Search

Objective: To assess racial and ethnic disparities in care for Medicare fee-for-service (FFS) beneficiaries and whether disparities differ between health system-affiliated physician organizations (POs) and nonaffiliated POs., Data Sources: We used Medicare Data on Provider Practice and Specialty (MD-PPAS), Medicare Provider Enrollment, Chain, and Ownership System (PECOS), IRS Form 990, 100% Medicare FFS claims, and race/ethnicity estimated using the Medicare Bayesian Improved Surname Geocoding 2.0 algorithm., Study Design: Using a sample of 16 007 POs providing primary care in 2015, we assessed racial/ethnic disparities on 12 measures derived from claims (2 cancer screenings; diabetic eye examinations; continuity of care; two medication adherence measures; three measures of follow-up visits after acute care; all-cause emergency department (ED) visits, all-cause readmissions, and ambulatory care-sensitive admissions). We decomposed these "total" disparities into within-PO and between-PO components using models with PO random effects. We then pair-matched 1853 of these POs that were affiliated with health systems to similar nonaffiliated POs. We examined differences in within-PO disparities by affiliation status by interacting each nonwhite race/ethnicity with an affiliation indicator., Data Collection/extraction Methods: Medicare Data on Provider Practice and Specialty identified POs billing Medicare; PECOS and IRS Form 990 identified health system affiliations. Beneficiaries age 18 and older were attributed to POs using a plurality visit rule., Principal Findings: We observed total disparities in 12 of 36 comparisons between white and nonwhite beneficiaries; nonwhites received worse care in 10. Within-PO disparities exceeded between-PO disparities and were substantively important (>=5 percentage points or>=0.2 standardized differences) in nine of the 12 comparisons. Among these 12, nonaffiliated POs had smaller disparities than affiliated POs in two comparisons (P < .05): 1.6 percentage points smaller black-white disparities in follow-up after ED visits and 0.6 percentage points smaller Hispanic-white disparities in breast cancer screening., Conclusions: We find no evidence that system-affiliated POs have smaller racial and ethnic disparities than nonaffiliated POs. Where differences existed, disparities were slightly larger in affiliated POs., (© Health Research and Educational Trust.)

Objective: To examine system integration with physician specialties across markets and the association between local system characteristics and their patterns of physician integration., Data Sources: Data come from the AHRQ Compendium of US Health Systems and IQVIA OneKey database., Study Design: We examined the change from 2016 to 2018 in the percentage of physicians in systems, focusing on primary care and the 10 most numerous nonhospital-based specialties across the 382 metropolitan statistical areas (MSAs) in the US. We also categorized systems by ownership, mission, and payment program participation and examined how those characteristics were related to their patterns of physician integration in 2018., Data Collection/extraction Methods: We examined local healthcare markets (MSAs) and the hospitals and physicians that are part of integrated systems that operate in these markets. We characterized markets by hospital and insurer concentration and systems by type of ownership and by whether they have an academic medical center (AMC), a 340B hospital, or accountable care organization., Principal Findings: Between 2016 and 2018, system participation increased for primary care and the 10 other physician specialties we examined. In 2018, physicians in specialties associated with lucrative hospital services were the most commonly integrated with systems including hematology-oncology (57%), cardiology (55%), and general surgery (44%); however, rates varied substantially across markets. For most specialties, high market concentration by insurers and hospital-systems was associated with lower rates of physician integration. In addition, systems with AMCs and publicly owned systems more commonly affiliated with specialties unrelated to the physicians' potential contribution to hospital revenue, and investor-owned systems demonstrated more limited physician integration., Conclusions: Variation in physician integration across markets and system characteristics reflects physician and systems' motivations. These integration strategies are associated with the financial interests of systems and other strategic goals (eg, medical education, and serving low-income populations)., (© 2020 The Author. Health Services Research published by Wiley Periodicals LLC on behalf of Health Research and Educational Trust.)

