Neonatologist staffing is related to the inter-hospital variation of risk-adjusted mortality of very low birth weight infants in Korea.

This study investigated whether hospital factors, including patient volume, unit level, and neonatologist staffing, were associated with variations in standardized mortality ratios (SMR) adjusted for patient factors in very-low-birth-weight infants (VLBWIs). A total of 15,766 VLBWIs born in 63 hospitals between 2013 and 2020 were analyzed using data from the Korean Neonatal Network cohort. SMRs were evaluated after adjusting for patient factors. High and low SMR groups were defined as hospitals outside the 95% confidence limits on the SMR funnel plot. The mortality rate of VLBWIs was 12.7%. The average case-mix SMR was 1.1; calculated by adjusting for six significant patient factors: antenatal steroid, gestational age, birth weight, sex, 5-min Apgar score, and congenital anomalies. Hospital factors of the low SMR group (N = 10) had higher unit levels, more annual volumes of VLBWIs, more number of neonatologists, and fewer neonatal intensive care beds per neonatologist than the high SMR group (N = 13). Multi-level risk adjustment revealed that only the number of neonatologists showed a significant fixed-effect on mortality besides fixed patient risk effect and a random hospital effect. Adjusting for the number of neonatologists decreased the variance partition coefficient and random-effects variance between hospitals by 11.36%. The number of neonatologists was independently associated with center-to-center differences in VLBWI mortality in Korea after adjustment for patient risks and hospital factors.

Variation in Risk-Adjusted Ventilator-Associated Pneumonia Days Within a Quality Collaborative.

INTRODUCTION: Ventilator-associated pneumonia (VAP) is associated with increased mortality, prolonged mechanical ventilation, and longer intensive care unit stays. The rate of VAP (VAPs per 1000 ventilator days) within a hospital is an important quality metric. Despite adoption of preventative strategies, rates of VAP in injured patients remain high in trauma centers. Here, we report variation in risk-adjusted VAP rates within a statewide quality collaborative. METHODS: Using Michigan Trauma Quality Improvement Program data from 35 American College of Surgeons-verified Level I and Level II trauma centers between November 1, 2020 and January 31, 2023, a patient-level Poisson model was created to evaluate the risk-adjusted rate of VAP across institutions given the number of ventilator days, adjusting for injury severity, physiologic parameters, and comorbid conditions. Patient-level model results were summed to create center-level estimates. We performed observed-to-expected adjustments to calculate each center's risk-adjusted VAP days and flagged outliers as hospitals whose confidence intervals lay above or below the overall mean. RESULTS: We identified 538 VAP occurrences among a total of 33,038 ventilator days within the collaborative, with an overall mean of 16.3 VAPs per 1000 ventilator days. We found wide variation in risk-adjusted rates of VAP, ranging from 0 (0-8.9) to 33.0 (14.4-65.1) VAPs per 1000 d. Several hospitals were identified as high or low outliers. CONCLUSIONS: There exists significant variation in the rate of VAP among trauma centers. Investigation of practices and factors influencing the differences between low and high outlier institutions may yield information to reduce variation and improve outcomes.

A Simple Risk Adjustment for Hospital-Level Nulliparous, Term, Singleton, Vertex, Cesarean Delivery Rates and Its Implications for Public Reporting.

