How Should Medicare Pay for Artificial Intelligence?

This Viewpoint examines artificial intelligence­enabled clinical services, existing payment structures, and the economics of artificial intelligence pricing.

Facility Fees for Colonoscopy Procedures at Hospitals and Ambulatory Surgery Centers.

This cross-sectional study investigates commercial facility fee differences for colonoscopy procedures between US hospitals and ambulatory surgery centers located within the same county and contracting with the same insurers.

Prices for Common Services at Quaternary vs Nonquaternary Hospitals.

This study uses commercial claims data to assess whether quaternary hospitals charge higher prices for common, unspecialized services also offered by nonquaternary hospitals.

Role of prices in driving the variation in spending across medical groups.

OBJECTIVE: To understand the relative role of prices versus utilization in the variation in total spending per patient across medical groups. DATA SOURCES: We conducted a cross-sectional analysis of medical claims for commercially insured adults from a large national insurer in 2018. STUDY DESIGN: After assigning patients to a medical group based on primary care visits in 2018, we calculated total medical spending for each patient in that year. Total spending included care provided by clinicians within the medical group and care provided by other providers, including hospitals. It did not include drug spending. We estimated the case mix adjusted spending per patient for each medical group. Within each market, we categorized medical groups into quartiles based on the group's spending per patient. To decompose spending variation into price versus utilization, we compared spending differences between highest and lowest quartile medical groups under two scenarios: (1) using actual prices (2) using a standardized price (same price used for a given service across the nation). PRINCIPAL FINDINGS: In total, 3,921,736 patients were assigned to 7284 medical groups. Per-patient spending in the highest quartile of spending medical groups was $1813 higher than per-patient spending in the lowest spending quartile of medical groups (50% higher relative spending). This overall difference was primarily driven by differences in inpatient care, imaging, and specialty care. In the scenario where we used standardized prices, the difference in spending between medical groups in the top and bottom quartiles decreased to $1425, implying that 79% of the $1813 difference in spending between the top and bottom quartile groups is explained by utilization and the remaining 21% by prices. The likely explanation for the modest impact of prices is that patients cared for by a given medical group receive care across a wide range of providers. CONCLUSIONS: Prices explained a modest fraction of the differences in spending between medical groups.

Benchmarking Changes And Selective Participation In The Medicare Shared Savings Program.

In 2017 the Medicare Shared Savings Program (MSSP) began incorporating regional spending into accountable care organization (ACO) benchmarks, thus favoring the participation of ACOs and practices with lower baseline spending than their region. To characterize providers' responses to these incentives, we isolated changes in spending due to changes in the mix of ACOs and practices participating in the MSSP. In contrast to earlier participation patterns, the composition of the MSSP after 2017 increasingly shifted to providers with lower preexisting levels of spending relative to their region, consistent with a selection response. Changes occurred through the entry of new ACOs with lower baseline spending, the exit of higher-spending ACOs, and the reconfiguration of participant lists favoring lower-spending practices within continuing ACOs. These participation patterns varied meaningfully by ACO type. Although compositional changes could not be definitively tied to benchmarking changes, the disproportionate participation of providers with lower baseline spending implies substantial costs and the need for ACO benchmarking reforms.

Hospital Survival In Rural Markets: Closures, Mergers, And Profitability.

Financial distress among rural hospitals in the US has increased in recent years. Using national hospital data, we investigated how the decline in profitability has affected hospital survival, either independently or with a merger. The answer has direct implications for access to care and competition in rural markets. We assessed the rate of hospital closures and mergers in predominantly rural markets during the period 2010-18, focusing on hospitals that were unprofitable at baseline. A minority of unprofitable hospitals (7 percent) closed. A larger share (17 percent) merged, most commonly with organizations from outside of their local geographic market. Most unprofitable hospitals (77 percent) continued to operate through 2018 without closure or merger. About half of these hospitals returned to profitability. At the market level, 22 percent of markets served by unprofitable hospitals lost a competitor to closure or within-market merger. Out-of-market mergers affected 33 percent of markets with an unprofitable hospital. Overall, our results suggest that rural markets are experiencing meaningful rates of hospital closures and mergers, yet many hospitals have survived despite poor financial performance. Policies targeting access to care will continue to be important. Similar attention will be needed to address the competitive effects of hospital closures and mergers on prices and quality.