Objective: To test the hypothesis that health systems provide better care to patients with high needs by comparing differences in quality between system-affiliated and nonaffiliated physician organizations (POs) and to examine variability in quality across health systems., Data Sources: 2015 Medicare Data on Provider Practice and Specialty linked physicians to POs. Medicare Provider Enrollment, Chain, and Ownership System (PECOS) and IRS Form 990 data identified health system affiliations. Fee-for-service Medicare enrollment and claims data were used to examine quality., Study Design: This cross-sectional analysis of beneficiaries with high needs, defined as having more than twice the expected spending of an average beneficiary, examined six quality measures: continuity of care, follow-up visits after hospitalizations and emergency department (ED) visits, ED visits, all-cause readmissions, and ambulatory care-sensitive hospitalizations. Using a matched-pair design, we estimated beneficiary-level regression models with PO random effects to compare quality of care in system-affiliated and nonaffiliated POs. We then limited the sample to system-affiliated POs and estimated models with system random effects to examine variability in quality across systems., Principal Findings: Among 2 323 301 beneficiaries with high needs, 52.3% received care from system-affiliated POs. Rates of ED visits were statistically significantly different in system-affiliated POs (117.5 per 100) and nonaffiliated POs (106.8 per 100, P < .0001). Small differences in the other five quality measures were observed across a range of sensitivity analyses. Among systems, substantial variation was observed for rates of continuity of care (90% of systems had rates between 70.8% and 89.4%) and follow-up after ED visits (90% of systems had rates between 56.9% and 73.5%)., Conclusions: Small differences in quality of care were observed among beneficiaries with high needs receiving care from system POs and nonsystem POs. Health systems may not confer hypothesized quality advantages to patients with high needs., (© Health Research and Educational Trust.)

Objective: We aim to assess whether system providers perform better than nonsystem providers under an alternative payment model that incentivizes high-quality, cost-efficient care. We posit that the payment environment and the incentives it provides can affect the relative performance of vertically integrated health systems. To examine this potential influence, we compare system and nonsystem hospitals participating in Medicare's Comprehensive Care for Joint Replacement (CJR) model., Data Sources: We used hospital cost and quality data from the Centers for Medicare & Medicaid Services linked to data from the Agency for Healthcare Research and Quality's Compendium of US Health Systems and hospital characteristics from secondary sources. The data include 706 hospitals in 67 metropolitan areas., Study Design: We estimated regressions that compared system and nonsystem hospitals' 2017 cost and quality performance providing lower joint replacements among hospitals required to participate in CJR., Principal Findings: Among CJR hospitals, system hospitals that provided comprehensive services in their local market had 5.8 percent ($1612) lower episode costs (P = .01) than nonsystem hospitals. System hospitals that did not provide such services had 3.5 percent ($967) lower episode costs (P = .14). Quality differences between system hospitals and nonsystem hospitals were mostly small and statistically insignificant., Conclusions: When operating under alternative payment model incentives, vertical integration may enable hospitals to lower costs with similar quality scores., (© Health Research and Educational Trust.)

Objective: To evaluate the impact of TEAM UP-an initiative that fully integrates behavioral health services into pediatric primary care in three Boston-area Community Health Centers (CHCs)-on health care utilization and costs., Data Sources: 2014-2017 claims data on continuously enrolled children from a Massachusetts Medicaid managed care plan., Study Design: We used a difference-in-difference approach with inverse probability of treatment weights to compare outcomes in children receiving primary care at TEAM UP CHCs versus comparison site CHCs, in the pre (2014-2016q2)- versus post (2016q3-2017)-intervention periods. Utilization outcomes included emergency department visits, inpatient admissions, primary care visits, and outpatient/professional visits (all cause and those with mental health (MH) diagnoses). Cost outcomes included total cost of care (inpatient, outpatient, professional, pharmacy). We further assessed differential effects by baseline MH diagnosis., Principal Findings: After 1.5 years, TEAM UP was associated with a relative increase in the rate of primary care visits (IRR = 1.15, 95% CI 1.04-1.27, or 115 additional visits/1000 patients/quarter), driven by children with a MH diagnosis at baseline. There was no significant change in avoidable health care utilization or cost., Conclusions: Expanding the TEAM UP behavioral health integration model to other sites has the potential to improve primary care engagement in low-income children with MH needs., (© Health Research and Educational Trust.)

