Opportunities for Savings in Risk Arrangements for Oncologic Care.

Importance: As the US accelerates adoption of alternative payment through global payment models such as Accountable Care Organizations (ACOs) or Medicare Advantage (MA), high spending for cancer care is a potential target for savings. Objective: To quantify the extent to which ACOs and other risk-bearing organizations operating in a specific geographic area (hospital referral region [HRR]) could achieve savings by steering patients to efficient medical oncology practices. Design, Setting, and Participants: This observational study included serial cross-sections of Medicare beneficiaries with cancer from 2010 to 2018. Data were analyzed from August 2021 to March 2023. Main Outcomes and Measures: Total spending and spending by category in the 1-year period following an index visit for a patient with newly diagnosed (incident) or poor-prognosis cancer. Results: The incident cohort included 1 309 825 patients with a mean age of 74.0 years; the most common cancer types were breast (21.4%), lung (16.7%), and colorectal cancer (10.0%). The poor prognosis cohort included 1 429 973 (mean age, 72.7 years); the most common cancer types were lung (26.6%), lymphoma (11.7%), and leukemia (7.3%). Options for steering varied across markets; the top quartile market had 10 or more oncology practices, but the bottom quartile had 3 or fewer oncology practices. Total spending (including Medicare Part D) in the incident cohort increased from a mean of $57 314 in 2009 to 2010 to $66 028 in 2016 to 2017. Within markets, total spending for practices in the highest spending quartile was 19% higher than in the lowest quartile. Hospital spending was the single largest component of spending in both time periods ($20 390 and $19 718, respectively) followed by Part B (infused) chemotherapy ($8022 and $11 699). Correlations in practice-level spending between the first-year (2009) and second-year (2010) spending were high (>0.90 in all categories with most >0.98), but these attenuated over time. Conclusions and Relevance: These results suggest there may be opportunities for ACOs and other risk-bearing organizations to select or drive referrals to lower-spending oncology practices in many local markets.

Organization of Cancer Specialists in US Physician Practices and Health Systems.

PURPOSE: To describe the supply of cancer specialists, the organization of cancer care within versus outside of health systems, and the distance to multispecialty cancer centers. METHODS: Using the 2018 Health Systems and Provider Database from the National Bureau of Economic Research and 2018 Medicare data, we identified 46,341 unique physicians providing cancer care. We stratified physicians by discipline (adult/pediatric medical oncologists, radiation oncologists, surgical/gynecologic oncologists, other surgeons performing cancer surgeries, or palliative care physicians), system type (National Cancer Institute [NCI] Cancer Center system, non-NCI academic system, nonacademic system, or nonsystem/independent practice), practice size, and composition (single disciplinary oncology, multidisciplinary oncology, or multispecialty). We computed the density of cancer specialists by county and calculated distances to the nearest NCI Cancer Center. RESULTS: More than half of all cancer specialists (57.8%) practiced in health systems, but 55.0% of cancer-related visits occurred in independent practices. Most system-based physicians were in large practices with more than 100 physicians, while those in independent practices were in smaller practices. Practices in NCI Cancer Center systems (95.2%), non-NCI academic systems (95.0%), and nonacademic systems (94.3%) were primarily multispecialty, while fewer independent practices (44.8%) were. Cancer specialist density was sparse in many rural areas, where the median travel distance to an NCI Cancer Center was 98.7 miles. Distances to NCI Cancer Centers were shorter for individuals living in high-income areas than in low-income areas, even for individuals in suburban and urban areas. CONCLUSION: Although many cancer specialists practiced in multispecialty health systems, many also worked in smaller-sized independent practices where most patients were treated. Access to cancer specialists and cancer centers was limited in many areas, particularly in rural and low-income areas.

Practice-Level Variation in Molecular Testing and Use of Targeted Therapy for Patients With Non-Small Cell Lung Cancer and Colorectal Cancer.

