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. 1999 Feb;32(1):11-23.
doi: 10.1007/s003910050076.

[The Geriatric Minimum Data Set (Gemidas) of the Federal Association of Clinical Geriatric Facilities e. V. as an instrument for quality assurance in inpatient geriatrics]

[Article in German]
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[The Geriatric Minimum Data Set (Gemidas) of the Federal Association of Clinical Geriatric Facilities e. V. as an instrument for quality assurance in inpatient geriatrics]

[Article in German]
M Borchelt et al. Z Gerontol Geriatr. 1999 Feb.

Abstract

Background: Geriatric medicine in Germany is faced with an increasing demand for continuous documentation and evaluation of its effectiveness and efficiency. Hence, the Federal Association (FA) of Clinical Geriatric Departments (Bundesarbeitsgemeinschaft der Klinisch-Geriatrischen Einrichtungen e.V.) has funded a working group on improving quality management in geriatrics by developing criteria for quality standards.

Methods: In 1996, the FA working group achieved consensus on the definition of the Geriatric Minimum Data Set (Gemidas) which covered (i) core information about a patient's age, sex, living arrangement, and (ii) basic characteristics of the hospital course such as location prior to admission and past discharge, leading and accompanying diagnoses, newly prescribed technical aids, objective functional status on admission and at discharge (e.g., Barthel Index (BI), Timed Up & Go (TUG), and intensity of professional care (PPR)), as well as subjectively evaluated attainment of treatment goals. This initial report describes the instrument and presents analyses of its feasibility for routine clinical practice and data consistency.

Results: Twenty out of 27 hospitals (74%) integrated Gemidas successfully in daily routine, 75% of which (15 hospitals, total n = 10,567 patients) instantaneously collected data on constant numbers of patients per month. Multivariate regression analyses used to decompose variances of the instrument's central indicators (e.g., BI, TUG, PPR) revealed a satisfactory dimensionality and high consistency (e.g., covering 59% of variance in BI with 53% of variance uniquely attributable to patient characteristics), as well as sensitivity to differences between hospitals (e.g., 12% of variance in duration of stay uniquely attributable to hospital differences after controlling for patients' characteristics).

Conclusion: Gemidas appears to be a feasible quality assurance instrument in geriatrics, suitable for compiling its data into a central registry database, which may then be used for analyses across and between hospitals. However, some modifications are still necessary and more detailed analyses needed, before final recommendations can be made.

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