Objective: Examine how behavioral health (BH) integration affects health care costs, emergency department (ED) visits, and inpatient admissions., Data Sources/study Setting: Truven Health MarketScan Research Databases., Study Design: Social network analysis identified "care communities" (providers sharing a high number of patients) and measured BH integration in terms of how connected, or central, BH providers were to other providers in their community. Multivariable generalized linear models adjusting for age, sex, number of prescriptions, and Charlson comorbidity score were used to estimate the relationship between the centrality of BH providers and health care utilization of BH patients., Data Collection/extraction Methods: Used outpatient, inpatient, and pharmacy claims data from six Medicaid plans from 2011 to 2013 to identify study outcomes, comorbidities, providers, and health care encounters., Principal Findings: Behavioral health centrality ranged from 0 (no BH providers) to 0.49. Relative to communities at the median BH centrality (0.06), in 2012, BH patients in communities at the 75th percentile of BH centrality (0.31) had 0.2 fewer admissions, 2.1 fewer all-cause ED visits, and accrued $1,947 fewer costs, on average., Conclusions: Increased behavioral centrality was significantly associated with a reduced number of ED visits, less frequent inpatient admissions, and lower overall health care costs., (© Health Research and Educational Trust.)

Objective: To examine self-reported financial strain in relation to pharmacy utilization adherence data., Data Sources/study Setting: Survey, administrative, and electronic medical data from Kaiser Permanente Northern California., Study Design: Retrospective cohort design (2006, n = 7,773)., Data Collection/extraction Methods: We compared survey self-reports of general and medication-specific financial strain to three adherence outcomes from pharmacy records, specifying adjusted generalized linear regression models., Principal Findings: Eight percent and 9 percent reported general and medication-specific financial strain. In adjusted models, general strain was significantly associated with primary nonadherence (RR = 1.37; 95 percent CI: 1.04-1.81) and refilling late (RR = 1.34; 95 percent CI: 1.07-1.66); and medication-specific strain was associated with primary nonadherence (RR = 1.42, 95 percent CI: 1.09-1.84)., Conclusions: Simple, minimally intrusive questions could be used to identify patients at risk of poor adherence due to financial barriers., (© Health Research and Educational Trust.)

Objectives: To fill an empirical gap in the literature by examining changes in quality of care measures occurring when multispecialty clinic systems were acquired by hospital-owned, vertically integrated health care delivery systems in the Twin Cities area., Data Sources/study Setting: Administrative data for health plan enrollees attributed to treatment and control clinic systems, merged with U.S. Census data., Study Design: We compared changes in quality measures for health plan enrollees in the acquired clinics to enrollees in nine control groups using a differences-in-differences model. Our dataset spans 2 years prior to and 4 years after the acquisitions. We estimated probit models with errors clustered within enrollees., Data Collection/extraction Methods: Data were assembled by the health plan's informatics team., Principal Findings: Vertical integration is associated with increased rates of colorectal and cervical cancer screening and more appropriate emergency department use. The probability of ambulatory care-sensitive admissions increased when the acquisition caused disruption in admitting patterns., Conclusions: Moving a clinic system into a vertically integrated delivery system resulted in limited increases in quality of care indicators. Caution is warranted when the acquisition causes disruption in referral patterns., (© Health Research and Educational Trust.)

Objective: To report 5 years of adverse events (AEs) identified using an enhanced Global Trigger Tool (GTT) in a large health care system., Study Setting: Records from monthly random samples of adults admitted to eight acute care hospitals from 2007 to 2011 with lengths of stay ≥3 days were reviewed., Study Design: We examined AE incidence overall and by presence on admission, severity, stemming from care provided versus omitted, preventability, and category; and the overlap with commonly used AE-detection systems., Data Collection: Professional nurse reviewers abstracted 9,017 records using the enhanced GTT, recording triggers and AEs. Medical record/account numbers were matched to identify overlapping voluntary reports or AHRQ Patient Safety Indicators (PSIs)., Principal Findings: Estimated AE rates were as follows: 61.4 AEs/1,000 patient-days, 38.1 AEs/100 discharges, and 32.1 percent of patients with ≥1 AE. Of 1,300 present-on-admission AEs (37.9 percent of total), 78.5 percent showed NCC-MERP level F harm and 87.6 percent were "preventable/possibly preventable." Of 2,129 hospital-acquired AEs, 63.3 percent had level E harm, 70.8 percent were "preventable/possibly preventable"; the most common category was "surgical/procedural" (40.5 percent). Voluntary reports and PSIs captured <5 percent of encounters with hospital-acquired AEs., Conclusions: AEs are common and potentially amenable to prevention. GTT-identified AEs are seldom caught by commonly used AE-detection systems., (© Health Research and Educational Trust.)

