Comparative Database Report

Sorra, J., Famolaro, T., Dyer, N., Nelson, D. andKhanna, K. 2009. Hospital Survey on Patient Safety Culture 2009 Comparative Database Report. Agency for Healthcare Research and CJrrality, AHRQ Ptrblication No. 09-0030, Rockville, MD. 

Agency for Healthcare Research and Quality (AHRQ 2012). 2012 User Comparative Database Report Hospital Survey on Patient Safety Culture. AHRQ, Rockville, MD. Available at http //www.ahrq.gov/qual/hospsurveyl2/ (last accessed on 14 June 2013). 

Since the first annual comparative database report, published in 2007, which included data voluntarily submitted from 382 US hospitals, the number of hospitals and staff respondents included in the database report has grown each year. The Hospital SOPS 2012 Comparative Database Report displays results from 1,128 hospitals and 567,703 hospital staff respondents (Sorra et al. 2012). This large number of hospitals provides for a much more reliable and comprehensive set of benchmarks. 

Mode of survey administration When the first comparative database report was released in 2007, most hospitals administered paper surveys (56 percent), followed by web (25 percent) and mixed mode (both paper and web -19 percent). By 2012, with rapidly advancing technology, web surveys became the predominant mode (66 percent), followed by paper (21 percent) and mixed mode (13 percent). The one caveat in this trend is that paper surveys still seem to get the highest response rates in US hospitals - on average at least 10 percentage points hi er than other modes. So if response rates are a concern and a hospital has the capabihty and resources to conduct a paper survey, it is still best to administer it by paper. But if costs are an issue, it can often be very eost-effective to administer a web snrvey. User s Guides, available for eaeh of the SOPS surveys on the AHRQ web site (www.ahrq.gov), contain tips and guidance on how best to administer the survey and present results. 

Findings fivm the Hospital SOPS 2012 Comparative Database Report... 

Hospital Survey on Patient Safety Culture Comparative Database Reports give benchmark data collected voluntarily from more than 1000 US hospitals. Survey results from these hospitals are averaged over the entire sample by topical composite or individual survey item. Two appendices report the average responses, which are broken down by hospital or respondent characteristics. 

The Medical Office Survey on Patient Safety Culture 2012 Comparative Database Report presents data from 23,679 staff within 934 US medical offices that completed the Medical Office Survey on Patient Safety Culture, so offices can compare their patient safety culture to other medical offices. The full report contains detailed comparative data for various medical office characteristics (number of providers, specialty, ownership, and region) and staff positions (AHRQ Publication No. 12-0052). 

The Nursing Home Survey on Patient Safety Culture 2011 User Comparative Database Report is based on data from 226 nursing homes in the United States and provides initial results that nursing homes can use to compare their patient safety culture to other US nursing homes. The report consists of a narrative description of the findings and four appendices presenting data by nursing home characteristics and respondent characteristics (AHRQ Publication No. 11-0030). 

Acute kidney injury (AKI) is easily defined as a syndrome characterized by a sudden decrease in GFR accompanied by azotemia [4]. However the reported incidence of AKI varies depending on a number of independent variables. For example, was the patient population surveyed derived from a community wide database or was it restricted to hospitalized patients What definition was adopted to designate acute kidney injury (AKI) The lack of a universally agreed upon definition of acute kidney injury (AKI), makes it difficult to compare clinical reports as to the incidence. 

If tube metal temperature data are not available, they can be calculated from process information and verified by correlation with the historic metal loss rates. This procedure is obviously one of second choice nevertheless, it has been adopted successfully many times. Alternatively, when the desired thermocouple tube metal temperature and tube wall thickness inspection data are available, one of the standard QA data procedures is to check that they are physically compatible. The authors have come across cases where either the thermocouple wall temperature data or the reported wall thickness data must be in error, as it was physically and thermodynamically impossible for them to co-exist. In such cases, the procedure is to compare the tube wall thinning history in question with an in-house database for all such similar alloys and compare the reported thermocouple data with calculated data from process information. It is generally clear which of the data sets is in major error. 

