A 37-Year-Old Man Trying to Choose a High-Quality Hospital: Review of Hospital Quality Indicators (vol 302, pg 2353, 2009)
ABSTRACT Mr A, a previously healthy 37-year-old man, was diagnosed as having Prinzmetal angina and a hypercoagulable state 3 years ago after an ST-elevation myocardial infarction. Now, his cardiologist is moving and Mr A must select a new physician and health system. Geographic relocation, insurance changes, and other events force millions in the United States to change physicians and hospitals every year. Mr A should begin by choosing a primary care physician, since continuity and coordination of care improves outcomes. Evidence for evaluating specific physicians is less robust, though a variety of sources are available. A broad range of detailed quality information, such as Medicare's Hospital Compare (http://www.hospitalcompare.hhs.gov/), is available for selecting a hospital. However, the relationship of these metrics to patient outcomes is variable, and different Web sites provide meaningfully different rankings and data interpretations. For Mr A in particular, a warfarin management team, the hospital's location, and a cardiologist with whom he feels comfortable and who can communicate with his primary care physician are important factors. Nevertheless, hospital quality information and metrics are an important component of the strategy Mr A should take to solve this challenging problem.
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ABSTRACT: Ranking of hospitals by lay media has attracted widespread attention but may not accurately reflect quality. Acute myocardial infarction (AMI) mortality is a straightforward measure of clinical outcome frequently used by ranking algorithms. Our aim was to assess whether ranking among top hospitals correlated with lower in-hospital risk-adjusted mortality following admission for AMI. Using a hierarchical regression model and the comprehensive nationwide database of hospital AMI admissions from 2004 to 2007 in France, we analysed crude and risk-adjusted hospital mortality rates in the ranked ('best') hospitals versus non-ranked hospitals. We subsequently restricted the comparison to non-ranked hospitals with matching on-site facilities. We analysed 192,372 admissions in 439 hospitals, 43 of which were in the ranked group. Patients admitted to the 396 non-ranked hospitals tended to be older with more comorbidities and underwent fewer revascularization procedures than patients admitted to ranked hospitals. Between hospital differences accounted for 10% of differences in mortality. Crude mortality was lower in ranked versus non-ranked hospitals (7.5% vs. 11.9%; P<0.001). The survival advantage associated with admission to ranked hospitals was reduced after adjustment for age and sex (5.7% vs. 6.4%; P=0.087) and comorbidities (4.9% vs. 5.5%; P=0.102). Ranked hospitals have similar adjusted AMI mortality rates to those not ranked and patient characteristics rather than hospital differences account for the variation in outcomes.Archives of cardiovascular diseases 10/2012; 105(10):489-98. DOI:10.1016/j.acvd.2012.05.007 · 1.66 Impact Factor
Article: Re: Let the patient revolution beginBMJ British medical journal 05/2013; 346:f2614. DOI:10.1136/bmj.f2614 · 16.30 Impact Factor
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ABSTRACT: The objective of this study was to determine adherence to incidentally detected lung nodule computed tomographic (CT) surveillance recommendations and identify demographic and clinical factors that increase the likelihood of CT surveillance. A total of 419 patients with incidentally detected lung nodules were included. Recorded data included patient demographic, radiologic, and clinical characteristics and outcomes at a 4-year follow-up. Multivariate logistic regression models determined the factors associated with likelihood of recommended CT surveillance. At least 1 recommended surveillance chest CT was performed on 48% of the patients (148/310). Computed tomographic result communication to the patient (odds ratio [OR], 2.2; P = 0.006; confidence interval [CI], 1.3-4.0) or to the referring physician (OR, 2.8; P = 0.001; CI, 1.7-4.5) and recommendation of a specific surveillance time interval (OR, 1.7; P = 0.023; CI, 1.08-2.72) increased the likelihood of surveillance. Other demographic, radiologic, and clinical factors did not influence surveillance. Documented physician and patient result communication as well as the recommendation of a specific surveillance time interval increased the likelihood of CT surveillance of incidentally detected lung nodules.Journal of computer assisted tomography 01/2014; 38(1):89-95. DOI:10.1097/RCT.0b013e3182a939a5 · 1.38 Impact Factor