• Journal of Digital Convergence
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• v.15 no.2
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• pp.273-283
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• 2017

This study aims to develop a convergence optimized LOS(Length of Stay) management system that can provide efficient by predicting LOS on outpatient information for inducing the LOS to manage their own activities. web program has been implemented to comput in real-time predicting LOS by using the predicted LOS model of outpatient information. The predict model was derived management targets of long term predicted patient group and intensive care patient group. The optimized LOS(Length of Stay) management system was confirmed efficient for optimizing management of LOS that can provide by the long-term predicting alarm and over LOS alarm service for long term predicted patient group and intensive care patient group. Therefore the trial operating policy alternative on extension of predicted LOS is needed to utilized convergence optimizing system on LOS.

• 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.

• Proceedings of the KAIS Fall Conference
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• 2010.11b
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• pp.509-512
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• 2010

본 논문에서는 지방환자의 서울진료의 추이와 치료결과를 살펴보기 위해 2005년, 2008년의 퇴원환자 조사 자료를 재원일수를 이용하여 분석하였다. 그 결과 2005년 퇴원환자 333,280명과 2008년 퇴원환자 419,873명의 성별, 연령별, 주진단 분포는 유사한 것으로 나타났으며 치료결과 재원일수는 2005년에 30일 이상이 7.2%, 20~29일이 5.9%인데 비해 2008년은 30일이상이 6.2%, 20~29일 6.0%로 나타나 재원일수는 절감되었다. 전체퇴원환자의 재원일수에 영향을 끼치는 요인에 대해 회귀분석 결과 연도, 성, 보험유형, 의료기관유형, 입원경로, 내원 경유, 주진단, 거주지의 효과를 통제한 후 지방환자의 진료지역에 따른 재원일수를 살펴본 결과 서울이 가장 낮은 것으로 나타났다. 또한 암환자의 재원일수에 영향을 끼치는 요인에 대해서도 연도, 성, 보험유형, 의료기관유형, 입원경로, 내원 경우, 주진단, 거주지의 효과를 통제한 후 지방환자의 진료지역에 따른 재원일수를 살펴본 결과 서울이 가장 낮은 것으로 나타났다. 즉, 지방환가 거주지에서 진료를 받는 것에 비해 서울에서 진료를 받는 것이 치료결과가 짧았다. 이는 타 지역 진료의 간접의료비 영향으로 서울지역에서 조기 퇴원하여 거주지에서 진료하였거나 서울 진료자가 중증도가 낮은 환자가 많아 재원일수가 낮을 수 있다는 것도 배제 할 수 없다. 이에 대한 중증도 보정 후 서울 진료환자의 재원일수가 낮은 요인을 분석하는 추후 연구가 필요하다.

• Journal of Digital Convergence
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• v.10 no.1
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• pp.413-422
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• 2012

In this study, we developed the severity-adjusted length of stay (LOS) model for acute myocardial infarction patients using data from the hospital discharge survey and proposed management of medical quality and development of policy. The dataset was taken from 2,309 database of the hospital discharge survey from 2004 to 2006. The severity-adjusted LOS model for the acute myocardial infarction (AMI) patients was developed by data mining analysis. From decision making tree model, the main reasons for LOS of AMI patients were CABG and comorbidity. The difference between severity-adjusted LOS from the ensemble model and real LOS was compared and it was confirmed that insurance type and location of hospital were statistically associated with LOS. And to conclude, hospitals should develop the severity-adjusted LOS model for frequent diseases to manage LOS variations efficiently and apply it into the medical information system.

• Journal of Digital Convergence
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• v.11 no.6
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• pp.221-233
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• 2013

This study aims to develop the severity-adjusted length of stay(LOS) model for acute stroke patients using data from the hospital discharge survey and propose management of length of stay(LOS) for acute stroke patients and using for Hospital management. The dataset was taken from 23,134 database of the hospital discharge survey from 2004 to 2009. The severity-adjusted LOS model for the acute stroke patients was developed by data mining analysis. From decision making tree model, the main reasons for LOS of acute stroke patients were acute stroke type. The difference between severity-adjusted LOS from the decision making tree model and real LOS was compared and it was confirmed that insurance type and bed number of hospital, location of hospital were statistically associated with LOS. And to conclude, hospitals should manage the LOS of acute stroke patients applying it into the medical information system.

