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Search All Research Studies
Topics
- Cardiovascular Conditions (2)
- Care Management (1)
- Children/Adolescents (1)
- (-) Clinical Decision Support (CDS) (8)
- Dementia (1)
- (-) Diagnostic Safety and Quality (8)
- Electronic Health Records (EHRs) (2)
- Emergency Department (2)
- (-) Health Information Technology (HIT) (8)
- Heart Disease and Health (2)
- Imaging (1)
- Neurological Disorders (1)
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- Obesity: Weight Management (1)
- Patient-Centered Outcomes Research (1)
- Patient Safety (1)
- Risk (1)
- Sepsis (1)
- Shared Decision Making (2)
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 8 of 8 Research Studies DisplayedSalwei ME, Carayon P, Wiegmann D
Usability barriers and facilitators of a human factors engineering-based clinical decision support technology for diagnosing pulmonary embolism.
The authors sought to identify and describe the usability barriers and facilitators of a human factors engineering (HFE)-based clinical decision support (CDS) prior to implementation in the emergency department. Through debrief interviews, they identified 271 occurrences of usability barriers and facilitators of the HFE-based CDS. They concluded that the systematic use of HFE principles in the design of CDS improves the usability of these technologies and recommended workflow integration in order to reduce usability barriers.
AHRQ-funded; HS026395; HS024558; HS022086.
Citation: Salwei ME, Carayon P, Wiegmann D .
Usability barriers and facilitators of a human factors engineering-based clinical decision support technology for diagnosing pulmonary embolism.
Int J Med Inform 2022 Feb;158:104657. doi: 10.1016/j.ijmedinf.2021.104657..
Keywords: Clinical Decision Support (CDS), Health Information Technology (HIT), Diagnostic Safety and Quality
Soares WE, Knee A, Gemme SR
SC, et al. A prospective evaluation of Clinical HEART score agreement, accuracy, and adherence in emergency department chest pain patients.
The HEART score is a risk stratification aid that may safely reduce chest pain admissions for emergency department patients. However, differences in interpretation of subjective components potentially alters the performance of the score. In this study, the investigators compared agreement between HEART scores determined during clinical practice with research-generated scores and estimated their accuracy in predicting 30-day major adverse cardiac events.
AHRQ-funded; HS024815.
Citation: Soares WE, Knee A, Gemme SR .
SC, et al. A prospective evaluation of Clinical HEART score agreement, accuracy, and adherence in emergency department chest pain patients.
Ann Emerg Med 2021 Aug;78(2):231-41. doi: 10.1016/j.annemergmed.2021.03.024..
Keywords: Heart Disease and Health, Cardiovascular Conditions, Emergency Department, Diagnostic Safety and Quality, Clinical Decision Support (CDS), Health Information Technology (HIT)
Carayon P, Hoonakker P, Hundt AS
Application of human factors to improve usability of clinical decision support for diagnostic decision-making: a scenario-based simulation study.
This study used a scenario-based simulation to compare a human factor (HF)-based clinician decision support (CDS) with a web-based CDS (MDCalc) for clinicians to diagnose pulmonary embolism (PE) in the emergency department. A total of 32 emergency physicians participated using both CDS types. Emergency physicians made more appropriate diagnoses decisions with the PE-Dx CDS (94%) than with the web-based CDS (84%). Experimental tasks were also performed faster (average 96 seconds per scenario versus 117 seconds). They also reported lower workload and higher satisfaction with the HF-based CDS.
AHRQ-funded; HS024342; HS024558; HS022086.
Citation: Carayon P, Hoonakker P, Hundt AS .
Application of human factors to improve usability of clinical decision support for diagnostic decision-making: a scenario-based simulation study.
BMJ Qual Saf 2020 Apr;29(4):329-40. doi: 10.1136/bmjqs-2019-009857..
Keywords: Clinical Decision Support (CDS), Shared Decision Making, Health Information Technology (HIT), Diagnostic Safety and Quality, Emergency Department
Meyer AND, Giardina TD, Spitzmueller C
Patient perspectives on the usefulness of an artificial intelligence-assisted symptom checker: cross-sectional survey study.
This study examined patients’ experiences using an artificial intelligence (AI)-assisted online symptom checker and their doctors’ reactions to that use. From March 2 through March 15, 2018 an online survey was conducted of US users of the Isabel Symptom Checker within 6 months of their use. The majority of users were women, white, and had a mean age of 48. Overall, patients had a positive experience with the symptom checker and felt they would use it again (91.4%). About 48% discussed the findings with their physician and felt about 40% of their physicians were interested. Patients who had previously experienced diagnostic errors were more likely to use the symptom checker to determine if they should seek care.
