Citations

AbstractPurposeBy assessing longitudinal associations between COVID‐19 census burdens and hospital characteristics, such as bed size and critical access status, we can explore whether pandemic‐era hospital quality benchmarking requires risk‐adjustment or stratification for hospital‐level characteristics.MethodsWe used hospital‐level data from the US Department of Health and Human Services including weekly total hospital and COVID‐19 censuses from August 2020 to August 2023 and the 2021 American Hospital Association survey. We calculated weekly percentages of total adult hospital beds containing COVID‐19 patients. We then calculated the number of weeks each hospital spent at Extreme (≥20% of beds occupied by COVID‐19 patients), High (10%–19%), Moderate (5%–9%), and Low (<5%) COVID‐19 stress. We assessed longitudinal hospital‐level COVID‐19 stress, stratified by 15 hospital characteristics including joint commission accreditation, bed size, teaching status, critical access hospital status, and core‐based statistical area (CBSA) rurality.FindingsAmong n = 2582 US hospitals, the median(IQR) weekly percentage of hospital capacity occupied by COVID‐19 patients was 6.7%(3.6%–13.0%). 80,268/213,383 (38%) hospital‐weeks experienced Low COVID‐19 census stress, 28% Moderate stress, 22% High stress, and 12% Extreme stress. COVID‐19 census burdens were similar across most hospital characteristics, but were significantly greater for critical access hospitals.ConclusionsUS hospitals experienced similar COVID‐19 census burdens across multiple institutional characteristics. Evidence‐based inclusion of pandemic‐era outcomes in hospital quality reporting may not require significant hospital‐level risk‐adjustment or stratification, with the exception of rural or critical access hospitals, which experienced differentially greater COVID‐19 census burdens and may merit hospital‐level risk‐adjustment considerations.

The availability of large state and federally run administrative health-care databases provides potentially comprehensive population-wide information that can dramatically impact both medical and health-policy decision-making. Specific opportunities and important limitations exist with all administrative databases based on what information is collected and how reliably specific data elements are reported. Access to patient identifiable-level information can be critical for certain long-term outcome studies but can be difficult (although not impossible) due to patient privacy protections, while more easily available de-identified information can provide important insights that may be more than sufficient for some short-term operative or in-hospital outcome questions. The first section of this paper by Sarah K. Meier and Benjamin D. Pollock discusses Medicare and the different data files available to health-care researchers. They describe what is and is not generally available from even the most granular Medicare Standard Analytic Files, and provide an analysis of the strengths and weaknesses of Medicare administrative data as well as the resulting best and inappropriate uses of these data. In the second section, the Nationwide Inpatient Sample and complementary State Inpatient Database programs are reviewed by Steven M. Kurtz and Edmund Lau, with insights into the origins of these programs, the data elements that are recorded relating to the operative procedure and hospital stay, and examples of the types of studies that optimally utilize these data sources. They also detail the limitations of these databases and identify studies that they are not well-suited for, especially those involving linkage or longitudinal studies over time. Both sections provide useful guidance on the best uses and pitfalls related to these important large representative national administrative data sources.

Background: It is standard of care and an accreditation requirement to screen for and address distress and psychosocial needs in patients with cancer. This study assessed the availability of mental health (MH) and chemical dependency (CD) services at US cancer centers. Methods: The 2017–2018 American Hospital Association (AHA) survey, Area Health Resource File, and Centers for Medicare & Medicaid Services Hospital Compare databases were used to assess availability of services and associations with hospital-level and health services area (HSA)–level characteristics. Results: Of 1,144 cancer centers surveyed, 85.4% offered MH services and 45.5% offered CD services; only 44.1% provided both. Factors associated with increased adjusted odds of offering MH services were teaching status (odds ratio [OR], 1.76; 95% CI, 1.18–2.62), being a member of a hospital system (OR, 2.00; 95% CI, 1.31–3.07), and having more beds (OR, 1.04 per 10-bed increase; 95% CI, 1.02–1.05). Higher population estimate (OR, 0.98; 95% CI, 0.97–0.99), higher percentage uninsured (OR, 0.90; 95% CI, 0.86–0.95), and higher Mental Health Professional Shortage Area level in the HSA (OR, 0.99; 95% CI, 0.98–1.00) were associated with decreased odds of offering MH services. Government-run (OR, 2.85; 95% CI, 1.30–6.22) and nonprofit centers (OR, 3.48; 95% CI, 1.78–6.79) showed increased odds of offering CD services compared with for-profit centers. Those that were members of hospital systems (OR, 1.61; 95% CI, 1.14–2.29) and had more beds (OR, 1.02; 95% CI, 1.01–1.03) also showed increased odds of offering these services. A higher percentage of uninsured patients in the HSA (OR, 0.92; 95% CI, 0.88–0.97) was associated with decreased odds of offering CD services. Conclusions: Patients’ ability to pay, membership in a hospital system, and organization size may be drivers of decisions to co-locate services within cancer centers. Larger organizations may be better able to financially support offering these services despite poor reimbursement rates. Innovations in specialty payment models highlight opportunities to drive transformation in delivering MH and CD services for high-need patients with cancer.

