The Drivers of 90-Day CABG Episode Payment Variation for Commercially Insured Patients

The Drivers of 90-Day CABG Episode Payment Variation for Commercially Insured Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Drivers of 90-Day CABG Episode Payment Variation for Commercially Insured Patients Saleh Alsarhan, Rigoberto I. Delgado, Trudy Millard Krause, David Aguilar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3891751/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Coronary artery bypass grafting (CABG) surgery has become a target for episode payment initiatives, and there is the need to understand what factors contribute to costs of CABG surgeries. 90-day episode payments for CABG surgery were estimated using commercial insurance claims of patients over 18 years of age in Texas. The study used a multiple linear regression model with a log-transformed 90-day CABG episode payments to model effect of patient factors on payment variation. The source of data was Optum’s de-identified Clinformatics® Data Mart Database (CDM): administrative claims data. A total of 999 CABG episodes were identified. The mean (SD) 90-day CABG episode payment per patient was $ 81,330 ( $ 47,382). Patient factors explained about 37% of the payment variation. Wide variation exists in 90-day CABG episode payments for commercially insured patients across Texas. Focusing on reducing variation of index hospitalization could be a potentially effective approach to improve efficiency. CABG drivers of payment variation Figures Figure 1 Figure 2 INTRODUCTION In 2018, the American Heart Association (AHA) reported that the $ 9 billion in annual medical costs associated with coronary artery disease (CAD) make it one of the ten most expensive health conditions treated in U.S. hospitals.[ 1 ] Medical costs related to CAD were projected to increase significantly.[ 1 ] For patients with CAD, coronary artery bypass grafting (CABG) surgery is a common and expensive invasive procedure. In Texas, for example, estimates from the Department of State Health Services place the average hospital charge for CABG surgery per case at $ 234,560.[ 2 ] Given that Texas had the third highest annual average spending growth rate (5.6%) in private health insurance between the years 2001–2020 in the country,[ 3 ] it is important to obtain precise estimates of costs for planning purposes under current value-added healthcare policies. But, as the case of Texas illustrates, estimation of costs represents several challenges. First, the number of costs assessments for CABG surgeries are limited, restricting the application of representative findings in the development of effective cost control policies. Second, most studies, not necessarily in Texas, rely on evaluating hospital billing charges, which is less accurate than other approaches such as through the analysis of insurance reimbursements. In an effort to focus on value-based medicine and quality of care in healthcare spending rather than on volume (i.e., quantity), researchers and policymakers have proposed clinical episode payment models for certain diagnoses and procedures, including episodes designed around CABG surgeries.[ 4 – 7 ] The episode payment typically is a bundled payment for all the care that a patient receives for a procedure or treatment of a particular disease or condition during a defined period of time. An episode may include inpatient stay as well as healthcare services provided within 90-days of the initial hospital discharge. Under the episode payment model, healthcare providers participate through an agreement with payers and receive a negotiated amount for the multiple services patients received during an episode of care. Therefore, reimbursement is linked to the healthcare performance within the episode of care. Healthcare providers are held accountable for the total costs of care and could be subject to financial consequences for low-quality care. Healthcare providers through the risk sharing strategy are incentivized to reduce unnecessary services and improve health care coordination across healthcare settings. Previous research has shown episode payments for surgical procedures might have the potential to decrease healthcare payments while maintaining or improving quality of care.[ 8 – 11 ] Studies that examined clinical episodes related to cardiac surgeries (i.e., cardiac valve replacement, CABG) found that episode payment initiatives were not associated with the index hospitalization costs but that they were associated with lowering post-acute care spending.[ 8 ][ 10 ] Furthermore, several studies have focused on CABG episode payments and the drivers of payment variation.[ 12 – 16 ] A recent study by Guduguntla et al. (2018) examined CABG episode payment variation and its components in seventy-six hospitals in Michigan, and these researchers found that a wide variation existed in 90-day episode payments.[ 13 ] The study found that patients with multiple readmissions as well as components such as index hospitalization, evaluation and management services (E&M), and inpatient rehabilitation contributed the most to variation in episode payments.[ 13 ] Furthermore, the findings of the study indicated that among all payment components, readmission had the highest difference rate between the highest payment hospitals compared with the lowest payment hospitals.[ 13 ] In another recent study in 2019, Shubeck et al. investigated CABG episode payment variation of Medicare nationwide beneficiaries at the hospital level.[ 16 ] These researchers found that CABG payment variation between the high- and low-payment hospitals was due to a threefold difference in index hospitalization payment.[ 16 ] In addition, the study demonstrated that patients with comorbidities incur higher payments than healthier patients.[ 16 ] Other studies on Medicare beneficiaries found that significant variation existed for CABG episodes payments, concluding that the index hospitalization component was the key driver of payment variation.[ 14 ][ 15 ] Because these CABG cost studies focused mainly on Medicare populations, their findings might not be generalizable for younger populations or for those with private insurance.[ 14 – 16 ] A study by Wynn-Jones et al. (2019) examined 90-day period CABG payment variation among TRICARE (health care program for military service members, retirees, and their families) adult patients and found significant regional-level variation in payment for CABG episodes.[ 12 ] The index hospitalization payment in this study was also found to be the main driver of payment variation.[ 12 ] Moreover, the readmission payment had the highest difference rate between the highest payment regions compared with the lowest payment regions.[ 12 ] Substantial gaps remain in our knowledge of CABG payments for commercially insured patients in Texas. Examining 90-day CABG payment variation for commercially insured patients across Texas might help establish policies to improve efficiency and decrease wasteful spending in healthcare in Texas. The purpose of this study was to describe CABG episode payments and to examine the drivers of payment variation using a representative commercially insured cohort for Texas. The results of this study should offer healthcare managers, health insurers, and policymakers critical baseline data to design appropriate strategies for improving value-based payments and episode payments for CABG surgeries. METHODS The study used Optum’s de-identified Clinformatics® Data Mart Database (CDM), administrative claims data of a commercially insured population. The Optum’s de-identified CDM is derived from a database of administrative health claims for members of large commercial and Medicare Advantage health plans. Data included patient characteristics, healthcare resource utilization, diagnoses codes, and standardized costs, allowing us to make proper comparisons of payments across Texas. The period of analysis was 2014–2018, but the claims datasets for this study included records for 2013 in order to track comorbid conditions. Similarly, patient data for cases discharged toward the end of 2018 were not included in the study if the 90-day post-procedure requirement occurred in 2019. The CMS MS-DRG CABG trigger codes (i.e., DRG 231, DRG 232, DRG 233, DRG 234, DRG 235, DRG 236) were used to identify patients for the 90-day CABG episodes.[ 17 ] Episode payments refer to cumulative standardized costs incurred during index hospitalization (initial admission for the CABG procedure) and the 90-day post-discharge care including any readmission. In order to estimate expenditure variation, episode costs were categorized into five components: 1) index hospitalization; 2) professional services; 3) post-acute care, 4) readmissions; and 5) prescription drugs. Professional services and post-acute care were further subdivided by the type of services provided. The analysis was done from the payer perspective, meaning only costs associated with reimbursements to providers were considered. Statistical Analyses The analysis included patient descriptive statistics. Winsorization was applied to episode payment data at the 1st and 99th percentiles to minimize the influence of outliers. To account for geographic variation, patient zip codes (i.e., patient’s 5-digit zip code at enrollment) were associated with the zip codes of Texas hospital referral regions (HRRs) using the Dartmouth crosswalk data. According to the Dartmouth Atlas of Health Care (DAHC), admissions commonly take place at a facility close to where patients live.[ 18 ] Each HRR represents a healthcare market encompassing at least one city having a hospital where major surgical procedures such as CABG surgeries are performed.[ 18 ] Depending on the frequency of CABG patients across Texas HRRs, six main HRRs were identified for simplicity of analysis. Patient comorbidities were identified according to diagnosis codes. In order to ensure stability of the analyses in the regression model, comorbidities that appeared in fewer than 20 episodes were not considered in the study. A multiple regression model with log transformed dependent variable (episode payment) was developed controlling for region, age, type of commercial insurance, comorbidities, DRG intensity, length of stay, and readmission stage. The type of commercial insurance variable was classified based on two lines of businesses (i.e., Medicare Advantage, non-Medicare Advantage). The readmission stage variable was classified into no readmission, readmission within 30-day period, readmission within 31-60-Day period, and readmission within 61-90-day period. Payment data was normalized with a log-transformation to correct for positively skewed data. The Least Absolute Shrinkage and Selection Operator (LASSO) method was used to select and simplify the number of variables to include in the regression model. The statistical analyses were performed using SAS software version 9.4 and ArcGIS version 10.6. Identifying High- and Low-payment Hospitals Hospitals were classified into four quartiles according to mean episode payment for each hospital to calculate payment variation following the literature.[ 13 ] After that, the mean payment of each episode cost component (i.e., index hospitalization, professional services, post-acute care, readmission, and pharmacy payments) were compared between the high- and low-payment quartiles to determine which respective component contributed to the greatest proportion of variation. In addition, readmission rates of high-and low-payment hospitals were identified. Lastly, key drivers of payment variation were determined by calculating the degree to which variation in component payments attributable to the total variation in 90-day episode payments between high- and low-payment hospitals. The rate of total payment variation contributed by each payment component was calculated following the current literature.