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Measures of health care quality were compared between 246 hospitals that were acquired by another hospital or health system during 2009–2013 and 1986 control hospitals that were not acquired. A composite measure of patient-reported experience worsened slightly in acquired hospitals relative to control hospitals. There were no significant changes in mortality or readmission rates.

In this study of abdominal aortic aneurysm repair, endovascular repair was shown to have an early survival advantage over open repair during the first three years. However, interventions related to aneurysm and ruptures were more common after endovascular repair. The use of endovascular repair of abdominal aortic aneurysms is increasing. By 2010, endovascular repair accounted for 78% of all intact repairs. 1 , 2 Randomized, controlled trials comparing endovascular repair with open repair generally have shown a perioperative benefit of endovascular repair over open repair. 3 – 5 Long-term survival, however, is similar with the two approaches. 6 – 9 As data on long-term outcomes accumulate, concerns have been raised about endovascular repair with respect to the increased rate of late failure leading to rupture and higher rates of reintervention. In our previous analyses performed with the use of Medicare data, which account for more . . .

Endovascular repair is a less invasive strategy than open repair for the management of abdominal aortic aneurysm. This observational study in a large Medicare population shows that perioperative survival is superior with endovascular repair but that the survival advantage gradually wanes over 3 years. The survival advantage is more durable in older patients. This observational study shows that perioperative survival is superior with endovascular repair but that the survival advantage gradually wanes over 3 years. Since the first report of endovascular repair of abdominal aortic aneurysm in 1991, the technique has become a mainstay in the repair of abdominal aortic aneurysm, accounting for over 40% of elective repairs of abdominal aortic aneurysm in 2003 (Figure 1). 1 – 3 Randomized trials have shown a perioperative survival benefit of endovascular repair over open repair, with fewer complications and a shorter recovery. 4 , 5 There are concerns, however, that longer-term outcomes of endovascular repair may not be as durable as those of open repair, with endovascular repair increasing the risk of late rupture of the abdominal aortic aneurysm and necessitating . . .

Hospital and registry data and national statistics showed a lower rate of abdominal aortic aneurysm repair and a larger mean aneurysm diameter at repair in England than in the United States; U.S. rates of aneurysm rupture and aneurysm-related death were lower. The decision about whether to repair an abdominal aortic aneurysm requires consideration of a balance of risks, including aneurysm rupture if surgery is not performed and death due to aneurysm repair itself, as well as consideration of an individual patient’s probable life expectancy. The decision is influenced by patient and clinician preference, medical management of coexisting conditions, and the availability of and access to endovascular procedures as an alternative to open repair. The aneurysm diameter is the best predictor of aneurysm rupture 1 , 2 ; the risk increases exponentially with an increasing diameter. 3 Therefore, the aneurysm diameter is a key determinant . . .

Although overuse in medicine is gaining increased attention, many questions remain unanswered. Dan Morgan and colleagues propose an agenda for coordinated research to improve our understanding of the problem

Background Risk adjustment models are employed to prevent adverse selection, anticipate budgetary reserve needs, and offer care management services to high-risk individuals. We aimed to address two unknowns about risk adjustment: whether machine learning (ML) and inclusion of social determinants of health (SDH) indicators improve prospective risk adjustment for health plan payments. Methods We employed a 2-by-2 factorial design comparing: (i) linear regression versus ML (gradient boosting) and (ii) demographics and diagnostic codes alone, versus additional ZIP code-level SDH indicators. Healthcare claims from privately-insured US adults (2016–2017), and Census data were used for analysis. Data from 1.02 million adults were used for derivation, and data from 0.26 million to assess performance. Model performance was measured using coefficient of determination (R 2 ), discrimination (C-statistic), and mean absolute error (MAE) for the overall population, and predictive ratio and net compensation for vulnerable subgroups. We provide 95% confidence intervals (CI) around each performance measure. Results Linear regression without SDH indicators achieved moderate determination (R 2 0.327, 95% CI: 0.300, 0.353), error ($6992; 95% CI: $6889, $7094), and discrimination (C-statistic 0.703; 95% CI: 0.701, 0.705). ML without SDH indicators improved all metrics (R 2 0.388; 95% CI: 0.357, 0.420; error $6637; 95% CI: $6539, $6735; C-statistic 0.717; 95% CI: 0.715, 0.718), reducing misestimation of cost by $3.5 M per 10,000 members. Among people living in areas with high poverty, high wealth inequality, or high prevalence of uninsured, SDH indicators reduced underestimation of cost, improving the predictive ratio by 3% (~$200/person/year). Conclusions ML improved risk adjustment models and the incorporation of SDH indicators reduced underpayment in several vulnerable populations.

