The Impact Of Telemedicine On Medicare Utilization, Spending, And Quality, 2019-22.

Telemedicine use remains substantially higher than it was before the COVID-19 pandemic, although it has fallen from pandemic highs. To inform the ongoing debate about whether to continue payment for telemedicine visits, we estimated the association of greater telemedicine use across health systems with utilization, spending, and quality. In 2020, Medicare patients receiving care at health systems in the highest quartile of telemedicine use had 2.5 telemedicine visits per person (26.8 percent of visits) compared with 0.7 telemedicine visits per person (9.5 percent of visits) in the lowest quartile of telemedicine use. In 2021-22, relative to those in the lowest quartile, Medicare patients of health systems in the highest quartile had an increase of 0.21 total outpatient visits (telemedicine and in-person) per patient per year (2.2 percent relative increase), a decrease of 14.4 annual non-COVID-19 emergency department visits per 1,000 patients per year (2.7 percent relative decrease), a $248 increase in per patient per year spending (1.6 percent relative increase), and increased adherence for metformin and statins. There were no clear differential changes in hospitalizations or receipt of preventive care.

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.

Changing Health Care Markets Have the Potential to Undermine Stroke Systems of Care.

This Viewpoint discusses the importance of prioritizing quality of care for patients with stroke in a changing health care environment.

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.

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.

Increases in Inpatient Psychiatry Beds Operated by Systems, For-Profits, and Chains, 2010-2016.

OBJECTIVE: This study explored trends in the quantity of inpatient psychiatry beds and in facility characteristics. METHODS: Using the National Bureau of Economic Research's Health Systems and Provider Database, the authors examined changes in the number of psychiatric facilities and beds, focusing on system ownership, profit status, facility type (general acute care versus freestanding), and affiliation with psychiatric hospital chains from 2010 to 2016. RESULTS: The number of psychiatric beds was relatively unchanged from 2010 (N=112,182 beds) to 2016 (N=111,184). However, the number of beds operated by systems increased by 39.8% (N=15,803); for-profits, by 56.9% (N=8,572); and chains, by 16.7% (N=6,256). Net increases in beds were primarily concentrated in for-profit freestanding psychiatric hospitals. In 2016, most for-profit beds were part of chains (70.2%) and systems (61.3%). CONCLUSIONS: Inpatient psychiatry has shifted toward increased ownership by systems, for-profits, and chains. Payers and policy makers should safeguard against profiteering, and future research should investigate the implications of these trends on quality of care.

Primary care quality and cost for privately insured patients in and out of US Health Systems: Evidence from four states.

OBJECTIVE: To characterize physician health system membership in four states between 2012 and 2016 and to compare primary care quality and cost between in-system providers and non-system providers for the commercially insured population. DATA SOURCES: Physician membership in health systems was obtained from a unique longitudinal database on health systems and matched at the provider level to 2014 all-payer claims data from Colorado, Massachusetts, Oregon, and Utah. STUDY DESIGN: Using an observational study design, we compared physicians in health systems to non-system physicians located in the same state and geography on average cost of care (risk-adjusted using the Johns Hopkins' Adjusted Clinical Grouper), five HEDIS quality measures, one measure of developmental screening, and two Prevention Quality Indicator Measures. DATA COLLECTION/EXTRACTION METHODS: Patients in commercial health plans were attributed to a primary care physician accounting for the plurality of office visits. A cohort for each quality measure was constructed based on appropriate measure specifications. PRINCIPAL FINDINGS: The share of physicians in health systems increased steadily from 2012 to 2016 and ranged from 48% in Colorado to 63% in Utah in 2016. Compared to physicians not in a system, system physicians performed similarly on most HEDIS quality metrics compared to non-system physicians. Patients attributed to in-system physicians had about 40% higher rates (P < .05) of Ambulatory Care Sensitive Admissions (measured in admissions per 100 000:921.33 in-system vs 674.61 not-in-system for acute composite; 2540.91 in-system vs 1972.17 for chronic composite In-system providers were associated with $29 (P < .05) higher average per member per month costs (453.37 vs 432.93). Overall, differences in performance by system membership were relatively small compared to differences across states and geography. CONCLUSION: A growing share of physicians is part of a health system from 2012 to 2016. Providers in health systems are not delivering primary care more efficiently than non-system providers for the commercially insured.

Changes in Quality of Care after Hospital Mergers and Acquisitions.

BACKGROUND: The hospital industry has consolidated substantially during the past two decades and at an accelerated pace since 2010. Multiple studies have shown that hospital mergers have led to higher prices for commercially insured patients, but research about effects on quality of care is limited. METHODS: Using Medicare claims and Hospital Compare data from 2007 through 2016 on performance on four measures of quality of care (a composite of clinical-process measures, a composite of patient-experience measures, mortality, and the rate of readmission after discharge) and data on hospital mergers and acquisitions occurring from 2009 through 2013, we conducted difference-in-differences analyses comparing changes in the performance of acquired hospitals from the time before acquisition to the time after acquisition with concurrent changes for control hospitals that did not have a change in ownership. RESULTS: The study sample included 246 acquired hospitals and 1986 control hospitals. Being acquired was associated with a modest differential decline in performance on the patient-experience measure (adjusted differential change, -0.17 SD; 95% confidence interval [CI], -0.26 to -0.07; P = 0.002; the change was analogous to a fall from the 50th to the 41st percentile) and no significant differential change in 30-day readmission rates (-0.10 percentage points; 95% CI, -0.53 to 0.34; P = 0.72) or in 30-day mortality (-0.03 percentage points; 95% CI, -0.20 to 0.14; P = 0.72). Acquired hospitals had a significant differential improvement in performance on the clinical-process measure (0.22 SD; 95% CI, 0.05 to 0.38; P = 0.03), but this could not be attributed conclusively to a change in ownership because differential improvement occurred before acquisition. CONCLUSIONS: Hospital acquisition by another hospital or hospital system was associated with modestly worse patient experiences and no significant changes in readmission or mortality rates. Effects on process measures of quality were inconclusive. (Funded by the Agency for Healthcare Research and Quality.).

Concentration of high-cost patients in hospitals and markets.

OBJECTIVES: Although we know that healthcare costs are concentrated among a small number of patients, we know much less about the concentration of these costs among providers or markets. This is important because it could help us to understand why some patients are higher-cost compared with others and enable us to develop interventions to reduce costs for these patients. STUDY DESIGN: Observational study. METHODS: We used a 20% sample of Medicare fee-for-service claims data from 2011 and 2012, and defined high-cost patients as those in the top 10% of standardized costs. We then characterized high-concentration hospitals as those with the highest proportion of high-cost patient claims, and high-concentration markets as the Hospital Referral Regions (HRRs) with the highest proportion of high-cost patients. We compared the characteristics and outcomes of each. RESULTS: High-concentration hospitals had 69% of their inpatient Medicare claims from high-cost Medicare beneficiaries compared with 51% for the remaining 90% of hospitals. These hospitals were more likely to be for-profit and major teaching hospitals, located in urban settings, and have higher readmission rates. High-concentration HRRs had 13% high-cost patients compared with 9.5% for the remaining 90% of HRRs. These HRRs had a smaller supply of total physicians, a greater supply of cardiologists, higher rates of emergency department visits, and significantly higher expenditures on care in the last 6 months of life. CONCLUSIONS: High-cost beneficiaries are only modestly concentrated in specific hospitals and healthcare markets.