Importance: All patients with newly diagnosed non-small cell lung cancer (NSCLC) and colorectal cancer (CRC) should receive molecular testing to identify those who can benefit from targeted therapies. However, many patients do not receive recommended testing and targeted therapies. Objective: To compare rates of molecular testing and targeted therapy use by practice type and across practices. Design, Setting, and Participants: This cross-sectional study used 100% Medicare fee-for-service data from 2015 through 2019 to identify beneficiaries with new metastatic NSCLC or CRC diagnoses receiving systemic therapy and to assign patients to oncology practices. Hierarchical linear models were used to characterize variation by practice type and across practices. Data analysis was conducted from June 2019 to October 2022. Exposures: Oncology practice providing care. Outcomes: Primary outcomes were rates of molecular testing and targeted therapy use for patients with NSCLC and CRC. Secondary outcomes were rates of multigene testing for NSCLC and CRC. Results: There were 106 228 Medicare beneficiaries with incident NSCLC (31 521 [29.7%] aged 65-69 years; 50 348 [47.4%] female patients; 2269 [2.1%] Asian, 8282 [7.8%] Black, and 91 215 [85.9%] White patients) and 39 512 beneficiaries with incident CRC (14 045 [35.5%] aged 65-69 years; 17 518 [44.3%] female patients; 896 [2.3%] Asian, 3521 [8.9%] Black, and 32 753 [82.9%] White patients) between 2015 and 2019. Among these beneficiaries, 18 435 (12.9%) were treated at National Cancer Institute (NCI)-designated centers, 8187 (5.6%) were treated at other academic centers, and 94 329 (64.7%) were treated at independent oncology practices. Molecular testing rates increased from 74% to 85% for NSCLC and 45% to 65% for CRC. First-line targeted therapy use decreased from 12% to 8% among patients with NSCLC and was constant at 5% for patients with CRC. For NSCLC, molecular testing rates were similar across practice types while rates of multigene panel use (13.2%) and targeted therapy use (16.6%) were highest at NCI-designated cancer centers. For CRC, molecular testing rates were 3.8 (95% CI: 1.2-6.5), 3.3 (95% CI, 0.4-6.1), and 12.2 (95% CI, 9.1-15.3) percentage points lower at hospital-owned practices, large independent practices, and small independent practices, respectively. Rates of targeted therapy use for CRC were similar across practice types. After adjusting for patient characteristics, there was moderate variation in molecular testing and targeted therapy use across oncology practices. Conclusions and Relevance: In this cross-sectional study of Medicare beneficiaries, molecular testing rates for NSCLC and CRC increased in recent years but remained lower than recommended levels. Rates of targeted therapy use decreased for NSCLC and remained stable for CRC. Variation across practices suggests that where a patient was treated may have affected access to recommended testing and efficacious treatments.

Racial and Ethnic Disparities in Opioid Access and Urine Drug Screening Among Older Patients With Poor-Prognosis Cancer Near the End of Life.

PURPOSE: To characterize racial and ethnic disparities and trends in opioid access and urine drug screening (UDS) among patients dying of cancer, and to explore potential mechanisms. METHODS: Among 318,549 non-Hispanic White (White), Black, and Hispanic Medicare decedents older than 65 years with poor-prognosis cancers, we examined 2007-2019 trends in opioid prescription fills and potency (morphine milligram equivalents [MMEs] per day [MMEDs]) near the end of life (EOL), defined as 30 days before death or hospice enrollment. We estimated the effects of race and ethnicity on opioid access, controlling for demographic and clinical factors. Models were further adjusted for socioeconomic factors including dual-eligibility status, community-level deprivation, and rurality. We similarly explored disparities in UDS. RESULTS: Between 2007 and 2019, White, Black, and Hispanic decedents experienced steady declines in EOL opioid access and rapid expansion of UDS. Compared with White patients, Black and Hispanic patients were less likely to receive any opioid (Black, -4.3 percentage points, 95% CI, -4.8 to -3.6; Hispanic, -3.6 percentage points, 95% CI, -4.4 to -2.9) and long-acting opioids (Black, -3.1 percentage points, 95% CI, -3.6 to -2.8; Hispanic, -2.2 percentage points, 95% CI, -2.7 to -1.7). They also received lower daily doses (Black, -10.5 MMED, 95% CI, -12.8 to -8.2; Hispanic, -9.1 MMED, 95% CI, -12.1 to -6.1) and lower total doses (Black, -210 MMEs, 95% CI, -293 to -207; Hispanic, -179 MMEs, 95% CI, -217 to -142); Black patients were also more likely to undergo UDS (0.5 percentage points; 95% CI, 0.3 to 0.8). Disparities in EOL opioid access and UDS disproportionately affected Black men. Adjustment for socioeconomic factors did not attenuate the EOL opioid access disparities. CONCLUSION: There are substantial and persistent racial and ethnic inequities in opioid access among older patients dying of cancer, which are not mediated by socioeconomic variables.

