951 resultados para information system evolution
Resumo:
Iowa Community Colleges transmit fall enrollment data to the Department of Education after the 14th day of the fall term start date. All data included in this report, except where noted, are taken from the Management Information System (MIS) electronic data files and are confirmed by the community college.
Resumo:
Iowa Community Colleges transmit fall enrollment data to the Department of Education after the 14th day of the fall term start date. All data included in this report, except where noted, are taken from the Management Information System (MIS) electronic data files and are confirmed by the community college.
Resumo:
Iowa Community Colleges transmit fall enrollment data to the Department of Education after the 14th day of the fall term start date. All data included in this report, except where noted, are taken from the Management Information System (MIS) electronic data files and are confirmed by the community college.
Resumo:
Iowa Community Colleges transmit fall enrollment data to the Department of Education after the 14th day of the fall term start date. All data included in this report, except where noted, are taken from the Management Information System (MIS) electronic data files and are confirmed by the community college.
Resumo:
Iowa Community Colleges transmit fall enrollment data to the Department of Education after the 14th day of the fall term start date. All data included in this report, except where noted, are taken from the Management Information System (MIS) electronic data files and are confirmed by the community college.
Resumo:
Iowa Community Colleges transmit fall enrollment data to the Department of Education after the 14th day of the fall term start date. All data included in this report, except where noted, are taken from the Management Information System (MIS) electronic data files and are confirmed by the community college.
Resumo:
Iowa Community Colleges transmit fall enrollment data to the Department of Education after the 14th day of the fall term start date. All data included in this report, except where noted, are taken from the Management Information System (MIS) electronic data files and are confirmed by the community college.
Resumo:
Iowa Community Colleges transmit fall enrollment data to the Department of Education after the 14th day of the fall term start date. All data included in this report, except where noted, are taken from the Management Information System (MIS) electronic data files and are confirmed by the community college.
Resumo:
Introduction: Therapeutic drug monitoring (TDM) aims at optimizing treatment by individualizing dosage regimen based on measurement of blood concentrations. Maintaining concentrations within a target range requires pharmacokinetic and clinical capabilities. Bayesian calculation represents a gold standard in TDM approach but requires computing assistance. In the last decades computer programs have been developed to assist clinicians in this assignment. The aim of this benchmarking was to assess and compare computer tools designed to support TDM clinical activities.¦Method: Literature and Internet search was performed to identify software. All programs were tested on common personal computer. Each program was scored against a standardized grid covering pharmacokinetic relevance, user-friendliness, computing aspects, interfacing, and storage. A weighting factor was applied to each criterion of the grid to consider its relative importance. To assess the robustness of the software, six representative clinical vignettes were also processed through all of them.¦Results: 12 software tools were identified, tested and ranked. It represents a comprehensive review of the available software's characteristics. Numbers of drugs handled vary widely and 8 programs offer the ability to the user to add its own drug model. 10 computer programs are able to compute Bayesian dosage adaptation based on a blood concentration (a posteriori adjustment) while 9 are also able to suggest a priori dosage regimen (prior to any blood concentration measurement), based on individual patient covariates, such as age, gender, weight. Among those applying Bayesian analysis, one uses the non-parametric approach. The top 2 software emerging from this benchmark are MwPharm and TCIWorks. Other programs evaluated have also a good potential but are less sophisticated (e.g. in terms of storage or report generation) or less user-friendly.¦Conclusion: Whereas 2 integrated programs are at the top of the ranked listed, such complex tools would possibly not fit all institutions, and each software tool must be regarded with respect to individual needs of hospitals or clinicians. Interest in computing tool to support therapeutic monitoring is still growing. Although developers put efforts into it the last years, there is still room for improvement, especially in terms of institutional information system interfacing, user-friendliness, capacity of data storage and report generation.
Resumo:
The Annual Condition of Education Report includes an expanded Background Demographics section. This section contains information on population, economic, and social data, providing a comparison between Iowa, other states, and the Nation. Geographic Information System (GIS) maps are presented to allow the reader to easily compare between two or more geographies. In some instances, trends are also reported. Information displayed in this section was obtained from a variety of sources and is noted on each table or figure. In all areas, the most recent data available were used.
Resumo:
Objectives: Therapeutic drug monitoring (TDM) aims at optimizing treatment by individualizing dosage regimen based on blood concentrations measurement. Maintaining concentrations within a target range requires pharmacokinetic (PK) and clinical capabilities. Bayesian calculation represents a gold standard in TDM approach but requires computing assistance. The aim of this benchmarking was to assess and compare computer tools designed to support TDM clinical activities.¦Methods: Literature and Internet were searched to identify software. Each program was scored against a standardized grid covering pharmacokinetic relevance, user-friendliness, computing aspects, interfacing, and storage. A weighting factor was applied to each criterion of the grid to consider its relative importance. To assess the robustness of the software, six representative clinical vignettes were also processed through all of them.¦Results: 12 software tools were identified, tested and ranked. It represents a comprehensive review of the available software characteristics. Numbers of drugs handled vary from 2 to more than 180, and integration of different population types is available for some programs. Nevertheless, 8 programs offer the ability to add new drug models based on population PK data. 10 computer tools incorporate Bayesian computation to predict dosage regimen (individual parameters are calculated based on population PK models). All of them are able to compute Bayesian a posteriori dosage adaptation based on a blood concentration while 9 are also able to suggest a priori dosage regimen, only based on individual patient covariates. Among those applying Bayesian analysis, MM-USC*PACK uses a non-parametric approach. The top 2 programs emerging from this benchmark are MwPharm and TCIWorks. Others programs evaluated have also a good potential but are less sophisticated or less user-friendly.¦Conclusions: Whereas 2 software packages are ranked at the top of the list, such complex tools would possibly not fit all institutions, and each program must be regarded with respect to individual needs of hospitals or clinicians. Programs should be easy and fast for routine activities, including for non-experienced users. Although interest in TDM tools is growing and efforts were put into it in the last years, there is still room for improvement, especially in terms of institutional information system interfacing, user-friendliness, capability of data storage and automated report generation.
Resumo:
This report summarizes joint enrollment in Iowa's community colleges. Jointly enrolled students are high school students enrolled in community college credit coursework. This report contains fiscal year data for the state's 15 community colleges reported through the Community College Management Information System (MIS) and confirmed by each college.
Resumo:
The Iowa Juvenile Court Services Offices are issuing their fourth annual statewide report. The the Iowa Division of Criminal and Juvenile Justice Planning (CJJP). This report would not be possible without the dedication of, and assistance from, all of the above-mentioned people. The eight Chief Juvenile Court Officers would like to take this opportunity to thank their staff for their dedication and their ability to enter accurate information on every youth referred to Juvenile Court Services; the staff at the Iowa Court Information System, without whom this report would not be possible; and CJJP for their maintenance of the Iowa Justice Data Warehouse and their support in preparing this document.