Diabetes-Focused Health Care Utilization Among Adolescents and Young Adults With Type 1 Diabetes.

OBJECTIVE: To describe the current rates of health services use with various types of providers among adolescents and young adults (AYA) with type 1 diabetes (T1D) and evaluate which patient factors are associated with rates of service use from different provider types. METHODS: Using 2012-16 claims data from a national commercial insurer, we identified 18,927 person-years of AYA with T1D aged 13 to 26 years and evaluated the frequency at which: 1) AYA skipped diabetes care for a year despite being insured; 2) received care from pediatric or non-pediatric generalists or endocrinologists if care was sought; and 3) received annual hemoglobin A1c (HbA1c) testing as recommended for AYA. We used descriptive statistics and multivariable regression to examine patient, insurance, and physician characteristics associated with utilization and quality outcomes. RESULTS: Between ages 13 and 26, the percentage of AYA with: any diabetes-focused visits declined from 95.3% to 90.3%; the mean annual number of diabetes-focused visits, if any, decreased from 3.5 to 3.0; receipt of ≥2 HbA1c tests annually decreased from 82.3% to 60.6%. Endocrinologists were the majority providers of diabetes care across ages, yet the relative proportion of AYA whose diabetes care was endocrinologist-dominated decreased from 67.3% to 52.7% while diabetes care dominated by primary care providers increased from 19.9% to 38.2%. The strongest predictors of diabetes care utilization were younger age and use of diabetes technology (pumps and continuous glucose monitors). CONCLUSIONS: Several provider types are involved in the care of AYA with T1D, though predominate provider type and care quality changes substantially across age in a commercially-insured population.

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.

Specialist use among privately insured children with disabilities.

OBJECTIVE: To investigate primary care practice ownership and specialist-use patterns for commercially insured children with disabilities. DATA SOURCES AND STUDY SETTING: A national commercial claims database and the Health Systems and Provider Database from 2012 to 2016 are the data sources for this study. STUDY DESIGN: This cross-sectional, descriptive study examines: (1) the most visited type of pediatric primary care physician and practice (independent or system-owned); (2) pediatric and non-pediatric specialist-use patterns; and (3) how practice ownership relates to specialist-use patterns. DATA COLLECTION/EXTRACTION METHODS: This study identifies 133,749 person-years of commercially insured children with disabilities aged 0-18 years with at least 24 months of continuous insurance coverage by linking a national commercial claims data set with the Health Systems and Provider Database and applying the validated Children with Disabilities Algorithm. PRINCIPAL FINDINGS: Three-quarters (75.9%) of children with disabilities received their pediatric primary care in independent practices. Nearly two thirds (59.6%) used at least one specialist with 45.1% using nonpediatric specialists, 28.8% using pediatric ones, and 17.0% using both. Specialist-use patterns varied by both child age and specialist type. Children with disabilities in independent practices were as likely to see a specialist as those in system-owned ones: 57.1% (95% confidence interval [95% CI] 56.7%-57.4%) versus 57.3% (95% CI 56.6%-58.0%), respectively (p = 0.635). The percent using two or more types of specialists was 46.1% (95% CI 45.4%-46.7%) in independent practices, comparable to that in systems 47.1% (95% CI 46.2%-48.0%) (p = 0.054). However, the mean number of specialist visits was significantly lower in independent practices than in systems-4.0 (95% CI 3.9%-4.0%) versus 4.4 (95% CI 4.3%-4.6%) respectively-reaching statistical significance with p < 0.0001. CONCLUSIONS: Recognizing how privately insured children with disabilities use pediatric primary care from pediatric and nonpediatric primary care specialists through both independent and system-owned practices is important for improving care quality and value.

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.

Organization and Performance of US Health Systems.

