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Search All Research Studies
Topics
- Adverse Events (3)
- Clinical Decision Support (CDS) (2)
- Elderly (8)
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- (-) Emergency Department (9)
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- (-) Falls (9)
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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.
Results
1 to 9 of 9 Research Studies DisplayedHekman DJ, Cochran AL, Maru AP
Effectiveness of an emergency department-based machine learning clinical decision support tool to prevent outpatient falls among older adults: protocol for a quasi-experimental study.
This article described a research protocol for evaluating the effectiveness of an automated screening and referral intervention tool for patients receiving falls risk intervention. The study will attempt to quantify the impact of a machine learning (ML) clinical decision support intervention on patient behavior and outcomes. The primary analysis will obtain referral completion rates from different emergency departments. The findings will inform ongoing discussion on the use of ML and artificial intelligence to augment medical decision-making.
AHRQ-funded; HS027735.
Citation: Hekman DJ, Cochran AL, Maru AP .
Effectiveness of an emergency department-based machine learning clinical decision support tool to prevent outpatient falls among older adults: protocol for a quasi-experimental study.
JMIR Res Protoc 2023 Aug 3; 12:e48128. doi: 10.2196/48128..
Keywords: Clinical Decision Support (CDS), Emergency Department, Health Information Technology (HIT), Elderly, Falls
Newgard CD, Lin A, Caughey AB
Falls in older adults requiring emergency services: mortality, use of healthcare resources, and prognostication to one year.
The purpose of this study was to assess the prognoses, healthcare use, transitions to skilled nursing or hospice, and mortality of older, community-living adults after a fall. The researchers conducted a secondary analysis of all adults in 7 Northwest U.S. counties greater than or equal to 65 years of age who had been transported to one of 51 hospitals after a fall. The study analyzed Medicare claims, state trauma registry data, state inpatient data, and death records for outcomes which included healthcare use, new claims for skilled nursing and hospice for one year, and mortality. The researchers found that in 3,159 older adults there were 147 deaths within 30 days and 665 deaths within one year, and the following predictors of mortality: respiratory diagnosis, serious brain injury, having a baseline disability, or a score of greater than or equal to 2 on the Charlson Comorbidity Index. The study concluded that in the year after experiencing a fall, community-living older adults who require ambulance transport to the hospital have increases in institutionalized living, the utilization of health care, and mortality.
AHRQ-funded; HS023796.
Citation: Newgard CD, Lin A, Caughey AB .
Falls in older adults requiring emergency services: mortality, use of healthcare resources, and prognostication to one year.
West J Emerg Med 2022 May 14;23(3):375-85. doi: 10.5811/westjem.2021.11.54327..
Keywords: Elderly, Falls, Emergency Department, Mortality, Healthcare Utilization
Jacobsohn GC, Leaf M, Liao F
Collaborative design and implementation of a clinical decision support system for automated fall-risk identification and referrals in emergency departments.
The authors used a collaborative and iterative approach to design and implement an automated clinical decision support system (CDS) for Emergency Department (ED) providers to identify and refer older adult ED patients at high risk of future falls. The system was developed using collaborative input from an interdisciplinary design team and integrated seamlessly into existing ED workflows. A key feature of development was the unique combination of patient experience strategies, human-centered design, and implementation science, which allowed for the CDS tool and intervention implementation strategies to be designed simultaneously. Challenges included: usability problems, data inaccessibility, time constraints, low appointment availability, high volume of patients, and others. The study concluded that using the collaborative, iterative approach was successful in achieving all project goals, and could be applied to other cases.
AHRQ-funded; HS024558.
Citation: Jacobsohn GC, Leaf M, Liao F .
Collaborative design and implementation of a clinical decision support system for automated fall-risk identification and referrals in emergency departments.
Healthc 2022 Mar;10(1):100598. doi: 10.1016/j.hjdsi.2021.100598..
Keywords: Elderly, Clinical Decision Support (CDS), Shared Decision Making, Falls, Risk, Emergency Department, Health Information Technology (HIT)
Newgard CD, Lin A, Caughey AB
The cost of a fall among older adults requiring emergency services.
Researchers evaluated medical expenditures to 1 year among community-dwelling older adults who fell and required ambulance transport, including acute versus post-acute periods, the primary drivers of cost, and comparison to baseline expenditures. They found that older adults who fall and require emergency services have increased healthcare expenditures compared with baseline, particularly during the post-acute period. Comorbidities, noninjury medical conditions, fracture type, and surgical interventions were independently associated with increased costs.
AHRQ-funded; HS023796.
Citation: Newgard CD, Lin A, Caughey AB .
The cost of a fall among older adults requiring emergency services.
J Am Geriatr Soc 2021 Feb;69(2):389-98. doi: 10.1111/jgs.16863..
Keywords: Elderly, Falls, Emergency Department, Healthcare Costs, Emergency Medical Services (EMS)
Patterson 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, Jacobsohn GC, Shah MN
Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department.
This study examined development and validation of a pragmatic natural language processing (NLP) approach to identify fall risk in older adults after emergency department (ED) visits. A single center retrospective review using data from 500 emergency department provider notes on older adults age 65 and older were random selected for analysis. The NLP algorithm successfully identified falls in ED notes with over 90% precision, and looks promising to reduce labor-intensive manual abstraction.
AHRQ-funded; HS024558.
Citation: Patterson BW, Jacobsohn GC, Shah MN .
Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department.
BMC Med Inform Decis Mak 2019 Jul 22;19(1):138. doi: 10.1186/s12911-019-0843-7..
Keywords: Adverse Events, Elderly, Emergency Department, Falls, Risk, Patient Safety
Patterson BW, Engstrom CJ, Sah V
Training and interpreting machine learning algorithms to evaluate fall risk after emergency department visits.
This study examined the potential of using machine learning algorithms to evaluate fall risk after an emergency department (ED) visit. They compared several machine learning methodologies for creation of a risk stratification algorithm to predict the outcome of a return visit for a fall within 6 months of an ED visit.
AHRQ-funded; HS024558; HS024342.
Citation: Patterson BW, Engstrom CJ, Sah V .
Training and interpreting machine learning algorithms to evaluate fall risk after emergency department visits.
Med Care 2019 Jul;57(7):560-66. doi: 10.1097/mlr.0000000000001140..
Keywords: Adverse Events, Elderly, Emergency Department, Falls, Risk, Patient Safety
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
Patterson BW, Smith MA, Repplinger MD
Using chief complaint in addition to diagnosis codes to identify falls in the emergency department.
The researchers compared incidence of falls in an emergency department (ED) cohort using a traditional International Classification of Diseases, Ninth Revision (ICD-9) code-based scheme and an expanded definition that included chief complaint information. They concluded that identifying individuals in the ED who have fallen based on diagnosis codes underestimates the true burden of falls.
AHRQ-funded; HS024558.
Citation: Patterson BW, Smith MA, Repplinger MD .
Using chief complaint in addition to diagnosis codes to identify falls in the emergency department.
J Am Geriatr Soc 2017 Sep;65(9):E135-E40. doi: 10.1111/jgs.14982.
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Keywords: Falls, Emergency Medical Services (EMS), Emergency Department