[ 13 ] This variation analysis approach was repeated but considering a subgroup of patients with non-Medicare Advantage insurance. The analysis of the non-Medicare Advantage subgroup (83% of the total sample) permitted examining the robustness of the model results, and ensuring the results did not vary by insurance type. The analysis helped to ensure that the calculations had reasonable validity and provided insights into how CABG episode payment varied across hospitals. This study involved de-identified retrospective data and received exempt approval from the University of Texas Institutional Review Board (IRB Number: HSC-SPH-19-0270). RESULTS During the 2014–2018 period, the data recorded a total of 4,623 CABG surgeries that were performed in Texas. After applying the inclusion and exclusion criteria at the patient level (Fig. 1 ), a total of 996 patients were included in the study, corresponding to 999 CABG episodes. Of the 996 identified patients who underwent CABG surgeries, there were 993 patients with one episode and 3 patients with two episodes. At the hospital level only 709 episodes, corresponding to 64 hospitals across the state of Texas, were included in the study after excluding hospitals with less than five episodes performed over the study period. This approach allowed us to make proper comparisons of hospital mean payments across Texas. As shown in Table 1 , average age of the CABG episode patient was 61 years, with a majority (81%) males. The three HRRs with the highest frequency of CABG episodes in Texas were Houston (26%), followed by Dallas (20.7%), and Austin (11.9%). Seventeen comorbid conditions were identified for the study population with mean Charlson index score of 1.89 and “diabetes without chronic complications” being the most common condition (25.43%). For DRG intensity, about 37% of the study sample underwent CABG surgery with major comorbidity or complications (MS-DRGs: 231, 233, 235) and about 73% without (MS-DRGs: 232, 234, 236). 83% of patients had non-Medicare Advantage while 17% had Medicare Advantage. Similarly, 82% had a POS health plan, and 18% had a PPO health plan. The average length of stay for CABG hospitalization was 8.4 days and around 14% of patients were readmitted within the 90-day care after hospital discharge. The mean (SD) 90-day CABG episode payment for all care provided after applying winsorization was $ 81,330. Table 1 Characteristics, Clinical Outcomes, and Payment Descriptive Statistics of the Study Population, 90-Day CABG Episodes (n = 999) Study Population Characteristics Frequency (SD or Percentage) Age Mean (SD) 61 (8.6) Median (IQR) 61 (12) Gender n (%) Male 808 (80.9%) Female 191 (19.1%) Regions* n (%) Houston 262 (26.23%) Austin 119 (11.91%) Dallas 207 (20.72%) Fort Worth 100 (10.01%) San Antonio 72 (7.21%) Corpus Christi 45 (4.50%) Other regions 194 (19.42%) CCI Score Mean (SD) 1.89 (2.03) Median (IQR) 1 (2) Comorbidities n (%) Diabetes without Chronic Complications 254 (25.43%) Myocardial Infarction 226 (22.62%) Cerebrovascular Disease 196 (19.62%) Diabetes with Chronic Complications 158 (15.82%) Chronic Pulmonary Disease 154 (15.42%) Congestive Heart Failure 152 (15.22%) Peripheral Vascular Disease 130 (13.01%) Renal Disease 101 (10.11%) Any Malignancy, Including Lymphoma and Leukemia, except Malignant Neoplasm of Skin 54 (5.41%) Mild Liver Disease 44 (4.40%) Rheumatologic Disease 23 (2.30%) Metastatic Solid Tumor 9 (0.90%) Peptic Ulcer Disease 5 (0.50%) Dementia 5 (0.50%) Hemiplegia or Paraplegia 4 (0.40%) Moderate or Severe Liver Disease 2 (0.20%) AIDS/HIV 1 (0.10%) DRG Intense n (%) With MCC (MS-DRGs: 231,233,235) 372 (37.24%) Without MCC (MS-DRGs :232,234,236) 627 (62.76%) Type of Commercial Insurance n (%) Medicare Advantage 170 (17.02%) N on-Medicare Advantage 829 (82.98%) Health Plan n (%) PPO 175 (17.52%) POS 824 (82.48%) Clinical Outcomes Length of Stay Mean (SD) 8.36 (4.43) Median (IQR) 7 (4) Readmission Stage n (%) No readmission 860 (86.06%) Within 30-Da y Period 68 (6.81%) Within 31- 60-Da y Period 36 (3.6%) Within 61- 90-Da y Period 35 (3.5%) Payment Descriptive Statistics 90-Day Episode Payment Mean (SD) $ 81,330 ( $ 47,382) Median (IQR) $ 69,056 ( $ 42,162) CABG indicates coronary artery bypass grafting; AIDS, acquired immunodeficiency syndrome; HIV, human immunodeficiency virus; CCI, Charlson comorbidity index; PPO, preferred provider organization; and POS, point of service plan. * Regions were identified according to patients 5-digit zip codes at enrollment. Patient-level Factors Influencing 90-day CABG Episode Payments As shown in Table 2 , regions in Texas, patient age, type of commercial insurance, vascular diseases, DRG intensity, length of stay and readmission stage had a significant influence on 90-day CABG episode payments. Payments were 22.78% lower for the Austin region patients than those patients in the Houston region (p < 0.0001). For every one-year increase in age, episode payments decreased by 1.25% (p < 0.0001). Episode payments were 16.58% lower for Medicare Advantage patients than those for non-Medicare Advantage patients (p < 0.0001). Episode payments were 9.99% higher for patients with peripheral vascular disease than those for patients without (p = 0.0309). Episode payments were 7.04% lower for patients with cerebrovascular disease than those for patients without (p = 0.0479). Episode payments were 8.87% higher for patients who had intense CABG MS-DRGs (i.e., DRG 231, DRG 233, DRG 235) at the time of index hospitalization than those for patients with the patients with low intensity MS-DRGs (i.e., DRG 232, DRG 234, DRG 236). For every one-day increase in length of stay, total episode payments increased by 4.3% (p < 0.0001). Episode payments for readmitted patients within the 30-days, 31-60-days, and 61- 90-days windows were higher than those for non-readmitted patients by 55.08%, 37.77%, and 39.93%, respectively (p < 0.0001). Overall, the model explained 37% of the variation in episode payments. As shown in Table 3 , the most common cause of readmissions, considering DRG codes, was DRG 857 which is related to postoperative & post-traumatic infections with operating room procedure with complication or comorbidity (4.71% of cases), followed by DRG 603 cellulitis without major complication or comorbidity (3.66%), DRG 291 heart failure and shock with major complication or comorbidity or peripheral extracorporeal membrane oxygenation (3.14%), DRG 293 heart failure and shock without complication or comorbidity (2.62%), and DRG 863 postoperative and post-traumatic infections without major complication or comorbidity (2.62%). Table 2 Linear Regression Model of Predictors of 90-Day CABG Episode Payments Variable Parameter Estimate† P Value* Region‡ Houston Reference Austin -22.78% < 0.0001* Dallas 4.30% 0.2626 Fort Worth 9.56% 0.0615 Other Regions -0.16% 0.9672 Age -1.25% < 0.0001* Type of Commercial Insurance Non-Medicare Advantage Reference Medicare Advantage -16.58% < 0.0001* Congestive Heart Failure 3.56% 0.3763 Peripheral Vascular Disease 9.99% 0.0309* Cerebrovascular Disease -7.04% 0.0479* Diabetes with Chronic Complications 6.47% 0.1073 Any Malignancy, Including Lymphoma and Leukemia, except Malignant Neoplasm of Skin -6.94% 0.2382 DRG Intensity No (without MCC) Reference Yes (with MCC) 8.87% 0.0051* Length of Stay 4.30% < 0.0001* Readmission Stage No Readmission Reference Within 30-Day Period 55.08% < 0.0001* Within 31-60-Day Period 37.77% < 0.0001* Within 61-90-Day Period 38.93% < 0.0001* DRG indicates Diagnosis Related Groups; MCC major complication or comorbidity. * Statistically significant at α = 0.05 level; n = 999; R-Square: 0.3711; Adj R-Square: 0.3608; F-value: 36.21; P < 0.0001 † Exponentiated parameter estimates (EXP(Coefficient)-1) *100, ‡ Regions were identified according to patients 5-digit zip codes at enrollment. Table 3 Most Common Causes of 90-day Readmissions of CABG Episodes, MS-DRGs Percentage Code MS-DRG Description 4.71% 857 Postoperative & Post-Traumatic Infections with O.R. Procedure with CC 3.66% 603 Cellulitis without MCC 3.14% 291 Heart Failure and Shock with MCC OR Peripheral Extracorporeal Membrane Oxygenation (ECMO) 2.62% 293 Heart Failure and Shock without CC/MCC 2.62% 863 Postoperative & Post-Traumatic Infections without MCC CC indicates complication or comorbidity; MCC, major complication or comorbidity; and MS-DRG, Medicare severity diagnosis-related group. Drivers of CABG Episode Payment Variation At the hospital level, payment variations between high- and low-payment hospitals were considerable (Table 4 ). The mean 90-day CABG episode payment for hospitals was $ 61,028 for the lowest payment quartile. In comparison to $ 106,148 in the highest quartile, the difference was of $ 45,121, or 74% of the lowest quartile total. All payment components for 90-day CABG episodes were higher in payments for the high-payment quartile than those for the low-payment quartile except for pharmacy (Table 4 ). Payments for index hospitalization accounted for the largest share of the total episode payment for CABG surgeries. Readmission rates for 90-day CABG episodes differed significantly between low- and high-payment hospitals (8.90% for low-payment hospitals vs. 14.92% for high-payment hospitals). Considering readmission payments, the difference between lowest payment quartile hospitals and the highest payment quartile hospitals had 796% higher readmission payments ( $ 10,069 vs. $ 1,124), 105% higher post-acute-care payments ( $ 12,859 vs. $ 6,259), 58% higher index hospitalization payments ( $ 59,851 vs. $ 37,946), 55% higher professional payments ( $ 21,955 vs. $ 14,179), and 7% lower pharmacy payments ( $ 1,414 vs. $ 1,519). Thus, payment difference between high- and low-payment hospitals for 90-day CABG episode payments was greatest for readmission payments, followed by post-acute care payments, index hospitalization payments, professional payments, and pharmacy payments. Therefore, the study demonstrated that the component with the highest difference rate between high- and low-payment hospitals was related to readmissions. Within professional service payments, payments for surgery accounted for the majority of payments across quartiles. Within post-acute care payments, payments for “outpatient facility surgery” accounted for a significant proportion of payments and was a key driver 6.2% of payment variation (Table 4 ). As for the drivers of these differences, index hospitalization contributed 48.6% to the variation in total episode payments between high-payment and low-payment hospitals (Fig. 2 ). Readmission, professional services, post-acute care, and pharmacy contributed to the variation in total episode payments between high-payment and low-payment hospitals by 19.8%, 17.2%, 14.6%, and 0.23%, respectively (Fig. 2 ). For the 90-day CABG episode subgroup analyses, there were 663 episodes where CABG surgery was performed for patients with non-Medicare Advantage insurance. After reclassifying hospitals into payment quartiles within the subgroup of non-Medicare Advantage insurance patients, index hospitalization remained the main driver of payment variation. The summary is presented in Table 5 . Table 4 The Contribution of CABG Payment Components and Subcomponents to Total Episode Payment Variation between High- and Low-payment Hospitals Payment Component Subcomponent Hospital Quartiles* Difference† Variation Attributed to Payment Component Lowest Quartile Highest Quartile ( $ ) (%) (Low Payment Hospitals) (High Payment Hospitals) Readmissions $1,124 $10,069 $8,945 796% 19.82% Post-acute Care OP Facility Surgery $ 306 $ 3,133 $ 2,828 6.27% Home Health $ 263 $ 671 $ 408 0.90% Ancillary $ 869 $ 1,214 $ 345 0.76% Rehab/Skilled Nursing Facility $ 849 $ 615 $ 235 0.52% Emergency Room $ 1,432 $ 1,616 $ 184 0.41% OP Facility Diagnostic $ 402 $ 263 $ 140 0.31% OP Facility Laboratory $ 123 $ 169 $ 45 0.10% OP Facility Radiology $ 180 $ 180 - 0.00% OP Facility Other $ 1,835 $ 4,999 $ 3,164 7.01% Subtotal Post-acute Care $6,259 $12,859 $6,600 105% 14.63% Index Hospitalization $37,946 $59,851 $21,904 58% 48.55% Professional Services Surgery $ 5,837 $ 8,834 $ 2,997 6.64% Anesthesia $ 4,710 $ 7,099 $ 2,389 5.29% E&M $ 2,327 $ 4,191 $ 1,864 4.13% Diagnostic Testing $ 442 $ 710 $ 268 0.59% Radiology $ 205 $ 301 $ 96 0.21% Emergency Room $ 213 $ 263 $ 50 0.11% Laboratory $ 93 $ 133 $ 40 0.09% Physical Medicine/Rehab $ 137 $ 104 $ 32 0.07% Professional Other $ 215 $ 321 $ 106 0.24% Subtotal Professional $14,179 $21,955 $7,777 55% 17.24% Pharmacy $1,519 $1,414 $105 7% 0.23% Total $61,028 $106,148 $45,121 74% 100.00% OP indicates outpatient; E&M, evaluation and management. * Hospitals that performed less than five episodes were excluded. † The absolute difference between lowest and highest payment hospitals was calculated. Table 5 Payment Components and Variation of Average 90-Day Episode Payments for CABG Surgeries between High- and Low-Payment Hospitals in the Commercial Patients Subgroup Payment Component Hospital Quartiles* Difference† % Variation Lowest Quartile (Low Payment Hospitals) Highest Quartile (High Payment Hospitals) Commercial CABG Index Hospitalization $ 39,416 $ 63,295 $ 23,878 52% Post-acute Care $ 5,424 $ 11,857 $ 6,434 14% Readmission $ 1,251 $ 10,991 $ 9,740 21% Professional Services $ 14,794 $ 21,399 $ 6,605 14% Pharmacy $ 1,758 $ 1,372 $ 386 1% Total Episode Payment $62,643 $108,915 $46,272 * Hospitals were ranked from lowest to highest average 90-day episode payments and then were categorized into low and high payment hospitals. † The absolute difference between lowest and highest payment hospitals was calculated. DISCUSSION The primary findings of this study regarding 90-day CABG episodes were established by aggregating episode payments and identifying payment components. Applying this approach allowed us to look beyond CABG surgery payments and evaluate a comprehensive picture of post-discharge payments for commercially insured patients in Texas. The findings showed patient factors have impact on 90-day CABG episode payment in Texas. In addition, there is a substantial variation in 90-day CABG episode payment across the hospitals of Texas. Variation in CABG episode payments appeared to be influenced by multiple patient factors, including patient region, age, certain comorbidities, readmissions, lengths of stay, and type of commercial insurance (i.e., Medicare Advantage, non-Medicare Advantage). The findings of the present study are similar to those of previous studies that showed patient factors had an impact on CABG payments.