Objective: To understand patient loyalty to providers over time, informing effective population health management., Study Setting: Patient care-seeking patterns over a 6-year timeframe in Minnesota, where care systems have a significant portion of their revenue generated by shared-saving contracts with public and private payers., Study Design: Weibull duration and probit models were used to examine patterns of patient attribution to a care system and the continuity of patient affiliation with a care system. Clustering of errors within family unit was used to account for within-family correlation in unobserved characteristics that affect patient loyalty., Data Collection: The payer provided data from health plan administrative files, matched to U.S. Census-based characteristics of the patient's neighborhood. Patients were retrospectively attributed to health care systems based on patterns of primary care., Principal Findings: I find significant patient loyalty, with past loyalty a very strong predictor of future relationship. Relationships were shorter when the patient's health status was complex and when the patient's care system was smaller., Conclusions: Population health management can be beneficial to the care system making this investment, particularly for patients exhibiting prior continuity in care system choice. The results suggest that co-located primary and specialty services are important in maintaining primary care loyalty., (© Health Research and Educational Trust.)

Objective: To determine the extent to which managed care has led to consolidation among hospitals and physicians., Data Sources: We use data from the American Hospital Association, American Medical Association, and government censuses., Study Design: Two stage least squares regression analysis examines how cross-section variation in managed care penetration affects provider consolidation, while controlling for the endogeneity of managed-care penetration. Specifically, we examine inpatient hospital markets and physician practice size in large metropolitan areas., Data Collection Methods: All data are from secondary sources, merged at the level of the Primary Metropolitan Statistical Area., Principal Findings: We find that higher levels of local managed-care penetration are associated with substantial increases in consolidation in hospital and physician markets. In the average market (managed-care penetration equaled 34 percent in 1994), managed care was associated with an increase in the Herfindahl of .054 between 1981 and 1994, moving from .096 in 1981 to .154. This is equivalent to moving from 10.4 equal-size hospitals to 6.5 equal-sized hospitals. In the physician market place, we estimate that at the mean, managed care resulted in a 14 percentage point decrease of physicians in solo practice between 1986 and 1995. This implies a decrease in the percentage of doctors in solo practice from 38 percent in 1986 to 24 percent by 1995.

Objective: To assess the impact of HMO market structure on the formation of physician-hospital strategic alliances from 1993 through 1995. The two trends, managed care and physician-hospital integration have been prominent in reshaping insurance and provider markets over the past decade., Study Design: Pooled cross-sectional data from the InterStudy HMO Census and the Annual Survey conducted by the American Hospital Association (AHA) between 1993 and the end of 1995 to examine the effects of HMO penetration and HMO numbers in a market on the formation of hospital-sponsored alliances with physicians. Because prior research has found nonlinear effects of HMOs on a variety of dependent variables, we operationalized HMO market structure two ways: using a Taylor series expansion and cross-classifying quartile distributions of HMO penetration and numbers into 16 dummy indicators. Alliance formation was operationalized using the presence of any alliance model (IPA, PHO, MSO, and foundation) and the sum of the four models present in the hospital. Because managed care and physician-hospital integration are endogenous (e.g., some hospitals also sponsor HMOs), we used an instrumental variables approach to model the determinants of HMO penetration and HMO numbers. These instruments were then used with other predictors of alliance formation: physician supply characteristics, the extent of hospital competition, hospital-level descriptors, population size and demographic characteristics, and indicators for each year. All equations were estimated at the MSA level using mixed linear models and first-difference models., Principal Findings: Contrary to conventional wisdom, alliance formation is shaped by the number of HMOs in the market rather than by HMO penetration. This confirms a growing perception that hospital-sponsored alliances with physicians are contracting vehicles for managed care: the greater the number of HMOs to contract with, the greater the development of alliances. The models also show that alliance formation is low in markets where a small number of HMOs have deeply penetrated the market. First-difference models further show that alliance formation is linked to HMO consolidation (drop in the number of HMOs in a market) and hospital downsizing. Alliance formation is not linked to changes in hospital costs, profitability, or market competition with other hospitals., Conclusions: Hospitals appear to form alliances with physicians for several reasons. Alliances serve to contract with the growing number of HMOs, to pose a countervailing bargaining force of providers in the face of HMO consolidation, and to accompany hospital downsizing and restructuring efforts., Implications for Policy, Delivery, or Practice: Physician-hospital integration is often mentioned as a provider response to increasing cost-containment pressures due to rising managed care penetration. Our findings do not support this view. Alliances appear to serve the hospital's interest in bargaining with managed care plans on a more even basis.