The Effect of Financial Incentives on Hospitals That Serve Poor Patients RESPONSE
ABSTRACT Providing financial incentives to hospitals to improve quality is increasingly common, yet its effect on hospitals that care for poor patients is largely unknown.
To determine how financial incentives for quality performance affect hospitals with more poor patients compared with those with fewer poor patients.
251 hospitals that participated in the Premier Hospital Quality Incentive Demonstration program and a national sample of 3017 hospitals.
The association between the disproportionate-share index, a marker of caring for poor patients, and baseline quality performance, changes in performance, and terminal performance for acute myocardial infarction, congestive heart failure, and pneumonia for hospitals in the pay-for-performance program and those in the national sample (which did not receive financial incentives).
Among both pay-for-performance hospitals and those in the national sample, hospitals with more poor patients had lower baseline performance than did those with fewer poor patients. A high disproportionate-share index was associated with greater improvements in performance for acute myocardial infarction and pneumonia but not for congestive heart failure, and the gains were greater among hospitals that received financial incentives than among the national sample. After 3 years, hospitals that had more poor patients and received financial incentives caught up for all 3 conditions, whereas those with more poor patients among the national sample continued to lag.
Hospitals in the Premier Hospital Quality Incentive Demonstration may be atypical, and these results may not be generalizable to all hospitals.
No evidence indicated that financial incentives widened the gap in performance between hospitals that serve poor patients and other hospitals. Pay-for-performance programs may be a promising quality improvement strategy for hospitals that serve poor patients.
Robert Wood Johnson Foundation.
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ABSTRACT: Background:Composite measures are useful for distilling quality data into summary scores; yet, there has been limited use of composite measures for cancer care.Objective:Compare multiple approaches for generating cancer care composite measures and evaluate how well composite measures summarize dimensions of cancer care and predict survival.Study Design:We computed hospital-level rates for 13 colorectal, lung, and prostate cancer process measures in 59 Veterans Affairs hospitals. We computed 4 empirical-factor (based on an exploratory factor analysis), 3 cancer-specific (colorectal, lung, prostate care), and 3 care modality-specific (diagnosis/evaluation, surgical, nonsurgical treatments) composite measures. We assessed correlations among all composite measures and estimated all-cause survival for colon, rectal, non-small cell lung, and small cell lung cancers as a function of composite scores, adjusting for patient characteristics.Results:Four factors emerged from the factor analysis: nonsurgical treatment, surgical treatment, colorectal early diagnosis, and prostate treatment. We observed strong correlations (r) among composite measures comprised of similar process measures (r=0.58-1.00, P<0.0001), but not among composite measures reflecting different care dimensions. Composite measures were rarely associated with survival.Conclusions:The empirical-factor domains grouped measures variously by cancer type and care modality. The evidence did not support any single approach for generating cancer care composite measures. Weak associations across different care domains suggest that low-quality and high-quality cancer care delivery may coexist within Veterans Affairs hospitals.Medical Care 11/2014; 53(1). DOI:10.1097/MLR.0000000000000257 · 2.94 Impact Factor
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ABSTRACT: Background The Centers for Medicaid and Medicare Services (CMS) and the National Cardiovascular Data Registry (NCDR) track primary percutaneous coronary intervention (PCI) performance in the form of door‐to‐balloon time. For quality assessment, exceptions are made for patients with “unavoidable delays” in both registries, yet it remains unclear how consistently such patients are identified. Methods and Results All primary PCI patients at 3 Massachusetts hospitals (Brigham and Women's, Massachusetts General, and North Shore Medical Center) from 2009 to 2011 were evaluated for CMS inclusion/exclusion and NCDR nonsystems delay (NSD) status. We subsequently analyzed patient characteristics and outcomes based on these strata. Among 456 total patients, 128 (28%) were excluded from CMS reporting, whereas 56 (12%) were listed in the NCDR registry as having an NSD. Forty of 56 (71%) patients with NSD were also excluded from CMS reporting, whereas 312 of 400 (78%) patients reported without NSD were included in CMS reports. Between‐registry agreement on patients with unavoidable delays was modest (κ=0.32). Among CMS‐included patients without NSD, 94% received PCI within 90 minutes compared with 29% of CMS‐excluded patients with NSD (P<0.001). Likewise, CMS‐included patients without NSD had a 4‐fold better 1‐year mortality rate compared with CMS‐excluded patients with NSD (P<0.001). Conclusions More than twice as many primary PCI patients are excluded from CMS quality analyses compared with NCDR. With the use of currently available cardiovascular quality registries, it is unclear how many patients truly require unavoidable delays during primary PCI. Patients with NSD had the worst outcomes regardless of CMS status.Journal of the American Heart Association 04/2014; 3(3). DOI:10.1161/JAHA.114.000944 · 2.88 Impact Factor
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ABSTRACT: Purpose: Critical care is often an integral part of rescue for patients with surgical complications. We sought to understand critical care characteristics predictive of failure-to-rescue (FTR) performance at the hospital level. Methods: Using 2009 to 2011 FTR data from Hospital Compare, we identified 144 outlier hospitals with significantly better/worse performance than the national average. We surveyed intensive care unit (ICU) directors and nurse managers regarding physical structures, patient composition, staffing, care protocols, and rapid response teams (RRTs). Hospitals were compared using descriptive statistics and logistic regression. Results: Of 67 hospitals completing the survey, 56.1% were low performing, and 43.9% were high performing. Responders were more likely to be teaching hospitals (40.9% vs 25.0%; P = .05) but were similar to nonresponders in terms of size, region, ownership, and FTR performance. Poor performers were more likely to serve higher proportions of Medicaid patients (68.4% vs 20.7%; P < .0001) and be level 1 trauma centers (55.9% vs 25.9%; P = .02). After controlling for these 2 characteristics, an intensivist on the RRT (adjusted odds ratio, 4.27; confidence interval, 1.45-23.02; P = .005) and an internist on staff in the ICU (adjusted odds ratio, 2.13; P = .04) were predictors of high performance. Conclusions: Intensivists on the RRT and internists in the ICU may represent discrete organizational strategies for improving patient rescue. Hospitals with high Medicaid burden fare poorly on the FTR metric.Journal of Critical Care 06/2014; 29(6). DOI:10.1016/j.jcrc.2014.06.010 · 2.19 Impact Factor