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At least 271 records · Page 15Linked to original sources

A joint hospital/vendor project brings CQI and point-of-care technology to home care.

A joint project team consisting of personnel from Parkview Episcopal Medical Center, Pueblo, Colorado, and Patient Care Technologies, Atlanta, Georgia, a software vendor, codeveloped a point-of-care based system of electronic patient records and administrative data capture for home health care. Well established continuous quality improvement techniques, in use at Parkview for approximately 6 years, guided the development project and the subsequent alpha and beta testing of the system. Significant results to date include an overall productivity gain approaching 20%, the potential to increase annual home care revenue $876,000 with the same staffing level, and an 83% reduction in billing errors. Although not directly measured as a part of the study, the project team believes the quality of charting has improved because it is now done at the point-of-care in the home rather than in the office--some period of time after care is delivered. Anticipated future development includes integration of the home care clinical record with the hospital's clinical data repository and explicit support of critical pathways.

Accounting↗

HELP the next generation: a new client-server architecture.

A new client-server based system which is centered around a lifetime data repository (LDR) is under construction. The goal of the new system is to maintain the patient centered decision support aspects of the existing HELP* system while providing an open architecture that supports faster application development and allows execution of applications to be distributed across many computers. These goals are achieved by implementing the system with software components that are commercially available or by adhering to national and international standards for software integration. Keys to successful integration include the use of MS-DOS @, OS/2#, and UNIX Section as operating systems, Microsoft OLE 2.0 as a standard interface to the clinical database, the use of TUXEDO as a transaction/communication manager, and the use of ORACLE [symbol: see text] RDBMS as the underlying database management system.

Computer Communication Networks↗

Using hub technology to facilitate information system integration in a health-care enterprise.

The deployment and maintenance of multiple point-to-point interfaces between a clinical information system, such as a laboratory information system, and other systems within a healthcare enterprise is expensive and time consuming. Moreover, the demand for such interfaces is increasing as hospitals consolidate and clinical laboratories participate in the development of regional laboratory networks and create host-to-host links with laboratory outreach clients. An interface engine, also called a hub, is an evolving technology that could replace multiple point-to-point interfaces from a laboratory information system with a single interface to the hub, preferably HL7 based. The hub then routes and translates laboratory information to other systems within the enterprise. Changes in application systems in an enterprise where a centralized interface engine has been implemented then amount to thorough analysis, an update of the enterprise's data dictionary, purchase of a single new vendor-supported interface, and table-based parameter changes on the hub. Two other features of an interface engine, support for structured query language and information store-and-forward, will facilitate the development of clinical data repositories and provide flexibility when interacting with other host systems. This article describes the advantages and disadvantages of an interface engine and lists some problems not solved by the technology. Finally, early developmental experience with an interface engine at the University of Michigan Medical Center and the benefits of the project on system integration efforts are described, not the least of which has been the enthusiastic adoption of the HL7 standard for all future interface projects.

Delivery of Health Care↗

Using the Java language to develop computer based patient records for use on the Internet.

The development of the Java Programming Language by Sun Microsystems has provided a new tool for the development of Internet based applications. Our preliminary work has shown how Java can be used to program an Internet based CBPR. Java is well suited to the needs of patient records and can interface with clinical data repositories written in MUMPS or SQL.

Computer Communication Networks↗

Genetic mapping of susceptibility loci in the genes involved in rheumatoid arthritis.

Eighty-nine multicase rheumatoid arthritis families, each containing at least one affected sib pair, have been studied for evidence of genetic linkage to a panel of 315 microsatellite DNA markers. The families were located through the UK national data repository of the Arthritis and Rheumatism Council. Microsatellites were genotyped by semiautomated technology using Applied Biosystems sequencers (ABI 373). Using the SIBPAIR statistical package, linkage to HLA was confirmed (p < 0.0003). Several possible linkages outside HLA were noted, including at least one (p < 0.004) that merits further investigation.

Arthritis, Rheumatoid↗

Elements for a report card: one institution's experience moving from theory to trial.

