Payer-Negotiated Price Variation and Relationship to Surgical Outcomes for the Most Common Cancers at NCI-Designated Cancer Centers.

BACKGROUND: Federal rules mandate that hospitals publish payer-specific negotiated prices for all services. Little is known about variation in payer-negotiated prices for surgical oncology services or their relationship to clinical outcomes. We assessed variation in payer-negotiated prices associated with surgical care for common cancers at National Cancer Institute (NCI)-designated cancer centers and determined the effect of increasing payer-negotiated prices on the odds of morbidity and mortality. MATERIALS AND METHODS: A cross-sectional analysis of 63 NCI-designated cancer center websites was employed to assess variation in payer-negotiated prices. A retrospective cohort study of 15,013 Medicare beneficiaries undergoing surgery for colon, pancreas, or lung cancers at an NCI-designated cancer center between 2014 and 2018 was conducted to determine the relationship between payer-negotiated prices and clinical outcomes. The primary outcome was the effect of median payer-negotiated price on odds of a composite outcome of 30 days mortality and serious postoperative complications for each cancer cohort. RESULTS: Within-center prices differed by up to 48.8-fold, and between-center prices differed by up to 675-fold after accounting for geographic variation in costs of providing care. Among the 15,013 patients discharged from 20 different NCI-designated cancer centers, the effect of normalized median payer-negotiated price on the composite outcome was clinically negligible, but statistically significantly positive for colon [aOR 1.0094 (95% CI 1.0051-1.0138)], lung [aOR 1.0145 (1.0083-1.0206)], and pancreas [aOR 1.0080 (1.0040-1.0120)] cancer cohorts. CONCLUSIONS: Payer-negotiated prices are statistically significantly but not clinically meaningfully related to morbidity and mortality for the surgical treatment of common cancers. Higher payer-negotiated prices are likely due to factors other than clinical quality.

Characterizing geographic variation in postoperative venous thromboembolism.

OBJECTIVE: Venous thromboembolism (VTE) after major surgery remains an important contributor to morbidity and mortality. Despite significant quality improvement efforts in prevention and prophylaxis strategies, the degree of hospital and regional variation in the United States remains unknown. METHODS: Medicare beneficiaries undergoing 13 different major surgeries at U.S. hospitals between 2016 and 2018 were included in this retrospective cohort study. We calculated the rates of 90-day VTE. We adjusted for a variety of patient and hospital covariates and used a multilevel logistic regression model to calculate the rates of VTE and coefficients of variation across hospitals and hospital referral regions (HRRs). RESULTS: A total of 4,115,837 patients from 4116 hospitals were included, of whom 116,450 (2.8%) experienced VTE within 90 days. The 90-day VTE rates varied substantially by procedure, from 2.5% for abdominal aortic aneurysm repair to 8.4% for pancreatectomy. Across the hospitals, there was a 6.6-fold variation in index hospitalization VTE and a 5.3-fold variation in the rate of postdischarge VTE. Across the HRRs, there was a 2.6-fold variation in 90-day VTE, with a 12.1-fold variation in the coefficient of variation. A subset of HRRs was identified with both higher VTE rates and higher variance across hospitals. CONCLUSIONS: Substantial variation exists in the rate of postoperative VTE across U.S. hospitals. Characterizing HRRs with high overall rates of VTE and those with significant variation across the hospitals will allow for targeted quality improvement efforts.

Changes in Hospital-acquired Conditions and Mortality Associated With the Hospital-acquired Condition Reduction Program.