Objective: To understand the organization of cardiologists in the United States and territories with an emphasis on identifying the characteristics of practices where cardiologists practice. Data: The Healthcare Organization Services database, which provides information on the designation, specialties and business associations, or affiliations, of healthcare professionals across the US and territories. These data were updated as of September 2013. Methods: Cardiologists were defined as providers with an MD or DO, who have an active clinical practice, and either: (1) have Cardiology as primary specialty, or (2) who have Internal Medicine, Pediatrics or Family Medicine indicated as primary specialty, with Cardiology as secondary specialty. Cardiologists were linked to businesses and descriptive analyses were conducted to characterize the organization of cardiologists. Findings: We identified 33,745 cardiologists; 22,461 of these cardiologists had at least one affiliation with an ambulatory practice or independent physician association (AP/IPA), while 30,567 had at least one hospital affiliation. Approximately 57.6% (19,453 of 33,745) of cardiologists were affiliated with both the AP/IPA and hospital settings. The overwhelming majority of ambulatory practices linked to hospital-AP/IPA affiliated cardiologists are medical groups (7,839 of 9,838), while independent physician practices comprise the next largest category (1,498 of 9,838). Of the 7,839 medical groups, 3,809 are cardiology specialty, 1,316 are multispecialty group practices, and 2,060 are family practice, general practice, geriatric medicine, internal medicine, pediatric medicine or primary care. Among all non-multispecialty medical groups (n=6,523), 59.2% (3,862 of 6,523) have 1-2 affiliated cardiologists, 21.7% (1,413 of 6,523) have 3-5, 12.6% (821 of 6,523) have 6-10 and 6.5% (427 of 6,523) have more than 10 affiliated cardiologists. Focusing at the provider level, 17,639 hospital-AP/IPA cardiologists had any affiliation with a medical group (MG), with 12,840 cardiologists with any cardiology specialty MG affiliation, 3,215 with any multispecialty group practice MG affiliation, and 3,051 with any family practice, general practice, geriatric medicine, internal medicine, pediatric medicine or primary care MG affiliation. Overall, 15,476 hospital-AP/IPA cardiologists had any affiliation with at least one non-multi-specialty group practice (non-MSGP), with 4,279 affiliated with one or more non-MSGP(s) with 1-2 cardiologists, 4,378 with one or more non-MSGP(s) with 3-5 cardiologists, and 4,861 and 5,099 affiliated with one or more non-MSGP(s) with 6-10 and >10 cardiologists, respectively. Conclusion: The majority of cardiologists were affiliated with both a hospital and ambulatory practice or IPA. Of this subset, most cardiologists were affiliated with at least one group practice.

Elderly seasonal migrators share time between homes in different states, presenting challenges for care coordination and patient attribution methods. Medicare has prioritized alternative payment models, putting health care providers at risk for quality and value of services delivered to their attributed patients, regardless of the location of care. Little research is available to guide providers and payers on the service use of seasonal migrators. The authors use claims data on fee-for-service (FFS) Medicare beneficiaries' locations throughout the year to (1) identify seasonal migrators and (2) describe the care they receive in each seasonal home, focusing on primary care and emergency department (ED) visits and the relationships between the two. In all, 5.5% of the Medicare aged FFS population were identified as seasonal migrators, with 4.1% following the traditional snowbird pattern of migration, spending warm months in the north and cold months in the south. Migrators had higher rates of ED visits and primary care treatable (PCT) ED visits than the nonmigratory groups, controlling for location, age, race, sex, Medicaid status, season, and comorbidities. They also had more visits with specialist physicians, more days with outpatient services, and more days seeing a physician in any setting. Having local primary care strongly reduced rates of both PCT ED visits and total ED visits for all migration categories, with the greatest reduction seen in PCT ED visits by migrators (local primary care was associated with a 58% reduction in PCT ED visits by snowbirds and a 65% reduction in PCT ED visits by other migrators).

We use administrative data from Wisconsin to determine the fraction of new Medicaid enrollees who have private health insurance at the time of enrollment in the program. Through the linkage of several administrative data sources not previously used for research, we are able to observe coverage status directly for a large fraction of enrollees and indirectly for the remainder. We provide strict bounds for the percentages in each status and find that the percentage of new enrollees with private insurance coverage at the time of enrollment lies between 16 percent and 29 percent, and the percentage that dropped private coverage in favor of public insurance lies between 4 percent and 18 percent. Our point estimates indicate that, among all new enrollees, 21 percent had private health insurance at the time of enrollment and that 10 percent dropped this coverage. Our results show substantially lower rates than previous studies of crowd-out following public health insurance expansions and significant rates of dual coverage, whereby new enrollees into public insurance retain their previously held private insurance coverage.