[ 13 – 15 ][ 19 ] The results also demonstrated variability in CABG payments by patient regions, highlighting, for example, lower episode payments for patients in the Austin region than patients in the Houston region. One explanation could be that Austin had a relatively lower readmission rate as compared to Houston or other Texas regions. Age was found to be negatively associated with episode payments. Although age had a minimal overall association with payments in the present study, this finding was contrary to results from other studies. No evidence in the literature was found suggesting that aging was negatively associated with episode payment for CABG surgeries. In fact, some previous studies revealed a positive relationship between age and payments.[ 19 – 21 ] Another study showed that age had a minimal overall association with cost.[ 16 ] Previous studies showed that healthcare spending for Medicare Advantage patients was lower than those patients having other type of commercial insurance plan.[ 22 ][ 23 ] The results of the present study agreed with the findings of other studies, which showed that payments for Medicare Advantage patients were lower than those for non-Medicare Advantage insured patients for CABG episodes.[ 22 ][ 23 ] This study found a strong collinearity between the type of insurance product (e.g., Medicare Advantage) and the health plan type. For example, it was more likely that a Medicare Advantage patient had a PPO health plan than a POS plan. On the other hand, it was more likely that a non-Medicare Advantage insurance patient had a POS health plan than PPO health plan. Furthermore, the results of the present study demonstrated that certain comorbid conditions were associated with 90-day episode payments. These results were in line with the literature.[ 16 ] The regression model showed that CABG episode payments were 10% higher for patients with peripheral vascular disease than for patients without this condition. This situation is likely a reflection of a strong correlation between CAD and peripheral vascular disease. However, the analysis demonstrated that patients with a history of cerebrovascular disease were associated with decreased 90-day CABG episode payments. Moreover, the present study showed that length of stay and readmission were significantly associated with higher payments for CABG episodes. Being readmitted is a predominant predictor of CABG episode payments. Episode payments for readmitted patients within a 30-day period, a 31-60-day period, and a 61-90-day period were significantly higher than those for non-readmitted patients. By quantifying the costs of readmission, this study highlighted the potential opportunities to provide more efficient care and significantly improve the quality of care for CABG episodes across Texas regions. In fact, the CMS has focused on readmission reduction as one of its main national healthcare policies. For example, the Hospital Readmission Reduction Program, which is a Medicare value-based purchasing program, supports the goal of linking payments to the quality of care by seeking to penalize hospitals with high rates of readmissions for select conditions. Because readmission stage was the predominant predictor of CABG episode payments, readmission causes were further analyzed. The most common causes for 90-day readmissions for CABG surgeries were identified by detecting the DRGs used for readmissions. The most common DRG codes of readmissions for CABG episodes were DRG 857, DRG 603, DRG 291, DRG 293, and DRG 863. Three of these DRG codes were related to postoperative infections (i.e., 857, 603, 863), and two of these DRG codes were related to heart failure (i.e., 291, 293). These causes of readmissions were in line with the literature.[ 13 ] The present study also showed a slightly higher readmission rate for CABG episode compared with another recent study on 90-day CABG episode payment variation for TRICARE beneficiaries (14% vs. 13%).[ 12 ] As the literature showed a wide variation in CABG episode payments for Medicare patients across hospitals performing CABG surgeries,[ 13 – 16 ] this study also showed a wide variation in CABG episode payments for commercially insured patients across Texas. The difference in average 90-day payments at hospitals in the highest and lowest payment quartiles was significant, representing 73.9% higher payments at high-payment hospitals versus low-payment hospitals. Of the five payment components, the index hospitalization was found to be the key driver of CABG episode payment variation, contributing 48.6% of the total payment variation between high- and low-payment hospitals. This finding was consistent with the reviewed studies that found the primary driver of payment variation in CABG episode was related to index hospitalization.[ 12 – 15 ] The second key driver of payment variation was readmission, contributing 19.8% of the total CABG episode payment variation between high- and low-payment hospitals. After this, professional services and post-acute care contributed to the total payment variation by 17.2% and 14.6%, respectively. The payment variation of professional services was driven in part by surgery payments, followed by anesthesia, and E&M. Private payers and healthcare providers moving to the episode payment model for CABG surgeries, should consider modeling their sources of variation by focusing on CABG hospitalizations. While CABG surgeries were not in episode payment models for commercially insured patients, they may eventually become the first heart invasive procedure to be included. In 2018, the CMS included these procedures as new voluntary episode payment models, which were qualified under the Medicare Quality Payment Program.[ 5 ][ 24 ][ 17 ] This initiative by the CMS could incentivize healthcare providers entering episode payment models for CABG. It could also motivate private payers to adapt the episode payment method to align provider incentives toward improving efficiency and healthcare quality. The findings of this research may contribute to the literature by giving insight into the variation of CABG episode payments across commercially insured patient factors. Even though this study did not determine how much savings could be achieved by implementing CABG episode payments, it determined the type of services that needed further investigation. This study also highlighted opportunities for improvement and cost reductions. Because readmission is a much more prominent factor in the 90-day episode payments, an excellent target for cost reduction would to reduce readmissions. Strategies to contain the CABG episode payments may need to differ depending on the main causes of readmission as well as on the performance of hospitals with high readmission rates. For example, if linking payment to healthcare performance within the episode of care helps reduce readmissions, which would subsequently reduce payment, then this practice should be encouraged from a policy perspective. Transferring some of the financial risk to healthcare providers might be helpful. For example, holding healthcare providers financially accountable for high readmissions might be more appropriate for reducing readmissions and, subsequently, the amounts of episode payments. Furthermore, healthcare policies, such as those that are relevant to improving the transparency of healthcare performance and payment data available to patients, might help reduce payment variation. For example, information about quality and payment, including out-of-pocket expenses, available to patients could serve as incentives for patients to request care from high-quality providers such as hospitals with lower readmission rates. As a result, hospitals with high readmissions would focus on improving their performance and reducing their readmission rates. The present study findings also provided insight about the distribution of 90-day episode payment across the five payment components for CABG surgeries. Index hospitalization payments not only comprised the largest proportion of CABG episode payments, but they also represented the key source of variation. Thus, healthcare providers entering episode-based payment models for CABG surgeries should consider the need to understand CABG hospitalization when developing initiatives to reduce related spending. Finally, this study might contribute to defining the essential targets of payment reform and improving efficiency in CABG episodes. Although this study provided an understanding of costs for CABG surgeries for commercially insured patients in Texas, it was not without limitations. The claims data were designed to justify payment, but they lacked the rich clinical details found in patient medical charts. As a result, detailed clinical variables that could have affected payments were not examined. This data, however, did include some valuable clinical information (i.e., diagnosis and procedure codes) that was used to track comorbid conditions. In particular, this study did not include information about the deaths of patients. In response to this, episodes with fewer than two claims after the discharge date were reviewed as a way of identifying any possible case that died during index hospitalization. There is, however, a possibility that some of the patients included in the study died during the 90-day post-discharge. Finally, the study was limited to a particular group of privately insured patients in Texas, which affects its generalizability to other populations outside of Texas. Despite these limitations, patient demographics and operative predictors were considered while determining CABG episode payments and identifying the drivers of payment variation across Texas. Thus, the claims data this study used were unique in that they provided comprehensive payment information for a large and broadly representative commercially insured population in Texas. As a result, this study might be generalizable to other commercially insured populations in Texas. CONCLUSIONS This study simulated potential episode payments in patients who underwent CABG surgeries in Texas. It provided insight into CABG episode payments across Texas. The findings indicated that CABG episode payments vary widely with a patients’ age, regions, and comorbidities. Payments also varied with other patient clinical factors, including length of stay and readmission, as well as commercial insurance type (Medicare Advantage, or non-Medicare Advantage). Readmission was the most influential independent predictor of higher payments for patients undergoing CABG surgeries. These results have contributed to a better understanding of the current variation of CABG payments across Texas as well as to the present association of patient factors to total episode payments. It was found that patient factors were significant drivers of variation in episode payments. Of the five payment components, payment variation was mainly attributable to differences in index hospitalization payments. As a result, payments for the index CABG hospital stays should be considered when developing initiatives to reduce healthcare spending. They could also serve to inform policymakers, payers, and healthcare providers about the implementation of episode payment initiatives. Declarations Ethics Approval and consent to participate This study involved de-identified retrospective data and received exempt approval from the University of Texas Institutional Review Board (IRB Number: HSC-SPH-19-0270). Consent for publication This declaration is not applicable. Competing interests The authors declare no conflict of interest Availability of data and materials This declaration is not applicable. Funding The study was partially funded by Kuwait Foundation for the Advancement of Sciences under project code “CB18-63MM-01” Authors’ contributions Saleh Alsarhan. and Rigoberto Delgado wrote the main manuscript text and Saleh Alsarhan prepared all figures and tables. All authors reviewed the manuscript and contributed to the final version of the manuscript. 