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Search All Research Studies
Topics
- Adverse Events (2)
- Clinical Decision Support (CDS) (4)
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- (-) Falls (11)
- (-) Health Information Technology (HIT) (11)
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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 11 of 11 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
Shear K, Rice H, Garabedian PM
Usability testing of an interoperable computerized clinical decision support tool for fall risk management in primary care.
The purpose of this study was to conduct usability testing of the ASPIRE fall risk management tool for use in divergent primary care clinics. Participants recruited from two sites with different electronic health records and clinical organizations used ASPIRE across two clinical scenarios; they rated ASPIRE usability as above average, based on usability benchmarks. Time spent on tasks decreased significantly between the first and second scenarios, indicating ease of learnability. The authors conclude that ASPIRE could be integrated into diverse organizations, since it allows a tailored implementation without the need to build a new system for each organization. ASPIRE is therefore well positioned to impact the challenge of falls at scale.
AHRQ-funded; HS027557.
Citation: Shear K, Rice H, Garabedian PM .
Usability testing of an interoperable computerized clinical decision support tool for fall risk management in primary care.
Appl Clin Inform 2023 Mar;14(2):212-26. doi: 10.1055/a-2006-4936.
Keywords: Clinical Decision Support (CDS), Shared Decision Making, Health Information Technology (HIT), Falls, Primary Care, Risk, Prevention
Rice H, Garabedian PM, Shear K
Clinical decision support for fall prevention: defining end-user needs.
The purpose of this study was to identify patient and primary care staff needs for development of a tool that will generate clinical decision support (CDS) to prevent falls and injuries in older adults. Community-dwelling patients aged 60 and over and primary care clinic staff were eligible to participate in the study; all were affiliated with the University of Florida Health Archer Family Health Care primary care clinic and the Brigham & Women's Hospital-affiliated primary care clinics. Through qualitative interviews with patients (n=18) and primary care clinic staff (n=24) user needs were identified and then categorized into the following themes: evidence-based safe exercises; expert guidance; individualized resources; in-person assessment of patient condition; motivational tools; patient understanding of fall risk; personal support networks; systematic communication and workload burden. The study concluded that personalized, actionable, and evidence-based clinical decision support may be able to address some of the many gaps that exist in fall prevention management in older adults.
AHRQ-funded; HS027557.
Citation: Rice H, Garabedian PM, Shear K .
Clinical decision support for fall prevention: defining end-user needs.
Appl Clin Inform 2022 May;13(3):647-55. doi: 10.1055/s-0042-1750360..
Keywords: Elderly, Falls, Prevention, Clinical Decision Support (CDS), Shared Decision Making, Health Information Technology (HIT)
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)
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, 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
Zhou S, Kang H, Gong Y
Design a learning-oriented fall event reporting system based on Kirkpatrick model.
Patient fall has been a severe problem in healthcare facilities around the world due to its prevalence and cost. Routine fall prevention training programs are not as effective as expected. Using event reporting systems is the trend for reducing patient safety events such as falls, although some limitations of the systems exist at current stage. The authors of this paper summarized these limitations through literature review, and developed an improved web-based fall event reporting system.
AHRQ-funded; HS022895.
Citation: Zhou S, Kang H, Gong Y .
Design a learning-oriented fall event reporting system based on Kirkpatrick model.
Stud Health Technol Inform 2017;245:828-32..
Keywords: Falls, Health Information Technology (HIT), Patient Safety, Web-Based, Adverse Events
Stone EE, Skubic M
Fall detection in homes of older adults using the Microsoft Kinect.
The researchers present a method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage fall detection system. The method is compared against five state-of-the-art fall detection algorithms and significantly better results are achieved.
AHRQ-funded; HS018477.
Citation: Stone EE, Skubic M .
Fall detection in homes of older adults using the Microsoft Kinect.
IEEE J Biomed Health Inform 2015 Jan;19(1):290-301. doi: 10.1109/jbhi.2014.2312180..
Keywords: Patient Safety, Falls, Elderly, Health Information Technology (HIT)
Wang F, Skubic M, Rantz M
Quantitative gait measurement with pulse-Doppler radar for passive in-home gait assessment.
The researchers proposed and validated a low-cost Doppler radar system for passive and continuous in-home gait assessment. Using signal processing techniques, they estimated human torso velocity and leg swing for step recognition. They found that the radar system has achieved a high accuracy on the step time estimation, while the walking speed estimation is systematically affected by the walking path direction.
AHRQ-funded; HS018477.
Citation: Wang F, Skubic M, Rantz M .
Quantitative gait measurement with pulse-Doppler radar for passive in-home gait assessment.
IEEE Trans Biomed Eng 2014 Sep;61(9):2434-43. doi: 10.1109/tbme.2014.2319333..
Keywords: Health Information Technology (HIT), Patient Safety, Falls, Elderly
Enayati M, Banerjee T, Popescu M
A novel web-based depth video rewind approach toward fall preventive interventions in hospitals.
The purpose of this study was to implement a web-based application to provide the ability to rewind and review depth videos captured in hospital rooms to investigate the event chains that led to patient’s fall at a specific time. It proposes a novel web application to ease the process of search and review of the videos by means of new visualization techniques to highlight video frames that contain potential risk of fall based on our previous research.
AHRQ-funded; HS018477.
Citation: Enayati M, Banerjee T, Popescu M .
A novel web-based depth video rewind approach toward fall preventive interventions in hospitals.
Conf Proc IEEE Eng Med Biol Soc 2014;2014:4511-4. doi: 10.1109/embc.2014.6944626..
Keywords: Health Information Technology (HIT), Web-Based, Falls, Hospitals
Stone EE, Skubic M, Back J
Automated health alerts from Kinect-based in-home gait measurements.
This paper details initial investigation of a method for automatically generating alerts to clinicians in response to changes in in-home gait parameters. The three case studies discussed illustrate the potential of automated alerts based on in-home gait data for notifying caregivers of changes in an individual's gait that may be indicative of changes in health status.
AHRQ-funded; HS018477.
Citation: Stone EE, Skubic M, Back J .
Automated health alerts from Kinect-based in-home gait measurements.
Conf Proc IEEE Eng Med Biol Soc 2014;2014:2961-4. doi: 10.1109/embc.2014.6944244..
Keywords: Patient Safety, Health Information Technology (HIT), Elderly, Falls