AHRQ-funded; HS025474; HS027363.
Citation: Meyer AND, Giardina TD, Spitzmueller C .
Patient perspectives on the usefulness of an artificial intelligence-assisted symptom checker: cross-sectional survey study.
J Med Internet Res 2020 Jan 30;22(1):e14679. doi: 10.2196/14679..
Keywords: Clinical Decision Support (CDS), Health Information Technology (HIT), Diagnostic Safety and Quality, Patient Safety
Barnes DE, Zhou J, Walker RL
Development and validation of eRADAR: a tool using EHR Data to detect unrecognized dementia.
The goal of this retrospective cohort study was to develop and validate an electronic health record (EHR)-based tool to help detect patients with unrecognized dementia. The tool was named EHR Risk of Alzheimer’s and Dementia Assessment Rule (eRADAR). This study was conducted at Kaiser Permanente Washington (KPWA) using participants in the Adult Changes in Thought (ACT) study who undergo comprehensive testing every 2 years to detect and diagnose dementia and have linked KPWA EHR data. Overall, 1015 ACT visits resulted in a diagnosis of incident dementia, of which 49% were previously unrecognized in the EHR. The final 31-predictor model included markers of dementia-related symptoms, healthcare utilization patterns, and dementia risk factors. The study showed good discrimination in the development interval and validation samples.
AHRQ-funded; HS022982.
Citation: Barnes DE, Zhou J, Walker RL .
Development and validation of eRADAR: a tool using EHR Data to detect unrecognized dementia.
J Am Geriatr Soc 2020 Jan;68(1):103-11. doi: 10.1111/jgs.16182..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Dementia, Neurological Disorders, Diagnostic Safety and Quality, Clinical Decision Support (CDS), Shared Decision Making
Levy AE, Shah NR, Matheny ME
Determining post-test risk in a national sample of stress nuclear myocardial perfusion imaging reports: implications for natural language processing tools.
The authors investigated whether Natural Language Processing (NLP) tools could potentially help estimate myocardial perfusion imaging (MPI) risk. Subjects were VA patients who underwent stress MPI and coronary angiography 2009-11; stress test reports were randomly selected for analysis. The authors found that post-test ischemic risk was determinable but rarely reported in this sample of stress MPI reports. They conclude that this supports the potential use of NLP to help clarify risk and recommend further study of NLP in this context.
AHRQ-funded; HS022998.
Citation: Levy AE, Shah NR, Matheny ME .
Determining post-test risk in a national sample of stress nuclear myocardial perfusion imaging reports: implications for natural language processing tools.
J Nucl Cardiol 2019 Dec;26(6):1878-85. doi: 10.1007/s12350-018-1275-y..
Keywords: Imaging, Risk, Clinical Decision Support (CDS), Health Information Technology (HIT), Diagnostic Safety and Quality, Cardiovascular Conditions, Heart Disease and Health
Makam AN, Nguyen OK, Auerbach AD
Diagnostic accuracy and effectiveness of automated electronic sepsis alert systems: a systematic review.
This review aimed to determine whether automated real-time electronic sepsis alerts can: (1) accurately identify sepsis and (2) improve process measures and outcomes. It found that automated sepsis alerts derived from electronic health data may improve care processes but tend to have poor positive predictive value and do not improve mortality or length of stay.
AHRQ-funded; HS022418.
Citation: Makam AN, Nguyen OK, Auerbach AD .
Diagnostic accuracy and effectiveness of automated electronic sepsis alert systems: a systematic review.
J Hosp Med 2015 Jun;10(6):396-402. doi: 10.1002/jhm.2347..
Keywords: Clinical Decision Support (CDS), Health Information Technology (HIT), Sepsis, Diagnostic Safety and Quality, Patient-Centered Outcomes Research
Shaikh U, Berrong J, Nettiksimmons J
Impact of electronic health record clinical decision support on the management of pediatric obesity.
The investigators assessed the impact of electronic health record-based clinical decision support in improving the diagnosis and management of pediatric obesity. They found a statistically significant increase in the diagnosis of overweight/obesity, scheduling of follow-up appointments, frequency of ordering recommended laboratory investigations, and assessment and counseling for nutrition and physical activity.
AHRQ-funded; HS018567.
Citation: Shaikh U, Berrong J, Nettiksimmons J .
Impact of electronic health record clinical decision support on the management of pediatric obesity.
Am J Med Qual 2015 Jan-Feb;30(1):72-80. doi: 10.1177/1062860613517926.
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Keywords: Care Management, Children/Adolescents, Clinical Decision Support (CDS), Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT), Obesity, Obesity: Weight Management