BACKGROUND: The Joint Commission uses nulliparous, term, singleton, vertex, cesarean delivery (NTSV-CD) rates to assess hospitals' perinatal care quality through the Cesarean Birth measurement (PC-02). However, these rates are not risk-adjusted for maternal health factors, putting this measure at odds with the risk adjustment paradigm of most publicly reported hospital quality measures. Here, the authors tested whether risk adjustment for readily documented maternal risk factors affected hospital-level NTSV-CD rates in a large health system. METHODS: Included were all consecutive NTSV pregnancies from January 2019 to April 2023 across 10 hospitals in one health system. Logistic regression, adjusting for age, obesity, diabetes, and hypertensive disorders. was used to calculate hospital-level risk-adjusted NTSV-CD rates by multiplying observed vs. expected ratios for each hospital by the systemwide unadjusted NTSV-CD rate. The authors calculated intrahospital risk differences between unadjusted and risk-adjusted rates and calculated the percentage of hospitals qualifying for different reporting status after risk adjustment using the 30% Joint Commission reporting threshold rate. RESULTS: Of 23,866 pregnancies, 6,550 (27.4%) had cesarean deliveries. Across 10 hospitals, the number of deliveries ranged from 393 to 7,671, with unadjusted NTSV-CD rates ranging from 21.0% to 30.5%. Risk-adjusted NTSV-CD rates ranged from 21.5% to 30.4%, with absolute intrahospital differences in risk-adjusted vs. unadjusted rates ranging from -1.33% (indicating lower rate after risk adjustment) to 3.37% (indicating higher rate after risk adjustment). Three of 10 (30.0%) hospitals qualified for different reporting statuses after risk adjustment. CONCLUSION: Risk adjustment for age, obesity, diabetes, and hypertensive disorders is feasible and resulted in meaningful changes in hospital-level NTSV-CD rates with potentially impactful consequences for hospitals near The Joint Commission reporting threshold.

Risk-adjusted trend in national inpatient fall rates observed from 2011 to 2019 in acute care hospitals in Switzerland: a repeated multicentre cross-sectional study.

OBJECTIVES: This study aimed to investigate whether a significant trend regarding inpatient falls in Swiss acute care hospitals between 2011 and 2019 could be confirmed on a national level, and whether the trend persists after risk adjustment for patient-related fall risk factors. DESIGN: A secondary data analysis was conducted based on annual multicentre cross-sectional studies carried out between 2011 and 2019. SETTING: All Swiss acute care hospitals were obliged to participate in the surveys. Except for emergency departments, outpatient wards and recovery rooms, all wards were included. PARTICIPANTS: All inpatients aged 18 or older who had given their informed consent and whose data were complete and available were included. OUTCOME MEASURE: Whether a patient had fallen in the hospital was retrospectively determined on the survey day by asking patients the following question: Have you fallen in this institution in the last 30 days? RESULTS: Based on data from 110 892 patients from 222 Swiss hospitals, a national inpatient fall rate of 3.7% was determined over the 9 survey years. A significant linear decreasing trend (p=0.004) was observed using the Cochran-Armitage trend test. After adjusting for patient-related fall risk factors in a two-level random intercept logistic regression model, a significant non-linear decreasing trend was found at the national level. CONCLUSIONS: A significant decrease in fall rates in Swiss hospitals, indicating an improvement in the quality of care provided, could be confirmed both descriptively and after risk adjustment. However, the non-linear trend, that is, an initial decrease in inpatient falls that flattens out over time, also indicates a possible future increase in fall rates. Monitoring of falls in hospitals should be maintained at the national level. Risk adjustment accounts for the observed increase in patient-related fall risk factors in hospitals, thus promoting a fairer comparison of the quality of care provided over time.

A Novel Risk-Adjusted Metric to Compare Hospitals on Their Antibiotic Prescribing at Hospital Discharge.

