Literature DB >> 8366244

Using large data bases in nursing and health policy research.

L L Lange1, A Jacox.   

Abstract

Concern about the quality, cost, and outcomes of health care has become a driving force in health policy research. The growing accessibility of large clinical and administrative health care data bases has led to an interest in using such data in health policy research. Clinical data bases are created by providers of care and contain data about episodes and outcomes of care, usually organized as patient records. Administrative data bases contain data about indirect care processes such as insurance claims processing, vital event recording, and quality assurance. Clinical and administrative data bases may contain millions of records, consist of data from multiple sites, and often have missing data issues that must be considered by researchers. These and other characteristics of large data bases require special data manipulation and analytic techniques. Large data bases have been used in epidemiological studies, risk assessment, and technology assessment and to study variations in caregiver practice patterns. Because the use of large data bases by nurse researchers has been constrained by the lack of nursing-relevant data in them, there is a need to reach consensus on useful and feasible nursing data elements and to include those data in ongoing data collection efforts by government agencies and private organizations.

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Year:  1993        PMID: 8366244     DOI: 10.1016/8755-7223(93)90037-d

Source DB:  PubMed          Journal:  J Prof Nurs        ISSN: 8755-7223            Impact factor:   2.104


  6 in total

1.  The era of patient safety: implications for nursing informatics curricula.

Authors:  Judith A Effken; Barbara Carty
Journal:  J Am Med Inform Assoc       Date:  2002 Nov-Dec       Impact factor: 4.497

2.  An introduction to tree-structured modeling with application to quality of life data.

Authors:  Xiaogang Su; Andres Azuero; June Cho; Elizabeth Kvale; Karen M Meneses; M Patrick McNees
Journal:  Nurs Res       Date:  2011 Jul-Aug       Impact factor: 2.381

Review 3.  Variables, variability, and variations research: implications for medical informatics.

Authors:  W L Holzemer; C A Reilly
Journal:  J Am Med Inform Assoc       Date:  1995 May-Jun       Impact factor: 4.497

4.  The use of rhBMP in spine surgery: is there a cancer risk?

Authors:  John G Devine; Joseph R Dettori; John C France; Erika Brodt; Robert A McGuire
Journal:  Evid Based Spine Care J       Date:  2012-05

5.  Validation of a method for identifying nursing home admissions using administrative claims.

Authors:  Ilene H Zuckerman; Masayo Sato; Van Doren Hsu; Jose J Hernandez
Journal:  BMC Health Serv Res       Date:  2007-12-10       Impact factor: 2.655

6.  The process and utility of classification and regression tree methodology in nursing research.

Authors:  Lisa Kuhn; Karen Page; John Ward; Linda Worrall-Carter
Journal:  J Adv Nurs       Date:  2013-11-17       Impact factor: 3.187

  6 in total

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