• 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.

• Health Policy and Management
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• v.27 no.2
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• pp.114-120
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• 2017

The hospital standardized mortality ratio (HSMR) is a widely used generic measure for assessing quality of hospital care in many countries. However, the validity of HSMR as a quality indicator is still controversial. We critically reviewed characteristics of HSMR and suggested how to use HSMR as a quality indicator in the Korean setting. The association between HSMR and other quality measures of hospital care is inconclusive. In addition current HSMR model has shortcomings in risk adjustment because of the lack of clinical data, accuracy of disease coding, coding variation among hospitals, end-of-life care issues, and so on. Therefore, HSMR should be used as an indicator for improvement, not for judgement such as public reporting and pay-for-performance. More efforts will be needed to tackle practical and methodological weaknesses of HSMR in the Korean setting.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.4
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• pp.1739-1750
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• 2012

This study was to develop the assessment of medical service outcome using administration data through compared with hospital standardized mortality ratios(HSMR) in various hospitals. This study analyzed 63,664 cases of Hospital Discharge Injury Data of 2007 and 2008, provided by Korea Centers for Disease Control and Prevention. We used data mining technique and compared decision tree and logistic regression for developing risk-adjustment model of in-hospital mortality. Our Analysis shows that gender, length of stay, Elixhauser comorbidity index, hospitalization path, and primary diagnosis are main variables which influence mortality ratio. By comparing hospital standardized mortality ratios(HSMR) with standardized variables, we found concrete differences (55.6-201.6) of hospital standardized mortality ratios(HSMR) among hospitals. This proves that there are quality-gaps of medical service among hospitals. This study outcome should be utilized more to achieve the improvement of the quality of medical service.

• Journal of Digital Convergence
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• v.13 no.12
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• pp.245-257
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• 2015

This study was to develop the predictive model for severity-adjusted mortality of inpatients with multiple chronic conditions and analyse the factors on the variation of hospital standardized mortality ratio(HSMR) to propose the plan to reduce the variation. We collect the data "Korean National Hospital Discharge In-depth Injury Survey" from 2008 to 2010 and select the final 110,700 objects of study who have chronic diseases for principal diagnosis and who are over the age of 30 with more than 2 chronic diseases including principal diagnosis. We designed a severity-adjusted mortality predictive model with using data-mining methods (logistic regression analysis, decision tree and neural network method). In this study, we used the predictive model for severity-adjusted mortality ratio by the decision tree using Elixhauser comorbidity index. As the result of the hospital standardized mortality ratio(HSMR) of inpatients with multiple chronic conditions, there were statistically significant differences in HSMR by the insurance type, bed number of hospital, and the location of hospital. We should find the method based on the result of this study to manage mortality ratio of inpatients with multiple chronic conditions efficiently as the national level. So we should make an effort to increase the quality of medical treatment for inpatients with multiple chronic diseases and to reduce growing medical expenses.

• Journal of Digital Convergence
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• v.11 no.10
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• pp.459-467
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• 2013

The purpose of this study was to identify the differences in medical care utilization by regional economic status using the National Hospital Discharge Patients Injury Survey. In order to determine economic status of each region, 234 cities and counties were categorized 5 quintiles according to their financial self-reliance ratio. The main results are as follows. First, low economic region has high age-standardized admission rate and standardized mortality rate. Second, of 16 major diseases, cerebrovascular and heart diseases, lung cancer, and stomach cancer reported greater changes in standardized mortality rate by regional economic status. Third, the rate of admission via emergency room in low economic region is higher than that of high economic region. Lastly, in the major illnesses, lower economic status led to an increase in average length of stay. Therefore, In order to bridge the gap in health inequality across regions, a regional medical policy tailored for each region and characteristics of the economic status should be established.