BACKGROUND: Antibiotic overuse at hospital discharge is common, but there is no metric to evaluate hospital performance at this transition of care. We built a risk-adjusted metric for comparing hospitals on their overall post-discharge antibiotic use. METHODS: This was a retrospective study across all acute-care admissions within the Veterans Health Administration during 2018-2021. For patients discharged to home, we collected data on antibiotics and relevant covariates. We built a zero-inflated, negative, binomial mixed model with 2 random intercepts for each hospital to predict post-discharge antibiotic exposure and length of therapy (LOT). Data were split into training and testing sets to evaluate model performance using absolute error. Hospital performance was determined by the predicted random intercepts. RESULTS: 1 804 300 patient-admissions across 129 hospitals were included. Antibiotics were prescribed to 41.5% while hospitalized and 19.5% at discharge. Median LOT among those prescribed post-discharge antibiotics was 7 (IQR, 4-10) days. The predictive model detected post-discharge antibiotic use with fidelity, including accurate identification of any exposure (area under the precision-recall curve = 0.97) and reliable prediction of post-discharge LOT (mean absolute error = 1.48). Based on this model, 39 (30.2%) hospitals prescribed antibiotics less often than expected at discharge and used shorter LOT than expected. Twenty-eight (21.7%) hospitals prescribed antibiotics more often at discharge and used longer LOT. CONCLUSIONS: A model using electronically available data was able to predict antibiotic use prescribed at hospital discharge and showed that some hospitals were more successful in reducing antibiotic overuse at this transition of care. This metric may help hospitals identify opportunities for improved antibiotic stewardship at discharge.

Impact of operative time on textbook outcome after minimally invasive esophagectomy, a risk-adjusted analysis from a high-volume center.

BACKGROUND: We aimed to study the impact of operative time on textbook outcome (TO), especially postoperative complications and length of postoperative stay in minimally invasive esophagectomy. METHODS: Patients undergoing esophagectomy for curative intent within a prospectively maintained database from 2016 to 2022 were retrieved. Relationships between operative time and outcomes were quantified using multivariable mixed-effects models with medical teams random effects. A restricted cubic spline (RCS) plotting was used to characterize correlation between operative time and the odds for achieving TO. RESULTS: Data of 2210 patients were examined. Median operative time was 270 mins (interquartile range, 233-313) for all cases. Overall, 902 patients (40.8%) achieved TO. Among non-TO patients, 226 patients (10.2%) had a major complication (grade ≥ III), 433 patients (19.6%) stayed postoperatively longer than 14 days. Multivariable analysis revealed operative time was associated with higher odds of major complications (odds ratio 1.005, P < 0.001) and prolonged postoperative stay (≥ 14 days) (odds ratio 1.003, P = 0.006). The relationship between operative time and TO exhibited an inverse-U shape, with 298 mins identified as the tipping point for the highest odds of achieving TO. CONCLUSIONS: Longer operative time displayed an adverse influence on postoperative morbidity and increased lengths of postoperative stay. In the present study, the TO displayed an inverse U-shaped correlation with operative time, with a significant peak at 298 mins. Potential factors contributing to prolonged operative time may potentiate targets for quality metrics and risk-adjustment process.

Risk adjusted EWMA control chart based on support vector machine with application to cardiac surgery data.

In the current study, we demonstrate the use of a quality framework to review the process for improving the quality and safety of the patient in the health care department. The researchers paid attention to assessing the performance of the health care service, where the data is usually heterogeneous to patient's health conditions. In our study, the support vector machine (SVM) regression model is used to handle the challenge of adjusting the risk factors attached to the patients. Further, the design of exponentially weighted moving average (EWMA) control charts is proposed based on the residuals obtained through SVM regression model. Analyzing real cardiac surgery patient data, we employed the SVM method to gauge patient condition. The resulting SVM-EWMA chart, fashioned via SVM modeling, revealed superior shift detection capabilities and demonstrated enhanced efficacy compared to the risk-adjusted EWMA control chart.

Using risk-adjusted cumulative sum to evaluate surgeon, divisional, and institutional outcomes-a feasibility study.