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Health Serv Res. 53, 632–648 (2018). https://doi.org/10.1111/1475-6773.12681 Dummit, L.A., Kahvecioglu, D., Marrufo, G., Rajkumar, R., Marshall, J., Tan, E., Press, M.J., Flood, S., Muldoon, L.D., Gu, Q., Hassol, A., Bott, D.M., Bassano, A., Conway, P.H.: Association Between Hospital Participation in a Medicare Bundled Payment Initiative and Payments and Quality Outcomes for Lower Extremity Joint Replacement Episodes. JAMA. 316, 1267 (2016). https://doi.org/10.1001/jama.2016.12717 Jubelt, L.E., Goldfeld, K.S., Blecker, S.B., Chung, W.-Y., Bendo, J.A., Bosco, J.A., Errico, T.J., Frempong-Boadu, A.K., Iorio, R., Slover, J.D., Horwitz, L.I.: Early Lessons on Bundled Payment at an Academic Medical Center. Journal of the American Academy of Orthopaedic Surgeons. 25, 654–663 (2017). https://doi.org/10.5435/JAAOS-D-16-00626 Navathe, A.S., Emanuel, E.J., Venkataramani, A.S., Huang, Q., Gupta, A., Dinh, C.T., Shan, E.Z., Small, D., Coe, N.B., Wang, E., Ma, X., Zhu, J., Cousins, D.S., Liao, J.M.: Spending And Quality After Three Years Of Medicare’s Voluntary Bundled Payment For Joint Replacement Surgery. Health Aff. 39, 58–66 (2020). https://doi.org/10.1377/hlthaff.2019.00466 Wynn-Jones, W., Koehlmoos, T.P., Tompkins, C., Navathe, A., Lipsitz, S., Kwon, N.K., Learn, P.A., Madsen, C., Schoenfeld, A., Weissman, J.S.: Variation in expenditure for common, high cost surgical procedures in a working age population: implications for reimbursement reform. BMC Health Serv Res. 19, 877 (2019). https://doi.org/10.1186/s12913-019-4729-2 Guduguntla, V., Syrjamaki, J.D., Ellimoottil, C., Miller, D.C., Prager, R.L., Norton, E.C., Theurer, P., Likosky, D.S., Dupree, J.M.: Drivers of Payment Variation in 90-Day Coronary Artery Bypass Grafting Episodes. JAMA Surg. 153, 14–19 (2018). https://doi.org/10.1001/jamasurg.2017.2881 Birkmeyer, J.D., Gust, C., Baser, O., Dimick, J.B., Sutherland, J.M., Skinner, J.S.: Medicare payments for common inpatient procedures: implications for episode-based payment bundling. Health Serv Res. 45, 1783–95 (2010). https://doi.org/10.1111/j.1475-6773.2010.01150.x Miller, D.C., Gust, C., Dimick, J.B., Birkmeyer, N., Skinner, J., Birkmeyer, J.D.: Large variations in Medicare payments for surgery highlight savings potential from bundled payment programs. Health Aff (Millwood). 30, 2107–15 (2011). https://doi.org/10.1377/hlthaff.2011.0783 Shubeck, S.P., Thumma, J.R., Dimick, J.B., Nathan, H.: Hot Spotting as a Strategy to Identify High-Cost Surgical Populations. Ann Surg. 269, 453–458 (2019). https://doi.org/10.1097/SLA.0000000000002663 Centers for Medicare & Medicaid Services: BPCI Advanced. (2018). https://www.cms.gov/priorities/innovation/innovation-models/bpci-advanced Wennberg JE, Cooper MM: The Dartmouth Atlas of Health Care. (1996) Saleh, S.S., Racz, M., Hannan, E.: The effect of preoperative and hospital characteristics on costs for coronary artery bypass graft. Ann Surg. 249, 335–41 (2009). https://doi.org/10.1097/SLA.0b013e318195e475 Shinjo, D., Fushimi, K.: Preoperative factors affecting cost and length of stay for isolated off-pump coronary artery bypass grafting: hierarchical linear model analysis. BMJ Open. 5, e008750 (2015). https://doi.org/10.1136/bmjopen-2015-008750 Krueger, H., Goncalves, J.L., Caruth, F.M., Hayden, R.I.: Coronary artery bypass grafting: how much does it cost? CMAJ. 146, 163–8 (1992) Maeda, J.L.K., Nelson, L.: How Do the Hospital Prices Paid by Medicare Advantage Plans and Commercial Plans Compare With Medicare Fee-for-Service Prices? Inquiry. 55, 46958018779654 (2018). https://doi.org/10.1177/0046958018779654 Baker, L.C., Bundorf, M.K., Devlin, A.M., Kessler, D.P.: Medicare Advantage Plans Pay Hospitals Less Than Traditional Medicare Pays. Health Aff. 35, 1444–1451 (2016). https://doi.org/10.1377/hlthaff.2015.1553 Advanced Alternative Payment Models (APMs) Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3891751","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":279843116,"identity":"970e8dc6-d369-484f-810e-4e02236a54e3","order_by":0,"name":"Saleh Alsarhan","email":"","orcid":"","institution":"Kuwait University","correspondingAuthor":false,"prefix":"","firstName":"Saleh","middleName":"","lastName":"Alsarhan","suffix":""},{"id":279843118,"identity":"b95cf544-070b-4de2-b505-16705d809f5d","order_by":1,"name":"Rigoberto I. Delgado","email":"data:image/png;base64,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","orcid":"","institution":"The University of Texas Health Science Center at Houston","correspondingAuthor":true,"prefix":"","firstName":"Rigoberto","middleName":"I.","lastName":"Delgado","suffix":""},{"id":279843120,"identity":"0b071046-e0e4-4d36-9f2e-9da023f02811","order_by":2,"name":"Trudy Millard Krause","email":"","orcid":"","institution":"The University of Texas Health Science Center at Houston","correspondingAuthor":false,"prefix":"","firstName":"Trudy","middleName":"Millard","lastName":"Krause","suffix":""},{"id":279843122,"identity":"fc62e329-e700-4f0f-bab8-07295ef97dd6","order_by":3,"name":"David Aguilar","email":"","orcid":"","institution":"The University of Texas Health Science Center at Houston","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Aguilar","suffix":""},{"id":279843125,"identity":"748eea1a-a073-4dac-bbe2-76f09949ac3f","order_by":4,"name":"Osama Mikhail","email":"","orcid":"","institution":"The University of Texas Health Science Center at Houston","correspondingAuthor":false,"prefix":"","firstName":"Osama","middleName":"","lastName":"Mikhail","suffix":""}],"badges":[],"createdAt":"2024-01-23 17:52:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3891751/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3891751/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53007237,"identity":"f1b126ee-67f3-4db0-8ae5-a65a455549cb","added_by":"auto","created_at":"2024-03-19 15:13:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":46716,"visible":true,"origin":"","legend":"\u003cp\u003eExclusion criteria for CABG episodes.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3891751/v1/f61039c3884a0b572ab9fe4e.jpg"},{"id":53007294,"identity":"56db9ba0-93d2-44ec-9758-b5f55b4b3264","added_by":"auto","created_at":"2024-03-19 15:13:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":38836,"visible":true,"origin":"","legend":"\u003cp\u003eCABG episode payment components and variation of average 90-day episode payments among hospital payment quartiles (\u003cstrong\u003eA\u003c/strong\u003e). The contribution of each payment component to the total CABG episode payment variation between high- and low-payment hospitals (\u003cstrong\u003eB\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3891751/v1/5ebbf2f3d52775050cd4bf44.jpg"},{"id":81696821,"identity":"ba2e01ad-cf42-4ba8-a619-6be57d0d77c3","added_by":"auto","created_at":"2025-04-30 12:28:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1620689,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3891751/v1/bb32ece1-7514-453f-a234-5105771b965f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Drivers of 90-Day CABG Episode Payment Variation for Commercially Insured Patients","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIn 2018, the American Heart Association (AHA) reported that the \u003cspan\u003e$\u003c/span\u003e9\u0026nbsp;billion in annual medical costs associated with coronary artery disease (CAD) make it one of the ten most expensive health conditions treated in U.S. hospitals.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] Medical costs related to CAD were projected to increase significantly.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] For patients with CAD, coronary artery bypass grafting (CABG) surgery is a common and expensive invasive procedure. In Texas, for example, estimates from the Department of State Health Services place the average hospital charge for CABG surgery per case at \u003cspan\u003e$\u003c/span\u003e234,560.[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Given that Texas had the third highest annual average spending growth rate (5.6%) in private health insurance between the years 2001\u0026ndash;2020 in the country,[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] it is important to obtain precise estimates of costs for planning purposes under current value-added healthcare policies. But, as the case of Texas illustrates, estimation of costs represents several challenges.\u003c/p\u003e \u003cp\u003eFirst, the number of costs assessments for CABG surgeries are limited, restricting the application of representative findings in the development of effective cost control policies. Second, most studies, not necessarily in Texas, rely on evaluating hospital billing charges, which is less accurate than other approaches such as through the analysis of insurance reimbursements.\u003c/p\u003e \u003cp\u003eIn an effort to focus on value-based medicine and quality of care in healthcare spending rather than on volume (i.e., quantity), researchers and policymakers have proposed clinical episode payment models for certain diagnoses and procedures, including episodes designed around CABG surgeries.[\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] The \u003cem\u003eepisode payment\u003c/em\u003e typically is a bundled payment for all the care that a patient receives for a procedure or treatment of a particular disease or condition during a defined period of time. An episode may include inpatient stay as well as healthcare services provided within 90-days of the initial hospital discharge. Under the episode payment model, healthcare providers participate through an agreement with payers and receive a negotiated amount for the multiple services patients received during an episode of care. Therefore, reimbursement is linked to the healthcare performance within the episode of care. Healthcare providers are held accountable for the total costs of care and could be subject to financial consequences for low-quality care. Healthcare providers through the risk sharing strategy are incentivized to reduce unnecessary services and improve health care coordination across healthcare settings. Previous research has shown episode payments for surgical procedures might have the potential to decrease healthcare payments while maintaining or improving quality of care.[\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Studies that examined clinical episodes related to cardiac surgeries (i.e., cardiac valve replacement, CABG) found that episode payment initiatives were not associated with the index hospitalization costs but that they were associated with lowering post-acute care spending.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFurthermore, several studies have focused on CABG episode payments and the drivers of payment variation.[\u003cspan additionalcitationids=\"CR13 CR14 CR15\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] A recent study by Guduguntla et al. (2018) examined CABG episode payment variation and its components in seventy-six hospitals in Michigan, and these researchers found that a wide variation existed in 90-day episode payments.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] The study found that patients with multiple readmissions as well as components such as index hospitalization, evaluation and management services (E\u0026amp;M), and inpatient rehabilitation contributed the most to variation in episode payments.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Furthermore, the findings of the study indicated that among all payment components, readmission had the highest difference rate between the highest payment hospitals compared with the lowest payment hospitals.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] In another recent study in 2019, Shubeck et al. investigated CABG episode payment variation of Medicare nationwide beneficiaries at the hospital level.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] These researchers found that CABG payment variation between the high- and low-payment hospitals was due to a threefold difference in index hospitalization payment.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] In addition, the study demonstrated that patients with comorbidities incur higher payments than healthier patients.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Other studies on Medicare beneficiaries found that significant variation existed for CABG episodes payments, concluding that the index hospitalization component was the key driver of payment variation.