Adoption of Innovative Therapies Across Oncology Practices-Evidence From Immunotherapy.

Importance: Immunotherapies reflect an important breakthrough in cancer treatment, substantially improving outcomes for patients with a variety of cancer types, yet little is known about which practices have adopted this novel therapy or the pace of adoption. Objective: To assess adoption of immunotherapies across US oncology practices and examine variation in adoption by practice type. Design, Setting, and Participants: This cohort study used data from Medicare fee-for-service beneficiaries undergoing 6-month chemotherapy episodes between 2010 and 2017. Data were analyzed January 19, 2021, to September 28, 2022, for patients with cancer types for which immunotherapy was approved by the US Food and Drug Administration (FDA) during the study period: melanoma, kidney cancer, lung cancer, and head and neck cancer. Exposures: Oncology practice location (rural vs urban), affiliation type (academic system, nonacademic system, independent), and size (1 to 5 physicians vs 6 or more physicians). Main Outcomes and Measures: The primary outcome was whether a practice adopted immunotherapy. Adoption rates for each practice type were estimated using multivariate linear models that adjusted for patient characteristics (age, sex, race and ethnicity, cancer type, Charlson Comorbidity Index, and median household income). Results: Data included 71 659 episodes at 1732 oncology practices. Of these, 264 practices (15%) were rural, 900 (52%) were independent, and 492 (28%) had 1 to 5 physicians. Most practices adopted immunotherapy within 2 years of FDA approval, but there was substantial variation in adoption rates across practice types. After FDA approval, adoption of immunotherapy was 11 (95% CI, -16 to -6) percentage points lower at rural practices than urban practices and 27 (95% CI, -32 to -22) percentage points lower at practices with 1 to 5 physicians than practices with 6 or more physicians. Adoption rates were similar at independent practices and nonacademic systems; however, both practice types had lower adoption than academic systems (independent practice difference, -6 [95% CI, -9 to -3] percentage points; nonacademic systems difference, -9 [95% CI, -11 to -6] percentage points). Conclusions and Relevance: In this cohort study of Medicare claims, practice characteristics, especially practice size and rural location, were associated with adoption of immunotherapy. These findings suggest that there may be geographic disparities in access to important innovations for treating patients with cancer.

Uncertainty in lung cancer stage for survival estimation via set-valued classification.

The difficulty in identifying cancer stage in health care claims data has limited oncology quality of care and health outcomes research. We fit prediction algorithms for classifying lung cancer stage into three classes (stages I/II, stage III, and stage IV) using claims data, and then demonstrate a method for incorporating the classification uncertainty in survival estimation. Leveraging set-valued classification and split conformal inference, we show how a fixed algorithm developed in one cohort of data may be deployed in another, while rigorously accounting for uncertainty from the initial classification step. We demonstrate this process using SEER cancer registry data linked with Medicare claims data.

Measuring Racial Inequities In The Quality Of Care Across Oncology Practices In The US.

