Performance of health care service area definitions for capturing variation in inpatient care and social determinants of health.

OBJECTIVE: To quantify the degree to which health care service area (HCSA) definitions captured hospitalizations and heterogeneity in social determinants of health (SDOH). DATA SOURCES AND STUDY SETTING: Geospatial data from the Centers for Medicare and Medicaid Services, the Census Bureau, and the Dartmouth Institute. Drive-time isochrones from MapBox. Area Deprivation Index (ADI) data. 2017 inpatient discharge data from Arizona, Florida, Iowa, Maryland, Nebraska, New Jersey, New York, and Wisconsin, State Emergency Department Databases and State Inpatient Databases, Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality; and Fee-For-Service Medicare data in 48 states. STUDY DESIGN: Cross-sectional, descriptive analysis. DATA COLLECTION/EXTRACTION METHODS: The capture rate was the percentage of inpatient discharges occurring in the same HCSA as the hospital. We compared capture rates for each HCSA definition for different populations and by hospital type. We measured SDOH heterogeneity using the coefficient of variation of the ADI among ZIP codes within each HCSA. PRINCIPAL FINDINGS: HCSA definitions captured a wide range of inpatient discharges, ranging from 20% to 50% for Public Use Microdata Areas (PUMAs) to 93%-97% for Metropolitan Statistical Areas (MSAs). Three-quarters of inpatient discharges were from facilities within the same county as the patient's residential ZIP code, while nearly two-thirds were within the same Hospital Service Area. From the hospital perspective, 74.7% of inpatient discharges originated from within a 30-min drive and 90.1% within a 60-min drive. Capture rates were the lowest for teaching hospitals. PUMAs and drive-time-based HCSAs encompassed more homogenous populations while MSAs, Commuting Zones, and Hospital Referral Regions captured the most variation. CONCLUSIONS: The proportion of hospital discharges captured by each HCSA varied, with MSAs capturing the highest proportion of discharges and PUMAs capturing the lowest. Additionally, researchers face a trade-off between capture rate and population homogeneity when deciding which HCSA to use.

Primary Care Physicians In Medicare Advantage Were Less Costly, Provided Similar Quality Versus Regional Average.

The use of many services is lower in Medicare Advantage (MA) compared with traditional Medicare, generating cost savings for insurers, whereas the quality of ambulatory services is higher. This study examined the role of selective contracting with providers in achieving these outcomes, focusing on primary care physicians. Assessing primary care physician costliness based on the gap between observed and predicted costs for their traditional Medicare patients, we found that the average primary care physician in MA networks was $433 less costly per patient (2.9 percent of baseline) compared with the regional mean, with less costly primary care physicians included in more networks than more costly ones. Favorable selection of patients by MA primary care physicians contributed partially to this result. The quality measures of MA primary care physicians were similar to the regional mean. In contrast, primary care physicians excluded from all MA networks were $1,617 (13.8 percent) costlier than the regional mean, with lower quality. Primary care physicians in narrow networks were $212 (1.4 percent) less costly than those in wide networks, but their quality was slightly lower. These findings highlight the potential role of selective contracting in reducing costs in the MA program.

Primary Care Physician Network Overlap Between Medicaid and the Patient Protection and Affordable Care Act Marketplace.

This cross-sectional study quantifies Medicaid and the Patient Protection and Affordable Care Act (ACA) Marketplace overlap among primary care physicians.

Unsolicited Patient Complaints Following the 21st Century Cures Act Information-Blocking Rule.