[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e][\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] Because these CABG cost studies focused mainly on Medicare populations, their findings might not be generalizable for younger populations or for those with private insurance.[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] A study by Wynn-Jones et al. (2019) examined 90-day period CABG payment variation among TRICARE (health care program for military service members, retirees, and their families) adult patients and found significant regional-level variation in payment for CABG episodes.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] The index hospitalization payment in this study was also found to be the main driver of payment variation.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Moreover, the readmission payment had the highest difference rate between the highest payment regions compared with the lowest payment regions.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Substantial gaps remain in our knowledge of CABG payments for commercially insured patients in Texas.\u003c/p\u003e \u003cp\u003eExamining 90-day CABG payment variation for commercially insured patients across Texas might help establish policies to improve efficiency and decrease wasteful spending in healthcare in Texas. The purpose of this study was to describe CABG episode payments and to examine the drivers of payment variation using a representative commercially insured cohort for Texas. The results of this study should offer healthcare managers, health insurers, and policymakers critical baseline data to design appropriate strategies for improving value-based payments and episode payments for CABG surgeries.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThe study used Optum\u0026rsquo;s de-identified Clinformatics\u0026reg; Data Mart Database (CDM), administrative claims data of a commercially insured population. The Optum\u0026rsquo;s de-identified CDM is derived from a database of administrative health claims for members of large commercial and Medicare Advantage health plans. Data included patient characteristics, healthcare resource utilization, diagnoses codes, and standardized costs, allowing us to make proper comparisons of payments across Texas. The period of analysis was 2014\u0026ndash;2018, but the claims datasets for this study included records for 2013 in order to track comorbid conditions. Similarly, patient data for cases discharged toward the end of 2018 were not included in the study if the 90-day post-procedure requirement occurred in 2019. The CMS MS-DRG CABG trigger codes (i.e., DRG 231, DRG 232, DRG 233, DRG 234, DRG 235, DRG 236) were used to identify patients for the 90-day CABG episodes.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eEpisode payments refer to cumulative standardized costs incurred during index hospitalization (initial admission for the CABG procedure) and the 90-day post-discharge care including any readmission. In order to estimate expenditure variation, episode costs were categorized into five components: 1) index hospitalization; 2) professional services; 3) post-acute care, 4) readmissions; and 5) prescription drugs. Professional services and post-acute care were further subdivided by the type of services provided. The analysis was done from the payer perspective, meaning only costs associated with reimbursements to providers were considered.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analyses\u003c/h2\u003e \u003cp\u003eThe analysis included patient descriptive statistics. Winsorization was applied to episode payment data at the 1st and 99th percentiles to minimize the influence of outliers. To account for geographic variation, patient zip codes (i.e., patient\u0026rsquo;s 5-digit zip code at enrollment) were associated with the zip codes of Texas hospital referral regions (HRRs) using the Dartmouth crosswalk data. According to the Dartmouth Atlas of Health Care (DAHC), admissions commonly take place at a facility close to where patients live.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Each HRR represents a healthcare market encompassing at least one city having a hospital where major surgical procedures such as CABG surgeries are performed.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] Depending on the frequency of CABG patients across Texas HRRs, six main HRRs were identified for simplicity of analysis. Patient comorbidities were identified according to diagnosis codes. In order to ensure stability of the analyses in the regression model, comorbidities that appeared in fewer than 20 episodes were not considered in the study. A multiple regression model with log transformed dependent variable (episode payment) was developed controlling for region, age, type of commercial insurance, comorbidities, DRG intensity, length of stay, and readmission stage. The type of commercial insurance variable was classified based on two lines of businesses (i.e., Medicare Advantage, non-Medicare Advantage). The readmission stage variable was classified into no readmission, readmission within 30-day period, readmission within 31-60-Day period, and readmission within 61-90-day period. Payment data was normalized with a log-transformation to correct for positively skewed data. The Least Absolute Shrinkage and Selection Operator (LASSO) method was used to select and simplify the number of variables to include in the regression model. The statistical analyses were performed using SAS software version 9.4 and ArcGIS version 10.6.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentifying High- and Low-payment Hospitals\u003c/h2\u003e \u003cp\u003eHospitals were classified into four quartiles according to mean episode payment for each hospital to calculate payment variation following the literature.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] After that, the mean payment of each episode cost component (i.e., index hospitalization, professional services, post-acute care, readmission, and pharmacy payments) were compared between the high- and low-payment quartiles to determine which respective component contributed to the greatest proportion of variation. In addition, readmission rates of high-and low-payment hospitals were identified. Lastly, key drivers of payment variation were determined by calculating the degree to which variation in component payments attributable to the total variation in 90-day episode payments between high- and low-payment hospitals. The rate of total payment variation contributed by each payment component was calculated following the current literature.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThis variation analysis approach was repeated but considering a subgroup of patients with non-Medicare Advantage insurance. The analysis of the non-Medicare Advantage subgroup (83% of the total sample) permitted examining the robustness of the model results, and ensuring the results did not vary by insurance type. The analysis helped to ensure that the calculations had reasonable validity and provided insights into how CABG episode payment varied across hospitals. This study involved de-identified retrospective data and received exempt approval from the University of Texas Institutional Review Board (IRB Number: HSC-SPH-19-0270).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eDuring the 2014\u0026ndash;2018 period, the data recorded a total of 4,623 CABG surgeries that were performed in Texas. After applying the inclusion and exclusion criteria at the patient level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), a total of 996 patients were included in the study, corresponding to 999 CABG episodes. Of the 996 identified patients who underwent CABG surgeries, there were 993 patients with one episode and 3 patients with two episodes. At the hospital level only 709 episodes, corresponding to 64 hospitals across the state of Texas, were included in the study after excluding hospitals with less than five episodes performed over the study period. This approach allowed us to make proper comparisons of hospital mean payments across Texas.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, average age of the CABG episode patient was 61 years, with a majority (81%) males. The three HRRs with the highest frequency of CABG episodes in Texas were Houston (26%), followed by Dallas (20.7%), and Austin (11.9%). Seventeen comorbid conditions were identified for the study population with mean Charlson index score of 1.89 and \u0026ldquo;diabetes without chronic complications\u0026rdquo; being the most common condition (25.43%). For DRG intensity, about 37% of the study sample underwent CABG surgery with major comorbidity or complications (MS-DRGs: 231, 233, 235) and about 73% without (MS-DRGs: 232, 234, 236). 83% of patients had non-Medicare Advantage while 17% had Medicare Advantage. Similarly, 82% had a POS health plan, and 18% had a PPO health plan. The average length of stay for CABG hospitalization was 8.4 days and around 14% of patients were readmitted within the 90-day care after hospital discharge. The mean (SD) 90-day CABG episode payment for all care provided after applying winsorization was \u003cspan\u003e$\u003c/span\u003e81,330.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics, Clinical Outcomes, and Payment Descriptive Statistics of the Study Population, 90-Day CABG Episodes (n\u0026thinsp;=\u0026thinsp;999)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eStudy Population Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (SD or Percentage)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (8.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e808 (80.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegions*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouston\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262 (26.23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (11.91%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDallas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207 (20.72%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFort Worth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 (10.01%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSan Antonio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (7.21%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorpus Christi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (4.50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e194 (19.42%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCCI Score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.89 (2.03)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes without Chronic Complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254 (25.43%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial Infarction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e226 (22.62%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebrovascular Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e196 (19.62%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes with Chronic Complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158 (15.82%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Pulmonary Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e154 (15.42%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive Heart Failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (15.22%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeripheral Vascular Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130 (13.01%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (10.11%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny Malignancy, Including Lymphoma and Leukemia, except Malignant Neoplasm of Skin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (5.41%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild Liver Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (4.40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatologic Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (2.30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetastatic Solid Tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (0.90%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeptic Ulcer Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDementia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (0.50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemiplegia or Paraplegia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate or Severe Liver Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.20%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIDS/HIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDRG Intense\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith MCC (MS-DRGs: 231,233,235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e372 (37.24%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout MCC (MS-DRGs :232,234,236)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e627 (62.76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of Commercial Insurance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare Advantage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170 (17.02%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003cb\u003eon-Medicare Advantage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e829 (82.98%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth Plan\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175 (17.52%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e824 (82.48%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical Outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of Stay\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.36 (4.