• Korea Journal of Hospital Management
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• v.26 no.1
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• pp.17-25
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• 2021

This study aimed to analysis factors related to in-hospital death of injured patients by patient safety accident. A total of 1,529 inpatients were selected from Korea Centers for Disease Control and Prevention database(2013-2017). Frequency, Fisher's exact test, t-test, ANOVA, logistic regression analyses by using STATA 12.0 were performed. Analysis results show that the mortality rate was lower for female than male but the mortality rate was higher for the older age, the higher the CCI, head (or neck), multiple, systemic damage sites, internal and others, metropolitan cities based on Seoul and 300-499 based on the bed size of 100-299. Based on these findings, the possibility of using the in-depth investigation of discharge damage from the Korea Centers for Disease Control and Prevention as a data source for the patient safety survey conducted to understand the actual status of patient safety accident types, frequency, and trends should be reviewed. Also, it is necessary to prevent injury and minimize death by identifying factors that affect death after injury by patient safety accident.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.271-282
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• 2021

This study was conducted to develop a model for predicting the length of stay for premature infants through machine learning. For the development of this model, 6,149 cases of premature infants discharged from the hospital from 2011 to 2016 of the discharge injury in-depth survey data collected by the Korea Centers for Disease Control and Prevention were used. The neural network model of the initial hospitalization was superior to other models with an explanatory power (R²) of 0.75. In the model added by converting the clinical diagnosis to CCS(Clinical class ification software), the explanatory power (R²) of the cubist model was 0.81, which was superior to the random forest, gradient boost, neural network, and penalty regression models. In this study, using national data, a model for predicting the length of stay for premature infants was presented through machine learning and its applicability was confirmed. However, due to the lack of clinical information and parental information, additional research is needed to improve future performance.

• Journal of Digital Convergence
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• v.11 no.5
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• pp.371-384
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• 2013

The purpose of this study was to develop the risk-adjusted mortality model using Korean Hospital Discharge Injury data and US National Hospital Discharge Survey data and to suggest some ways to manage hospital mortality rates through comparison of Korea and United States Hospital Standardized Mortality Ratios(HSMR). This study used data mining techniques, decision tree and logistic regression, for developing Korea and United States risk-adjustment model of in-hospital mortality. By comparing Hospital Standardized Mortality Ratio(HSMR) with standardized variables, analysis shows the concrete differences between the two countries. While Korean Hospital Standardized Mortality Ratio(HSMR) is increasing every year(101.0 in 2006, 101.3 in 2007, 103.3 in 2008), HSMR appeared to be reduced in the United States(102.3 in 2006, 100.7 in 2007, 95.9 in 2008). Korean Hospital Standardized Mortality Ratios(HSMR) by hospital beds were higher than that of the United States. A two-aspect approach to management of hospital mortality rates is suggested; national and hospital levels. The government is to release Hospital Standardized Mortality Ratio(HSMR) of large hospitals and to offer consulting on effective hospital mortality management to small and medium hospitals.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.345-352
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• 2019

This study aims to analyze the factors affecting the length of stay in elderly pediatric inpatients in traffic accidents. We used Korean National Hospital Discharge In-depth Injury data on the discharged from 2012 to 2016. Statistically significant factors affecting the length of stay are admission route, Charlson Comorbidity Index(CCI), injury parts, operation, results, hospital area, and beds for hospitals. The length of stay was shorter in the case of the admission route of the outpatient department than the emergency room, the results were not improved or death rather than improved, and the bed size was 500-999 beds or over 1000 beds rather than 100-299 beds. However, the length of stay was longer in the case of CCI score was 1-2 or over 3 rather than 0, injury parts were other parts rather than head/neck, when the operation was yes, and when the hospital area was a province, metropolitan rather than Seoul. This study intends to understand the medical characteristics of inpatient to prevent pedestrian traffic accidents in accordance with the population aging. Based on this finding, we wish to be used as the basic data for the establishment of policies to effectively manage traffic safety and medical resources in consideration of the characteristics of the elderly people.

• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.487-497
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• 2021

The purpose of this study is to identify factors affecting length of stay(LOS) and death in tuberculosis(TB) patients by disease type, patient characteristic, admission and disease characteristic, and hospital characteristic from 2008 to 2017. Survey data was using Korean national hospital discharge in-depth survey data produced by Korea Disease Control and Prevention Agency. Study subjects were 10,634 inpatients with TB(A15, A16, A17, A18, A19, U88.0, U88.1, U84.30, U84.31) and analyzed frequency, chi-square test, Fisher's exact test, and logistic regression by using STATA 13.0. As a study result, the type of TB(extrapulmonary TB, multidrug-resistant TB, extensively drug-resistant TB), sex(woman), age(35-49, 50-64, 65-74, 75 years old or older), admission type(outpatient department), CCI(1-2 point, 3 point over), hospital location(metropolitan city) and bed size(300-499, 500-999, over 1000) were significantly influence LOS. Also, the type of TB(extrapulmonary TB, extensively drug-resistant TB), sex(woman), age(50-64, 65-74, 75 years old or older), residence(small town/rural), admission type(outpatient department), CCI(1-2 point, 3 point over), hospital location(provincial) were significantly influence death. In conclusion, the existing tuberculosis management has been patient management with rapid diagnosis and treatment following early detection. But other studies should be carried out for the system that identifies and supports high-risk groups of the long-term length of stay in hospital or high mortality rates as a result of treatment.