Figure 6 examines differences in the distribution of days away from work for these industry sectors. Compared with the private sector and all manufacturing, both the semiconductor industry and the OHS database reported greater proportions of cases involving only one or two days way from work. This experience tends to indicate that lost workday cases involving semiconductor workers are less severe than for workers in the other comparison industry sectors. 

European regulation should create the framework so that each Member State uses an equivalent reporting procedures and templates to enable fully compatible and comparable databases. A unified safety reporting across Europe, using the same economic principles and cost components, would be a great benefit. 

Due to these characteristics, the possibility to build databases of NMR-based metabolic profiles of foodstuffs, in order to assess their quality, is becoming more and more actual [9]. However, when we apply multivariate analysis to our data fields or wish to compare databases obtained by NMR spectroscopy by other researchers, it is very important to consider the source of extrinsic variability originated by the employed experimental conditions, in terms of sample preparation, NMR acquisition parameters, signal-to-noise ratio, NMR spectral pre-treatment, NMR data pre-processing, as well as the strategy for NMR-based metabolomics (for an exhaustive introduction to the reported critical cmicepts see the manual by Axelson [10]). 

A standardization of the experimental protocols to obtain standard sample is desirable in order to compare database acquired in different laboratories. In this context, several initiatives, carried out by the Standard Metabolic Reporting Structures group, were focused to produce draft policy documents covering all aspects of a metabolomic study that are recommended for recording, from the raw biological sample, the analysis of material from that sample, and che-mometric and statistical approaches to retrieve informaticHi from the sample data [19-21]. 

Historical DataBase Subsystem We have discussed the use of on-hne databases. An historical database is built similar to an on-line database. Unlike their on-line counterparts, the information stored in a historical database is not normally accessed directly by other subsystems for process control and monitoring. Periodic reports and longterm trends are generated based on the archived data. The reports are often used for long-term planning and system performance evaluations such as statistical process (quality) control. The trends may be used to detect process drifts or to compare process variations at different times. 

A portion of the database for this polymer is shown in Figure 6. Literature reports that this polymer follows second-order Markov statistics ( 21 ). And, in fact, probabilities that produced simulated spectra comparable to the experimental spectrum could not be obtained with Bernoullian or first-order Markov models. Figure 7 shows the experimental and simulated spectra for these ten pentads using the second-order Markov probabilities Pil/i=0.60, Piv/i=0.35, Pvi/i=0.40, and Pvv/i=0.55 and a linewidth of 14.8 Hz. 

Incorporating the Kirtas system with the International Plant Names Index and SNOW-MED allows movement of the historic text into an electronic format, identihcation of current plant names, and identihcation of the symptoms treated with the plants. To complete the mining of historic herbal texts for novel drug leads we use the Natural Products Alert (NAPRALERT ) database to compare the information extracted from the historic herbal text to the reports of plant use in the current literature. The NAPRALERT database provides a summary of plants ethnopharmacological use, biochemical activities, and isolated compounds [27]. By querying each plant (with the current plant name) it is possible to identify any reports in the current literature regarding the plant. As an example, Table 4.1 shows the NAPRALERT output for Cycas rumphii. 

Temporal Relationships of Adverse Events. The temporal relationship between duration of product exposure and development of an adverse event is important in assessing causality. But how can data on temporal relationships be systematically summarized in a database containing thousands or even hundreds of thousands of subjects Temporal relationships cannot be clearly elicited if only frequencies of adverse events between treatment and control groups are compared. There can be many disparities in the subjects time of exposure or time at risk. Toxic manifestations of drugs may not occur until several months or even years after the initial exposure to the drug. How do we systematically assess delayed toxicity of a previously prescribed drug from the effect of a newly prescribed drug Such a scenario occurred with reported cases of pancreatitis associated with valproic acid therapy, in which some cases appeared several years after therapy [2]. 

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