BACKGROUND: Few objective, real-time measurements of surgeon performance exist. The risk-adjusted cumulative sum is a novel method that can track surgeon-level outcomes on a continuous basis. The objective of this study was to demonstrate the feasibility of using risk-adjusted cumulative sum to monitor outcomes after colorectal operations and identify clinically relevant performance variations. METHODS: The National Surgical Quality Improvement Program was queried to obtain patient-level data for 1,603 colorectal operations at a high-volume center from 2011 to 2020. For each case, expected risks of morbidity, mortality, reoperation, readmission, and prolonged length of stay were estimated using the National Surgical Quality Improvement Program risk calculator. Risk-adjusted cumulative sum curves were generated to signal observed-to-expected odds ratios of 1.5 (poor performance) and 0.5 (exceptional performance). Control limits were set based on a false positive rate of 5% (α = 0.05). RESULTS: The cohort included data on 7 surgeons (those with more than 20 cases in the study period). Institutional observed versus expected outcomes were the following: morbidity 12.5% (vs 15.0%), mortality 2.5% (vs 2.0%), prolonged length of stay 19.7% (vs 19.1%), reoperation 11.1% (vs 11.3%), and 30-day readmission 6.1% (vs 4.8%). Risk-adjusted cumulative sum accurately demonstrated within- and between-surgeon performance variations across these metrics and proved effective when considering division-level data. CONCLUSION: Risk-adjusted cumulative sum adjusts for patient-level risk factors to provide real-time data on surgeon-specific outcomes. This approach enables prompt identification of performance outliers and can contribute to quality assurance, root-cause analysis, and incentivization not only at the surgeon level but at divisional and institutional levels as well.

A modified CEUS risk stratification model for adnexal masses with solid components: prospective multicenter study and risk adjustment.

OBJECTIVES: To evaluate the additional advantages of integrating contrast-enhanced ultrasound (CEUS) into the Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound (US) for the characterization of adnexal lesions with solid components. MATERIALS AND METHODS: This prospective multicenter study recruited women suspected of having adnexal lesions with solid components between September 2021 and December 2022. All patients scheduled for surgery underwent preoperative CEUS and US examinations. The lesions were categorized according to the O-RADS US system, and quantitative CEUS indexes were recorded. Pathological results served as the reference standard. Univariable and multivariable analyses were performed to identify risk factors for malignancy in adnexal lesions with solid components. Receiver operating characteristic (ROC) curve analysis was employed to assess diagnostic performance. RESULTS: A total of 180 lesions in 175 women were included in the study. Among these masses, 80 were malignant and 100 were benign. Multivariable analysis revealed that serum CA-125, the presence of acoustic shadowing, and peak intensity (PI) ratio (PImass/PIuterus) of solid components on CEUS were independently associated with adnexal malignancy. The modified CEUS risk stratification model demonstrated superior diagnostic value in assessing adnexal lesions with solid components compared to O-RADS US (AUC: 0.91 vs 0.78, p < 0.001) and exhibited comparable performance to the Assessment of Different NEoplasias in the adnexa (ADNEX) model (AUC 0.91 vs 0.86, p = 0.07). CONCLUSION: Our findings underscore the potential value of CEUS as an adjunctive tool for enhancing the precision of diagnostic evaluations of O-RADS US. CLINICAL RELEVANCE STATEMENT: The promising performance of the modified CEUS risk stratification model suggests its potential to mitigate unnecessary surgeries in the characterization of adnexal lesions with solid components. KEY POINTS: • The additional value of CEUS to O-RADS US in distinguishing between benign and malignant adnexal lesions with solid components requires further evaluation. • The modified CEUS risk stratification model displayed superior diagnostic value and specificity in characterizing adnexal lesions with solid components when compared to O-RADS US. • The inclusion of CEUS demonstrated potential in reducing the need for unnecessary surgeries in the characterization of adnexal lesions with solid components.

The mechanics of risk adjustment and incentives for coding intensity in Medicare.