• Journal of Preventive Medicine and Public Health
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• v.56 no.2
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• pp.180-189
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• 2023

Objectives: The coronavirus disease 2019 (COVID-19) pandemic has led to a global shortage of medical resources; therefore, we investigated whether COVID-19 impacted the quality of non-COVID-19 hospital care in Korea by comparing hospital standardized mortality rates (HSMRs) before and during the pandemic. Methods: This retrospective cohort study analyzed Korean National Health Insurance discharge claim data obtained from January to June in 2017, 2018, 2019, and 2020. Patients' in-hospital deaths were classified according to the most responsible diagnosis categories. The HSMR is calculated as the ratio of expected deaths to actual deaths. The time trend in the overall HSMR was analyzed by region and hospital type. Results: The final analysis included 2 252 824 patients. In 2020, the HSMR increased nationwide (HSMR, 99.3; 95% confidence interval [CI], 97.7 to 101.0) in comparison to 2019 (HSMR, 97.3; 95% CI, 95.8 to 98.8). In the COVID-19 pandemic zone, the HSMR increased significantly in 2020 (HSMR, 112.7; 95% CI, 107.0 to 118.7) compared to 2019 (HSMR, 101.7; 95% CI, 96.9 to 106.6). The HSMR in all general hospitals increased significantly in 2020 (HSMR, 106.4; 95% CI, 104.3 to 108.5) compared to 2019 (HSMR, 100.3; 95% CI, 98.4 to 102.2). Hospitals participating in the COVID-19 response had a lower HSMR (HSMR, 95.6; 95% CI, 93.9 to 97.4) than hospitals not participating in the COVID-19 response (HSMR, 124.3; 95% CI, 119.3 to 129.4). Conclusions: This study suggests that the COVID-19 pandemic may have negatively impacted the quality of care in hospitals, especially general hospitals with relatively few beds. In light of the COVID-19 pandemic, it is necessary to prevent excessive workloads in hospitals and to properly employ and coordinate the workforce.

• Journal of Preventive Medicine and Public Health
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• v.44 no.4
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• pp.185-189
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• 2011

Objectives: In 1995, an outbreak survey in Gozan-dong concluded that an association between fiberglass exposure in drinking water and cancer outbreak cannot be established. This study follows the subjects from a study in 1995 using a data linkage method to examine whether an association existed. The authors will address the potential benefits and methodological issues following outbreak surveys using data linkage, particularly when informed consent is absent. Methods: This is a follow-up study of 697 (30 exposed) individuals out of the original 888 (31 exposed) participants (78.5%) from 1995 to 2007 assessing the cancer outcomes and deaths of these individuals. The National Cancer Registry (KNCR) and death certificate data were linked using the ID numbers of the participants. The standardized incidence ratio (SIR) and standardized mortality ratio (SMR) from cancers were calculated by the KNCR. Results: The SIR values for all cancer or gastrointestinal cancer (GI) occurrences were the lowest in the exposed group (SIR, 0.73; 95% CI, 0.10 to 5.21; 0.00 for GI), while the two control groups (control 1: external, control 2: internal) showed slight increases in their SIR values (SIR, 1.18 and 1.27 for all cancers; 1.62 and 1.46 for GI). All lacked statistical significance. All-cause mortality levels for the three groups showed the same pattern (SMR 0.37, 1.29, and 1.11). Conclusions: This study did not refute a finding of non-association with a 13-year follow-up. Considering that many outbreak surveys are associated with a small sample size and a cross-sectional design, follow-up studies that utilize data linkage should become standard procedure.