IMPORTANCE: To improve patient safety, the Centers for Medicare and Medicaid Services announced the Hospital-Acquired Condition Reduction Program (HACRP) in August 2013. The program reduces Medicare payments by 1% for hospitals in the lowest performance quartile related to hospital-acquired conditions. Performance measures are focused on perioperative care. OBJECTIVE: To evaluate changes in HACs and 30-day mortality after the announcement of the HACRP. DESIGN: Interrupted time-series design using Medicare Provider and Analysis Review (MEDPAR) claims data. We estimated models with linear splines to test for changes in HACs and 30-day mortality before the Affordable Care Act (ACA), after the ACA, and after the HACRP. SETTING: Fee-for-service Medicare 2009-2015. PARTICIPANTS: Medicare beneficiaries undergoing surgery and discharged from an acute care hospital between January 2009 and August 2015 (N = 8,857,877). MAIN OUTCOME AND MEASURE: Changes in HACs and 30-day mortality after the announcement of the HACRP. RESULTS: Patients experienced HACs at a rate of 13.39 per 1000 discharges [95% confidence interval (CI), 13.10 to 13.68] in the pre-ACA period. This declined after the ACA was passed and declined further after the HACRP announcement [adjusted difference in annual slope, -1.34 (95% CI, -1.64 to -1.04)]. Adjusted 30-day mortality was 3.69 (95% CI, 3.64 to 3.74) in the pre-ACA period among patients receiving surgery. Thirty-day mortality continued to decline after the ACA [adjusted annual slope -0.04 (95% CI, -0.05 to -0.02)] but was flat after the HACRP [adjusted annual slope -0.01 (95% CI, -0.04 to 0.02)]. CONCLUSIONS AND RELEVANCE: Although hospital-acquired conditions targeted under the HACRP declined at a greater rate after the program was announced, 30-day mortality was unchanged.

Evaluation of US Hospital Episode Spending for Acute Inpatient Conditions After the Patient Protection and Affordable Care Act.

Importance: Under the Patient Protection and Affordable Care Act (ACA), US hospitals were exposed to a number of reforms intended to reduce spending, many of which, beginning in 2012, targeted acute care hospitals and often focused on specific diagnoses (eg, acute myocardial infarction, heart failure, and pneumonia) for Medicare patients. Other provisions enacted in the ACA and under budget sequestration (beginning in 2013) mandated Medicare fee cuts. Objective: To evaluate the association between the enactment of ACA reforms and 30-day price-standardized hospital episode spending. Design, Setting, and Participants: This policy evaluation included index discharges between January 1, 2008, and August 31, 2015, from a national random 20% sample of Medicare beneficiaries. Data analysis was performed from February 1, 2019 to July 8, 2020. Exposure: Payment reforms after passage of the ACA. Main Outcomes and Measures: 30-day price-standardized episode payments. Three alternative estimation approaches were used to evaluate the association between reforms following the ACA and episode spending: (1) a difference-in-difference (DID) analysis among acute care hospitals, comparing spending for diagnoses commonly targeted by ACA programs with nontargeted diagnoses; (2) a DID analysis comparing acute care hospitals and critical access hospitals (not exposed to reforms); and (3) a generalized synthetic control analysis, comparing acute care and critical access hospitals. Supplemental analysis examined the degree to which Medicare fee cuts contributed to spending reductions. Results: A total of 7 634 242 index discharges (4 525 630 [59.2%] female patients; mean [SD] age, 79.31 [8.02] years) were included. All 3 approaches found that reforms following the ACA were associated with a significant reduction in episode spending. The DID estimate comparing targeted and untargeted diagnoses suggested that reforms following the ACA were associated with a -$431 (95% CI, -$492 to -$369; -2.87%) change in total spending, while the generalized synthetic control analysis suggested that reforms were associated with a -$1232 (95% CI, -$1488 to -$965; -10.12%) change in total episode spending, amounting in a total annual savings of $5.68 billion. Cuts to Medicare fees accounted for most of these savings. Conclusions and Relevance: In this policy evaluation, the ACA was associated with large reductions in US hospital episode spending.

Target Prices Influence Hospital Participation And Shared Savings In Medicare Bundled Payment Program.

To reduce episode spending for twenty-nine predefined clinical conditions, the Centers for Medicare and Medicaid Services (CMS) implemented the voluntary Bundled Payments for Care Improvement (BPCI) Advanced Model program in 2018. Under this program, hospitals gain or lose revenue depending on their episode spending relative to target prices set by CMS. The relationship between target prices and hospital participation in BPCI Advanced is unknown, as are the financial implications for CMS. Using Medicare claims, we estimate that each $1,000 increase in target prices increased the probability of participation by 0.78 percentage points across all episodes. We then used Medicare claims before the start of BPCI Advanced to evaluate mean reversion, or the tendency for episode spending at individual hospitals to move closer to average episode spending over time, especially for hospitals having higher target prices. Hospitals with spending that was 10 percent more than target prices at baseline could expect spending to decline by 7.43 percent in the performance period, hospitals with spending that was 20 percent more saw spending decline by 9.80 percent, and hospitals with spending that was 30 percent more saw spending decline by 11.93 percent. Our findings suggest that CMS will end up paying substantial bonuses to hospitals that resulted from mean reversion rather than from meaningful reductions in costs.

