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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 4 of 4 Research Studies DisplayedDowns SM, Bauer NS, Saha C
Effect of a computer-based decision support intervention on autism spectrum disorder screening in pediatric primary care clinics: a cluster randomized clinical trial.
This study examined outcomes for implementation of a decision support system called CHICA (Child Health Improvement Through Computer Automation) to improve screening rates for autism in children aged 18 to 24 months. A random sample of 274 children in four urban clinics was used. Two clinics participated in the intervention, and two served as controls. Because participating clinics requested intervention be discontinued for children aged 18 months, only results for those aged 24 months was analyzed. Of the 263 children with reviewed results, 92% were enrolled in Medicaid, 52.5% were African American, and 36.5% were Hispanic. Screening rates increased from 0% at baseline to 100% in 24 months during the study period of November 2010 to November 2012. Screening results were positive for 265 of 980 children screened by CHICA in the time period, with 2 children from the intervention group positively diagnosed in the time frame of the study.
AHRQ-funded; HS018453.
Citation: Downs SM, Bauer NS, Saha C .
Effect of a computer-based decision support intervention on autism spectrum disorder screening in pediatric primary care clinics: a cluster randomized clinical trial.
JAMA Netw Open 2019 Dec 2;2(12):e1917676. doi: 10.1001/jamanetworkopen.2019.17676..
Keywords: Autism, Clinical Decision Support (CDS), Decision Making, Health Information Technology (HIT), Primary Care, Children/Adolescents, Screening
Gu Y, Leroy G, Pettygrove S
Optimizing corpus creation for training word embedding in low resource domains: a case study in Autism Spectrum Disorder (ASD).
Automating the extraction of behavioral criteria indicative of Autism Spectrum Disorder (ASD) in electronic health records (EHRs) can contribute significantly to the effort to monitor the condition. Word embedding algorithms such as Word2Vec can encode semantic meanings of words in vectors and assist in automated vocabulary discovery from EHRs. However, text available for training word embeddings for ASD is miniscule compared to the billions of tokens typically used. In this study, the investigators evaluated the importance of corpus specificity versus size and hypothesized that for specific domains small corpora can generate excellent word embeddings.
AHRQ-funded; HS024988.
Citation: Gu Y, Leroy G, Pettygrove S .
Optimizing corpus creation for training word embedding in low resource domains: a case study in Autism Spectrum Disorder (ASD).
AMIA Annu Symp Proc 2018 Dec 5;2018:508-17..
Keywords: Autism, Electronic Health Records (EHRs), Health Information Technology (HIT)
Bush RA, Connelly CD, Perez A
Extracting autism spectrum disorder data from the electronic health record.
This study uses electronic health record (EHR) data to examine medical utilization and track outcomes among children with Autism Spectrum Disorder (ASD). The study also identifies challenges inherent in designing inclusive algorithms for identifying individuals with ASD and demonstrates the utility of employing multiple extractions to improve the completeness and quality of EHR data when conducting research.
AHRQ-funded; HS022404.
Citation: Bush RA, Connelly CD, Perez A .
Extracting autism spectrum disorder data from the electronic health record.
Appl Clin Inform 2017 Jul 19;8(3):731-41. doi: 10.4338/aci-2017-02-ra-0029..
Keywords: Autism, Children/Adolescents, Data, Health Information Technology (HIT), Electronic Health Records (EHRs)
Bauer NS, Carroll AE, Saha C
Computer decision support changes physician practice but not knowledge regarding autism spectrum disorders.
This study examined whether adding an autism module promoting adherence to clinical guidelines to an existing computer decision support system (CDSS) changed physician knowledge and self-reported clinical practice. It found that a CDSS module to improve primary care management of ASD in pediatric practice led to significant improvements in physician-reported use of validated screening tools to screen for ASDs.
AHRQ-funded; HS018453.
Citation: Bauer NS, Carroll AE, Saha C .
Computer decision support changes physician practice but not knowledge regarding autism spectrum disorders.
Appl Clin Inform 2015;6(3):454-65. doi: 10.4338/aci-2014-09-ra-0084.
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Keywords: Health Information Technology (HIT), Practice Patterns, Clinical Decision Support (CDS), Children/Adolescents, Autism