Importance: The 21st Century Cures Act includes an information-blocking rule (IBR) that requires health systems to provide patients with immediate access to their health information in the electronic medical record upon request. Patients accessing their health information before they receive an explanation from their health care team may experience confusion and may be more likely to share unsolicited patient complaints (UPCs) with their health care organization. Objective: To evaluate the quantity of UPCs about physicians before and after IBR implementation and to identify themes in UPCs that may identify patient confusion, fear, or anger related to the release of information. Design, Setting, and Participants: This retrospective cohort study was conducted with an interrupted time-series analysis of UPCs spanning January 1, 2020, to June 30, 2022. The data were obtained from a single academic medical center, Vanderbilt University Medical Center, at which the IBR was implemented on January 20, 2021. Data analysis was performed from January 11 to July 15, 2023. Exposure: Implementation of the IBR on January 20, 2021. Main Outcomes and Measures: The primary outcome was the monthly rate of UPCs before and after IBR implementation. A qualitative analysis was performed for UPCs received after IBR implementation. The Wilcoxon rank-sum test was used to compare monthly complaints between the pre- and post-IBR groups. The Pearson χ2 test was used to compare proportions of complaints by UPC category between time periods. Results: The medical center received 8495 UPCs during the study period: 3022 over 12 months before and 5473 over 18 months after institutional IBR implementation. There was no difference in the monthly proportions of UPCs per 1000 patient encounters before (median, 0.81 [IQR, 0.75-0.88]) and after (median, 0.83 [IQR, 0.77-0.89]) IBR implementation (difference in medians, -0.02 [95% CI, -0.12 to 0.07]; P =.86). Segmented regression analysis revealed no difference in monthly UPCs (ß [SE], 0.03 [0.09]; P =.72). Conclusions and Relevance: In this cohort study, implementation of the Cures Act IBR was not associated with an increase in monthly rates of UPCs. These findings suggest that review of UPCs identified as IBR-specific complaints may allow clinicians and organizations to prepare patients that their test and procedure results may be available before clinicians are able to review them and respond.

Population Genomic Screening for Three Common Hereditary Conditions : A Cost-Effectiveness Analysis.

BACKGROUND: The cost-effectiveness of screening the U.S. population for Centers for Disease Control and Prevention (CDC) Tier 1 genomic conditions is unknown. OBJECTIVE: To estimate the cost-effectiveness of simultaneous genomic screening for Lynch syndrome (LS), hereditary breast and ovarian cancer syndrome (HBOC), and familial hypercholesterolemia (FH). DESIGN: Decision analytic Markov model. DATA SOURCES: Published literature. TARGET POPULATION: Separate age-based cohorts (ages 20 to 60 years at time of screening) of racially and ethnically representative U.S. adults. TIME HORIZON: Lifetime. PERSPECTIVE: U.S. health care payer. INTERVENTION: Population genomic screening using clinical sequencing with a restricted panel of high-evidence genes, cascade testing of first-degree relatives, and recommended preventive interventions for identified probands. OUTCOME MEASURES: Incident breast, ovarian, and colorectal cancer cases; incident cardiovascular events; quality-adjusted survival; and costs. RESULTS OF BASE-CASE ANALYSIS: Screening 100 000 unselected 30-year-olds resulted in 101 (95% uncertainty interval [UI], 77 to 127) fewer overall cancer cases and 15 (95% UI, 4 to 28) fewer cardiovascular events and an increase of 495 quality-adjusted life-years (QALYs) (95% UI, 401 to 757) at an incremental cost of $33.9 million (95% UI, $27.0 million to $41.1 million). The incremental cost-effectiveness ratio was $68 600 per QALY gained (95% UI, $41 800 to $88 900). RESULTS OF SENSITIVITY ANALYSIS: Screening 30-, 40-, and 50-year-old cohorts was cost-effective in 99%, 88%, and 19% of probabilistic simulations, respectively, at a $100 000-per-QALY threshold. The test costs at which screening 30-, 40-, and 50-year-olds reached the $100 000-per-QALY threshold were $413, $290, and $166, respectively. Variant prevalence and adherence to preventive interventions were also highly influential parameters. LIMITATIONS: Population averages for model inputs, which were derived predominantly from European populations, vary across ancestries and health care environments. CONCLUSION: Population genomic screening with a restricted panel of high-evidence genes associated with 3 CDC Tier 1 conditions is likely to be cost-effective in U.S. adults younger than 40 years if the testing cost is relatively low and probands have access to preventive interventions. PRIMARY FUNDING SOURCE: National Human Genome Research Institute.

Physician patient sharing relationships within insurance plan networks.