• Journal of Digital Convergence
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• v.13 no.5
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• pp.89-99
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• 2015

This study aims to develop a LOS(Length of Stay) bench-making system that can provide efficient by comparing the LOS management of other hospital and level evaluation for inducing the LOS to manage their own activities. The convergence LOS bench-making web program has been implemented to compare a variety of beds, regional group, followed reporting with excel files downloads by using the severity-adjusted LOS model of Korean National Hospital Discharge in-depth Injury Survey data. Features that are computed in real-time severity-adjusted LOS was also implemented. Trial operating results, bench-making system was confirmed efficient for management of LOS on the long-term care and group of disease in hospital from the staff or medical department, receive requests comparative statistics by area and disease group. Therefore the policy alternative on extension of severity-adjusted LOS is needed to utilized bench-making system on LOS.

• The Journal of Korean Society for School & Community Health Education
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• v.23 no.1
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• pp.29-40
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• 2022

Objectives: The purpose of this study is to identify the relationship between metabolic syndrome factor diseases and falls in the elderly aged 65 years or older and use them as basic data to reduce the risk of falls. Methods: The method of this study was to compare the injury-related characteristics of the fall and non-fall groups with a factor disease of metabolic syndrome in Korea over 65 years of age. Data from the 14th National Hospital Discharge In-depth Injury Survey in 2018 were used to conduct the study. A total of 7,991 data were analyzed using SPSS 23.0. Results: Among the total injuries, the fall group with metabolic syndrome factor disease accounted for 69.0% and the non-fall group 31.0%. Falls occurred in 86.3% of households. In the fall group with metabolic syndrome factor disease, the number of females was 1.9~2.1 times higher than that of males. Compared to 65~69 years of age, the incidence of falls was 1.4~1.5 times higher in 70~79 years, 1.7~2.2 times higher in 80~89 years, and 2.5~3.6 times higher in 90-year-olds and older. In NISS, the incidence of falls was 1.7 times higher in moderate compared to mild. In principle diagnosis, the incidence of falls was 2.2 times higher in S40-S99 compared to S00-S19. Conclusion: The elderly with metabolic syndrome factor disease should continue to promote health through light exercise that can strengthen muscle strength to prevent falls.

• Journal of the Health Care and Life Science
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• v.10 no.2
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• pp.275-283
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• 2022

This study aims to identify the factors affecting medical use in Seoul metropolitan region (SMR) by patients with a congenital malformation in the Jeollabuk-do region and suggest methods to improve equity in regional healthcare utilization. The study conducted the chi-square test and logistic regression analysis using Korean National Hospital's in-depth injury survey data from 2016 to 2020. The results are as follows. First, medical utilization in SMR was high in the age group of 19-44. Second, the bigger the bed size, the higher the medical utilization in SMR. Third, in the case of the principal diagnosis, the medical utilization in SMR was high in the patients with congenital Anomaly of the Tracheobronchial System. Fourth, the medical utilization in SMR was high in the group of inpatients with higher severity after surgery. Based on these results, government-run healthcare policies and planning should be established to achieve regional equity of medical utilization.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.126-136
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• 2018

The purpose of this study was to develop a severity-adjustment model for predicting mortality in acute stroke patients using machine learning. Using the Korean National Hospital Discharge In-depth Injury Survey from 2006 to 2015, the study population with disease code I60-I63 (KCD 7) were extracted for further analysis. Three tools were used for the severity-adjustment of comorbidity: the Charlson Comorbidity Index (CCI), the Elixhauser comorbidity index (ECI), and the Clinical Classification Software (CCS). The severity-adjustment models for mortality prediction in patients with acute stroke were developed using logistic regression, decision tree, neural network, and support vector machine methods. The most common comorbid disease in stroke patients were hypertension, uncomplicated (43.8%) in the ECI, and essential hypertension (43.9%) in the CCS. Among the CCI, ECI, and CCS, CCS had the highest AUC value. CCS was confirmed as the best severity correction tool. In addition, the AUC values for variables of CCS including main diagnosis, gender, age, hospitalization route, and existence of surgery were 0.808 for the logistic regression analysis, 0.785 for the decision tree, 0.809 for the neural network and 0.830 for the support vector machine. Therefore, the best predictive power was achieved by the support vector machine technique. The results of this study can be used in the establishment of health policy in the future.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.217-230
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• 2019

This study was conducted to develop a customized severity-adjustment method and to evaluate their validity for acute myocardial infarction(AMI) patients to complement the limitations of the existing severity-adjustment method for comorbidities. For this purpose, the subjects of KCD-7 code I20.0 ~ I20.9, which is the main diagnosis of acute myocardial infarction were extracted using the Korean National Hospital Discharge In-depth Injury survey data from 2006 to 2015. Three tools were used for severity-adjustment method of comorbidities : CCI (charlson comorbidity index), ECI (Elixhauser comorbidity index) and the newly proposed CCS (Clinical Classification Software). The results showed that CCS was the best tool for the severity correction, and that support vector machine model was the most predictable. Therefore, we propose the use of the customized method of severity correction and machine learning techniques from this study for the future research on severity adjustment such as assessment of results of medical service.