• Archives of Plastic Surgery
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• v.41 no.2
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• pp.116-121
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• 2014

Background Understanding risk factors that increase readmission rates may help enhance patient education and set system-wide expectations. We aimed to provide benchmark data on causes and predictors of readmission following inpatient plastic surgery. Methods The 2011 National Surgical Quality Improvement Program dataset was reviewed for patients with both "Plastics" as their recorded surgical specialty and inpatient status. Readmission was tracked through the "Unplanned Readmission" variable. Patient characteristics and outcomes were compared using chi-squared analysis and Student's t-tests for categorical and continuous variables, respectively. Multivariate regression analysis was used for identifying predictors of readmission. Results A total of 3,671 inpatient plastic surgery patients were included. The unplanned readmission rate was 7.11%. Multivariate regression analysis revealed a history of chronic obstructive pulmonary disease (COPD) (odds ratio [OR], 2.01; confidence interval [CI], 1.12- 3.60; P=0.020), previous percutaneous coronary intervention (PCI) (OR, 2.69; CI, 1.21-5.97; P=0.015), hypertension requiring medication (OR, 1.65; CI, 1.22-2.24; P<0.001), bleeding disorders (OR, 1.70; CI, 1.01-2.87; P=0.046), American Society of Anesthesiologists (ASA) class 3 or 4 (OR, 1.57; CI, 1.15-2.15; P=0.004), and obesity (body mass index ${\geq}30$) (OR, 1.43; CI, 1.09-1.88, P=0.011) to be significant predictors of readmission. Conclusions Inpatient plastic surgery has an associated 7.11% unplanned readmission rate. History of COPD, previous PCI, hypertension, ASA class 3 or 4, bleeding disorders, and obesity all proved to be significant risk factors for readmission. These findings will help to benchmark inpatient readmission rates and manage patient and hospital system expectations.

• 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.

• Journal of Korean Critical Care Nursing
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• v.12 no.1
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• pp.36-45
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• 2019

Purpose : The purpose of this study was to identify status and characteristics of patients who have been readmitted to ICU, and to analyze risk factors associated with the readmission to ICU within 48hours. Method: Data were collected from patient's electronic medical reports from one hospital in B city. Participants were 2,937 patients aged 18 years old or older admitted to the ICU. Data were analyzed using odd ratios (ORs) from multivariate logistic regressions. Results: 2.2% of the 2,937 patients were early readmitted to ICU. Risk factors for early readmission to ICU were existence of respiratory disease, use of mechanical ventilator, and duration of hospitalization (longer). Conclusion: The assessment on the respiratory system of the patient who will be discharged from the ICU was identified as an important nursing activity. Therefore, the respiratory system management and education should be actively conducted. In addition, early ICU readmission may be prevented and decreased if a link was built to share the information on patient condition between the ICU and general wards.

• Health Policy and Management
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• v.31 no.4
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• pp.451-461
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• 2021

Background: Readmissions related to lack of quality care harm both patients and health insurance finances. If the factors affecting readmission are identified, the readmission can be managed by controlling those factors. This paper aims to identify factors that affect readmissions of convalescent rehabilitation patients. Methods: Health Insurance Review and Assessment Service claims data were used to identify readmissions of convalescent patients who were admitted in hospitals and long-term care hospitals nationwide in 2018. Based on prior research, the socio-demographics, clinical, medical institution, and staffing levels characteristics were included in the research model as independent variables. Readmissions for convalescent rehabilitation treatment within 30 days after discharge were analyzed using logistic regression and generalization estimation equation. Results: The average readmission rate of the study subjects was 24.4%, and the risk of readmission decreases as age, length of stay, and the number of patients per physical therapist increase. In the patient group, the risk of readmission is lower in the spinal cord injury group and the musculoskeletal system group than in the brain injury group. The risk of readmission increases as the severity of patients and the number of patients per rehabilitation medicine specialist increases. Besides, the readmission risk is higher in men than women and long-term care hospitals than hospitals. Conclusion: "Reducing the readmission rate" is consistent with the ultimate goal of the convalescent rehabilitation system. Thus, it is necessary to prepare a mechanism for policy management of readmission.

• 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.

• 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 Korean Biological Nursing Science
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• v.24 no.4
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• pp.262-271
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• 2022

Purpose: The purpose of this study was to identify the factors affecting readmission in stroke patients. Methods: A retrospective study design was used. Participants were 3,675 adult cerebral stroke patients in the inpatient wards of the Department of Neurology and Neurosurgery of G University Hospital located in C city. Data were collected from January 1, 2016 to December 31, 2021 and data were analyzed using χ² test, independent t-test, and multivariate logistic regression with SPSS/WIN 24.0. Results: After discharge for stroke, the readmission rate was 23.7%, and the mortality rate was 0.3%. The variables with significant differences between the readmission group and non-readmission group were age, type of stroke, surgery, ICU treatment, mRS score, blood pressure, diabetes, and heart disease. Factors influencing an readmission in stroke patients were Age 65-74 (OR 1.30, 95% CI=1.03-1.64), ≥ 75 (OR 1.28, 95% CI=1.02-1.62), mRS score 2points (OR 2.50, 95% CI=1.99-3.13), HTN status (OR 1.26, 95% CI=1.07-1.50), CVD status (OR 1.38, 95% CI=1.01-1.90), TC (OR 1.60, 95% CI=1.05-2.44). Conclusion: To lower the readmission rate of stroke patients, it is essential to control lifestyle, including whether or not to take treatment drugs, after diagnosing risk factors such as high blood pressure, diabetes, and heart disease, hyperlipidemia. Nursing interventions that can provide information on risk factor management and coping strategies are urgently needed as symptoms change. In addition, research is needed to develop and implement an intervention strategy that can improve the function of stroke patients as much as possible at home or in society so that they can lead an independent life without the help of others, and verify their effectiveness.

• 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.

• Tuberculosis and Respiratory Diseases
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• v.83 no.4
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• pp.303-311
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• 2020

Background: If the duration of mechanical ventilation (MV) is related with the intensive care unit (ICU) readmission must be clarified. The purpose of this study was to elucidate if prolonged MV duration increases ICU readmission rate. Methods: The present observational cohort study analyzed national healthcare claims data from 2006 to 2015. Critically ill patients who received MV in the ICU were classified into five groups according to the MV duration: MV for <7 days, 7-13 days, 14-20 days, 21-27 days, and ≥28 days. The rate and risk of the ICU readmission were estimated according to the MV duration using the unadjusted and adjusted analyses. Results: We found that 12,929 patients had at least one episode of MV in the ICU. There was a significant linear relationship between the MV duration and the ICU readmission (R²=0.85, p=0.025). The total readmission rate was significantly higher as the MV duration is prolonged (MV for <7 days, 13.9%; for 7-13 days, 16.7%; for 14-20 days, 19.4%; for 21-27 days, 20.4%; for ≥28 days, 35.7%; p<0.001). The analyses adjusted by covariables and weighted with the multinomial propensity scores showed similar results. In the adjusted regression analysis with a Cox proportional hazards model, the MV duration was significantly related to the ICU readmission (hazard ratio, 1.058 [95% confidence interval, 1.047-1.069], p<0.001). Conclusion: The rate of readmission to the ICU was significantly higher in patients who received longer durations of the MV in the ICU. In the clinical setting, closer observation of patients discharged from the ICU after prolonged periods of MV is required.

• 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.