Reducing Medicare Advantage Benchmarks Will Decrease Plan Generosity, But Those Effects Will Likely Be Modest.

Concerns that Medicare Advantage (MA) plans are overpaid have motivated calls to reduce MA benchmarks-the dollar amounts set by the Centers for Medicare and Medicaid Services (CMS) against which MA plans bid to set premiums and fund extra benefits. However, cutting benchmarks may lead to higher MA enrollee premiums and decreased plan generosity. We assessed the relationships between MA benchmarks and plan generosity and benefits. We estimated that a $1,000 per year decrease in benchmarks would lead to small increases in annual premiums of about $60 and increases in annual deductibles of about $27. Copays would also increase modestly, and the propensity to offer benefits would generally decline by less than 5 percentage points, with the greatest impact being on the availability of dental, hearing, and vision benefits. These results suggest that although cuts to MA benchmarks would adversely affect plan generosity, those effects would be modest.

Association of Evaluation and Management Payment Policy Changes With Medicare Payment to Physicians by Specialty.

Importance: US primary care physicians (PCPs) have lower mean incomes than specialists, likely contributing to workforce shortages. In 2021, the Centers for Medicare & Medicaid Services increased payment for evaluation and management (E/M) services and relaxed documentation requirements. These changes may have reduced the gap between primary care and specialist payment. Objectives: To simulate the effect of the E/M payment policy change on total Medicare physician payments while holding volume constant and to compare these simulated changes with observed changes in total Medicare payments and E/M coding intensity, before (July-December 2020) and after (July-December 2021) the E/M payment policy change. Design, Setting, and Participants: Retrospective observational study of US office-based physicians who were in specialties with 5000 or more physicians billing Medicare and who had 50 or more fee-for-service Medicare visits before and after the E/M payment policy change. Exposures: E/M payment policy changes. Main Outcomes and Measures: Outcomes included physician-level simulated volume-constant payment change, total observed Medicare payment change, and share of high-intensity (ie, level 4 or 5) E/M visits before and after the E/M payment policy change. For each specialty, the median change in each outcome was reported. The payment gap between primary care and specialty physicians was calculated as the difference between total Medicare payments to the median primary care and median specialty physician. Results: The study population included 180 624 physicians. Repricing 2020 services yielded a simulated volume-constant payment change ranging from a 3.3% (-$4557.0) decrease for the median radiologist to an 11.0% ($3683.1) increase for the median family practice physician. After the E/M payment change, the median high-intensity share of E/M visits increased for physicians of nearly all specialties, ranging from a -4.4 percentage point increase (dermatology) to a 17.8 percentage point increase (psychiatry). The median change in total Medicare payments by specialty ranged from -4.2% (-$1782.9) for general surgery to 12.1% ($3746.9) for family practice. From July-December 2020 to July-December 2021, the payment gap between the median primary care physician and the median specialist shrank by $825.1, from $40 259.8 to $39 434.7 (primary care, $41 193.3 in July-December 2020 and $45 962.4 in July-December 2021; specialist, $81 453.1 in July-December 2020 and $85 397.1 in July-December 2021)-a relative decrease of 2.0%. Conclusions and Relevance: Among US office-based physicians receiving Medicare payments in 2020 and 2021, E/M payment policy changes were associated with changes in Medicare payment by specialty, although the payment gap between primary care physicians and specialists decreased only modestly. The findings may have been influenced by the COVID-19 pandemic, and further research in subsequent years is needed.

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.

Changes in use of low-value services during the COVID-19 pandemic.

The COVID-19 pandemic led to a significant disruption, then recovery, of health care services use. Prior research has not examined the relative rates of resumption of high-value and low-value care. We examined the use of 6 common low-value services that received a D grade from the US Preventive Services Task Force compared with clinically comparable high-value services in a large commercially insured population nationwide from before the pandemic to April 1, 2021. We found that, overall, low-value services and high-value services were disrupted similarly. In aggregate, low-value care declined to 56.2% and high-value care to 53.2% in the initial month of the pandemic (April 2020) relative to baseline (number of visits in 2019 normalized by relevant enrolled population), then rebounded to 83.1% of baseline for low-value services and 95.0% of baseline for high-value services by January 2021. Substantial heterogeneity appeared across clinical contexts, such as prostate cancer screening for men 70 years and older rebounding to 111.8% of baseline and asymptomatic chronic obstructive pulmonary disease screening remaining at 38.5% of baseline in January 2021. This suggests that although, on average, resuming lower-value services may have been perceived to be a lesser priority by providers and patients, the pandemic may have had heterogeneous effects on consumer and provider decision-making along the dimension of clinical value. This enhances our understanding of how disruptions affect the relationship between clinical value and usage of different services and suggests the need for more targeted interventions to reduce low-value care.