The emerging computerized patient record, the movement toward a unified nursing language, and increasing accountability to the public for clinical outcomes are converging forces that require a coherent plan if nursing is to evaluate outcomes. The article describes a nursing service and school of nursing's approach and beginning experiences in identifying concepts and indicators that are useful to clinicians. These concepts and indicators have the potential to be embedded in a concurrent information system that would feed a data repository used retrospectively by clinicians and researchers. The article also discusses results to date and lessons learned.

Databases, Factual↗

Generic database design for patient management information.

Patient management information tracks general facts about the location of the patient and the providers assigned to care for the patient. The Clinical Data Repository at Columbia Presbyterian Medical Center employs a generic schema to record patient management events. The schema is extremely simple, yet can support several different views of patient information, as required by different applications: a longitudinal view of patient visits, including both inpatient and outpatient encounters; a visit-oriented view, to record facts related to a current encounter; a location-based view to provide a census of a nursing ward; and a provider-based view to give a list of the patients currently being cared for by a given clinician. All of these views can be supported in a highly efficient manner by the use of appropriate indexes.

Databases as Topic↗

ANA standards for nursing data sets in information systems.

The American Nurses Association has established the Nursing Information and Data Set Evaluation Center. The purpose of this Center is to develop and disseminate standards pertaining to information systems that support the documentation of nursing practice, and to evaluate voluntarily submitted information systems against these standards. The need for an evaluation center arises out of a long history of calls for standards pertaining to nursing data and information systems. These calls have come from the Secretary of Health and Human Services Commission on Nursing, the National Commission on Nursing Implementation Project, and a joint Task Force appointed by the Congress of Nursing Practice Steering Committee on Databases to Support Clinical Nursing Practice and the Congress of Nursing Practice Committee on Nursing Practice Standards and Guidelines. Standards have been developed to evaluate the completeness, accuracy and appropriateness of four dimensions of nursing data sets and the systems that contain them: (1) nomenclature, (2) clinical content, (3) clinical data repository, and (4) general system characteristics.

Academies and Institutes↗

Setting standards for nursing data sets in information systems.

The American Nurses Association (ANA) has established the Nursing Information and Data Set Evaluation Center (NIDSEC). The purpose of this Center is to develop and disseminate standards pertaining to information systems that support the documentation of nursing practice, and to evaluate voluntarily submitted information systems against these standards. The need for an evaluation center arises out of a long history of calls for standards pertaining to nursing data and to information systems. Standards have been developed to evaluate the completeness, accuracy and appropriateness of four dimensions of nursing data sets and the systems that contain them 1) nomenclature, 2) clinical content, 3) clinical data repository, and 4) general system characteristics. Once the standards and scoring mechanisms were established, they were tested for reliability and validity. A Content Validity Index (CVI) of 0.875 was established for the scoring tool.

American Nurses' Association↗

Integration of DICOM images into an electronic medical record using thin viewing clients.

Over the past five years the University of Washington has created a clinical data repository. This repository combines in a distributed relational database information from multiple departmental databases (MIND). MINDscape provides a platform independent, web browser view of the MIND dataset that can easily be linked to other information resources on the network.

Computer Security↗

Describing patient problems & nursing treatment patterns using nursing minimum data sets (NMDS & NMMDS) & UHDDS repositories.

Dramatic changes in health care have intensified practitioners' efforts to access and use information to determine more efficacious approaches to patient outcomes. The overall goal of the study is to measure the influence of nursing informatics clinical reasoning decision support interventions on patient outcomes. This paper describes Phases I of the study: the methodology for establishing and testing the usefulness of large data repositories comprised of three minimum data sets, including the Nursing Minimum Data Set (NMDS), the Nursing Management Minimum Data Set (NMMDS), and the Uniform Hospital Discharge Data Set (UHDDS), and the American Nurses Association Quality Indicators to support effectiveness research. The use of generic data modeling to construct a clinical nursing repository of more than 477,000 electronic records is discussed. Patient problem and treatment profiles, patterns, and variations based on standardized analyzing classifications are described for inpatient adult samples, and nursing and medical diagnosis groups.

Databases, Factual↗

Consistently processed RNA sequencing data from 50 sources enriched for pediatric data.