Racial inequities in clinical performance diminish overall health care system performance; however, quality assessments have rarely incorporated reliable measures of racial inequities. We studied care for more than one million Medicare fee-for-service beneficiaries with cancer to assess the feasibility of calculating reliable practice-level measures of racial inequities in chemotherapy-associated emergency department (ED) visits and hospitalizations. Specifically, we used hierarchical models to estimate adjusted practice-level Black-White differences in these events and described differences across practices. We calculated reliable inequity measures for 426 and 322 practices, depending on the measure. These practices reflected fewer than 10 percent of practices treating Medicare beneficiaries with chemotherapy, but they treated approximately half of all White and Black Medicare beneficiaries receiving chemotherapy and two-thirds of Black Medicare beneficiaries receiving chemotherapy. Black patients experienced chemotherapy-associated ED visits and hospitalizations at higher rates (54.2 percent and 35.8 percent, respectively) than White patients (45.7 percent and 31.9 percent, respectively). The median within-practice Black-White difference was 8.1 percentage points for chemotherapy-associated ED visits and 2.7 percentage points for chemotherapy-associated hospitalizations. Additional research is needed to identify other reliable measures of racial inequities in health care quality, measure care inequities in smaller practices, and assess whether providing practice-level feedback could improve equity.

Impact of the Oncology Care Model on Use of Supportive Care Medications During Cancer Treatment.

PURPOSE: The Oncology Care Model (OCM) is an episode-based alternative payment model for cancer care that seeks to reduce Medicare spending while maintaining care quality. We evaluated the impact of OCM on appropriate use of supportive care medications during cancer treatment. METHODS: We evaluated chemotherapy episodes assigned to OCM (n = 201) and comparison practices (n = 534) using Medicare claims (2013-2019). We assessed denosumab use for beneficiaries with bone metastases from breast, lung, or prostate cancer; prophylactic WBC growth factor use for beneficiaries receiving chemotherapy for breast, lung, or colorectal cancer; and prophylactic use of neurokinin-1 (NK1) antagonists and long-acting serotonin antagonists for beneficiaries receiving chemotherapy for any cancer type. Analyses used a difference-in-difference approach. RESULTS: After its launch in 2016, OCM led to a relative reduction in the use of denosumab for beneficiaries with bone metastases receiving bone-modifying medications (eg, 5.0 percentage point relative reduction in breast cancer episodes [90% CI, -7.1 to -2.8]). There was no OCM impact on use of prophylactic WBC growth factors during chemotherapy with high or low risk for febrile neutropenia. Among beneficiaries receiving chemotherapy with intermediate febrile neutropenia risk, OCM led to a 7.6 percentage point reduction in the use of prophylactic WBC growth factors during breast cancer episodes (90% CI, -12.6 to -2.7); there was no OCM impact in lung or colorectal cancer episodes. Among beneficiaries receiving chemotherapy with high or moderate emetic risk, OCM led to reductions in the prophylactic use of NK1 antagonists and long-acting serotonin antagonists (eg, 6.0 percentage point reduction in the use of NK1 antagonists during high emetic risk chemotherapy [90% CI, -9.0 to -3.1]). CONCLUSION: OCM led to the reduced use of some high-cost supportive care medications, suggesting more value-conscious care.

Association of the Oncology Care Model With Value-Based Changes in Use of Radiation Therapy.

