The Resident Assessment Instrument-Minimum Data Set 2.0 quality indicators: a systematic review
Alison M. Hutchinson,Doris L. Milke,Suzanne Maisey,Cynthia Johnson,Janet E. Squires,Gary F. Teare,Carole A. Estabrooks +6 more
TLDR
Evidence for the reliability and validity of the RAI-MDS QIs remains inconclusive, but the QIs provide a useful tool for quality monitoring and to inform quality improvement programs and initiatives, however, caution should be exercised when interpreting the QI results.Abstract:
The Resident Assessment Instrument-Minimum Data Set (RAI-MDS) 2.0 is designed to collect the minimum amount of data to guide care planning and monitoring for residents in long-term care settings. These data have been used to compute indicators of care quality. Use of the quality indicators to inform quality improvement initiatives is contingent upon the validity and reliability of the indicators. The purpose of this review was to systematically examine published and grey research reports in order to assess the state of the science regarding the validity and reliability of the RAI-MDS 2.0 Quality Indicators (QIs). We systematically reviewed the evidence for the validity and reliability of the RAI-MDS 2.0 QIs. A comprehensive literature search identified relevant original research published, in English, prior to December 2008. Fourteen articles and one report examining the validity and/or reliability of the RAI-MDS 2.0 QIs were included. The studies fell into two broad categories, those that examined individual quality indicators and those that examined multiple indicators. All studies were conducted in the United States and included from one to a total of 209 facilities. The number of residents included in the studies ranged from 109 to 5758. One study conducted under research conditions examined 38 chronic care QIs, of which strong evidence for the validity of 12 of the QIs was found. In response to these findings, the 12 QIs were recommended for public reporting purposes. However, a number of observational studies (n = 13), conducted in "real world" conditions, have tested the validity and/or reliability of individual QIs, with mixed results. Ten QIs have been studied in this manner, including falls, depression, depression without treatment, urinary incontinence, urinary tract infections, weight loss, bedfast, restraint, pressure ulcer, and pain. These studies have revealed the potential for systematic bias in reporting, with under-reporting of some indicators and over-reporting of others. Evidence for the reliability and validity of the RAI-MDS QIs remains inconclusive. The QIs provide a useful tool for quality monitoring and to inform quality improvement programs and initiatives. However, caution should be exercised when interpreting the QI results and other sources of evidence of the quality of care processes should be considered in conjunction with QI results.read more
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References
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Book
Practical statistics for medical research
TL;DR: Practical Statistics for Medical Research is a problem-based text for medical researchers, medical students, and others in the medical arena who need to use statistics but have no specialized mathematics background.
Standards for educational and psychological testing
Alija Kulenović,Vesna Buško +1 more
TL;DR: For example, Standardi pružaju okvir koje ukazuju na ucinkovitost kvalitetnih instrumenata u onim situacijama u kojima je njihovo koristenje potkrijepljeno validacijskim podacima.
Journal ArticleDOI
MDS Cognitive Performance Scale
John N. Morris,Brant E. Fries,David R. Mehr,Catherine Hawes,Charles D. Phillips,Vincent Mor,Lewis A. Lipsitz +6 more
TL;DR: A Cognitive Performance Scale (CPS) is designed that uses MDS data to assign residents into easily understood cognitive performance categories, and should prove useful to clinicians and investigators using the MDS to determine a resident's cognitive assets.
Journal ArticleDOI
Tools for assessing quality and susceptibility to bias in observational studies in epidemiology: a systematic review and annotated bibliography
TL;DR: A number of useful assessment tools have been identified and a need to agree on critical elements for assessing susceptibility to bias in observational epidemiology and to develop appropriate evaluation tools.