Climate Change ; Health ; Humans ; Risk Adjustment

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.
Clinical Coding ; Korea* ; Mortality* ; Quality of Health Care ; Risk Adjustment

BACKGROUND: The learning curve cumulative sum (LC-CUSUM) test is an innovative tool that allows quantitative monitoring of individual medical performance during the learning process by determining when a predefined acceptable level of performance is reached. This study used the LC-CUSUM test to monitor the learning process and failure rate of anesthesia residents training for specific subspecialty anesthesia procedures. METHODS: The study included 490 tracheal punctures (TP) for jet ventilation, 340 thoracic epidural analgesia (TEA) procedures, and 246 fiberoptic nasal intubations (FONI) performed by 18 residents during their single 6-month rotation. RESULTS: Overall, 27 (14–52), 19 (5–41), and 14 (6–33) TP, TEA, and FONI procedures were performed, respectively, by each resident. In total, 2 of 18 residents achieved an acceptable failure rate for TEA according to the literature and 4 of 18 achieved an acceptable failure rate for FONI, while none of the residents attained an acceptable rate for TP. CONCLUSIONS: A single 6-month rotation in a reference teaching center may not be sufficient to train residents to perform specific or sub-specialty procedures as required. A regional learning network may be useful. More patient-based data are necessary to conduct a risk adjustment analysis for such specific procedures.
Analgesia, Epidural ; Anesthesia ; Anesthesiology* ; Intubation ; Learning Curve* ; Learning* ; Punctures ; Risk Adjustment ; Tea ; Ventilation

BACKGROUND: The learning curve cumulative sum (LC-CUSUM) test is an innovative tool that allows quantitative monitoring of individual medical performance during the learning process by determining when a predefined acceptable level of performance is reached. This study used the LC-CUSUM test to monitor the learning process and failure rate of anesthesia residents training for specific subspecialty anesthesia procedures. METHODS: The study included 490 tracheal punctures (TP) for jet ventilation, 340 thoracic epidural analgesia (TEA) procedures, and 246 fiberoptic nasal intubations (FONI) performed by 18 residents during their single 6-month rotation. RESULTS: Overall, 27 (14–52), 19 (5–41), and 14 (6–33) TP, TEA, and FONI procedures were performed, respectively, by each resident. In total, 2 of 18 residents achieved an acceptable failure rate for TEA according to the literature and 4 of 18 achieved an acceptable failure rate for FONI, while none of the residents attained an acceptable rate for TP. CONCLUSIONS: A single 6-month rotation in a reference teaching center may not be sufficient to train residents to perform specific or sub-specialty procedures as required. A regional learning network may be useful. More patient-based data are necessary to conduct a risk adjustment analysis for such specific procedures.
Analgesia, Epidural ; Anesthesia ; Anesthesiology* ; Intubation ; Learning Curve* ; Learning* ; Punctures ; Risk Adjustment ; Tea ; Ventilation

BACKGROUND: This study aimed to estimate risk adjusted mortality rate in the ICUs (Intensive care units) by APACHE (Acute Physiology And Chronic Health Evaluation) III for revealing the performance variation in ICUs. METHODS: This study focused on 1,090 patients in the ICUs of 18 hospitals. For establishing risk adjusted mortality predictive model, logistic regression analysis was performed. APACHE III, surgery experience, admission route, and major disease categories were used as independent variables. The performance of each model was evaluated by c-statistic and goodness-of-fit test of Hosmer-Lemeshow. Using this predictive model, the performance of each ICU was tested as ratio of predictive mortality rate and observed mortality rate. RESULTS: The average observed mortality rate was 24.1%. The model including APACHE III score, admission route, and major disease categories was signified as the fittest one. After risk adjustment, the ratio of predictive mortality rate and observed mortality rate was distributed from 0.49 to 1.55. CONCLUSIONS: The variation in risk adjusted mortality among ICUs was wide. The effort to reduce this quality difference is needed.
APACHE ; Humans ; Critical Care ; Intensive Care Units ; Logistic Models ; Risk Adjustment

Forecasting ; Genetic Predisposition to Disease ; prevention & control ; Genome-Wide Association Study ; Humans ; Risk Adjustment ; trends

Health outcomes evaluation seeks to compare a new treatment or novel programme with the current standard of care, or to identify variation of outcomes across different healthcare providers. In the real world, it is not always possible to conduct randomised controlled trials to address the issue of comparator groups being different with respect to baseline risk factors for the outcomes. Therefore, risk adjustment is required to address patient factors that may lead to biases in estimates of treatment effects. It is essential when conducting outcomes evaluation of more than trivial significance. Risk adjustment begins by asking 4 questions: what outcome, what time frame, what population, and what purpose. Next, design issues are considered. This involves choosing the data source, planning data collection, defining the sample required, and selecting the variables carefully. Finally, analytical issues are considered. Regression modelling is central to every analytic strategy. Other methods that may augment regression include restriction, stratification, propensity scores, instrumental variables, and difference-in-differences. The construction of risk adjustment models is an iterative process requiring both art and science. Derived models should be validated. Limitations of risk adjustment include reliance on data availability and quality, imperfect method, ineffectiveness when comparators are very different, and sensitivity to different methods used. Thoughtful application of risk adjustment can improve the validity of comparisons between different treatments, programmes and providers. The extent of risk adjustment should be guided by its purpose. Finally, its methodology should be made explicit, so that informed readers can judge the robustness of results obtained.
Health Services Research ; Outcome Assessment (Health Care) ; Regression Analysis ; Risk Adjustment ; standards