OBJECTIVE: To quantify shared patient relationships between primary care physicians (PCPs) and cardiologists and oncologists and the degree to which those relationships were captured within insurance networks. DATA SOURCES: Secondary analysis of Vericred data on physician networks, CareSet data on physicians' shared Medicare patients, and insurance plan attributes from Health Insurance Compare. Data validation exercises used data from Physician Compare and IQVIA. STUDY DESIGN: Cross-sectional study of the PCP-to-specialist in-network shared patient percentage (primary outcome). We also categorized networks by insurance market segment (Medicare Advantage [MA], Medicaid managed care, small-group or individually purchased), insurance plan type, and network breadth. DATA EXTRACTION: We analyzed data on 219,982 PCPs, 29,400 cardiologists, and 22,745 oncologists who, in 2021, accepted MA (n = 941 networks), Medicaid managed care (n = 293), and individually-purchased (n = 332) and small-group (n = 501) plans. PRINCIPAL FINDINGS: Networks captured, on average, 64.6% of PCP-cardiology shared patient ties, and 61.8% of PCP-oncologist ties. Less than half of in-network ties (44.5% and 38.9%, respectively) were among physicians with a common organizational affiliation. After adjustment for network breadth, we found no evidence of differences in the shared patient percentage across insurance market segments or networks of different types (p-value >0.05 for all comparisons). An exception was among national versus local and regional networks, where we found that national plans captured fewer shared patient ties, particularly among the narrowest networks (58.4% for national networksvs. 64.7% for local and regional networks for PCP-cardiology). CONCLUSIONS: Given recent trends toward narrower networks, our findings underscore the importance of incorporating additional and nuanced measures of network composition to aid plan selection (for patients) and to guide regulatory oversight.

Access to In-Network Hospitals in Tennessee During the COVID-19 Pandemic.

This cross-sectional study assesses the daily percentage of floor and intensive care unit bed available at in-network hospitals for patients with COVID-19 in Tennessee.

Cost-effectiveness of population-wide genomic screening for Lynch syndrome in the United States.

PURPOSE: Genomic screening for Lynch syndrome (LS) could prevent colorectal cancer (CRC) by identifying high-risk patients and instituting intensive CRC screening. We estimated the cost-effectiveness of a population-wide LS genomic screening vs family history-based screening alone in an unselected US population. METHODS: We developed a decision-analytic Markov model including health states for precancer, stage-specific CRC, and death and assumed an inexpensive test cost of $200. We conducted sensitivity and threshold analyses to evaluate model uncertainty. RESULTS: Screening unselected 30-year-olds for LS variants resulted in 48 (95% credible range [CR] = 35-63) fewer overall CRC cases per 100,000 screened individuals, leading to 187 quality-adjusted life-years (QALYs; 95% CR = 123-260) gained at an incremental cost of $24.6 million (95% CR = $20.3 million-$29.1 million). The incremental cost-effectiveness ratio was $132,200, with an 8% and 71% probability of being cost-effective at $100,000 and $150,000 per QALY willingness-to-pay thresholds, respectively. CONCLUSION: Population LS screening may be cost-effective in younger patient populations under a $150,000 willingness-to-pay per QALY threshold and with a relatively inexpensive test cost. Further reductions in testing costs and/or the inclusion of LS testing within a broader multiplex screening panel are needed for screening to become highly cost-effective.

Difference-in-differences for categorical outcomes.

OBJECTIVE: To discuss and develop difference-in-difference estimators for categorical outcomes and apply them to estimate the effect of the Affordable Care Act's Medicaid expansion on insurance coverage. DATA SOURCES: Secondary analysis of Survey on Income and Program Participation (SIPP) data on health insurance coverage types before (January 2013) and after (December 2015) Medicaid expansion in 39 US states (19 expansion and 20 non-expansion). STUDY DESIGN: We develop difference-in-difference methods for repeated measures (panel data) of categorical outcomes. We discuss scale-dependence of DID assumptions for marginal and transition effect estimates and specify a new target estimand: the difference between outcome category transitions under treatment versus no treatment. We establish causal assumptions about transitions that are sufficient to identify this and a marginal target estimand. We contrast the marginal estimands identified by the transition approach versus an additive assumption only about marginal evolution. We apply both the marginal and transition approaches to estimate the effects of Medicaid expansion on health insurance coverage types (employer-sponsored; other private, non-group; public; and uninsured). DATA EXTRACTION: We analyzed 16,027 individual survey responses from people aged 18-62 years in the 2014 SIPP panel. PRINCIPAL FINDINGS: We show that the two identifying assumptions are equivalent (on the scale of the marginals) if either the baseline marginal distributions are identical or the marginals are constant in both groups. Applying our transitions approach to the SIPP data, we estimate a differential increase in transitions from uninsured to public coverage and differential decreases in transitions from uninsured to private, non-group coverage and in remaining uninsured. CONCLUSIONS: By comparing the assumption that marginals are evolving in parallel to an assumption about transitions across outcome values, we illustrate the scale-dependence of difference-in-differences. Our application shows that studying transitions can illuminate nuances obscured by changes in the marginals.

Breadth and Exclusivity of Hospital and Physician Networks in US Insurance Markets.

