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Achieving breakthroughs of key core technologies in the aero-engine industry is urgently required for guaranteeing national strategic security, and it demands effective organization of innovation forces. This study first analyses the major innovation forces and their problems regarding collaborative innovation in the current aero-engine scientific research and production system in China. Subsequently, it clarifies the roles of relevant forces in the research process of key core technologies, including the government, the army, the Aero Engine Corporation of China, professional maintenance enterprises, upstream and downstream enterprises, universities, and research institutes. Furthermore, we suggest that the breakthrough practice should be planned and organized in an overall manner, corresponding institutions and mechanisms should be created to encourage the innovation forces to closely align with national strategies, the driving role of the demand side should be emphasized, a number of innovation consortia aiming at user needs and product problems should be established by leading enterprises, and an innovation ecosystem should be established relying on major projects.

Purpose: Performance measurement systems for nursing homes assume that facility performance contributes heavily to individual outcomes. This research illustrates how that assumption can be assessed using the change in residents' activities of daily living (ADLs). Design and Methods: The data used in these analyses were all from residents with both an admission and a quarterly assessment in a sample of all admissions to a randomly chosen 10% of Medicare- or Medicaid-certified nursing homes operating during 2002. Results: Models including both facility and individual variables explained up to 20% of the variation in ADL change after admission. Facility identity in isolation explained between 8% and 14% of the variation in ADL change. Implications: The results suggest that quality indicators based on change in ADLs may be problematic when used in nursing home performance measurement systems. More generally, the results recommend that the level of variation in performance measures attributable to facility identity or performance become a much more central consideration when researchers evaluate quality indicators for use in nursing home performance measurement systems. Key Words: Nursing homes, Quality indicators, Long-term care, Quality of care

The routine use of sequential methods is well established in clinical studies. Recently, there has been increasing interest in applying these methods to prospectively monitor the safety of newly approved drugs through accrual of real-world data. However, the application to marketed drugs using real-world data has been limited and work is needed to determine which sequential approaches are most suited to such data. In this study, the conditional sequential sampling procedure (CSSP), a group sequential method, was compared with a log-linear model with Poisson distribution (LLMP) through a SAS procedure (PROC GENMOD) combined with an alpha-spending function on two large longitudinal US administrative health claims databases. Relative performance in identifying known drug-outcome associations was examined using a set of 50 well-studied drug-outcome pairs. The study finds that neither method correctly identified all pairs but that LLMP often provides better ability and shorter time for identifying the known drug-outcome associations with superior computational performance when compared with CSSP, albeit with more false positives. With the features of flexible confounding control and ease of implementation, LLMP may be a good alternative or complement to CSSP. [ABSTRACT FROM AUTHOR]

Background: Information exists as unstructured medical text in healthcare databases. Such information is not routinely considered in safety surveillance but typically relies solely on structured (coded) data. Natural language processing (NLP) may allow the capture of concepts from unstructured data and thus enhance safety surveillance capability.Objectives: We sought to assess the added contribution of unstructured data extracted from medical text by NLP for detecting acute liver dysfunction (ALD) in patients with inflammatory bowel disease (IBD).Methods: Using a previously developed rule, we evaluated structured and unstructured NLP-extracted terms from a commercially available electronic medical record (EMR) system. The rule was intended to identify ALD diagnosis and timing of onset and was the result of three iterations of rule development using 150 ALD candidate cases. We evaluated the performance of the rule with or without NLP among all candidate cases and among 50 new cases with clinical adjudication.Results: NLP terms were necessary for the diagnosis of 9% of cases and for ruling out 3% of false-positive cases. Inclusion of NLP terms led to an identification of an additional  9% of ALD-onset dates, with consequent earlier recognition in 5%.Conclusions: NLP-derived terms in one large commercially available EMR system modestly improved the sensitivity and specificity in the identification of ALD and identified earlier onset. [ABSTRACT FROM AUTHOR]