OBJECTIVES: To assess whether the risk-adjusted inhospital mortality rates for non-emergent and isolated coronary artery bypass graft surgery (CABG) patients exhibited a consistent trend from 2001 to 2003. METHODS: The data used in this study came from CABG claims that were submitted to a Korean Health Insurance Review Agency (HIRA) in 2001, 2002, and 2003. Study datasets included data from 17 tertiary hospitals, which had at least 25 claims each year over 3 years. The interhospital differences in patients' risk-factors were identified and controlled in the risk-adjustment model. Actual and predicted mortality rates for each hospital were calculated in 2001, 2002, 2003, and 2001+2002, and were then examined to identify consistent rate patterns over time. Kappa analysis was applied to assess the agreements between rates. RESULTS: Hospitals with lower-than-expected inpatient mortality rates showed more consistent rates than those with higher-than-expected mortality rates. The mortality rates that were calculated based on data obtained over multiple years had less variation among hospitals than rates based on single year data. Based on the Kappa score, the highest agreement was found when the rates were compared between the 2-year combined data (2001+2002) and 2003. CONCLUSIONS: Consistent patterns over 3 years were most evident for hospitals which had lower-than expected mortality rates. Policy makers can use this information to identify the degree of outcomes in hospitals and help motivate or channel the behaviors of providers.
Risk Assessment ; Risk Adjustment ; Male ; Korea/epidemiology ; Humans ; Hospital Mortality/*trends ; Female ; Coronary Artery Bypass/*mortality/trends

To compare mortality rate, the adjustment of case-mix variables is needed. The Pediatric Index of Mortality (PIM) 3 score is a widely used case-mix adjustment system of a pediatric intensive care unit (ICU), but there has been no validation study of it in Korea. We aim to validate the PIM3 in a Korean pediatric ICU, and extend the validation of the score from those aged 0–16 to 0–18 years, as patients aged 16–18 years are admitted to pediatric ICU in Korea. A retrospective cohort study of 1,710 patients was conducted in a tertiary pediatric ICU. To validate the score, the discriminatory power was assessed by calculating the area under the receiver-operating characteristic (ROC) curve, and calibration was evaluated by the Hosmer-Lemeshow goodness-of-fit (GOF) test. The observed mortality rate was 8.47%, and the predicted mortality rate was 6.57%. For patients aged < 18 years, the discrimination was acceptable (c-index = 0.76) and the calibration was good, with a χ² of 9.4 in the GOF test (P = 0.313). The observed mortality rate in the hemato-oncological subgroup was high (18.73%), as compared to the predicted mortality rate (7.13%), and the discrimination was unacceptable (c-index = 0.66). In conclusion, the PIM3 performed well in a Korean pediatric ICU. However, the application of the PIM3 to a hemato-oncological subgroup needs to be cautioned. Further studies on the performance of PIM3 in pediatric patients in adult ICUs and pediatric ICUs of primary and secondary hospitals are needed.
Adult ; Benchmarking ; Calibration ; Child ; Cohort Studies ; Critical Care* ; Discrimination (Psychology) ; Humans ; Intensive Care Units* ; Korea* ; Mortality* ; Retrospective Studies ; Risk Adjustment

PURPOSE: The purpose of this study was to measure home health resource utilization using a Case-Mix Adjustor Model developed in the U.S. METHOD: The subjects of this study were 484 patients who had received home health care more than 4 visits during a 60-day episode at 31 home health care institutions. Data on the 484 patients had to be merged onto a 60-day payment segment. Based on the results, the researcher classified home health resource groups (HHRG). RESULT: The subjects were classified into 34 HHRGs in Korea. Home health resource utilization according to clinical severity was in order of Minimum (C0) < 'Low (C1) < 'Moderate (C2) < 'High (C3), according to dependency in daily activities was in order of Minimum (F0) < 'High (F3) < 'Medium (F2) < 'Low (F1) < 'Maximum (F4). Resource utilization by HHRGs was the highest 564,735 won in group C0F0S2 (clinical severity minimum, dependency in daily activity minimum, service utilization moderate), and the lowest 97,000 won in group C2F3S1, so the former was 5.82 times higher than the latter. CONCLUSION: Resource utilization in home health care has become an issue of concern due to rising costs for home health care. The results suggest the need for more analytical attention on the utilization and expenditures for home care using a Case-Mix Adjustor Model.
Risk Adjustment ; Middle Aged ; Male ; Korea ; Humans ; Home Care Services/*utilization ; Health Resources/*utilization ; Female ; Aged, 80 and over ; Aged ; Adult