OBJECTIVE: To study diagnosis coding intensity across Medicare programs, and to examine the impacts of changes in the risk model adopted by the Centers for Medicare and Medicaid Services (CMS) for 2024. DATA SOURCES AND STUDY SETTING: Claims and encounter data from the CMS data warehouse for Traditional Medicare (TM) beneficiaries and Medicare Advantage (MA) enrollees. STUDY DESIGN: We created cohorts of MA enrollees, TM beneficiaries attributed to Accountable Care Organizations (ACOs), and TM non-ACO beneficiaries. Using the 2019 Hierarchical Condition Category (HCC) software from CMS, we computed HCC prevalence and scores from base records, then computed incremental prevalence and scores from health risk assessments (HRA) and chart review (CR) records. DATA COLLECTION/EXTRACTION METHODS: We used CMS's 2019 random 20% sample of individuals and their 2018 diagnosis history, retaining those with 12 months of Parts A/B/D coverage in 2018. PRINCIPAL FINDINGS: Measured health risks for MA and TM ACO individuals were comparable in base records for propensity-score matched cohorts, while TM non-ACO beneficiaries had lower risk. Incremental health risk due to diagnoses in HRA records increased across coverage cohorts in line with incentives to maximize risk scores: +0.9% for TM non-ACO, +1.2% for TM ACO, and + 3.6% for MA. Including HRA and CR records, the MA risk scores increased by 9.8% in the matched cohort. We identify the HCC groups with the greatest sensitivity to these sources of coding intensity among MA enrollees, comparing those groups to the new model's areas of targeted change. CONCLUSIONS: Consistent with previous literature, we find increased health risk in MA associated with HRA and CR records. We also demonstrate the meaningful impacts of HRAs on health risk measurement for TM coverage cohorts. CMS's model changes have the potential to reduce coding intensity, but they do not target the full scope of hierarchies sensitive to coding intensity.

Risk adjustment for regional healthcare funding allocations with ensemble methods: an empirical study and interpretation.

We experiment with recent ensemble machine learning methods in estimating healthcare costs, utilizing Finnish data containing rich individual-level information on healthcare costs, socioeconomic status and diagnostic data from multiple registries. Our data are a random 10% sample (553,675 observations) from the Finnish population in 2017. Using annual healthcare cost in 2017 as a response variable, we compare the performance of Random forest, Gradient Boosting Machine (GBM) and eXtreme Gradient Boosting (XGBoost) to linear regression. As machine learning methods are often seen as unsuitable in risk adjustment applications because of their relative opaqueness, we also introduce visualizations from the machine learning literature to help interpret the contribution of individual variables to the prediction. Our results show that ensemble machine learning methods can improve predictive performance, with all of them significantly outperforming linear regression, and that a certain level of interpretation can be provided for them. We also find individual-level socioeconomic variables to improve prediction accuracy and that their effect is larger for machine learning methods. However, we find that the predictions used for funding allocations are sensitive to model selection, highlighting the need for comprehensive robustness testing when estimating risk adjustment models used in applications.

Catheterization for Congenital Heart Disease Adjustment for Risk Method II.

BACKGROUND: Current metrics used to adjust for case mix complexity in congenital cardiac catheterization are becoming outdated due to the introduction of novel procedures, innovative technologies, and expanding patient subgroups. This study aims to develop a risk adjustment methodology introducing a novel, clinically meaningful adverse event outcome and incorporating a modern understanding of risk. METHODS: Data from diagnostic only and interventional cases with defined case types were collected for patients ≤18 years of age and ≥2.5 kg at all Congenital Cardiac Catheterization Project on Outcomes participating centers. The derivation data set consisted of cases performed from 2014 to 2017, and the validation data set consisted of cases performed from 2019 to 2020. Severity level 3 adverse events were stratified into 3 tiers by clinical impact (3a/b/c); the study outcome was clinically meaningful adverse events, severity level ≥3b (3bc/4/5). RESULTS: The derivation data set contained 15 224 cases, and the validation data set included 9462 cases. Clinically meaningful adverse event rates were 4.5% and 4.2% in the derivation and validation cohorts, respectively. The final risk adjustment model included age <30 days, Procedural Risk in Congenital Cardiac Catheterization risk category, and hemodynamic vulnerability score (C statistic, 0.70; Hosmer-Lemeshow P value, 0.83; Brier score, 0.042). CONCLUSIONS: CHARM II (Congenital Heart Disease Adjustment for Risk Method II) risk adjustment methodology allows for equitable comparison of clinically meaningful adverse events among institutions and operators with varying patient populations and case mix complexity performing pediatric cardiac catheterization.