Importance: Little is known about the breadth of health care networks or the degree to which different insurers' networks overlap. Objective: To quantify network breadth and exclusivity (ie, overlap) among primary care physician (PCP), cardiology, and general acute care hospital networks for employer-based (large group and small group), individually purchased (marketplace), Medicare Advantage (MA), and Medicaid managed care (MMC) plans. Design, Setting, and Participants: This cross-sectional study included 1192 networks from Vericred. The analytic unit was the network-zip code-clinician type-market, which captured attributes of networks from the perspective of a hypothetical patient seeking access to in-network clinicians or hospitals within a 60-minute drive. Exposures: Enrollment in a private insurance plan. Main Outcomes and Measures: Percentage of in-network physicians and/or hospitals within a 60-minute drive from a hypothetical patient in a given zip code (breadth). Number of physicians and/or hospitals within each network that overlapped with other insurers' networks, expressed as a percentage of the total possible number of shared connections (exclusivity). Descriptive statistics (mean, quantiles) were produced overall and by network breadth category, as follows: extra-small (<10%), small (10%-25%), medium (25%-40%), large (40%-60%), and extra-large (>60%). Networks were analyzed by insurance type, state, and insurance, physician, and/or hospital market concentration level, as measured by the Hirschman-Herfindahl index. Results: Across all US zip code-network observations, 415 549 of 511 143 large-group PCP networks (81%) were large or extra-large compared with 138 485 of 202 702 MA (68%), 191 918 of 318 082 small-group (60%), 60 425 of 149 841 marketplace (40%), and 21 781 of 66 370 MMC (40%) networks. Large-group employer networks had broader coverage than all other network plans (mean [SD] PCP breadth: large-group employer-based plans, 57.3% [20.1]; small-group employer-based plans, 45.7% [21.4]; marketplace, 36,4% [21.2]; MMC, 32.3% [19.3]; MA, 47.4% [18.3]). MMC networks were the least exclusive (a mean [SD] overlap of 61.3% [10.5] for PCPs, 66.5% [9.8] for cardiology, and 60.2% [12.3] for hospitals). Networks were narrowest (mean [SD] breadth 42.4% [16.9]) and most exclusive (mean [SD] overlap 47.7% [23.0]) in California and broadest (79.9% [16.6]) and least exclusive (71.1% [14.6]) in Nebraska. Rising levels of insurer and market concentration were associated with broader and less exclusive networks. Markets with concentrated primary care and insurance markets had the broadest (median [interquartile range {IQR}], 75.0% [60.0%-83.1%]) and least exclusive (median [IQR], 63.7% [52.4%-73.7%]) primary care networks among large-group commercial plans, while markets with least concentration had the narrowest (median [IQR], 54.6% [46.8%-67.6%]) and most exclusive (median [IQR], 49.4% [41.9%-56.9%]) networks. Conclusions and Relevance: In this study, narrower health care networks had a relatively large degree of overlap with other networks in the same geographic area, while broader networks were associated with physician, hospital, and insurance market concentration. These results suggest that many patients could switch to a lower-cost, narrow network plan without losing in-network access to their PCP, although future research is needed to assess the implications for care quality and clinical integration across in-network health care professionals and facilities in narrow network plans.

Cost-effectiveness of Population-Wide Genomic Screening for Hereditary Breast and Ovarian Cancer in the United States.

