Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “data integration”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6Linked to original sources
PubMed1996

Integrating data from natural language processing into a clinical information system.

Demographic data extracted from discharge summaries by natural language processing was compared to data gathered by a conventional hospital admitting system. Discrepancies in data were noted in names, age, sex, race, and ethnicity. Some differences are attributable to errors in collection: interaction with patient, dictation, transcription, and data entry. Very few differences were due to errors in natural language processing. Other differences can be used to critique existing data, or to enhance data with more detailed information. Discrepancies in data as elementary as patient demographics raise the issue of resolving conflicts when neither source of data is known to be more reliable. Clinical repositories can represent conflicting data from multiple sources, but clinical information systems must bear the cost of increased complexity in the application programs that will use the data.

Evaluation Studies as Topic↗
PubMed2001

Integrated patient data for optimal patient management: the value of laboratory data in quality improvement.

Managed care organizations are shifting from traditional utilization management programs to focus on initiatives that improve the health of an insured population. This strategy requires sophisticated data integration to identify at-risk individuals and track outcomes. Laboratory data are becoming increasingly valuable tools for managed care organizations and healthcare providers. The HEDIS Effectiveness of Care measures have incorporated laboratory data into several key performance indicators. By building a comprehensive repository of laboratory data that includes both procedure codes and laboratory values, managed care organizations can realize substantial savings by avoiding the costly medical record reviews required when administrative data are incomplete. In addition to tracking clinical outcomes, laboratory data provide the ability to risk-stratify a population to target high-risk individuals for case management and disease management interventions. Healthcare organizations face several challenges in the integration of laboratory data into medical databases and practice management software. Confidentiality is a key consideration in view of recent healthcare regulations. Providers of laboratory services should work collaboratively with organizations setting standards for healthcare informatics to facilitate the pooling of data for quality improvement and outcomes research. Health Level Seven, Inc. (HL7), Logical Observation Identifier Names and Codes (LOINC), and Systematized Nomenclature of Medicine (SNOMED) will likely play a key role in this process.

Clinical Laboratory Techniques↗
PubMed1997

Using case management systems to integrate clinical and financial data.

Integrated delivery systems can use automated case management information systems to better manage relevant clinical and financial data across the continuum of care. Effective case management systems can help caregivers track clinical and financial information; match appropriate resources to patient needs; and analyze populations to identify risk, enhance adherence to clinical guidelines, and understand provider treatment profiles.

Case Management↗
PubMed1995

An object-oriented model for the integration of knowledge-based systems.

This paper discusses functional integration, data integration, and knowledge integration as basic problems concerning the integration of knowledge-based systems into a hospital information system. A system model for an integrated knowledge-based system is introduced. Object-oriented models for the systems meta-database, patient database, and knowledge-base are presented. It is expected that the reader is familiar with the basic concepts of the object-oriented approach.

Artificial Intelligence↗
PubMed1990

Integrity of small data bases in computer analysis of dietary data.

The integrity of data bases to support microcomputer-based dietary analysis programs has become increasingly important to developers and users of nutritional analysis software. This paper reviews critical issues in maintaining data integrity during development of small nutritional data bases. Because a limited number of large, source data bases provides the data for smaller, special-purpose data bases, this review initially focuses on factors that affect the quality and precision of methodologies used in establishing large data bases. Issues discussed are accuracy of source data as determined by analytical methodology and imputation procedures, and methods for insuring representativeness of data. The effect of data transfer procedures on small data base integrity are discussed, including use of multiple sources and standardization of naming and coding conventions. Also reviewed are procedures for selecting reduced numbers of foods and nutrients without sacrificing accuracy of analysis, and methods currently in use for validating small data bases.

Databases, Factual↗
PubMed2003

Contextualizing heterogeneous data for integration and inference.

Systems that attempt to integrate and analyze data from multiple data sources are greatly aided by the addition of specific semantic and metadata "context" that explicitly describes what a data value means. In this paper, we describe a systematic approach to constructing models of data and their context. Our approach provides a generic "template" for constructing such models. For each data source, a developer creates a customized model by filling in the tem-plate with predefined attributes and value. This approach facilitates model construction and provides consistent syntax and semantics among models created with the template. Systems that can process the template structure and attribute values can reason about any model so described. We used the template to create a detailed knowledge base for syndromic surveillance data integration and analysis. The knowledge base provided support for data integration, translation, and analysis methods.

Database Management Systems↗