PURPOSE: Radiation utilization for breast cancer and metastatic bone disease varies in modality, fractionation, and cost, despite evidence demonstrating equal effectiveness and consensus recommendations such as Choosing Wisely that advocate for higher value care. We assessed whether the Oncology Care Model (OCM), an alternative payment model for practices providing chemotherapy to patients with cancer, affected the overall use and value of radiation therapy in terms of Choosing Wisely recommendations. METHODS AND MATERIALS: We used Centers for Medicare & Medicaid Services administrative data to identify beneficiaries enrolled in traditional fee-for-service Medicare who initiated chemotherapy episodes at OCM and propensity-matched comparison practices. Difference-in-difference (DID) analyses evaluated the effect of OCM on overall use of postoperative radiation for breast cancer, use of intensity modulated radiation therapy and hypofractionation for breast cancer, and fractionation patterns for treatment of metastatic bone disease from breast or prostate cancer. We performed additional analyses stratified by the presence or absence of a radiation oncologist in the practice. RESULTS: Among 27,859 postoperative breast cancer episodes, OCM had no effect on overall use of radiation therapy after breast surgery (DID percentage point difference = 0.4%; 90% confidence interval [CI], -1.7%, 2.4%) or on use of intensity modulated radiation therapy in this setting (DID = -0.6; 90% CI, -3.1, 2.0). Among 19,366 metastatic bone disease episodes, OCM had no effect on fractionation patterns for palliation of bone metastases (DID for ≤10 fractions = -1.1%; 90% CI, -2.6%, 0.4% and DID for single fraction = -0.2%; 90% CI, -1.9%, 1.6%). Results were similar for practices with and without a radiation oncologist. We did not evaluate the effect of OCM on hypofractionated radiation after breast-conserving surgery owing to evidence of differential baseline trends. CONCLUSIONS: OCM had no effect on use of radiation therapy after breast-conserving surgery for breast cancer or on fractionation patterns for metastatic bone disease. Future payment models directly focused on radiation oncology providers may be better poised to improve the value of radiation oncology care.

The Oncology Care Model and Adherence to Oral Cancer Drugs: A Difference-in-Differences Analysis.

BACKGROUND: Adherence to oral cancer drugs is suboptimal. The Oncology Care Model (OCM) offers oncology practices financial incentives to improve the value of cancer care. We assessed the impact of OCM on adherence to oral cancer therapy for chronic myelogenous leukemia (CML), prostate cancer, and breast cancer. METHODS: Using 2014-2019 Medicare data, we studied chemotherapy episodes for Medicare fee-for-service beneficiaries prescribed tyrosine kinase inhibitors (TKIs) for CML, antiandrogens (ie, enzalutamide, abiraterone) for prostate cancer, or hormonal therapies for breast cancer in OCM-participating and propensity-matched comparison practices. We measured adherence as the proportion of days covered and used difference-in-difference (DID) models to detect changes in adherence over time, adjusting for patient, practice, and market-level characteristics. RESULTS: There was no overall impact of OCM on improved adherence to TKIs for CML (DID = -0.3%, 90% confidence interval [CI] = -1.2% to 0.6%), antiandrogens for prostate cancer (DID = 0.4%, 90% CI = -0.3% to 1.2%), or hormonal therapy for breast cancer (DID = 0.0%, 90% CI = -0.2% to 0.2%). Among episodes for Black beneficiaries in OCM practices, for whom adherence was lower than for White beneficiaries at baseline, we observed small improvements in adherence to high cost TKIs (DID = 3.0%, 90% CI = 0.2% to 5.8%) and antiandrogens (DID = 2.2%, 90% CI = 0.2% to 4.3%). CONCLUSIONS: OCM did not impact adherence to oral cancer therapies for Medicare beneficiaries with CML, prostate cancer, or breast cancer overall but modestly improved adherence to high-cost TKIs and antiandrogens for Black beneficiaries, who had somewhat lower adherence than White beneficiaries at baseline. Patient navigation and financial counseling are potential mechanisms for improvement among Black beneficiaries.

Association of Participation in the Oncology Care Model With Medicare Payments, Utilization, Care Delivery, and Quality Outcomes.

