• 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.

• 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.

• Korea Journal of Hospital Management
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• v.15 no.4
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• pp.125-142
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• 2010

Objective : To determine demographic, clinical, health care utilization factors predicting unplanned readmission(within 28 days) to the hospital. Methods : A case-control study was conducted from January to December 2009. Multiple logistic regression was used to examine risk factors for readmission. 180 patients who had been readmitted within 28 days and 1,784 controls were recruited from an university hospital in Seoul. Results : Six risk factors associated with readmission risk were identified and include mail sex, medical service rather than surgical service, number of comorbid diseases, type of patient's room, lenth of stay, number of admissions in the prior 12 months. Conclusions : One of the association with readmission risk identified was the number of hospital admissions in the previous year. This factor may be the only risk factor necessary for assessing prior risk and has the additional advantage of being easily accessible from computerized medical records without requiring other medical record review. This risk factor may be useful in identifying a group at high readmission risk, which could be targeted in intervention studies. Multiple risk factors intervention approach should be considered in designing future prevention strategies.