Patient Coded Severity and Payment Penalties Under the Hospital Readmissions Reduction Program: A Machine Learning Approach.

OBJECTIVE: The objective of this study was to examine variation in hospital responses to the Centers for Medicare and Medicaid's expansion of allowable secondary diagnoses in January 2011 and its association with financial penalties under the Hospital Readmission Reduction Program (HRRP). DATA SOURCES/STUDY SETTING: Medicare administrative claims for discharges between July 2008 and June 2011 (N=3102 hospitals). RESEARCH DESIGN: We examined hospital variation in response to the expansion of secondary diagnoses by describing changes in comorbidity coding before and after the policy change. We used random forest machine learning regression to examine hospital characteristics associated with coded severity. We then used a 2-part model to assess whether variation in coded severity was associated with readmission penalties. RESULTS: Changes in severity coding varied considerably across hospitals. Random forest models indicated that greater baseline levels of condition categories, case-mix index, and hospital size were associated with larger changes in condition categories. Hospital coding of an additional condition category was associated with a nonsignificant 3.8 percentage point increase in the probability for penalties under the HRRP (SE=2.2) and a nonsignificant 0.016 percentage point increase in penalty amount (SE=0.016). CONCLUSION: Changes in patient coded severity did not affect readmission penalties.

A Comparison of Estimated Cost Savings from Potential Reductions in Hospital-Acquired Conditions to Levied Penalties Under the CMS Hospital-Acquired Condition Reduction Program.

BACKGROUND: The Hospital-Acquired Condition Reduction Program (HACRP) from the Centers for Medicare & Medicaid Services (CMS) reduces Medicare payments to hospitals with high rates of hospital-acquired conditions (HACs) by 1% each year. It is not known how the savings accruing to CMS from such penalties compare to savings resulting from a reduction in HACs driven by this program. This study compares the reported savings to CMS from financial penalties levied under the HACRP with savings resulting from potential reductions in HACs. METHODS: Using a random sample of 20% of Medicare claims data (January 1, 2009-September 30, 2014), the research team evaluated the association between HACs and 90-day episode spending (adjusted to 2015 dollars), then estimated potential annual savings to CMS if there was a relative decrease in incidence of all HACs by 1%-20%. These savings were then compared to the actual collected HACRP penalties reported by CMS in 2015. RESULTS: All HACs were associated with significant increases in total 90-day episode spending, ranging from $3,183 for iatrogenic pneumothorax to $21,654 for postoperative hip fracture. The total estimated savings to Medicare from potential reduction in all HACs ranged from $2.2 million to $44 million per year, an amount much lower than the $361 million in penalties levied on hospitals per year for HACs. CONCLUSION: The penalties levied under the HACRP far exceed the potential cost savings accruing from a 1%-20% reduction in HACs that might result from hospitals' efforts in response to the program.

Most Patients Undergoing Ground And Air Ambulance Transportation Receive Sizable Out-Of-Network Bills.

"Surprise" out-of-network bills have come under close scrutiny, and while ambulance transportation is known to be a large component of the problem, its impact is poorly understood. We measured the prevalence and financial impact of out-of-network billing in ground and air ambulance transportation. For members of a large national insurance plan in 2013-17, 71 percent of all ambulance rides involved potential surprise bills. For both ground and air ambulances, out-of-network charges were substantially greater than in-network prices, resulting in median potential surprise bills of $450 for ground transportation and $21,698 for air transportation. Though out-of-network air ambulance bills were larger, out-of-network ground ambulance bills were more common, with an aggregate impact of $129 million per year. Out-of-network air ambulance bills averaged $91 million per year, rising from $41 million in 2013 to $143 million in 2017. Federal proposals to limit surprise out-of-network billing should incorporate protections for patients undergoing ground or air ambulance transportation.

Is social capital protective against hospital readmissions?