IMPORTANCE: In 2016, the US Centers for Medicare & Medicaid Services initiated the Oncology Care Model (OCM), an alternative payment model designed to improve the value of care delivered to Medicare beneficiaries with cancer. OBJECTIVE: To assess the association of the OCM with changes in Medicare spending, utilization, quality, and patient experience during the OCM's first 3 years. DESIGN, SETTING, AND PARTICIPANTS: Exploratory difference-in-differences study comparing care during 6-month chemotherapy episodes in OCM participating practices and propensity-matched comparison practices initiated before (January 2014 through June 2015) and after (July 2016 through December 2018) the start of the OCM. Participants included Medicare fee-for-service beneficiaries with cancer treated at these practices through June 2019. EXPOSURES: OCM participation. MAIN OUTCOMES AND MEASURES: Total episode payments (Medicare spending for Parts A, B, and D, not including monthly payments for enhanced oncology services); utilization and payments for hospitalizations, emergency department (ED) visits, office visits, chemotherapy, supportive care, and imaging; quality (chemotherapy-associated hospitalizations and ED visits, timely chemotherapy, end-of-life care, and survival); and patient experiences. RESULTS: Among Medicare fee-for-service beneficiaries with cancer undergoing chemotherapy, 483 319 beneficiaries (mean age, 73.0 [SD, 8.7] years; 60.1% women; 987 332 episodes) were treated at 201 OCM participating practices, and 557 354 beneficiaries (mean age, 72.9 [SD, 9.0] years; 57.4% women; 1 122 597 episodes) were treated at 534 comparison practices. From the baseline period, total episode payments increased from $28 681 for OCM episodes and $28 421 for comparison episodes to $33 211 for OCM episodes and $33 249 for comparison episodes during the intervention period (difference in differences, -$297; 90% CI, -$504 to -$91), less than the mean $704 Monthly Enhanced Oncology Services payments. Relative decreases in total episode payments were primarily for Part B nonchemotherapy drug payments (difference in differences, -$145; 90% CI, -$218 to -$72), especially supportive care drugs (difference in differences, -$150; 90% CI, -$216 to -$84). The OCM was associated with statistically significant relative reductions in total episode payments among higher-risk episodes (difference in differences, -$503; 90% CI, -$802 to -$204) and statistically significant relative increases in total episode payments among lower-risk episodes (difference in differences, $151; 90% CI, $39-$264). The OCM was not significantly associated with differences in hospitalizations, ED visits, or survival. Of 22 measures of utilization, 10 measures of quality, and 7 measures of care experiences, only 5 were significantly different. CONCLUSIONS AND RELEVANCE: In this exploratory analysis, the OCM was significantly associated with modest payment reductions during 6-month episodes for Medicare beneficiaries receiving chemotherapy for cancer in the first 3 years of the OCM that did not offset the monthly payments for enhanced oncology services. There were no statistically significant differences for most utilization, quality, and patient experience outcomes.

US Trends in Opioid Access Among Patients With Poor Prognosis Cancer Near the End-of-Life.

PURPOSE: Heightened regulations have decreased opioid prescribing across the United States, yet little is known about trends in opioid access among patients dying of cancer. METHODS: Among 270,632 Medicare fee-for-service decedents with poor prognosis cancers, we used part D data to examine trends from 2007 to 2017 in opioid prescription fills and opioid potency (morphine milligram equivalents per day [MMED]) near the end-of-life (EOL), defined as the 30 days before death or hospice enrollment. We used administrative claims to evaluate trends in pain-related emergency department (ED) visits near EOL. RESULTS: Between 2007 and 2017, the proportion of decedents with poor prognosis cancers receiving ≥ 1 opioid prescription near EOL declined 15.5% (relative percent difference [RPD]), from 42.0% (95% CI, 41.4 to 42.7) to 35.5% (95% CI, 34.9 to 36.0) and the proportion receiving ≥ 1 long-acting opioid prescription declined 36.5% (RPD), from 18.1% (95% CI, 17.6 to 18.6) to 11.5% (95% CI, 11.1 to 11.9). Among decedents receiving opioids near EOL, the mean daily dose fell 24.5%, from 85.6 MMED (95% CI, 82.9 to 88.3) to 64.6 (95% CI, 62.7 to 66.6) MMED. Overall, the total amount of opioids prescribed per decedent near EOL (averaged across those who did and did not receive an opioid) fell 38.0%, from 1,075 morphine milligram equivalents per decedent (95% CI, 1,042 to 1,109) to 666 morphine milligram equivalents per decedent (95% CI, 646 to 686). Simultaneously, the proportion of patients with pain-related ED visits increased 50.8% (RPD), from 13.2% (95% CI, 12.7 to 13.6) to 19.9% (95% CI, 19.4 to 20.4). Sensitivity analyses demonstrated similar declines in opioid utilization in the 60 and 90 days before death or hospice, and suggested that trends in opioid access were not confounded by secular trends in hospice utilization. CONCLUSION: Opioid use among patients dying of cancer has declined substantially from 2007 to 2017. Rising pain-related ED visits suggests that EOL cancer pain management may be worsening.

