare service delivery emerges from the interaction between technological progress and institutional changes. Building a healthcare system which enables effective and efficient patient care is a imperative to a sustainable operation of a society. Identifying of a promising medical technologies and diffusing them consists of the basic tasks of a good healthcare system. Inducing of a promising innovation in healthcare and utilization of the innovation requires a deep understanding of healthcare innovation system and delicate governmental intervention to the effective functioning of the system. Therefore, the support for R&D in healthcare field should be given to social and institutional technologies for the better organization of healthcare delivery and consumption system as well as basic and applied medical sciences.

• Korea Journal of Hospital Management
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• v.18 no.3
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• pp.27-42
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• 2013

Purpose: This study is aimed to provide the fundamental data for building the patient safety culture by identifying the perceptions of patient safety culture of hospital nurses. Methods: this study was a cross-sectional survey. For this study, 816 nurses participated from three general hospital and one university hospital located in Gwangju and Chonnam. The data were collected from April to June, 2012 by self-administrated questionnaires. The 'Hospital Survey on Patient Safety Culture'developed AHRQ(2004) and translated in Korean and edited by Je(2006), was used to measure the patient safety culture which the nurses were perceived. The collected data were analyzed with descriptive statistics, t-test, ANOVA, Scheffe test using SPSS window 18.0. Results: With a possible score of 5 points, the average score for nurses'perceived patient safety culture 3.32. In the sub dimension of patient safety culture, the score hospital-level aspects was the highest level of 3.27(0.50) and reporting system medical errors was the lowest of 3.08(0.40). The difference of perception level on patient safety culture were statistically significant depending on demographic and job-related characteristics such as age, hospital level, work experience in present hospital, work experience in present unit, work experience in present area, positions, work hours of week. Conclusion: The scores of perception of which were shown to be relatively low in this study, needed to be improved through continuous education, evaluation and researches. We suggest developing a new tool on patient safety culture fit our country which will help to manage ongoing patient safety culture.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.92-102
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• 2017

In this paper, we consider a typical health care system via the help of Wireless Sensor Network (WSN) for wireless patient tracking. The wireless patient tracking module of this system performs localization out of samples of Received Signal Strength (RSS) variations and tracking through a Particle Filter (PF) for WSN assisted by multiple transmit-power information. We propose a modified PF, Kullback-Leibler Distance (KLD)-resampling PF, to ameliorate the effect of RSS variations by generating a sample set near the high-likelihood region for improving the wireless patient tracking. The key idea of this method is to approximate a discrete distribution with an upper bound error on the KLD for reducing both location error and the number of particles used. To determine this bound error, an optimal algorithm is proposed based on the maximum gap error between the proposal and Sampling Important Resampling (SIR) algorithms. By setting up these values, a number of simulations using the health care system's data sets which contains the real RSSI measurements to evaluate the location error in term of various power levels and density nodes for all methods. Finally, we point out the effect of different power levels vs. different density nodes for the wireless patient tracking.