How Emerging Telehealth Models Challenge Policymaking.

Policy Points Current telehealth policy discussions are focused on synchronous video and audio telehealth visits delivered by traditional providers and have neglected the growing number of alternative telehealth offerings. These alternative telehealth offerings range from simply supporting traditional brick-and-mortar providers to telehealth-only companies that directly compete with them. We describe policy challenges across this range of alternative telehealth offerings in terms of using the appropriate payment model, determining the payment amount, and ensuring the quality of care.

The merits of administrative benchmarks for population-based payment programs.

The different approaches to setting benchmarks for population-based payment models (empirical, bidding based, and administratively set) have unique advantages and challenges.

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.

Regulating Hospital Prices Based On Market Concentration Is Likely To Leave High-Price Hospitals Unaffected.

Concern about high hospital prices for commercially insured patients has motivated several proposals to regulate these prices. Such proposals often limit regulations to highly concentrated hospital markets. Using a large sample of 2017 US commercial insurance claims, we demonstrate that under the market definition commonly used in these proposals, most high-price hospitals are in markets that would be deemed competitive or "moderately concentrated," using antitrust guidelines. Limiting policy actions to concentrated hospital markets, particularly when those markets are defined broadly, would likely result in poor targeting of high-price hospitals. Policies that target the undesired outcome of high price directly, whether as a trigger or as a screen for action, are likely to be more effective than those that limit action based on market concentration.

Public Payment Rates For Hospitals And The Potential For Consolidation-Induced Cost Shifting.

The theory of hospital cost shifting posits that reductions in public prices lead to higher commercial prices. The cost-shifting narrative and the empirical strategies used to evaluate it typically assume no connection between public prices and the number of hospitals operating in the market (market structure). We raise the possibility of "consolidation-induced cost shifting," which recognizes that changes in public prices for hospital care can affect market structure and, through that mechanism, affect commercial prices. We investigated the first leg of that argument: that public payment may affect hospital market structure. After controlling for many confounders, we found that hospitals with a higher share of Medicare patients had lower and more rapidly declining profits and an increased likelihood of closure or acquisition compared with hospitals that were less reliant on Medicare. This is consistent with the existence of consolidation-induced cost shifting and implies that reductions in public prices must be undertaken cautiously. Mechanisms to limit closure- or acquisition-induced increases in commercial hospital prices may be important.

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.

Variation in spending associated with primary care practices.

OBJECTIVES: To measure variation in spending and inpatient prices associated with the primary care physician (PCP) practice to which patients are attributed. STUDY DESIGN: Cross-sectional analysis of claims data. METHODS: We used random effect models to estimate case mix-adjusted spending across large PCP practices within 3-digit zip codes. We compare inpatient prices for patients in high-spending practices with those in low-spending practices. RESULTS: The physician practice to which a patient was attributed is associated with significant differences in spending after controlling for patient comorbidities and geography. Patients attributed to practices in the top quartile of total medical expenses have about 30% higher spending than patients attributed to practices in the bottom quartile of adjusted spending in their 3-digit zip code. If patients attributed to practices in the top 2 quartiles had spending equivalent to those in the median practice, total spending would drop by 8%. Price variation accounts for a meaningful amount of the variation, with inpatient prices 17% higher in top-quartile vs bottom-quartile practices. We cannot disaggregate the large variation in utilization into practice patterns and unmeasured case mix (including unmeasured differences in patients' socioeconomic status) vs random health shocks, but correlation in spending patterns across years suggests that some persistent differences in spending patterns exist. CONCLUSIONS: There are meaningful opportunities to reduce spending by changing patient PCP selection, encouraging patients to use lower-priced specialists and hospitals, and eliminating wasteful care. Attention must be paid to the best ways to reap these savings.