Evaluation of Reliability and Correlations of Quality Measures in Cancer Care.

Importance: Measurement of the quality of care is important for alternative payment models in oncology, yet the ability to distinguish high-quality from low-quality care across oncology practices remains uncertain. Objective: To assess the reliability of cancer care quality measures across oncology practices using registry and claims-based measures of process, utilization, end-of-life (EOL) care, and survival, and to assess the correlations of practice-level performance across measure and cancer types. Design, Setting, and Participants: This cross-sectional study used the Surveillance, Epidemiology, and End Results (SEER) Program registry linked to Medicare administrative data to identify individuals with lung cancer, breast cancer, or colorectal cancer (CRC) that was newly diagnosed between January 1, 2011, and December 31, 2015, and who were treated in oncology practices with 20 or more patients. Data were analyzed from January 2018 to December 2020. Main Outcomes and Measures: Receipt of guideline-recommended treatment and surveillance, hospitalizations or emergency department visits during 6-month chemotherapy episodes, care intensity in the last month of life, and 12-month survival were measured. Summary measures for each domain in each cohort were calculated. Practice-level rates for each measure were estimated from hierarchical linear models with practice-level random effects; practice-level reliability (reproducibility) for each measure based on the between-measure variance, within-measure variance, and distribution of patients treated in each practice; and correlations of measures across measure and cancer types. Results: In this study of SEER registry data linked to Medicare administrative data from 49 715 patients with lung cancer treated in 502 oncology practices, 21 692 with CRC treated in 347 practices, and 52 901 with breast cancer treated in 492 practices, few practices had 20 or more patients who were eligible for most process measures during the 5-year study period. Patients were 65 years or older; approximately 50% of the patients with lung cancer and CRC and all of the patients with breast cancer were women. Most measures had limited variability across practices. Among process measures, 0 of 6 for lung cancer, 0 of 6 for CRC, and 3 of 11 for breast cancer had a practice-level reliability of 0.75 or higher for the median-sized practice. No utilization, EOL care, or survival measure had reliability across practices of 0.75 or higher. Correlations across measure types were low (r ≤ 0.20 for all) except for a correlation between the CRC process and 1-year survival summary measures (r = 0.35; P < .001). Summary process measures had limited or no correlation across lung cancer, breast cancer, and CRC (r ≤ 0.16 for all). Conclusions and Relevance: This study found that quality measures were limited by the small numbers of Medicare patients with newly diagnosed cancer treated in oncology practices, even after pooling 5 years of data. Measures had low reliability and had limited to no correlation across measure and cancer types, suggesting the need for research to identify reliable quality measures for practice-level quality assessments.

How Do Claims-Based Measures of End-of-Life Care Compare to Family Ratings of Care Quality?

OBJECTIVES: Assess whether frequently-used claims-based end-of-life (EOL) measures are associated with higher ratings of care quality. DESIGN: Retrospective cohort study. SETTING/PARTICIPANTS: Deceased fee-for-service Medicare beneficiaries with cancer who underwent chemotherapy during July 2016 to January 2017 and died within 12 months and their caregiver respondents to an after-death survey (n = 2,559). MEASUREMENTS: We examined claims-based measures of EOL care: chemotherapy 14 days or more before death; inpatient admissions, intensive care unit (ICU) use, and emergency department (ED) visits 30 days or more before death; hospice election and the timing of election before death. Primary outcomes are family ratings of "excellent" care in the last month of life and reports that hospice care began "at the right time." Associations were assessed with logistic regression, adjusted by patient characteristics. RESULTS: Family rated EOL care as excellent less often, if within 30 days before death the cancer patient had inpatient admissions (1 hospitalization = 41.5% vs 51.5% none, adjusted difference -10.1 percentage points), ICU use (38.6% for any ICU use vs 47.4% none; adjusted difference -8.8 percentage points), ED visits (41.0% 1 visit vs 51.6% no visits; adjusted difference -10.6 percentage points), or elected hospice within 7 days before death. Among hospice enrollees, family more often reported that hospice began at the right time if it started at least 7 days before death (hospice 1-2 days before death 60.2% vs hospice 7-13 days 74.9%; adjusted difference +14.7 percentage points). CONCLUSIONS: Claims-based measures of EOL care for cancer patients that reflect avoidance of hospital-based care and earlier hospice enrollment are associated with higher ratings of care quality by bereaved family members.