Larger cohorts improve the power of tumor gene expression analysis, but the signal is muddied if datasets are processed using different methods or have inaccurate metadata. Here we present five compendia containing consistently processed gene expression data derived from 16,446 diverse RNA sequencing datasets. To create the compendia, we obtained access to RNA sequence data from repositories containing public data as well as clinical partners with access to non-published data. We then assessed the quality, quantified gene expression, harmonized clinical metadata, and released the expression values and metadata without access restrictions. These datasets have been used for diverse projects ranging from identifying similarities between tumor types to assessing how well cell lines recapitulate tumors. They have also been used for n-of-1 analysis to identify genes with unusual expression patterns in a single sample and to infer molecular diagnosis. The comparison to new data is enabled by our dockerized, freely available pipeline. The compendia have been cited in at least 20 publications.

Humans↗

NASCArrays: a repository for microarray data generated by NASC's transcriptomics service.

NASC operates an Affymetrix 'GeneChip' (microarray) service for the Arabidopsis thaliana community. All data produced by the service are publicly available through our microarray data base 'NASCArrays' published at http://affymetrix. arabidopsis.info. The data are accessible through text searching and a series of data mining tools. All data are annotated with sample preparation details, and the original Affymetrix data are available for download. The database aims to be MIAME supportive and provide a coordinated resource for re searchers interested in the transcriptome of Arabidopsis. Using this database, data produced will be shared with other databases worldwide.

Arabidopsis↗

GICPIdb: an archival repository of multimodal data focusing on pathological images for gastrointestinal cancers.

INTRODUCTION: Deep learning (DL) shows great potential for predicting biomarkers from routine histopathological slides of gastrointestinal (GI) cancers. Yet most existing models are validated on limited patient cohorts, while pathological image annotation and molecular marker standardization demand substantial professional expertise. To address these gaps, we constructed the Gastrointestinal Cancer Pathological Image Archive (GICPIdb, gicpidb.shubuzuo.top), a dedicated database and web platform covering seven major GI cancer types. METHODS: High-quality hematoxylin and eosin (H&E)-stained whole-slide images were collected from multiple sources and uniformly processed. Image annotations were performed by board-certified pathologists following standardized protocols. GICPIdb offers five interactive web modules for data uploading, quality control, feature extraction, online annotation and AI-based prediction. Its intuitive interface supports data browsing, retrieval, visualization and downloading. RESULTS: The database houses 2,863 pathologist-annotated, uniformly processed, high-quality H&E stained images collected from 2,655 patients. Of these, 1,699 patients were sourced from The Cancer Genome Atlas (TCGA), 182 from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and 424 from China-Japan Friendship Hospital and 350 from Chifeng Municipal Hospital in Inner Mongolia, China. It also integrates data on over 50 key molecular markers (e.g., MSI, TMB) and prognostic labels related to survival, recurrence and metastasis. DISCUSSION: GICPIdb aims to promote the development of DL-driven AI tools for cancer research and clinical translation. The multi-institutional data collection and standardized annotation pipeline are expected to enhance the generalizability and reproducibility of AI-based prediction models across diverse patient populations.

deep learning↗

Development and application of a sorption data base for the performance assessment of a radwaste repository.

A sorption data base (SDB) provides readily available data for the performance assessment of a radwaste repository when site-specific and/or reference data are needed. The software developed at KAERI, SDB-21C, is a graphic user interface (GUI) program that provides efficient and user-friendly tools for evaluating large amount of sorption data. In addition, the most comprehensive sorption data base that contains about 11,000 NEA data and 2,000 KAERI data was compiled in the program. Besides the simple Kd approach, a parametric model and its compiled data sets are also included in the SDB-21C. In order to evaluate the versatility of SDB-21C, several applications were performed for relevant hypothetical situations.

Adsorption↗

Sending data to a central repository.

The Veterinary Medical DataBases (VMDB) has been collecting data from participating institutions for 40 years. We have recently started a program to collect clinical case data from not only veterinary schools but from veterinary practitioners as well. In so doing, we are using commonly accepted means of data representation and transmission. Thus, we use the Standard Nomenclature of Medicine (SNOMED) as a standard vocabulary language and Health Level 7 (HL 7) as a standard transmission protocol. We report on the planning and implementation of these standards and the problems encountered in their implementation.

Animal Diseases↗