42 research outputs found

    Assessing the accuracy of an inter-institutional automated patient-specific health problem list

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    <p>Abstract</p> <p>Background</p> <p>Health problem lists are a key component of electronic health records and are instrumental in the development of decision-support systems that encourage best practices and optimal patient safety. Most health problem lists require initial clinical information to be entered manually and few integrate information across care providers and institutions. This study assesses the accuracy of a novel approach to create an inter-institutional automated health problem list in a computerized medical record (MOXXI) that integrates three sources of information for an individual patient: diagnostic codes from medical services claims from all treating physicians, therapeutic indications from electronic prescriptions, and single-indication drugs.</p> <p>Methods</p> <p>Data for this study were obtained from 121 general practitioners and all medical services provided for 22,248 of their patients. At the opening of a patient's file, all health problems detected through medical service utilization or single-indication drug use were flagged to the physician in the MOXXI system. Each new arising health problem were presented as 'potential' and physicians were prompted to specify if the health problem was valid (Y) or not (N) or if they preferred to reassess its validity at a later time.</p> <p>Results</p> <p>A total of 263,527 health problems, representing 891 unique problems, were identified for the group of 22,248 patients. Medical services claims contributed to the majority of problems identified (77%), followed by therapeutic indications from electronic prescriptions (14%), and single-indication drugs (9%). Physicians actively chose to assess 41.7% (n = 106,950) of health problems. Overall, 73% of the problems assessed were considered valid; 42% originated from medical service diagnostic codes, 11% from single indication drugs, and 47% from prescription indications. Twelve percent of problems identified through other treating physicians were considered valid compared to 28% identified through study physician claims.</p> <p>Conclusion</p> <p>Automation of an inter-institutional problem list added over half of all validated problems to the health problem list of which 12% were generated by conditions treated by other physicians. Automating the integration of existing information sources provides timely access to accurate and relevant health problem information. It may also accelerate the uptake and use of electronic medical record systems.</p

    Rapid quantification of underivatized amino acids in plasma by hydrophilic interaction liquid chromatography (HILIC) coupled with tandem mass-spectrometry

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    Background: Amino acidopathies are a class of inborn errors of metabolism (IEM) that can be diagnosed by analysis of amino acids (AA) in plasma. Current strategies for AA analysis include cation exchange HPLC with post-column ninhydrin derivatization, GC-MS, and LC-MS/MS-related methods. Major drawbacks of the current methods are time-consuming procedures, derivative problems, problems with retention, and MS-sensitivity. The use of hydrophilic interaction liquid chromatography (HILIC) columns is an ideal separation mode for hydrophilic compounds like AA. Here we report a HILIC-method for analysis of 36 underivatized AA in plasma to detect defects in AA metabolism that overcomes the major drawbacks of other methods. Methods: A rapid, sensitive, and specific method was developed for the analysis of AA in plasma without derivatization using HILIC coupled with tandem mass-spectrometry (Xevo TQ, Waters). Results: Excellent separation of 36 AA (24 quantitative/12 qualitative) in plasma was achieved on an Acquity BEH Amide column (2.1×100 mm, 1.7 ÎŒm) in a single MS run of 18 min. Plasma of patients with a known IEM in AA metabolism was analyzed and all patients were correctly identified. Conclusion: The reported method analyzes 36 AA in plasma within 18 min and provides baseline separation of isomeric AA such as leucine and isoleucine. No separation was obtained for isoleucine and allo-isoleucine. The method is applicable to study defects in AA metabolism in plasma

    A new method to measure the polymerization shrinkage kinetics of light cured composites

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    summary This study was undertaken to develop a new measurement method to determine the initial dynamic volumetric shrinkage of composite resins during polymerization, and to investigate the effect of curing light intensity on the polymerization shrinkage kinetics. The instrument was basically an electromagnetic balance that was constructed with a force transducer using a position sensitive photo detector (PSPD) and a negative feedback servo amplifier. The volumetric change of composites during polymerization was detected continuously as a buoyancy change in distilled water by means of the Archimedes' principle. Using this new instrument, the dynamic patterns of the polymerization shrinkage of seven commercial composite resins were measured. The polymerization shrinkage of the composites was 1·92∌4·05 volume %. The shrinkage of a packable composite was the lowest, and that of a flowable composite was the highest. The maximum rate of polymerization shrinkage increased with increasing light intensity but the peak shrinkage rate time decreased with increasing light intensity. A strong positive relationship was observed between the square root of the light intensity and the maximum shrinkage rate. The shrinkage rate per unit time, dVol%/dt, showed that the instrument can be a valuable research method for investigating the polymerization reaction kinetics. This new shrinkage-measuring instrument has some advantages that it was insensitive to temperature changes and could measure the dynamic volumetric shrinkage in real time without complicated processes. Therefore, it can be used to characterize the shrinkage kinetics in a wide range of commercial and experimental visible-light-cure materials in relation to their composition and chemistr
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