Analyzing Hospital Transfers Using INTERACT Acute Care Transfer Tools: Lessons from MOQI

OBJECTIVES We explored the differences in potentially avoidable/unavoidable hospital transfers in a retrospective analysis of Interventions to Reduce Acute Care Transfers (INTERACT) Acute Transfer Tools (ACTs) completed by advanced practice registered nurses (APRNs) working in the Missouri Quality I...

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Bibliographic Details
Published in:Journal of the American Geriatrics Society (JAGS) 2019-09, Vol.67 (9), p.1953-1959
Main Authors: Popejoy, Lori L., Vogelsmeier, Amy A., Alexander, Greg L., Galambos, Colleen M., Crecelius, Charles A., Ge, Bin, Flesner, Marcia, Canada, Kelli, Rantz, Marilyn
Format: Article
Language:English
Subjects:
Advance directives
Advanced Practice Nursing - standards
Advanced Practice Nursing - statistics & numerical data
Aged
Aged, 80 and over
avoidable transfers
Cross-Sectional Studies
Decision making
Emergency medical care
Female
Health Services Misuse - statistics & numerical data
Hemorrhage
Homes for the Aged - standards
Homes for the Aged - statistics & numerical data
Hospitalization
Hospitalization - statistics & numerical data
Humans
INTERACT
Male
Missouri
Nausea
Nursing homes
Nursing Homes - standards
Nursing Homes - statistics & numerical data
Older people
Palliative care
Patient admissions
Patient care planning
Patient Transfer - standards
Patient Transfer - statistics & numerical data
Preferences
Quality control
Quality Improvement - statistics & numerical data
Retrospective Studies
Vomiting
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Summary:OBJECTIVES We explored the differences in potentially avoidable/unavoidable hospital transfers in a retrospective analysis of Interventions to Reduce Acute Care Transfers (INTERACT) Acute Transfer Tools (ACTs) completed by advanced practice registered nurses (APRNs) working in the Missouri Quality Improvement (QI) Initiative (MOQI). DESIGN Cross‐sectional descriptive study of 3996 ACTs for 32.5 calendar months from 2014 to 2016. Univariate analyses examined differences between potentially avoidable vs unavoidable transfers. Multivariate logistic regression analysis of candidate factors identified those contributing to avoidable transfers. Setting Sixteen nursing homes (NHs), ranging from 120 to 321 beds, in urban, metro, and rural communities within 80 miles of a large midwestern city. PARTICIPANTS A total of 5168 residents with a median age of 82 years. MEASUREMENTS Data from 3946 MOQI‐adapted ACTs. RESULTS A total of 54% of hospital transfers were identified as avoidable. QI opportunities related to avoidable transfers were earlier detection of new signs/symptoms (odds ratio [OR] = 2.35; 95% confidence interval [CI] = 1.61‐3.42; P
ISSN:0002-8614
1532-5415
DOI:10.1111/jgs.15996