Importance: Genomic screening for hereditary breast and ovarian cancer (HBOC) in unselected women offers an opportunity to prevent cancer morbidity and mortality, but the potential clinical impact and cost-effectiveness of such screening have not been well studied. Objective: To estimate the lifetime incremental incidence of HBOC and the quality-adjusted life-years (QALYs), costs, and cost-effectiveness of HBOC genomic screening in an unselected population vs family history-based testing. Design, Setting, and Participants: In this study conducted from October 27, 2017, to May 3, 2020, a decision analytic Markov model was developed that included health states for precancer, for risk-reducing mastectomy (RRM) and risk-reducing salpingo-oophorectomy (RRSO), for earlier- and later-stage HBOC, after cancer, and for death. A complimentary cascade testing module was also developed to estimate outcomes in first-degree relatives. Age-specific RRM and RRSO uptake probabilities were estimated from the Geisinger MyCode Community Health Initiative and published sources. Parameters including RRM and RRSO effectiveness, variant-specific cancer risk, costs, and utilities were derived from published sources. Sensitivity and scenario analyses were conducted to evaluate model assumptions and uncertainty. Main Outcomes and Measures: Lifetime cancer incidence, QALYs, life-years, and direct medical costs for genomic screening in an unselected population vs family history-based testing only were calculated. The incremental cost-effectiveness ratio (ICER) was calculated as the difference in cost between strategies divided by the difference in QALYs between strategies. Earlier-stage and later-stage cancer cases prevented and total cancer cases prevented were also calculated. Results: The model found that population screening of 30-year-old women was associated with 75 (95% credible range [CR], 60-90) fewer overall cancer cases and 288 QALYs (95% CR, 212-373 QALYs) gained per 100 000 women screened, at an incremental cost of $25 million (95% CR, $21 millon to $30 million) vs family history-based testing; the ICER was $87 700 (78% probability of being cost-effective at a threshold of $100 000 per QALY). In contrast, population screening of 45-year-old women was associated with 24 (95% CR, 18-29) fewer cancer cases and 97 QALYs (95% CR, 66-130 QALYs) gained per 100 000 women screened, at an incremental cost of $26 million (95% CR, $22 million to $30 million); the ICER was $268 200 (0% probability of being cost-effective at a threshold of $100 000 per QALY). A scenario analysis without cascade testing increased the ICER to $92 600 for 30-year-old women and $354 500 for 45-year-old women. A scenario analysis assuming a 5% absolute decrease in mammography screening in women without a variant was associated with the potential for net harm (-90 QALYs per 100 000 women screened; 95% CR, -180 to 10 QALYs). Conclusions and Relevance: The results of this study suggest that population HBOC screening may be cost-effective among younger women but not among older women. Cascade testing of first-degree relatives added a modest improvement in clinical and economic value. The potential for harm conferred by inappropriate reduction in mammography among noncarriers should be quantified.

How The ACA Dented The Cost Curve.

Numerous provisions of the Affordable Care Act (ACA) were designed to make health care more affordable, yet the act's cumulative effects on health care costs are still debated. A key question is whether or not the ACA reduced the annual rate at which total national health care spending increased and brought per capita spending growth rates down. We review the direct and indirect effects of the ACA on spending across segments of the health insurance market. We highlight areas where the ACA has affected spending, but we emphasize that the ACA's long-run impact on spending will depend on sustaining the adjustments made to provider payment systems and expanding the emphasis on value across payers throughout the ACA's second decade and beyond.

Medicaid Expansion Slowed Rates Of Health Decline For Low-Income Adults In Southern States.

Of the fourteen states that have not expanded eligibility for Medicaid, nine are in the southern census region, and two others border that region. Ongoing debate over the merits of Medicaid expansion in these states has focused, in part, on whether the safety net provides sufficient access for uninsured low-income Americans. We analyzed longitudinal survey and vital status data from the twelve-state Southern Community Cohort Study (SCCS) for 15,356 nonelderly adult participants with low incomes, 86 percent of whom were enrolled at community health centers. In difference-in-differences analyses, we compared changes in self-reported health between participants in four expansion and eight nonexpansion states before (2008-13) and after (2015-17) Medicaid expansion. We found that a higher proportion of SCCS participants in expansion states reported increases in Medicaid coverage (a differential change of 7.6 percentage points), a lower proportion experienced a health status decline (-1.8 percentage points), and a higher proportion maintained their baseline health status (1.4 percentage points). The magnitude of estimated reductions in health declines would meaningfully affect a nonexpansion state's health ranking in our sample if that state elected to expand Medicaid. Our results suggest that for low-income adults in the South, Medicaid expansion yielded health benefits-even for those with established access to safety-net care.

A Decision-Theoretic Approach to Panel-Based, Preemptive Genotyping.

We discuss a decision-theoretic approach to building a panel-based, preemptive genotyping program. The method is based on findings that a large percentage of patients are prescribed medications that are known to have pharmacogenetic associations, and over time, a substantial proportion are prescribed additional such medication. Preemptive genotyping facilitates genotype-guided therapy at the time medications are prescribed; panel-based testing allows providers to reuse previously collected genetic data when a new indication arises. Because it is cost-prohibitive to conduct panel-based genotyping on all patients, we describe a three-step approach to identify patients with the highest anticipated benefit. First, we construct prediction models to estimate the risk of being prescribed one of the target medications using readily available clinical data. Second, we use literature-based estimates of adverse event rates, variant allele frequencies, secular death rates, and costs to construct a discrete event simulation that estimates the expected benefit of having an individual's genetic data in the electronic health record after an indication has occurred. Finally, we combine medication prescription risk with expected benefit of genotyping once a medication is indicated to calculate the expected benefit of preemptive genotyping. For each patient-clinic visit, we calculate this expected benefit across a range of medications and select patients with the highest expected benefit overall. We build a proof of concept implementation using a cohort of patients from a single academic medical center observed from July 2010 through December 2012. We then apply the results of our modeling strategy to show the extent to which we can improve clinical and economic outcomes in a cohort observed from January 2013 through December 2015.