BACKGROUND: To evaluate the association between social capital and 30-day readmission to the hospital among Medicare beneficiaries overall, beneficiaries with dementia and related memory disorders, and beneficiaries with dual eligibility for Medicaid. METHODS: Using Health and Retirement Study (HRS) data linked with 2008-2015 Medicare claims from traditional Medicare beneficiaries hospitalized during the study period (1246 unique respondents, 2212 total responses), we examined whether dementia and related memory disorders and dual eligibility were associated with social capital. We then estimated a multiple regression model to test whether social capital was associated with a reduced likelihood of readmission. RESULTS: Dementia was associated with an - 0.241 standard deviation (sd) change in social capital (95% CI: - 0.378, - 0.103), dual eligibility with a - 0.461 sd change (95% CI: - 0.611, - 0.310), and the occurrence of both was associated with an additional - 0.236 sd change (95% CI: - 0.525, - 0.053). 30-day readmission rates were 14.47% over the study period. In both adjusted and unadjusted models, social capital was associated with small and nonsignificant differences in 30-day readmissions. These effects did not vary across dementia status and socioeconomic status. CONCLUSIONS: Dementia and dual eligibility were associated with lower social capital, but social capital was not associated with the risk of readmission for any population.

Out-of-Network Bills for Privately Insured Patients Undergoing Elective Surgery With In-Network Primary Surgeons and Facilities.

Importance: Privately insured patients who receive care from in-network physicians may receive unexpected out-of-network bills ("surprise bills") from out-of-network clinicians they did not choose. In elective surgery, this can occur if patients choose in-network surgeons and hospitals but receive out-of-network bills from other involved clinicians. Objective: To evaluate out-of-network billing across common elective operations performed with in-network primary surgeons and facilities. Design, Setting, and Participants: Retrospective analysis of claims data from a large US commercial insurer, representing 347 356 patients who had undergone 1 of 7 common elective operations (arthroscopic meniscal repair [116 749]; laparoscopic cholecystectomy [82 372]; hysterectomy [67 452]; total knee replacement [42 313]; breast lumpectomy [18 018]; colectomy [14 074]; coronary artery bypass graft surgery [6378]) by an in-network primary surgeon at an in-network facility between January 1, 2012, and September 30, 2017. Follow-up ended November 8, 2017. Exposure: Patient, clinician, and insurance factors potentially related to out-of-network bills. Main Outcomes and Measures: The primary outcome was the proportion of episodes with out-of-network bills. The secondary outcome was the estimated potential balance bill associated with out-of-network bills from each surgical procedure, calculated as total out-of-network charges less the typical in-network price for the same service. Results: Among 347 356 patients (mean age, 48 [SD, 11] years; 66% women) who underwent surgery with in-network primary surgeons and facilities, 20.5% of episodes (95% CI, 19.4%-21.7%) had an out-of-network bill. In these episodes, the mean potential balance bill per episode was $2011 (95% CI, $1866-$2157) when present. Out-of-network bills were associated with surgical assistants in 37% of these episodes; when present, the mean potential balance bill was $3633 (95% CI, $3384-$3883). Out-of-network bills were associated with anesthesiologists in 37% of episodes; when present, the mean potential balance bill was $1219 (95% CI, $1049-$1388). Membership in health insurance exchange plans, compared with nonexchange plans, was associated with a significantly higher risk of out-of-network bills (27% vs 20%, respectively; risk difference, 6% [95% CI, 3.9%-8.9%]; P < .001). Surgical complications were associated with a significantly higher risk of out-of-network bills, compared with episodes with no complications (28% vs 20%, respectively; risk difference, 7% [95% CI, 5.8%-8.8%]; P < .001). Among 83 021 procedures performed at ambulatory surgery centers with in-network primary surgeons, 6.7% (95% CI, 5.8%-7.7%) included an out-of-network facility bill and 17.2% (95% CI, 15.7%-18.8%) included an out-of-network professional bill. Conclusions and Relevance: In this retrospective analysis of commercially insured patients who had undergone elective surgery at in-network facilities with in-network primary surgeons, a substantial proportion of operations were associated with out-of-network bills.

Local Referral of High-Risk Pancreatectomy Patients to Improve Surgical Outcomes and Minimize Travel Burden.