Importance: Health systems play a central role in the delivery of health care, but relatively little is known about these organizations and their performance. Objective: To (1) identify and describe health systems in the United States; (2) assess differences between physicians and hospitals in and outside of health systems; and (3) compare quality and cost of care delivered by physicians and hospitals in and outside of health systems. Evidence Review: Health systems were defined as groups of commonly owned or managed entities that included at least 1 general acute care hospital, 10 primary care physicians, and 50 total physicians located within a single hospital referral region. They were identified using Centers for Medicare & Medicaid Services administrative data, Internal Revenue Service filings, Medicare and commercial claims, and other data. Health systems were categorized as academic, public, large for-profit, large nonprofit, or other private systems. Quality of preventive care, chronic disease management, patient experience, low-value care, mortality, hospital readmissions, and spending were assessed for Medicare beneficiaries attributed to system and nonsystem physicians. Prices for physician and hospital services and total spending were assessed in 2018 commercial claims data. Outcomes were adjusted for patient characteristics and geographic area. Findings: A total of 580 health systems were identified and varied greatly in size. Systems accounted for 40% of physicians and 84% of general acute care hospital beds and delivered primary care to 41% of traditional Medicare beneficiaries. Academic and large nonprofit systems accounted for a majority of system physicians (80%) and system hospital beds (64%). System hospitals were larger than nonsystem hospitals (67% vs 23% with >100 beds), as were system physician practices (74% vs 12% with >100 physicians). Performance on measures of preventive care, clinical quality, and patient experience was modestly higher for health system physicians and hospitals than for nonsystem physicians and hospitals. Prices paid to health system physicians and hospitals were significantly higher than prices paid to nonsystem physicians and hospitals (12%-26% higher for physician services, 31% for hospital services). Adjusting for practice size attenuated health systems differences on quality measures, but price differences for small and medium practices remained large. Conclusions and Relevance: In 2018, health system physicians and hospitals delivered a large portion of medical services. Performance on clinical quality and patient experience measures was marginally better in systems but spending and prices were substantially higher. This was especially true for small practices. Small quality differentials combined with large price differentials suggests that health systems have not, on average, realized their potential for better care at equal or lower cost.

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.

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.

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.

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 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.

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.

Coding-Driven Changes In Measured Risk In Accountable Care Organizations.

Claims data, which form the foundation of risk adjustment in payment for health care services, may reflect efforts to capture more-or more severe-clinical conditions rather than true changes in health status. This can distort payments. We quantify this in the context of Medicare's accountable care organization (ACO) program by comparing risk scores derived from two different measurement approaches. One approach uses diagnoses coded on claims based on Centers for Medicare and Medicaid Services Hierarchical Condition Categories (HCC), and the other uses self-reported, survey-based health data from the Consumer Assessment of Healthcare Providers and Systems (CAHPS). During 2013-16 HCC-based risk scores grew faster than CAHPS-based risk scores (2.1 percent versus 0.3 percent annually), and the gap in HCC- and CAHPS-based risk score growth varied widely across ACOs. The average gap in risk score growth appears to be the result primarily of HCC coding practices rather than poor performance of the CAHPS model, suggesting that coding practices (not necessarily driven by ACO contracts) may account for most of the observed risk score growth for ACO 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.

Using Consistently Low Performance to Identify Low-Quality Physician Groups.

Importance: There has been a growth in the use of performance-based payment models in the past decade, but inherently noisy and stochastic quality measures complicate the assessment of the quality of physician groups. Examining consistently low performance across multiple measures or multiple years could potentially identify a subset of low-quality physician groups. Objective: To identify low-performing physician groups based on consistently low performance after adjusting for patient characteristics across multiple measures or multiple years for 10 commonly used quality measures for diabetes and cardiovascular disease (CVD). Design, Setting, and Participants: This cross-sectional study used medical and pharmacy claims and laboratory data for enrollees ages 18 to 65 years with diabetes or CVD in an Aetna health insurance plan between 2016 and 2019. Each physician group's risk-adjusted performance for a given year was estimated using mixed-effects linear probability regression models. Performance was correlated across measures and time, and the proportion of physician groups that performed in the bottom quartile was examined across multiple measures or multiple years. Data analysis was conducted between September 2020 and May 2021. Exposures: Primary care physician groups. Main Outcomes and Measures: Performance scores of 6 quality measures for diabetes and 4 for CVD, including hemoglobin A1c (HbA1c) testing, low-density lipoprotein testing, statin use, HbA1c control, low-density lipoprotein control, and hospital-based utilization. Results: A total of 786 641 unique enrollees treated by 890 physician groups were included; 414 655 (52.7%) of the enrollees were men and the mean (SD) age was 53 (9.5) years. After adjusting for age, sex, and clinical and social risk variables, correlations among individual measures were weak (eg, performance-adjusted correlation between any statin use and LDL testing for patients with diabetes, r = -0.10) to moderate (correlation between LDL testing for diabetes and LDL testing for CVD, r = .43), but year-to-year correlations for all measures were moderate to strong. One percent or fewer of physician groups performed in the bottom quartile for all 6 diabetes measures or all 4 cardiovascular disease measures in any given year, while 14 (4.0%) to 39 groups (11.1%) were in the bottom quartile in all 4 years for any given measure other than hospital-based utilization for CVD (1.1%). Conclusions and Relevance: A subset of physician groups that was consistently low performing could be identified by considering performance measures across multiple years. Considering the consistency of group performance could contribute a novel method to identify physician groups most likely to benefit from limited resources.

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.