Few studies have examined rates and causes of short-term readmissions among adults across age and insurance types. We compared rates, characteristics, and costs of 30-day readmission after all-cause hospitalizations across insurance types in the US. We retrospectively evaluated alive patients ≥18 years old, discharged for any cause, 1/1/13-11/31/13, 2006 non-federal hospitals in 21 states in the Nationwide Readmissions Database. The primary stratification variable of interest was primary insurance. Comorbid conditions were assessed based on Elixhauser comorbidities, as defined by administrative billing codes. Additional measures included diagnoses for index hospitalizations leading to rehospitalization. Hierarchical multivariable logistic regression models, with hospital site as a random effect, were used to calculate the adjusted odds of 30-day readmissions by age group and insurance categories. Cost and discharge estimates were weighted per NRD procedures to reflect a nationally representative sample. Diagnoses for index hospitalizations leading to rehospitalization were determined. Among 12,533,551 discharges, 1,818,093 (14.5%) resulted in readmission within 30 days. Medicaid insurance was associated with the highest adjusted odds ratio (AOR) for readmission both in those ≥65 years old (AOR 1.12, 95%CI 1.10-1.14; p <0.001), and 45-64 (AOR 1.67, 95% CI 1.66-1.69; p < 0.001), and Medicare in the 18-44 group (Medicare vs. private insurance: AOR 1.99, 95% CI 1.96-2.01; p <0.001). Discharges for psychiatric or substance abuse disorders, septicemia, and heart failure accounted for the largest numbers of readmissions, with readmission rates of 24.0%, 17.9%, 22.9% respectively. Total costs for readmissions were 50.7 billion USD, highest for Medicare (29.6 billion USD), with non-Medicare costs exceeding 21 billion USD. While Medicare readmissions account for more than half of the total burden of readmissions, costs of non-Medicare readmissions are nonetheless substantial. Medicaid patients have the highest odds of readmission in individuals older than age 44, commonly due to hospitalizations for psychiatric illness and substance abuse disorders. Medicaid patients represent a population at uniquely high risk for readmission.

In 2009, Blue Cross Blue Shield of Massachusetts implemented a global payment system, the Alternative Quality Contract (AQC). In the first year, the AQC was associated with reduced growth in medical spending; estimated payments to AQC groups exceeded estimated savings. The growth of health care spending is a major concern for households, businesses, and state and federal policymakers. 1 – 3 In response to the continued growth in spending in Massachusetts after health care reform, Blue Cross Blue Shield of Massachusetts (BCBS), the state's largest commercial payer, implemented the Alternative Quality Contract (AQC) in January 2009. 4 The AQC is a contracting model that is based on global payment and pay for performance. It is similar to the two-sided model for accountable care organizations specified by the Centers for Medicare and Medicaid Services (CMS) in its proposed regulations for those organizations. 5 Global payment . . .

Background: There is substantial variation in the cost and intensity of care delivered by US hospitals. We assessed how the structure of patient-sharing networks of physicians affiliated with hospitals might contribute to this variation. Methods: We constructed hospital-based professional networks based on patient-sharing ties among 61,461 physicians affiliated with 528 hospitals in 51 hospital referral regions in the US using Medicare data on clinical encounters during 2006. We estimated linear regression models to assess the relationship between measures of hospital network structure and hospital measures of spending and care intensity in the last 2 years of life. Results: The typical physician in an average-sized urban hospital was connected to 187 other doctors for every 100 Medicare patients shared with other doctors. For the average-sized urban hospital an increase of 1 standard deviation (SD) in the median number of connections per physician was associated with a 17.8% increase in total spending, in addition to 17.4% more hospital days, and 23.8% more physician visits (all P < 0.001). In addition, higher \"centrality\" of primary care providers within these hospital networks was associated with 14.7% fewer medical specialist visits (P < 0.001) and lower spending on imaging and tests (-9.2% and -12.9% for 1 SD increase in centrality, P < 0.001). Conclusions: Hospital-based physician network structure has a significant relationship with an institution's care patterns for their patients. Hospitals with doctors who have higher numbers of connections have higher costs and more intensive care, and hospitals with primary care-centered networks have lower costs and care intensity.

Hospitalizations account for the largest share of health care spending. New payment models increasingly encourage health care providers to reduce hospital admissions. Although emergency department (ED) physicians play a major role in the decision to admit a patient, the extent to which admission rates vary among ED physicians even within the same hospital remains poorly understood. In this study we examined physician-level variation in ED admission rates for Medicare patients. We found meaningful variation in admission rates: The mean physician-level adjusted admission rate was 38.9 percent and ranged from 32.2 percent to 45.6 percent for physicians at the tenth and ninetieth percentiles, respectively, of the estimated distribution within the same hospital. In contrast, the predicted risk for admission based on patient characteristics varied little among these physicians, suggesting that the variation in admission rates was not due to differences in patients seen. Our results suggest that strategies targeting physician decision making could modify (by either increasing or decreasing when appropriate) rates of admissions.