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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 3 of 3 Research Studies DisplayedPatterson BW, Jacobsohn GC, Maru AP
Comparing strategies for identifying falls in older adult emergency department visits using EHR data.
This study compared seven different strategies for identifying falls in older adult emergency department (ED) visits using electronic health record (EHR) data. This retrospective cohort study used randomly selected data from 500 ED visits by patients 65 and older at an academic medical center from December 2016 to April 2017. The seven strategies tested were: Chief complaint (CC), ICD codes, Restrictive ICD codes, Broad ICD codes, Combined approaches, Natural language processing (NLP), and Manual abstraction (gold standard). When compared with manual chart review, NLP was found to be the most accurate fall identification strategy, followed by a combination of a restrictive ICD code-based definition with CC.
AHRQ-funded; HS024558.
Citation: Patterson BW, Jacobsohn GC, Maru AP .
Comparing strategies for identifying falls in older adult emergency department visits using EHR data.
J Am Geriatr Soc 2020 Dec;68(12):2965-67. doi: 10.1111/jgs.16831..
Keywords: Elderly, Falls, Emergency Department, Electronic Health Records (EHRs), Health Information Technology (HIT)
Patterson BW, Repplinger MD, Pulia MS
Using the Hendrich II Inpatient Fall Risk Screen to predict outpatient falls after emergency department visits.
This study examined the utility of using the Hendrich II Inpatient Fall Risk Screen to predict outpatient falls in elderly patients after emergency department (ED) visits. Individuals aged 65 and older seen in the ED from January 2013 to September 30, 2015 participated in the study. The Hendrich II screen was found to correlate with outpatient falls, but it is likely it would have little utility as a stand-alone fall screen. When the screen was combined with other potential confounders or predictors, the screen performed much better.
AHRQ-funded; HS024558.
Citation: Patterson BW, Repplinger MD, Pulia MS .
Using the Hendrich II Inpatient Fall Risk Screen to predict outpatient falls after emergency department visits.
J Am Geriatr Soc 2018 Apr;66(4):760-65. doi: 10.1111/jgs.15299..
Keywords: Elderly, Falls, Risk, Emergency Department, Electronic Health Records (EHRs), Health Information Technology (HIT), Prevention, Patient Safety, Adverse Events
Marier A, Olsho LE, Rhodes W
AHRQ Author: Spector WD
Improving prediction of fall risk among nursing home residents using electronic medical records.
To identify individuals at highest risk for falls, the authors applied a repeated events survival model to analyze The Minimum Data Set ( MDS 3.0 and EMR data for 5129 residents in 13 nursing homes within a single large California chain. They found that incorporating EMR data improves ability to identify those at highest risk for falls relative to prediction using MDS data alone.
AHRQ-funded; AHRQ-authored; 290201000031I.
Citation: Marier A, Olsho LE, Rhodes W .
Improving prediction of fall risk among nursing home residents using electronic medical records.
J Am Med Inform Assoc 2016 Mar;23(2):276-82. doi: 10.1093/jamia/ocv061..
Keywords: Falls, Electronic Health Records (EHRs), Risk, Nursing Homes, Prevention