Effects of Affordable Care Act Marketplaces and Medicaid Eligibility Expansion on Access to Cancer Care.

PURPOSE: The aim of this study was to inform oncologists about how repealing the Affordable Care Act (ACA) may affect their ability to provide cancer therapies for people with cancer enrolled in ACA health plans and why proposals to change Medicaid funding may make it even more difficult for Medicaid beneficiaries to access cancer treatments. METHODS: We examined the regulations and provisions of the ACA related to how health insurance impacts access to diagnostic testing and treatments for people with cancer, including access to clinical trials. Similarly, we examined federal and state rules affecting Medicaid beneficiaries' access to cancer treatments. RESULTS: Repealing various provisions of the ACA will restrict who has access to both current and new cancer treatments. Such changes also will impact oncology research that depends on having heterogeneous people in clinical trials. CONCLUSIONS: Significant changes to the ACA will affect oncology treatment choices of everyone with health insurance-not only the 10 million people newly covered by ACA health plans and the 70 million people with Medicaid coverage.

Sepsis-Associated 30-Day Risk-Standardized Readmissions: Analysis of a Nationwide Medicare Sample.

OBJECTIVES: To determine national readmission rates among sepsis survivors, variations in rates between hospitals, and determine whether measures of quality correlate with performance on sepsis readmissions. DESIGN: Cross-sectional study of sepsis readmissions between 2008 and 2011 in the Medicare fee-for-service database. SETTING: Acute care, Medicare participating hospitals from 2008 to 2011. PATIENTS: Septic patients as identified by International Classification of Diseases, Ninth Revision codes using the Angus method. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We generated hospital-level, risk-standardized, 30-day readmission rates among survivors of sepsis and compared rates across region, ownership, teaching status, sepsis volume, hospital size, and proportion of underserved patients. We examined the relationship between risk-standardized readmission rates and hospital-level composite measures of quality and mortality. From 633,407 hospitalizations among 3,315 hospitals from 2008 to 2011, median risk-standardized readmission rates was 28.7% (interquartile range, 26.1-31.9). There were differences in risk-standardized readmission rates by region (Northeast, 30.4%; South, 29.6%; Midwest, 28.8%; and West, 27.7%; p < 0.001), teaching versus nonteaching status (31.1% vs 29.0%; p < 0.001), and hospitals serving the highest proportion of underserved patients (30.6% vs 28.7%; p < 0.001). The best performing hospitals on a composite quality measure had highest risk-standardized readmission rates compared with the lowest (32.0% vs 27.5%; p < 0.001). Risk-standardized readmission rates was lower in the highest mortality hospitals compared with those in the lowest (28.7% vs 30.7%; p < 0.001). CONCLUSIONS: One third of sepsis survivors were readmitted and wide variation exists between hospitals. Several demographic and structural factors are associated with this variation. Measures of higher quality in-hospital care were correlated with higher readmission rates. Several potential explanations are possible including poor risk standardization, more research is needed.

Uncovering waste in US healthcare: Evidence from ambulance referral patterns.

There is widespread agreement that the US healthcare system wastes as much as 5% of GDP, yet much less agreement on the source of the waste. This paper uses the effectively random assignment of patients to ambulance companies to generate comparisons across similar patients treated at different hospitals. We find that assignment to hospitals whose patients receive large amounts of care over the three months following a health emergency have only modestly better survival outcomes compared to hospitals whose patients receive less. Outcomes are related to different forms of spending. Patients assigned to hospitals with high levels of inpatient spending are more likely to survive to one year, while high levels of outpatient spending result in lower survival. In particular, we discovered that downstream spending at skilled nursing facilities (SNF) is a strong predictor of mortality. Our results highlight SNF admissions as a quality measure to complement the commonly used measure of hospital readmissions and suggest that in the search for waste in the US healthcare, post-acute SNF care is a prime candidate.