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReadmission Stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo readmission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e860 (86.06%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin 30-Da\u003cb\u003ey\u003c/b\u003e Period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (6.81%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin 31- 60-Da\u003cb\u003ey\u003c/b\u003e Period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin 61- 90-Da\u003cb\u003ey\u003c/b\u003e Period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePayment Descriptive Statistics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90-Day Episode Payment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e81,330 (\u003cspan\u003e$\u003c/span\u003e47,382)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e69,056 (\u003cspan\u003e$\u003c/span\u003e42,162)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCABG indicates coronary artery bypass grafting; AIDS, acquired immunodeficiency syndrome; HIV, human immunodeficiency virus; CCI, Charlson comorbidity index; PPO, preferred provider organization; and POS, point of service plan.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e* Regions were identified according to patients 5-digit zip codes at enrollment.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePatient-level Factors Influencing 90-day CABG Episode Payments\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, regions in Texas, patient age, type of commercial insurance, vascular diseases, DRG intensity, length of stay and readmission stage had a significant influence on 90-day CABG episode payments. Payments were 22.78% lower for the Austin region patients than those patients in the Houston region (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). For every one-year increase in age, episode payments decreased by 1.25% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Episode payments were 16.58% lower for Medicare Advantage patients than those for non-Medicare Advantage patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Episode payments were 9.99% higher for patients with peripheral vascular disease than those for patients without (p\u0026thinsp;=\u0026thinsp;0.0309). Episode payments were 7.04% lower for patients with cerebrovascular disease than those for patients without (p\u0026thinsp;=\u0026thinsp;0.0479). Episode payments were 8.87% higher for patients who had intense CABG MS-DRGs (i.e., DRG 231, DRG 233, DRG 235) at the time of index hospitalization than those for patients with the patients with low intensity MS-DRGs (i.e., DRG 232, DRG 234, DRG 236). For every one-day increase in length of stay, total episode payments increased by 4.3% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Episode payments for readmitted patients within the 30-days, 31-60-days, and 61- 90-days windows were higher than those for non-readmitted patients by 55.08%, 37.77%, and 39.93%, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Overall, the model explained 37% of the variation in episode payments. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the most common cause of readmissions, considering DRG codes, was DRG 857 which is related to postoperative \u0026amp; post-traumatic infections with operating room procedure with complication or comorbidity (4.71% of cases), followed by DRG 603 cellulitis without major complication or comorbidity (3.66%), DRG 291 heart failure and shock with major complication or comorbidity or peripheral extracorporeal membrane oxygenation (3.14%), DRG 293 heart failure and shock without complication or comorbidity (2.62%), and DRG 863 postoperative and post-traumatic infections without major complication or comorbidity (2.62%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear Regression Model of Predictors of 90-Day CABG Episode Payments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameter Estimate\u0026dagger;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP Value*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u0026Dagger;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouston\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-22.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDallas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFort Worth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Regions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9672\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eType of Commercial Insurance\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Medicare Advantage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare Advantage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-16.58%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCongestive Heart Failure\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3763\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeripheral Vascular Disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0309*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCerebrovascular Disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0479*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes with Chronic Complications\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.1073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAny Malignancy, Including Lymphoma and Leukemia, except Malignant Neoplasm of Skin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.94%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2382\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDRG Intensity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo (without MCC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes (with MCC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0051*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of Stay\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReadmission Stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Readmission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin 30-Day Period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin 31-60-Day Period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin 61-90-Day Period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eDRG indicates Diagnosis Related Groups; MCC major complication or comorbidity.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e* Statistically significant at α\u0026thinsp;=\u0026thinsp;0.05 level; n\u0026thinsp;=\u0026thinsp;999; R-Square: 0.3711; Adj R-Square: 0.3608; F-value: 36.21; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003e\u0026dagger;\u003c/b\u003e Exponentiated parameter estimates (EXP(Coefficient)-1) *100,\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003e\u0026Dagger;\u003c/b\u003e Regions were identified according to patients 5-digit zip codes at enrollment.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMost Common Causes of 90-day Readmissions of CABG Episodes, MS-DRGs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMS-DRG Description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePostoperative \u0026amp; Post-Traumatic Infections with O.R. Procedure with CC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCellulitis without MCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeart Failure and Shock with MCC OR Peripheral Extracorporeal Membrane Oxygenation (ECMO)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeart Failure and Shock without CC/MCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePostoperative \u0026amp; Post-Traumatic Infections without MCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eCC indicates complication or comorbidity; MCC, major complication or comorbidity; and MS-DRG, Medicare severity diagnosis-related group.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDrivers of CABG Episode Payment Variation\u003c/h2\u003e \u003cp\u003eAt the hospital level, payment variations between high- and low-payment hospitals were considerable (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The mean 90-day CABG episode payment for hospitals was \u003cspan\u003e$\u003c/span\u003e61,028 for the lowest payment quartile. In comparison to \u003cspan\u003e$\u003c/span\u003e106,148 in the highest quartile, the difference was of \u003cspan\u003e$\u003c/span\u003e45,121, or 74% of the lowest quartile total. All payment components for 90-day CABG episodes were higher in payments for the high-payment quartile than those for the low-payment quartile except for pharmacy (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Payments for index hospitalization accounted for the largest share of the total episode payment for CABG surgeries. Readmission rates for 90-day CABG episodes differed significantly between low- and high-payment hospitals (8.90% for low-payment hospitals vs. 14.92% for high-payment hospitals). Considering readmission payments, the difference between lowest payment quartile hospitals and the highest payment quartile hospitals had 796% higher readmission payments (\u003cspan\u003e$\u003c/span\u003e10,069 vs. \u003cspan\u003e$\u003c/span\u003e1,124), 105% higher post-acute-care payments (\u003cspan\u003e$\u003c/span\u003e12,859 vs. \u003cspan\u003e$\u003c/span\u003e6,259), 58% higher index hospitalization payments (\u003cspan\u003e$\u003c/span\u003e59,851 vs. \u003cspan\u003e$\u003c/span\u003e37,946), 55% higher professional payments (\u003cspan\u003e$\u003c/span\u003e21,955 vs. \u003cspan\u003e$\u003c/span\u003e14,179), and 7% lower pharmacy payments (\u003cspan\u003e$\u003c/span\u003e1,414 vs. \u003cspan\u003e$\u003c/span\u003e1,519). Thus, payment difference between high- and low-payment hospitals for 90-day CABG episode payments was greatest for readmission payments, followed by post-acute care payments, index hospitalization payments, professional payments, and pharmacy payments. Therefore, the study demonstrated that the component with the highest difference rate between high- and low-payment hospitals was related to readmissions. Within professional service payments, payments for surgery accounted for the majority of payments across quartiles. Within post-acute care payments, payments for \u0026ldquo;outpatient facility surgery\u0026rdquo; accounted for a significant proportion of payments and was a key driver 6.2% of payment variation (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As for the drivers of these differences, index hospitalization contributed 48.6% to the variation in total episode payments between high-payment and low-payment hospitals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Readmission, professional services, post-acute care, and pharmacy contributed to the variation in total episode payments between high-payment and low-payment hospitals by 19.8%, 17.2%, 14.6%, and 0.23%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For the 90-day CABG episode subgroup analyses, there were 663 episodes where CABG surgery was performed for patients with non-Medicare Advantage insurance. After reclassifying hospitals into payment quartiles within the subgroup of non-Medicare Advantage insurance patients, index hospitalization remained the main driver of payment variation. The summary is presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Contribution of CABG Payment Components and Subcomponents to Total Episode Payment Variation between High- and Low-payment Hospitals\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePayment Component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSubcomponent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHospital Quartiles*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eDifference\u0026dagger;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariation Attributed to Payment Component\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLowest Quartile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHighest Quartile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Low Payment Hospitals)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(High Payment Hospitals)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReadmissions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e$1,124\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e$10,069\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e$8,945\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e796%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e19.82%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cb\u003ePost-acute Care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOP Facility Surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e3,133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e2,828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.27%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHome Health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.90%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAncillary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e1,214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRehab/Skilled Nursing Facility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.52%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmergency Room\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e1,432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e1,616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.41%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOP Facility Diagnostic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.31%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOP Facility Laboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOP Facility Radiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOP Facility Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e1,835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e4,999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e3,164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.01%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSubtotal Post-acute Care\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e$6,259\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e$12,859\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e$6,600\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e105%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e14.63%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndex Hospitalization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e$37,946\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e$59,851\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e$21,904\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e58%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e48.55%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cb\u003eProfessional Services\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e5,837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e8,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e2,997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnesthesia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e4,710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e7,099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e2,389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.29%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE\u0026amp;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e2,327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e4,191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e1,864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiagnostic Testing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRadiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.21%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmergency Room\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLaboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhysical Medicine/Rehab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessional Other\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.24%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSubtotal Professional\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e$14,179\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e$21,955\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e$7,777\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e55%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e17.24%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePharmacy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e$1,519\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e$1,414\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e$105\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e7%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.23%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e$61,028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e$106,148\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e$45,121\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e74%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e100.00%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eOP indicates outpatient; E\u0026amp;M, evaluation and management.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e* Hospitals that performed less than five episodes were excluded.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003e\u0026dagger;\u003c/b\u003e The absolute difference between lowest and highest payment hospitals was calculated.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePayment Components and Variation of Average 90-Day Episode Payments for CABG Surgeries between High- and Low-Payment Hospitals in the Commercial Patients Subgroup\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePayment Component\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHospital Quartiles*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDifference\u0026dagger;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e% Variation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest Quartile\u003c/p\u003e \u003cp\u003e(Low Payment Hospitals)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHighest Quartile\u003c/p\u003e \u003cp\u003e(High Payment Hospitals)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial CABG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex Hospitalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e39,416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e63,295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e23,878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-acute Care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e5,424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e11,857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e6,434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReadmission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e1,251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e10,991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e9,740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfessional Services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e14,794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e 21,399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e6,605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePharmacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e1,758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e1,372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Episode Payment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e$62,643\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e$108,915\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e$46,272\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* Hospitals were ranked from lowest to highest average 90-day episode payments and then were categorized into low and high payment hospitals.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003e\u0026dagger;\u003c/b\u003e The absolute difference between lowest and highest payment hospitals was calculated.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe primary findings of this study regarding 90-day CABG episodes were established by aggregating episode payments and identifying payment components. Applying this approach allowed us to look beyond CABG surgery payments and evaluate a comprehensive picture of post-discharge payments for commercially insured patients in Texas. The findings showed patient factors have impact on 90-day CABG episode payment in Texas. In addition, there is a substantial variation in 90-day CABG episode payment across the hospitals of Texas.\u003c/p\u003e \u003cp\u003eVariation in CABG episode payments appeared to be influenced by multiple patient factors, including patient region, age, certain comorbidities, readmissions, lengths of stay, and type of commercial insurance (i.e., Medicare Advantage, non-Medicare Advantage). The findings of the present study are similar to those of previous studies that showed patient factors had an impact on CABG payments.[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e][\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] The results also demonstrated variability in CABG payments by patient regions, highlighting, for example, lower episode payments for patients in the Austin region than patients in the Houston region. One explanation could be that Austin had a relatively lower readmission rate as compared to Houston or other Texas regions. Age was found to be negatively associated with episode payments. Although age had a minimal overall association with payments in the present study, this finding was contrary to results from other studies. No evidence in the literature was found suggesting that aging was negatively associated with episode payment for CABG surgeries. In fact, some previous studies revealed a positive relationship between age and payments.[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] Another study showed that age had a minimal overall association with cost.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] Previous studies showed that healthcare spending for Medicare Advantage patients was lower than those patients having other type of commercial insurance plan.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e][\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] The results of the present study agreed with the findings of other studies, which showed that payments for Medicare Advantage patients were lower than those for non-Medicare Advantage insured patients for CABG episodes.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e][\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] This study found a strong collinearity between the type of insurance product (e.g., Medicare Advantage) and the health plan type. For example, it was more likely that a Medicare Advantage patient had a PPO health plan than a POS plan. On the other hand, it was more likely that a non-Medicare Advantage insurance patient had a POS health plan than PPO health plan.\u003c/p\u003e \u003cp\u003eFurthermore, the results of the present study demonstrated that certain comorbid conditions were associated with 90-day episode payments. These results were in line with the literature.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] The regression model showed that CABG episode payments were 10% higher for patients with peripheral vascular disease than for patients without this condition. This situation is likely a reflection of a strong correlation between CAD and peripheral vascular disease. However, the analysis demonstrated that patients with a history of cerebrovascular disease were associated with decreased 90-day CABG episode payments.\u003c/p\u003e \u003cp\u003eMoreover, the present study showed that length of stay and readmission were significantly associated with higher payments for CABG episodes. Being readmitted is a predominant predictor of CABG episode payments. Episode payments for readmitted patients within a 30-day period, a 31-60-day period, and a 61-90-day period were significantly higher than those for non-readmitted patients. By quantifying the costs of readmission, this study highlighted the potential opportunities to provide more efficient care and significantly improve the quality of care for CABG episodes across Texas regions. In fact, the CMS has focused on readmission reduction as one of its main national healthcare policies. For example, the Hospital Readmission Reduction Program, which is a Medicare value-based purchasing program, supports the goal of linking payments to the quality of care by seeking to penalize hospitals with high rates of readmissions for select conditions. Because readmission stage was the predominant predictor of CABG episode payments, readmission causes were further analyzed. The most common causes for 90-day readmissions for CABG surgeries were identified by detecting the DRGs used for readmissions. The most common DRG codes of readmissions for CABG episodes were DRG 857, DRG 603, DRG 291, DRG 293, and DRG 863. Three of these DRG codes were related to postoperative infections (i.e., 857, 603, 863), and two of these DRG codes were related to heart failure (i.e., 291, 293). These causes of readmissions were in line with the literature.