• The Journal of the Korea Contents Association
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• v.11 no.9
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• pp.372-383
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• 2011

Our study was carried out to develop the severity-adjustment model for length of stay in hospital for percutaneous coronary interventions so that we would analysis the factors on the variation in length of stay(LOS). The subjects were 1,011 percutaneous coronary interventions inpatients of the Korean National Hospital Discharge In-depth Injury Survey 2004-2006 data. The data were analyzed using t-test and ANOVA and the severity-adjustment model was developed using data mining technique. After yielding the standardized value of the difference between crude and expected length of stay, we analysed the variation of length of stay for percutaneous coronary interventions. There was variation of LOS in regional differences, size of sickbed and insurance type. The variation of length of stay controlling the case mix or severity of illness can be explained the factors of provider. This supply factors in LOS variations should be more studied for individual practice style or patient management practices and healthcare resources or environment. We expect that the severity-adjustment model using administrative databases should be more adapted in other diseases in practical.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.6
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• pp.2668-2676
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• 2011

In order to analyze the variation in length of stay(LOS) of injury inpatients, we developed severity-adjusted LOS model using Korean National Discharge In-depth Injury Survey data of Center for Disease Control. Appling this model, we calculated predicted values and, after standardizing LOS using the differences from the actual values, analyzed the variation in LOS. Major factors affecting severity-adjusted LOS of injury inpatients were found to be severity, surgery(or no surgery), age, injury mechanism and channel of hospitalization. Result of analysis of the differences between the actual values and predicted values adjusted by decision tree model suggested that there were statistically significant differences by hospital size(number of beds), type of insurance and location of institution. In order to reduce the variation in LOS, efforts should be exerted in developing nationwide treatment protocol, inducing medical institutions to utilize it, and furthermore systematically evaluating it to reduce the variation continually.

• Journal of Digital Convergence
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• v.13 no.2
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• pp.215-225
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• 2015

This study was conducted to develop a severity-adjusted LOS(Length of Stay) model for knee replacement patients and identify factors that can influence the LOS by using the Korean National Hospital Discharge in-depth Injury Survey data. The comorbidity scoring systems and data-mining methods were used to design a severity-adjusted LOS model which covered 4,102 knee replacement patients. In this study, a decision tree model using CCS comorbidity scoring index was chosen for the final model that produced superior results. Factors such as presence of arthritis, patient sex and admission route etc. influenced patient length of stay. And there was a statistically significant difference between real LOS and adjusted LOS resulted from health-insurance type, bed size, and hospital location. Therefore the policy alternative on excessive medical utilization is needed to reduce variation in length of hospital stay in patients who undergo knee replacement.

• Journal of Digital Convergence
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• v.16 no.1
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• pp.231-242
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• 2018

The efficient management of the Length of Stay(LOS) is important in hospital. It is import to reduce medical cost for patients and increase profitability for hospitals. In order to efficiently manage LOS, it is necessary to develop an artificial intelligence-based prediction model that supports hospitals in benchmarking and reduction ways of LOS. In order to develop a predictive model of LOS for acute stroke patients, acute stroke patients were extracted from 2013 and 2014 discharge injury patient data. The data for analysis was classified as 60% for training and 40% for evaluation. In the model development, we used traditional regression technique such as multiple regression analysis method, artificial intelligence technique such as interactive decision tree, neural network technique, and ensemble technique which integrate all. Model evaluation used Root ASE (Absolute error) index. They were 23.7 by multiple regression, 23.7 by interactive decision tree, 22.7 by neural network and 22.7 by esemble technique. As a result of model evaluation, neural network technique which is artificial intelligence technique was found to be superior. Through this, the utility of artificial intelligence has been proved in the development of the prediction LOS model. In the future, it is necessary to continue research on how to utilize artificial intelligence techniques more effectively in the development of LOS prediction model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.354-362
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• 2017

Due to population aging and westernized lifestyle, ischemic heart diseases are increasing, and Korea has one of the highest lengths of stay for ischemic heart diseases. Since the increase in the length of stay is a major cause of the increase in medical expenses, it is necessary to prepare a plan to manage the length of stay. Accordingly, this study was conducted to identify the factors influencing the length of stay of ischemic heart disease, and provide the elementary resources necessary for the management of the length of stay. The study subjects were 566 ischemic heart disease patients of a tertiary hospital. As the result of the study, first, the number of inpatients with chest pain as the chief complaint was the largest. Second, the average length of stay was 4.89 days, and the length of stay varied depending on the type of ischemic heart disease. Third, the age of over 75 years, diabetes, and dyspnea were the factors increasing the length of stay. Therefore, for management of adequate length of stay for ischemic heart disease, it is important to prevent the progression of illness through blood sugar control for ischemic heart disease patients with diabetes. Also, it is necessary to prepare a system where patients can visit hospitals as fast as possible if they have any symptoms.