• Health Policy and Management
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• v.25 no.3
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• pp.197-206
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• 2015

Background: The purpose of this study was to propose a method for developing a measure of hospital-wide all-cause risk-standardized readmissions using administrative claims data in Korea and to discuss further considerations in the refinement and implementation of the readmission measure. Methods: By adapting the methodology of the United States Center for Medicare & Medicaid Services for creating a 30-day readmission measure, we developed a 6-step approach for generating a comparable measure using Korean datasets. Using the 2010 Korean National Health Insurance (NHI) claims data as the development dataset, hierarchical regression models were fitted to calculate a hospital-wide all-cause risk-standardized readmission measure. Six regression models were fitted to calculate the readmission rates of six clinical condition groups, respectively and a single, weighted, overall readmission rate was calculated from the readmission rates of these subgroups. Lastly, the case mix differences among hospitals were risk-adjusted using patient-level comorbidity variables. The model was validated using the 2009 NHI claims data as the validation dataset. Results: The unadjusted, hospital-wide all-cause readmission rate was 13.37%, and the adjusted risk-standardized rate was 10.90%, varying by hospital type. The highest risk-standardized readmission rate was in hospitals (11.43%), followed by general hospitals (9.40%) and tertiary hospitals (7.04%). Conclusion: The newly developed, hospital-wide all-cause readmission measure can be used in quality and performance evaluations of hospitals in Korea. Needed are further methodological refinements of the readmission measures and also strategies to implement the measure as a hospital performance indicator.

• Tuberculosis and Respiratory Diseases
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• v.83 no.2
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• pp.147-156
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• 2020

Background: Limited studies have been performed to assess readmission following hospitalization for community-acquired pneumonia (CAP) in an Asian population. We evaluated the rates, reasons, and risk factors for 30-day readmission following hospitalization for CAP in the general adult population of Korea. Methods: We performed a retrospective observational study of 1,021 patients with CAP hospitalized at Yeungnam University from March 2012 to February 2014. The primary end point was all-cause hospital readmission within 30 days following discharge after the initial hospitalization. Hospital readmission was classified as pneumonia-related or pneumonia-unrelated readmission. Results: During the study period, 862 patients who survived to hospital discharge were eligible for inclusion and among them 72 (8.4%) were rehospitalized within 30 days. In the multivariable analysis, pneumonia-related readmission was associated with para/hemiplegia, malignancy, pneumonia severity index class ≥4 and clinical instability ≥1 at hospital discharge. Comorbidities such as chronic lung disease and chronic kidney disease, treatment failure, and decompensation of comorbidities were associated with the pneumonia-unrelated 30-day readmission rate. Conclusion: Rehospitalizations within 30 days following discharge were frequent among patients with CAP. The risk factors for pneumonia-related and -unrelated readmission were different. Aspiration prevention, discharge at the optimal time, and close monitoring of comorbidities may reduce the frequency of readmission among patients with CAP.

• Health Policy and Management
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• v.26 no.2
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• pp.148-152
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• 2016

The hospital readmission rate has been widely used as an indicator of the quality of hospital care in many countries. However, the transferrability of this indicator that has been developed in a different health care system can be questioned. We reviewed what should be considered when using the risk-standardized readmission rate (RSRR) as a generic quality indicator in the Korean setting. We addressed the relationship between RSRR and the quality of hospital care, methodological aspects of RSRR, and use of RSRR for external purposes. These issues can influence the validity of the readmission rate as a generic quality indicator. Therefore RSRR should be used with care and further studies are needed to enhance the validity of the readmission rate indicator.

• 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.

