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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 DisplayedHumbert-Droz M, Izadi Z, Schmajuk G
Development of a natural language processing system for extracting rheumatoid arthritis outcomes from clinical notes using the national rheumatology informatics system for effectiveness registry.
Researchers developed and evaluated a natural language processing pipeline for extracting outcome measures in rheumatology from free-text outpatient rheumatology notes within the ACR's Rheumatology Informatics System for Effectiveness (RISE) registry. All patients in RISE from 2015 to 2018 were included. The researchers found the pipeline to have good internal and external validity and they concluded that it could facilitate measurement of clinical and patient reported outcomes for use in both research and quality measurement.
AHRQ-funded; HS025638.
Citation: Humbert-Droz M, Izadi Z, Schmajuk G .
Development of a natural language processing system for extracting rheumatoid arthritis outcomes from clinical notes using the national rheumatology informatics system for effectiveness registry.
Arthritis Care Res 2023 Mar; 75(3):608-15. doi: 10.1002/acr.24869..
Keywords: Arthritis, Electronic Health Records (EHRs), Health Information Technology (HIT), Outcomes, Patient-Centered Outcomes Research, Evidence-Based Practice
Norgeot B, Glicksberg BS, Trupin L
Assessment of a deep learning model based on electronic health record data to forecast clinical outcomes in patients with rheumatoid arthritis.
This study researched the use of artificial intelligence learning models to predict clinical outcomes in patients with rheumatoid arthritis (RA). Patients from a university hospital (UH) and a public safety-net hospital (SNH). The populations were quite different from each other. A total of 578 UH patients and 242 SNH patients were included in the study. Patients at the UH were seen more frequently than the SNH patients and were often prescribed high-class medications (63% vs. 28.9%). The model that was used showed a statistically random performance based on each patients’ most recent disease activity score.
AHRQ-funded; HS024412.
Citation: Norgeot B, Glicksberg BS, Trupin L .
Assessment of a deep learning model based on electronic health record data to forecast clinical outcomes in patients with rheumatoid arthritis.
JAMA Netw Open 2019 Mar;2(3):e190606. doi: 10.1001/jamanetworkopen.2019.0606..
Keywords: Arthritis, Electronic Health Records (EHRs), Health Information Technology (HIT), Outcomes
Yazdany J, Robbins M, Schmajuk G
Development of the American College of Rheumatology's rheumatoid arthritis electronic clinical quality measures.
The researchers sought to develop and test electronic clinical quality measures for rheumatoid arthritis. Disease activity assessment, functional status assessment, disease-modifying antirheumatic durg use, and tuberculosis screening measures have achieved national endorsement and are recommended for use in federal quality reporting programs.
AHRQ-funded; HS024412.
Citation: Yazdany J, Robbins M, Schmajuk G .
Development of the American College of Rheumatology's rheumatoid arthritis electronic clinical quality measures.
Arthritis Care Res 2016 Nov;68(11):1579-90. doi: 10.1002/acr.22984.
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Keywords: Electronic Health Records (EHRs), Medication, Quality Measures, Arthritis, Outcomes