Physician and facility drivers of spending variation in locoregional prostate cancer.

BACKGROUND: Prostate cancer is the most common male cancer, with a wide range of treatment options. Payment reform to reduce unnecessary spending variation is an important strategy for reducing waste, but its magnitude and drivers within prostate cancer are unknown. METHODS: In total, 38,971 men aged ≥66 years with localized prostate cancer who were enrolled in Medicare fee-for-service and were included in the Surveillance, Epidemiology, and End Results-Medicare database from 2009 to 2014 were included. Multilevel linear regression with physician and facility random effects was used to examine the contributions of urologists, radiation oncologists, and their affiliated facilities to variation in total patient spending in the year after diagnosis within geographic region. The authors assessed whether spending variation was driven by patient characteristics, disease risk, or treatments. Physicians and facilities were sorted into quintiles of adjusted patient-level spending, and differences between those that were high-spending and low-spending were examined. RESULTS: Substantial variation in spending was driven by physician and facility factors. Differences in cancer treatment modalities drove more variation across physicians than differences in patient and disease characteristics (72% vs 2% for urologists, 20% vs 18% for radiation oncologists). The highest spending physicians spent 46% more than the lowest and had more imaging tests, inpatient care, and radiotherapy spending. There were no differences across spending quintiles in the use of robotic surgery by urologists or the use of brachytherapy by radiation oncologists. CONCLUSIONS: Significant differences were observed for patients with similar demographics and disease characteristics. This variation across both physicians and facilities suggests that efforts to reduce unnecessary spending must address decision making at both levels.

Multimodality cancer care and implications for episode-based payments in cancer.

Most patients receiving multimodality cancer care receive care from different practices. Therefore, episode-based payments in oncology must hold multiple providers accountable for costs and quality.

Early Findings From the Oncology Care Model Evaluation.

PURPOSE: The Oncology Care Model (OCM) is an alternative payment model administered by the Centers for Medicare & Medicaid Services (CMS) that is structured around 6-month chemotherapy treatment episodes. This report describes the CMS-sponsored OCM evaluation and summarizes early evaluation findings. METHODS: The OCM evaluation examines health care spending and use, quality of care, and patient experience during chemotherapy treatment episodes. Because OCM participation is voluntary, the evaluation compares participating physician practices with a propensity-matched group of nonparticipating practices by using a difference-in-differences approach. This report examines 6-month episodes initiated during the first OCM performance period (July 1, 2016, through January 1, 2017). RESULTS: During the first OCM performance period, there was no statistically significant impact of OCM on total episode payments. There were small declines in intensive care unit (ICU) admissions (7 per 1,000 episodes) and emergency department visits (15 per 1,000 episodes); there was no statistically significant impact on hospitalizations or 30-day readmissions. Analyses of care quality and end-of-life care showed statistically significant impacts of OCM on the proportion of patients with inpatient hospitalizations in the last 30 days of life (1.5% absolute decrease) and ICU admissions in the last 30 days of life (2.1% decrease). There was no significant OCM impact on measures of hospice use. CONCLUSION: Early findings from the OCM evaluation demonstrate modest program-related impacts on some acute care services and no change in total episode payments. Early findings may not reflect practice redesign efforts that were phased in after the beginning of OCM.