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] The present study also showed a slightly higher readmission rate for CABG episode compared with another recent study on 90-day CABG episode payment variation for TRICARE beneficiaries (14% vs. 13%).[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eAs the literature showed a wide variation in CABG episode payments for Medicare patients across hospitals performing CABG surgeries,[\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] this study also showed a wide variation in CABG episode payments for commercially insured patients across Texas. The difference in average 90-day payments at hospitals in the highest and lowest payment quartiles was significant, representing 73.9% higher payments at high-payment hospitals versus low-payment hospitals. Of the five payment components, the index hospitalization was found to be the key driver of CABG episode payment variation, contributing 48.6% of the total payment variation between high- and low-payment hospitals. This finding was consistent with the reviewed studies that found the primary driver of payment variation in CABG episode was related to index hospitalization.[\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] The second key driver of payment variation was readmission, contributing 19.8% of the total CABG episode payment variation between high- and low-payment hospitals. After this, professional services and post-acute care contributed to the total payment variation by 17.2% and 14.6%, respectively. The payment variation of professional services was driven in part by surgery payments, followed by anesthesia, and E\u0026amp;M. Private payers and healthcare providers moving to the episode payment model for CABG surgeries, should consider modeling their sources of variation by focusing on CABG hospitalizations.\u003c/p\u003e \u003cp\u003eWhile CABG surgeries were not in episode payment models for commercially insured patients, they may eventually become the first heart invasive procedure to be included. In 2018, the CMS included these procedures as new voluntary episode payment models, which were qualified under the Medicare Quality Payment Program.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e][\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e][\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] This initiative by the CMS could incentivize healthcare providers entering episode payment models for CABG. It could also motivate private payers to adapt the episode payment method to align provider incentives toward improving efficiency and healthcare quality. The findings of this research may contribute to the literature by giving insight into the variation of CABG episode payments across commercially insured patient factors. Even though this study did not determine how much savings could be achieved by implementing CABG episode payments, it determined the type of services that needed further investigation.\u003c/p\u003e \u003cp\u003eThis study also highlighted opportunities for improvement and cost reductions. Because readmission is a much more prominent factor in the 90-day episode payments, an excellent target for cost reduction would to reduce readmissions. Strategies to contain the CABG episode payments may need to differ depending on the main causes of readmission as well as on the performance of hospitals with high readmission rates. For example, if linking payment to healthcare performance within the episode of care helps reduce readmissions, which would subsequently reduce payment, then this practice should be encouraged from a policy perspective. Transferring some of the financial risk to healthcare providers might be helpful. For example, holding healthcare providers financially accountable for high readmissions might be more appropriate for reducing readmissions and, subsequently, the amounts of episode payments.\u003c/p\u003e \u003cp\u003eFurthermore, healthcare policies, such as those that are relevant to improving the transparency of healthcare performance and payment data available to patients, might help reduce payment variation. For example, information about quality and payment, including out-of-pocket expenses, available to patients could serve as incentives for patients to request care from high-quality providers such as hospitals with lower readmission rates. As a result, hospitals with high readmissions would focus on improving their performance and reducing their readmission rates.\u003c/p\u003e \u003cp\u003eThe present study findings also provided insight about the distribution of 90-day episode payment across the five payment components for CABG surgeries. Index hospitalization payments not only comprised the largest proportion of CABG episode payments, but they also represented the key source of variation. Thus, healthcare providers entering episode-based payment models for CABG surgeries should consider the need to understand CABG hospitalization when developing initiatives to reduce related spending. Finally, this study might contribute to defining the essential targets of payment reform and improving efficiency in CABG episodes.\u003c/p\u003e \u003cp\u003eAlthough this study provided an understanding of costs for CABG surgeries for commercially insured patients in Texas, it was not without limitations. The claims data were designed to justify payment, but they lacked the rich clinical details found in patient medical charts. As a result, detailed clinical variables that could have affected payments were not examined. This data, however, did include some valuable clinical information (i.e., diagnosis and procedure codes) that was used to track comorbid conditions. In particular, this study did not include information about the deaths of patients. In response to this, episodes with fewer than two claims after the discharge date were reviewed as a way of identifying any possible case that died during index hospitalization. There is, however, a possibility that some of the patients included in the study died during the 90-day post-discharge. Finally, the study was limited to a particular group of privately insured patients in Texas, which affects its generalizability to other populations outside of Texas. Despite these limitations, patient demographics and operative predictors were considered while determining CABG episode payments and identifying the drivers of payment variation across Texas. Thus, the claims data this study used were unique in that they provided comprehensive payment information for a large and broadly representative commercially insured population in Texas. As a result, this study might be generalizable to other commercially insured populations in Texas.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study simulated potential episode payments in patients who underwent CABG surgeries in Texas. It provided insight into CABG episode payments across Texas. The findings indicated that CABG episode payments vary widely with a patients\u0026rsquo; age, regions, and comorbidities. Payments also varied with other patient clinical factors, including length of stay and readmission, as well as commercial insurance type (Medicare Advantage, or non-Medicare Advantage). Readmission was the most influential independent predictor of higher payments for patients undergoing CABG surgeries. These results have contributed to a better understanding of the current variation of CABG payments across Texas as well as to the present association of patient factors to total episode payments. It was found that patient factors were significant drivers of variation in episode payments.\u003c/p\u003e \u003cp\u003eOf the five payment components, payment variation was mainly attributable to differences in index hospitalization payments. As a result, payments for the index CABG hospital stays should be considered when developing initiatives to reduce healthcare spending. They could also serve to inform policymakers, payers, and healthcare providers about the implementation of episode payment initiatives.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involved de-identified retrospective data and received exempt approval from the University of Texas Institutional Review Board (IRB Number: HSC-SPH-19-0270).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis declaration is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis declaration is not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was partially funded by Kuwait Foundation for the Advancement of Sciences under project code \u0026ldquo;CB18-63MM-01\u0026rdquo;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSaleh Alsarhan. and Rigoberto Delgado wrote the main manuscript text and Saleh Alsarhan prepared all figures and tables. All authors reviewed the manuscript and contributed to the final version of the manuscript. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBenjamin, E.J., Virani, S.S., Callaway, C.W., Chamberlain, A.M., Chang, A.R., Cheng, S., Chiuve, S.E., Cushman, M., Delling, F.N., Deo, R., de Ferranti, S.D., Ferguson, J.F., Fornage, M., Gillespie, C., Isasi, C.R., Jim\u0026eacute;nez, M.C., Jordan, L.C., Judd, S.E., Lackland, D., Lichtman, J.H., Lisabeth, L., Liu, S., Longenecker, C.T., Lutsey, P.L., Mackey, J.S., Matchar, D.B., Matsushita, K., Mussolino, M.E., Nasir, K., O\u0026rsquo;Flaherty, M., Palaniappan, L.P., Pandey, A., Pandey, D.K., Reeves, M.J., Ritchey, M.D., Rodriguez, C.J., Roth, G.A., Rosamond, W.D., Sampson, U.K.A., Satou, G.M., Shah, S.H., Spartano, N.L., Tirschwell, D.L., Tsao, C.W., Voeks, J.H., Willey, J.Z., Wilkins, J.T., Wu, J.HY., Alger, H.M., Wong, S.S., Muntner, P.: Heart Disease and Stroke Statistics\u0026mdash;2018 Update: A Report From the American Heart Association. 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Health Aff. 35, 1444\u0026ndash;1451 (2016). https://doi.org/10.1377/hlthaff.2015.1553\u003c/li\u003e\n\u003cli\u003eAdvanced Alternative Payment Models (APMs)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CABG, drivers of payment variation","lastPublishedDoi":"10.21203/rs.3.rs-3891751/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3891751/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCoronary artery bypass grafting (CABG) surgery has become a target for episode payment initiatives, and there is the need to understand what factors contribute to costs of CABG surgeries. 90-day episode payments for CABG surgery were estimated using commercial insurance claims of patients over 18 years of age in Texas. The study used a multiple linear regression model with a log-transformed 90-day CABG episode payments to model effect of patient factors on payment variation. The source of data was Optum\u0026rsquo;s de-identified Clinformatics\u0026reg; Data Mart Database (CDM): administrative claims data. A total of 999 CABG episodes were identified. The mean (SD) 90-day CABG episode payment per patient was \u003cspan\u003e$\u003c/span\u003e81,330 (\u003cspan\u003e$\u003c/span\u003e47,382). Patient factors explained about 37% of the payment variation.\u003c/p\u003e \u003cp\u003eWide variation exists in 90-day CABG episode payments for commercially insured patients across Texas. Focusing on reducing variation of index hospitalization could be a potentially effective approach to improve efficiency.\u003c/p\u003e","manuscriptTitle":"The Drivers of 90-Day CABG Episode Payment Variation for Commercially Insured Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-19 15:13:08","doi":"10.21203/rs.3.rs-3891751/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aa350dd3-b6e6-40f2-82ce-54053482b518","owner":[],"postedDate":"March 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-28T06:54:06+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-19 15:13:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3891751","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3891751","identity":"rs-3891751","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}