• Journal of Korean Clinical Nursing Research
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• v.19 no.2
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• pp.265-274
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• 2013

Purpose: The aim of this study was to determine the risk factors contributed to unplanned readmission to intensive care unit (ICU) and to investigate the prediction model of unplanned readmission. Methods: We retrospectively reviewed the electronic medical records which included the data of 3,903 patients who had discharged from ICUs in a university hospital in Seoul from January 2011 to April 2012. Results: The unplanned readmission rate was 4.8% (n=186). The nine variables were significantly different between the unplanned readmission and no readmission groups: age, clinical department, length of stay at 1st ICU, operation, use of ventilator during 24 hours a day, APACHE II score at ICU admission and discharge, direct nursing care hours and Glasgow coma scale total score at 1st ICU discharge. The clinical department, length of stay at 1st ICU, operation and APACHE II score at ICU admission were the significant predictors of unplanned ICU readmission. The predictive model's area under the curve was .802 (p<.001). Conclusion: We identified the risk factors and the prediction model associated with unplanned ICU readmission. Better patient assessment tools and knowledge about risk factors could contribute to reduce unplanned ICU readmission rate and mortality.

• Journal of Preventive Medicine and Public Health
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• v.32 no.3
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• pp.289-296
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• 1999

Objectives: This study describes the risk factors affecting the unexpected readmission of 261 patients who were discharged from a university hospital in Seoul. Methods: This case-control study reviewed medical records of inpatients who had been discharged from a hospital between 1 August 1995 and 31 October 1995 after the treatment for general diseases. The cases were 68 patients who were readmitted unexpectedly within 28 days of discharge from an index stay, and the controls were 193 Patients who were discharged without readmission during the study period. Results: Logistic regression analysis results were as follows; Patients who had no operation during their hospital stay were more likely to be readmitted unexpectedly than patients who had operation. Patients who had 1 or 2 parts of their body being involved in treatment were more likely to be readmitted unexpectedly than patients who hand more than 3 parts of their body being involved in treatment. Patients who had complications after surgery were more likely to be readmitted unexpectedly than patients who had no complications. Insufficient discharge planning caused unexpected readmissions. Conclusions: Discharge planning education should be extended to health care providers. And the assessment of discharge planning should be evaluated. Readmission is often necessary for the treatment of related problems of originating from initial hospitalization, which causes cost problems. Unexpected readmission is preventable and the models for readmission can serve as a valuable clinical tool for high risk patients.

• Quality Improvement in Health Care
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• v.4 no.2
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• pp.242-259
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• 1997

This study describes associated factors of readmission of 213 inpatients from an university hospital in Seoul. This retrospective study reviewed medical records of patients who discharged from a hospital stay for general diseases between 1 August 1995 and 31 October 1995, Cases were 68 discharge patients with an unplanned readmission within 30 days of discharge from an index stay. And the other cases are 145 patients who had more than two discharges and didn't have an unplanned readmission within 30 days. Logistic regression model was analyzed and the results were as follows; 1. duration of readmission, rate of unpayed, room, path, and risk of disease were more likely to be readmitted unexpectedly than the expected readmission patients. 2. early readmission, low risk condition group, and inadquateness of discharge plann for patients had unplanned radmissions rather than planned readmissions. Therefore, discharge planning education to health care provider is required and assessement of discharge planning should be evaluated. Readmissions are usually for related problems that arose during the original hopitaliztion and caused cost problems. Especially the unplanned readmissions are frequently preventable. Ultimately, models for readmissions can serve as a valuable clinical tool for target high-risk patients and older patients and with this kind of tools we can reduce hospital readmissions and maintain high-quality of inpatient care.

• Journal of Korean Public Health Nursing
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• v.16 no.1
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• pp.201-212
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• 2002

This study was designed to identify the risk factors of unplanned readmission in a university hospital. The six-month discharge information from January to June, 2000 in a tertiary university hospital was used as a source of data through the medical record and hospital information system. To increase the effect of comparison. the data were collected by sampling 192 couples (384 patients) of unplanned readmission group through the matching by its disease groups, sex, and age. The accuracy of prediction for unplanned readmission was analyzed by constructing the predicted model of unplanned readmission through the logistic regression. The study results are as follows. The conditional logistic regression analysis was performed with nine variables at the significance level 0.05 through univariate analysis including residence, days after discharge, initial admission route, previous admission, transfer to special care unite, hospital stay days, medical care expenses, special cares, and laboratory and imaging services. As a result, the closer the patients live in Seoul and Gyeong-in area (Odds ratio=2.529, p=0.003), the shorter the days after discharge was (Odds ratio=0.600, p=0.000), and the more frequent admission rate was (Odds ratio=2.317, p=0.004), the more unplanned readmission was resulted. Also, the accuracy of prediction for data classification of this regression model showed $70.3\%$(032+83/306).