BACKGROUND: Referring patients to high-quality hospitals for complex procedures may improve outcomes. This is most feasible within small geographic areas. However, access to specialized surgical procedures may be an implementation barrier. We sought to determine the availability of high-quality hospitals performing pancreatectomy and the potential benefit and travel burden of referral within small geographic areas. METHODS: We identified elderly Medicare beneficiaries undergoing pancreatectomy between 2012 and 2014. Hospitals were stratified into quintiles of quality based on postoperative complication rates. Patient risk was assessed by modeling the predicted risk of developing a postoperative complication. The geographic unit of analysis was Metropolitan Statistical Area (MSA). Hospitals were categorized into MSA by zip code. Travel distance was calculated using patient and hospital zip code. RESULTS: Among high-risk patients, 40.7% received care at the lowest-quality hospitals even though 80% had a high-quality hospital in the same MSA. Shifting these patients from low- to high-quality hospitals would decrease serious complications from 46.6 to 21.9% (P < 0.001) and mortality from 10.9 to 8.9% (P = 0.047). Three quarters of high-risk patients treated at low-quality hospitals could reach a high-quality hospital by extending their travel < 5 miles, and nearly 60% traveled farther to a low-quality hospital than was necessary to reach a high-quality hospital. CONCLUSIONS: High-risk pancreatectomy patients often receive care at low-quality hospitals despite the availability of high-quality hospitals in the area or within an acceptable distance. Referral of high-risk patients to high-quality hospitals within small geographic areas may be an effective strategy to improve outcomes following pancreatic surgery.

Local Referral of High-risk Patients to High-quality Hospitals: Surgical Outcomes, Cost Savings, and Travel Burdens.

OBJECTIVE: We sought to assess the potential changes in Medicare payments and clinical outcomes of referring high-risk surgical patients to local high-quality hospitals within small geographic areas. SUMMARY BACKGROUND DATA: Previous studies have documented a benefit in referring high-risk patients to high-quality hospitals on a national basis, suggesting selective referral as a mechanism to improve the value of surgical care. Practically, referral of patients should be done within small geographic regions; however, the benefit of local selective referral has not been studied. METHODS: We analyzed data on elderly Medicare beneficiaries undergoing any of 4 elective inpatient surgical procedures between 2012 and 2014. Hospitals were categorized into Metropolitan Statistical Areas by zip code and stratified into quintiles of quality based on rates of postoperative complications. Patient risk was calculated by modeling the predicted risk of a postoperative complication. Medicare payments for each surgical episode were calculated. Distances between patients' home zip code and high- and low-quality hospitals were calculated. RESULTS: One quarter of high-risk patients underwent surgery at a low-quality hospital despite the availability of a high-quality hospital in their small geographic area. Shifting these patients to a local high-quality hospital would decrease spending 12% to 37% ($2,500 for total knee and hip replacement, $6,700 for colectomy, and $11,400 for lung resection). Approximately 45% of high-risk patients treated at low-quality hospitals could travel a shorter distance to reach a high-quality hospital than the low-quality hospital they received care at. CONCLUSIONS: Complication rates and Medicare payments are significantly lower for high-risk patients treated at local high-quality hospitals. This suggests triaging high-risk patients to local high-quality hospitals within small geographic areas may serve as a template for improving the value of surgical care.

Who Will be the Costliest Patients? Using Recent Claims to Predict Expensive Surgical Episodes.

INTRODUCTION: Surgery accounts for almost half of inpatient spending, much of which is concentrated in a subset of high-cost patients. To study the effects of surgeon and hospital characteristics on surgical expenditures, a way to adjust for patient characteristics is essential. DESIGN: Using 100% Medicare claims data, we identified patients aged 66-99 undergoing elective inpatient surgery (coronary artery bypass grafting, colectomy, and total hip/knee replacement) in 2014. We calculated price-standardized Medicare payments for the surgical episode from admission through 30 days after discharge (episode payments). On the basis of predictor variables from 2013, that is, Elixhauser comorbidities, hierarchical condition categories, Medicare's Chronic Conditions Warehouse (CCW), and total spending, we constructed models to predict the costs of surgical episodes in 2014. RESULTS: All sources of comorbidity data performed well in predicting the costliest cases (Spearman correlation 0.86-0.98). Models on the basis of hierarchical condition categories had slightly superior performance. The costliest quintile of patients as predicted by the model captured 35%-45% of the patients in each procedure's actual costliest quintile. For example, in hip replacement, 44% of the costliest quintile was predicted by the model's costliest quintile. CONCLUSIONS: A significant proportion of surgical spending can be predicted using patient factors on the basis of readily available claims data. By adjusting for patient factors, this will facilitate future research on unwarranted variation in episode payments driven by surgeons, hospitals, or other market forces.