Comparison of diagnosis-based risk adjustment methods for episode-based costs to apply in efficiency measurement.

BACKGROUND: The recent rising health spending intrigued efficiency and cost-based performance measures. However, mortality risk adjustment methods are still under consideration in cost estimation, though methods specific to cost estimate have been developed. Therefore, we aimed to compare the performance of diagnosis-based risk adjustment methods based on the episode-based cost to utilize in efficiency measurement. METHODS: We used the Health Insurance Review and Assessment Service-National Patient Sample as the data source. A separate linear regression model was constructed within each Major Diagnostic Category (MDC). Individual models included explanatory (demographics, insurance type, institutional type, Adjacent Diagnosis Related Group [ADRG], diagnosis-based risk adjustment methods) and response variables (episode-based costs). The following risk adjustment methods were used: Refined Diagnosis Related Group (RDRG), Charlson Comorbidity Index (CCI), National Health Insurance Service Hierarchical Condition Categories (NHIS-HCC), and Department of Health and Human Service-HCC (HHS-HCC). The model accuracy was compared using R-squared (R2), mean absolute error, and predictive ratio. For external validity, we used the 2017 dataset. RESULTS: The model including RDRG improved the mean adjusted R2 from 40.8% to 45.8% compared to the adjacent DRG. RDRG was inferior to both HCCs (RDRG adjusted R2 45.8%, NHIS-HCC adjusted R2 46.3%, HHS-HCC adjusted R2 45.9%) but superior to CCI (adjusted R2 42.7%). Model performance varied depending on the MDC groups. While both HCCs had the highest explanatory power in 12 MDCs, including MDC P (Newborns), RDRG showed the highest adjusted R2 in 6 MDCs, such as MDC O (pregnancy, childbirth, and puerperium). The overall mean absolute errors were the lowest in the model with RDRG ($1,099). The predictive ratios showed similar patterns among the models regardless of the  subgroups according to age, sex, insurance type, institutional type, and the upper and lower 10th percentiles of actual costs. External validity also showed a similar pattern in the model performance. CONCLUSIONS: Our research showed that either NHIS-HCC or HHS-HCC can be useful in adjusting comorbidities for episode-based costs in the process of efficiency measurement.

An Update to the Kaiser Permanente Inpatient Risk Adjustment Methodology Accurately Predicts In-Hospital Mortality: a Retrospective Cohort Study.

BACKGROUND: Methods to accurately predict the risk of in-hospital mortality are important for applications including quality assessment of healthcare institutions and research. OBJECTIVE: To update and validate the Kaiser Permanente inpatient risk adjustment methodology (KP method) to predict in-hospital mortality, using open-source tools to measure comorbidity and diagnosis groups, and removing troponin which is difficult to standardize across modern clinical assays. DESIGN: Retrospective cohort study using electronic health record data from GEMINI. GEMINI is a research collaborative that collects administrative and clinical data from hospital information systems. PARTICIPANTS: Adult general medicine inpatients at 28 hospitals in Ontario, Canada, between April 2010 and December 2022. MAIN MEASURES: The outcome was in-hospital mortality, modeled by diagnosis group using 56 logistic regressions. We compared models with and without troponin as an input to the laboratory-based acute physiology score. We fit and validated the updated method using internal-external cross-validation at 28 hospitals from April 2015 to December 2022. KEY RESULTS: In 938,103 hospitalizations with 7.2% in-hospital mortality, the updated KP method accurately predicted the risk of mortality. The c-statistic at the median hospital was 0.866 (see Fig. 3) (25th-75th 0.848-0.876, range 0.816-0.927) and calibration was strong for nearly all patients at all hospitals. The 95th percentile absolute difference between predicted and observed probabilities was 0.038 at the median hospital (25th-75th 0.024-0.057, range 0.006-0.118). Model performance was very similar with and without troponin in a subset of 7 hospitals, and performance was similar with and without troponin for patients hospitalized for heart failure and acute myocardial infarction. CONCLUSIONS: An update to the KP method accurately predicted in-hospital mortality for general medicine inpatients in 28 hospitals in Ontario, Canada. This updated method can be implemented in a wider range of settings using common open-source tools.