• Korean Journal of Clinical Pharmacy
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• v.33 no.1
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• pp.1-7
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• 2023

Background: Along with the increase in the elderly population, concerns about polypharmacy, which can cause medication-related problems, are increasing. This study aimed to find out the association between drug-related factors and readmission in elderly patients within 30 days after discharge. Methods: Data of patients aged ≥65 years who were discharged from the respiratory medicine ward of a tertiary hospital between January and March 2016 were retrospectively obtained. The medication regimen complexity at discharge was calculated using the medication regimen complexity index (MRCI) score, comorbidity status was assessed using the Charlson comorbidity index (CCI), potentially inappropriate medications (PIMs) were evaluated based on the Beer 2019 criteria, and adverse drug events (ADEs) were examined using the ADE reporting system. Multivariable logistic regression analysis was used to evaluate the effect of medication-related problems on hospital readmission after controlling for other variables. Results: Of the 206 patients included, 84 (40.8%) used PIMs, 31 (15%) had ADEs, and 32 (15.5%) were readmitted. The mean age, total medications, MRCI, CCI, and PIMs in the readmission group were significantly higher than those in the non-readmission group. Age significantly decreased the risk of readmission (odds ratio [OR], 0.89; 95% confidence interval [CI], 0.84-0.96) after adjusting for sex, length of hospital stay, and ADEs. The use of PIMs (OR, 2.38; 95% CI, 1.10-5.16) and increased CCI (OR, 1.50; 95% CI, 1.16-1.93) and MRCI (OR, 1.04; 95% CI, 1.01-1.07) were associated with an increased occurrence of readmission. Conclusion: PIMs were associated with a significantly greater risk for readmission than MRCI.

• Clinical and Experimental Pediatrics
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• v.52 no.8
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• pp.888-892
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• 2009

Purpose : To evaluate the risk factors for hospital readmission during the neonatal period among late preterm infants who were discharged after nursery care. Methods : In this retrospective study, we reviewed medical records of 135 late preterm infants readmitted to the neonatal intensive care unit (NICU) during the neonatal period, after discharge from nursery of IL Sin Christian Hospital from January 2003 to December 2008. We compared the risk factors of the hospital readmission group with the control group. Results : The gestational age and birth weight of 135 study infants were $36^{+1}{\pm}0.5$ weeks and $2,718.4{\pm}296.9gm$, respectively. Identified risk factors of hospital readmission were breastfeeding (71.9% vs. 44.4%), short duration of nursery stay ($3.3{\pm}1.6$ days vs. $4.1{\pm}2.0$ days), firstborn (60.0% vs. 45.3%), and maternal pregnancy complication (31.9% vs. 18.8%). Jaundice accounted for the majority of hospital readmissions (83.7%), and the age at hospital readmission was $6.2{\pm}3.6$ postnatal days, mostly at 5-6 postnatal days (40.7%). Identified risk factors of hospital readmission due to jaundice were spontaneous normal vaginal delivery (43.4% vs. 1.8%), younger maternal age ($29.8{\pm}3.4$ yrs vs. $32.1{\pm}4.2$ yrs), and lower maternal pregnancy complication (28.3% vs. 50%). Conclusion : Identified risk factors of hospital readmission were breastfeeding, short duration of nursery stay, firstborn, and maternal pregnancy complication. Jaundice accounted for the majority of hospital readmissions, and the age at hospital readmission was $6.2{\pm}3.6$ postnatal days.