• Journal of Preventive Medicine and Public Health
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• v.47 no.4
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• pp.206-215
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• 2014

Objectives: The hospital standardized mortality ratio (HSMR) has been widely used because it allows for robust risk adjustment using administrative data and is important for improving the quality of patient care. Methods: All inpatients discharged from hospitals with more than 700 beds (66 hospitals) in 2008 were eligible for inclusion. Using the claims data, 29 most responsible diagnosis (MRDx), accounting for 80% of all inpatient deaths among these hospitals, were identified, and inpatients with those MRDx were selected. The final study population included 703 571 inpatients including 27 718 (3.9% of all inpatients) in-hospital deaths. Using logistic regression, risk-adjusted models for predicting in-hospital mortality were created for each MRDx. The HSMR of individual hospitals was calculated for each MRDx using the model coefficients. The models included age, gender, income level, urgency of admission, diagnosis codes, disease-specific risk factors, and comorbidities. The Elixhauser comorbidity index was used to adjust for comorbidities. Results: For 26 out of 29 MRDx, the c-statistics of these mortality prediction models were higher than 0.8 indicating excellent discriminative power. The HSMR greatly varied across hospitals and disease groups. The academic status of the hospital was the only factor significantly associated with the HSMR. Conclusions: We found a large variation in HSMR among hospitals; therefore, efforts to reduce these variations including continuous monitoring and regular disclosure of the HSMR are required.

• Journal of Korean Academy of Nursing
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• v.35 no.1
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• pp.47-55
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• 2005

Purpose: This study was to evaluate the validity of the Pediatric Index of Mortality Ⅱ(PIM Ⅱ). Method: The first values on PIM Ⅱ variables following ICU admission were collected from the patient's charts of 548 admissions retrospectively in three ICUs(medical, surgical, and neurosurgical) at P University Hospital and a cardiac ICU at D University Hospital in Busan from January 1, 2002 to December 31, 2003. Data was analyzed with the SPSSWIN 10.0 program for the descriptive statistics, correlation coefficient, standardized mortality ratio(SMR), validity index(sensitivity, specificity, positive predictive value, negative predictive value), and AUC of ROC curve. Result: The mortality rate was 10.9% (60 cases) and the predicted death rate was 9.5%. The correlation coefficient(r) between observed and expected death rates was .929(p<.01) and SMR was 1.15. Se, Sp, pPv, nPv, and the correct classification rate were .80, .96, .70, .98, and 94.0% respectively. In addition, areas under the curve (AUC) of the receiver operating characteristic(ROC) was 0.954 (95% CI=0.919～0.989). According to demographic characteristics, mortality was underestimated in the medical group and overestimated in the surgical group. In addition, the AUCs of ROC curve were generally high in all subgroups. Conclusion: The PIM Ⅱ showed a good, so it can be utilized for the subject hospital. better.

• Journal of Preventive Medicine and Public Health
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• v.54 no.5
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• pp.352-359
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• 2021

Objectives: The aim of this study was to examine the geographic distribution of diabetes mortality in Japan and identify socioeconomic factors affecting differences in municipality-specific diabetes mortality. Methods: Diabetes mortality data by year and municipality from 2013 to 2017 were extracted from Japanese Vital Statistics, and the socioeconomic characteristics of municipalities were obtained from government statistics. We calculated the standardized mortality ratio (SMR) of diabetes for each municipality using the empirical Bayes method and represented geographic differences in SMRs in a map of Japan. Multiple linear regression was conducted to identify the socioeconomic factors affecting differences in SMR. Statistically significant socioeconomic factors were further assessed by calculating the relative risk of mortality of quintiles of municipalities classified according to the degree of each socioeconomic factor using Poisson regression analysis. Results: The geographic distribution of diabetes mortality differed by gender. Of the municipality-specific socioeconomic factors, high rates of single-person households and unemployment and a high number of hospital beds were associated with a high SMR for men. High rates of fatherless households and blue-collar workers were associated with a high SMR for women, while high taxable income per-capita income and total population were associated with low SMR for women. Quintile analysis revealed a complex relationship between taxable income and mortality for women. The mortality risk of quintiles with the highest and lowest taxable per-capita income was significantly lower than that of the middle-income quintile. Conclusions: Socioeconomic factors of municipalities in Japan were found to affect geographic differences in diabetes mortality.