Improving diagnosis-based cost groups in the Dutch risk equalization model: the effects of a new clustering method and allowing for multimorbidity.

Health insurance markets with community-rated premiums typically use risk equalization (RE) to compensate insurers for predictable profits on people in good health and predictable losses on those with a chronic disease. Over the past decades RE models have evolved from simple demographic models to sophisticated health-based models. Despite the improvements, however, non-trivial predictable profits and losses remain. This study examines to what extent the Dutch RE model can be further improved by redesigning one key morbidity adjuster: the Diagnosis-based Cost Groups (DCGs). This redesign includes (1) revision of the underlying hospital diagnoses and treatments ('dxgroups'), (2) application of a new clustering procedure, and (3) allowing multi-qualification. We combine data on spending, risk characteristics and hospital claims for all individuals with basic health insurance in the Netherlands in 2017 (N = 17 m) with morbidity data from general practitioners (GPs) for a subsample (N = 1.3 m). We first simulate a baseline RE model (i.e., the RE model of 2020) and then modify three important features of the DCGs. In a second step, we evaluate the effect of the modifications in terms of predictable profits and losses for subgroups of consumers that are potentially vulnerable to risk selection. While less prominent results are found for subgroups derived from the GP data, our results demonstrate substantial reductions in predictable profits and losses at the level of dxgroups and for individuals with multiple dxgroups. An important takeaway from our paper is that smart design of morbidity adjusters in RE can help mitigate selection incentives.

NSQIP Quality Benchmarking and Evaluation of Potential Bias Associated with Higher- or Lower-Risk Operation Case Mix.

BACKGROUND: To ensure validity and acceptance of NSQIP risk-adjusted benchmarking, it is important that adjustments adequately control for hospitals that vary in their proportions of lower- or higher-risk operations (combined risk for procedure and patient). This issue was addressed in separate empirical and simulation studies. STUDY DESIGN: For the empirical study, potential miscalibration bias favoring hospitals that do lower-risk operations or disfavoring hospitals that do higher-risk operations was evaluated for 14 modeled outcomes using NSQIP data. A determination was also made as to whether there was a relationship between mean hospital operation risk and benchmarking results (log odds ratio). In the simulation study of the same 14 outcomes, hospital benchmarked performance was evaluated when sampled cases were reconstituted to include either a larger proportion of lower-risk operations or a larger proportion of higher-risk operations. RESULTS: Miscalibration favoring either lower- or higher-risk operations was absent, as were important associations between operative risk and hospital log odds ratios (most model R 2 less than 0.01). In the simulation, there were no substantial changes in log odds ratios when greater percentages of either lower- or higher-risk operations were included in a hospital's sample (nonsignificant p values and effect sizes less than 0.1). CONCLUSIONS: These results should enhance NSQIP participants' confidence in the adequacy of NSQIP patient and procedure risk-adjustment methods. NSQIP participants may rely on benchmarking findings, and implement quality improvement efforts based on them, without concern that they are biased by a preponderance of lower or higher risk operations.

Evaluation of risk adjustment performance of diagnosis-based and medication-based comorbidity indices in patients with chronic obstructive pulmonary disease.

