223 research outputs found

    General Hospitals, Specialty Hospitals and Financially Vulnerable Patients

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    Examines whether specialty hospitals draw well-insured patients away from general and safety-net hospitals, reducing their ability to cross-subsidize less profitable services and uncompensated care, in three cities. Notes challenges and implications

    Virtual colonoscopy; real misses

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    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/72356/1/j.1572-0241.2003.08448.x.pd

    Relation between hospital orthopaedic specialisation and outcomes in patients aged 65 and older: retrospective analysis of US Medicare data

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    Objective To explore the relation between hospital orthopaedic specialisation and postoperative outcomes after total hip or knee replacement surgery

    COST-EFFECTIVENESS OF AIRLINE DEFIBRILLATORS: IS PEACE OF MIND MORE IMPORTANT THAN SAVING LIVES?

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    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74922/1/j.1524-4733.2001.40201-5.x.pd

    Uncompensated care provided by for-profit, not-for-profit, and government owned hospitals

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    <p>Abstract</p> <p>Background</p> <p>There is growing concern certain not-for-profit hospitals are not providing enough uncompensated care to justify their tax exempt status. Our objective was to compare the amount of uncompensated care provided by not-for-profit (NFP), for-profit (FP) and government owned hospitals.</p> <p>Methods</p> <p>We used 2005 state inpatient data (SID) for 10 states to identify patients hospitalized for three common conditions: acute myocardial infarction (AMI), coronary artery bypass grafting (CABG), or childbirth. Uncompensated care was measured as the proportion of each hospital's total admissions for each condition that were classified as being uninsured. Hospitals were categorized as NFP, FP, or government owned based upon data obtained from the American Hospital Association. We used bivariate methods to compare the proportion of uninsured patients admitted to NFP, FP and government hospitals for each diagnosis. We then used generalized linear mixed models to compare the percentage of uninsured in each category of hospital after adjusting for the socioeconomic status of the markets each hospital served.</p> <p>Results</p> <p>Our cohort consisted of 188,117 patients (1,054 hospitals) hospitalized for AMI, 82,261 patients (245 hospitals) for CABG, and 1,091,220 patients for childbirth (793 hospitals). The percentage of admissions classified as uninsured was lower in NFP hospitals than in FP or government hospitals for AMI (4.6% NFP; 6.0% FP; 9.5% government; P < .001), CABG (2.6% NFP; 3.3% FP; 7.0% government; P < .001), and childbirth (3.1% NFP; 4.2% FP; 11.8% government; P < .001). In adjusted analyses, the mean percentage of AMI patients classified as uninsured was similar in NFP and FP hospitals (4.4% vs. 4.3%; P = 0.71), and higher for government hospitals (6.0%; P < .001 for NFP vs. government). Likewise, results demonstrated similar proportions of uninsured patients in NFP and FP hospitals and higher levels of uninsured in government hospitals for both CABG and childbirth.</p> <p>Conclusions</p> <p>For the three conditions studied NFP and FP hospitals appear to provide a similar amount of uncompensated care while government hospitals provide significantly more. Concerns about the amount of uncompensated care provided by NFP hospitals appear warranted.</p

    Integration of deep transcriptome and proteome analyses reveals the components of alkaloid metabolism in opium poppy cell cultures

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    <p>Abstract</p> <p>Background</p> <p><it>Papaver somniferum </it>(opium poppy) is the source for several pharmaceutical benzylisoquinoline alkaloids including morphine, the codeine and sanguinarine. In response to treatment with a fungal elicitor, the biosynthesis and accumulation of sanguinarine is induced along with other plant defense responses in opium poppy cell cultures. The transcriptional induction of alkaloid metabolism in cultured cells provides an opportunity to identify components of this process via the integration of deep transcriptome and proteome databases generated using next-generation technologies.</p> <p>Results</p> <p>A cDNA library was prepared for opium poppy cell cultures treated with a fungal elicitor for 10 h. Using 454 GS-FLX Titanium pyrosequencing, 427,369 expressed sequence tags (ESTs) with an average length of 462 bp were generated. Assembly of these sequences yielded 93,723 unigenes, of which 23,753 were assigned Gene Ontology annotations. Transcripts encoding all known sanguinarine biosynthetic enzymes were identified in the EST database, 5 of which were represented among the 50 most abundant transcripts. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) of total protein extracts from cell cultures treated with a fungal elicitor for 50 h facilitated the identification of 1,004 proteins. Proteins were fractionated by one-dimensional SDS-PAGE and digested with trypsin prior to LC-MS/MS analysis. Query of an opium poppy-specific EST database substantially enhanced peptide identification. Eight out of 10 known sanguinarine biosynthetic enzymes and many relevant primary metabolic enzymes were represented in the peptide database.</p> <p>Conclusions</p> <p>The integration of deep transcriptome and proteome analyses provides an effective platform to catalogue the components of secondary metabolism, and to identify genes encoding uncharacterized enzymes. The establishment of corresponding transcript and protein databases generated by next-generation technologies in a system with a well-defined metabolite profile facilitates an improved linkage between genes, enzymes, and pathway components. The proteome database represents the most relevant alkaloid-producing enzymes, compared with the much deeper and more complete transcriptome library. The transcript database contained full-length mRNAs encoding most alkaloid biosynthetic enzymes, which is a key requirement for the functional characterization of novel gene candidates.</p

    Hospital Variation in Survival Trends for In‐hospital Cardiac Arrest

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    Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/139094/1/jah3561.pd
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