Changes in coding of pneumonia and impact on the Hospital Readmission Reduction Program.

OBJECTIVE: To evaluate whether changes in diagnosis assignment explain reductions in 30-day readmission for patients with pneumonia following the Hospital Readmission Reduction Program (HRRP). DATA SOURCES: 100 percent MedPAR, 2008-2015. STUDY DESIGN: Retrospective cohort study of Medicare discharges in HRRP-eligible hospitals. Outcomes were 30-day readmission rates for pneumonia under a "narrow" definition (used for the HRRP until October 2015; n = 2 288 644) and a "broad" definition that included certain diagnoses of sepsis and aspiration pneumonia (used since October 2015; n = 3 618 215). We estimated changes in 30-day readmissions in the pre-HRRP period (January 2008-March 2010), the HRRP implementation period (April 2010-September 2012), and the HRRP penalty period (October 2012-June 2015). PRINCIPAL FINDINGS: Under the narrow definition, adjusted annual readmission rates changed by +0.07 percentage points (pp) during the pre-HRRP period (95% CI: -0.03 pp, +0.18 pp), -1.07 pp during HRRP implementation (95% CI: -1.15 pp, -0.99 pp), and -0.09 pp during the penalty period (95% CI: -0.18 pp, -0.00 pp). Under the broad definition, 30-day readmissions changed by +0.21 pp during the pre-HRRP period (95% CI: +0.12 pp, +0.30 pp), -1.28 pp during HRRP implementation (95% CI: -1.35 pp, -1.21 pp), and -0.09 pp during the penalty period (95% CI: -0.16 pp, -0.02 pp). CONCLUSIONS: Changes in the coding of inpatient pneumonia admissions do not explain readmission reduction following the HRRP.

Changes in hospital safety following penalties in the US Hospital Acquired Condition Reduction Program: retrospective cohort study.

OBJECTIVE: To evaluate the association between hospital penalization in the US Hospital Acquired Condition Reduction Program (HACRP) and subsequent changes in clinical outcomes. DESIGN: Regression discontinuity design applied to a retrospective cohort from inpatient Medicare claims. SETTING: 3238 acute care hospitals in the United States. PARTICIPANTS: Medicare fee-for-service beneficiaries discharged from acute care hospitals between 23 July 2014 and 30 November 2016 and eligible for at least one targeted hospital acquired condition (n=15 470 334). INTERVENTION: Hospital receipt of a penalty in the first year of the HACRP. MAIN OUTCOME MEASURES: Episode level count of targeted hospital acquired conditions per 1000 episodes, 30 day readmissions, and 30 day mortality. RESULTS: Of 724 hospitals penalized under the HACRP in fiscal year 2015, 708 were represented in the study. Mean counts of hospital acquired conditions were 2.72 per 1000 episodes for penalized hospitals and 2.06 per 1000 episodes for non-penalized hospitals; 30 day readmissions were 14.4% and 14.0%, respectively, and 30 day mortality was 9.0% for both hospital groups. Penalized hospitals were more likely to be large, teaching institutions, and have a greater share of patients with low socioeconomic status than non-penalized hospitals. HACRP penalties were associated with a non-significant change of -0.16 hospital acquired conditions per 1000 episodes (95% confidence interval -0.53 to 0.20), -0.36 percentage points in 30 day readmission (-1.06 to 0.33), and -0.04 percentage points in 30 day mortality (-0.59 to 0.52). No clear patterns of clinical improvement were observed across hospital characteristics. CONCLUSIONS: Penalization was not associated with significant changes in rates of hospital acquired conditions, 30 day readmission, or 30 day mortality, and does not appear to drive meaningful clinical improvements. By disproportionately penalizing hospitals caring for more disadvantaged patients, the HACRP could exacerbate inequities in care.