OBJECTIVES: This study assessed risk adjustment performance of six comorbidity indices in two categories of comorbidity measures: diagnosis-based comorbidity indices and medication-based ones in patients with chronic obstructive pulmonary disease (COPD). METHODS: This was a population-based retrospective cohort study. Data used in this study were sourced from the Taiwan National Health Insurance Research Database. The study population comprised all patients who were hospitalized due to COPD for the first time in the target year of 2012. Each qualified patient was individually followed for one year starting from the index date to assess two outcomes of interest, medical expenditures within one year after discharge and in-hospital mortality of patients. To assess how well the added comorbidity measures would improve the fitted model, we calculated the log-likelihood ratio statistic G2. Subsequently, we compared risk adjustment performance of the comorbidity indices by using the Harrell c-statistic measure derived from multiple logistic regression models. RESULTS: Analytical results demonstrated that that comorbidity measures were significant predictors of medical expenditures and mortality of COPD patients. Specifically, in the category of diagnosis-based comorbidity indices the Elixhauser index was superior to other indices, while the RxRisk-V index was a stronger predictor in the framework of medication-based codes, for gauging both medical expenditures and in-hospital mortality by utilizing information from the index hospitalization only as well as the index and prior hospitalizations. CONCLUSIONS: In conclusion, this work has ascertained that comorbidity indices are significant predictors of medical expenditures and mortality of COPD patients. Based on the study findings, we propose that when designing the payment schemes for patients with chronic diseases, the health authority should make adjustments in accordance with the burden of health care caused by comorbid conditions.

Is the Centers for Medicare and Medicaid Services Hierarchical Condition Category Risk Adjustment Model Satisfactory for Quantifying Risk After Spine Surgery?

BACKGROUND: The Centers for Medicare and Medicaid Services (CMS) hierarchical condition category (HCC) coding is a risk adjustment model that allows for the estimation of risk-and cost-associated with health care provision. Current models may not include key factors that fully delineate the risk associated with spine surgery. OBJECTIVE: To augment CMS HCC risk adjustment methodology with socioeconomic data to improve its predictive capabilities for spine surgery. METHODS: The National Inpatient Sample was queried for spinal fusion, and the data was merged with county-level coverage and socioeconomic status variables obtained from the Brookings Institute. We predicted outcomes (death, nonroutine discharge, length of stay [LOS], total charges, and perioperative complication) with pairs of hierarchical, mixed effects logistic regression models-one using CMS HCC score alone and another augmenting CMS HCC scores with demographic and socioeconomic status variables. Models were compared using receiver operating characteristic curves. Variable importance was assessed in conjunction with Wald testing for model optimization. RESULTS: We analyzed 653 815 patients. Expanded models outperformed models using CMS HCC score alone for mortality, nonroutine discharge, LOS, total charges, and complications. For expanded models, variable importance analyses demonstrated that CMS HCC score was of chief importance for models of mortality, LOS, total charges, and complications. For the model of nonroutine discharge, age was the most important variable. For the model of total charges, unemployment rate was nearly as important as CMS HCC score. CONCLUSION: The addition of key demographic and socioeconomic characteristics substantially improves the CMS HCC risk-adjustment models when modeling spinal fusion outcomes. This finding may have important implications for payers, hospitals, and policymakers.

Should administrative costs in health insurance be included in the risk-equalization? An analysis of five countries.

Most countries that apply risk-equalization in their health insurance market(s) perform risk-equalization on medical claims but do not include other components of the insurance premium, such as administrative costs. Using fixed effects panel regressions from individual insurers in Australia, Germany, the Netherlands, Switzerland, and the US, we find evidence that health insurers with a high morbidity population on average have higher administrative costs. We argue that administrative costs should also be included in risk-equalization and we show that such equalization results in additional equalization payments nontrivial in size. Using examples from Germany and the US, we show how in practice policymakers can include administrative costs in risk-equalization. We are skeptical about applying risk-equalization to other components of the insurance premium, such as profits or costs related to solvency requirements of insurers.