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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 DisplayedWissel BD, Greiner HM, Glauser TA
Automated, machine learning-based alerts increase epilepsy surgery referrals: a randomized controlled trial.
Researchers conducted a prospective, randomized controlled trial of a natural language processing-based clinical decision support system in the electronic health record at 14 pediatric neurology outpatient clinics to determine whether automated, electronic alerts increased referrals for epilepsy surgery. Children with epilepsy and at least two prior neurology visits were screened by the system prior to their scheduled visit to identify potential surgical candidates, and the potential candidates randomized 2:1 for their providers to receive an alert or standard of care (no alert). The results showed that patients whose providers received an alert were more likely to be referred for a presurgical evaluation. The researchers concluded that machine learning-based automated alerts may improve the utilization of referrals for epilepsy surgery evaluations.
AHRQ-funded; HS024977.
Citation: Wissel BD, Greiner HM, Glauser TA .
Automated, machine learning-based alerts increase epilepsy surgery referrals: a randomized controlled trial.
Epilepsia 2023 Jul; 64(7):1791-99. doi: 10.1111/epi.17629..
Keywords: Neurological Disorders, Surgery, Health Information Technology (HIT)
Wissel BD, Greiner HM, Glauser TA
Early identification of epilepsy surgery candidates: a multicenter, machine learning study.
Epilepsy surgery is underutilized. Automating the identification of potential surgical candidates may facilitate earlier intervention. The study objective was to develop site-specific machine learning (ML) algorithms to identify candidates before they undergo surgery. The investigators concluded that site-specific machine learning algorithms could identify candidates for epilepsy surgery early in the disease course in diverse practice settings.
AHRQ-funded; HS024977.
Citation: Wissel BD, Greiner HM, Glauser TA .
Early identification of epilepsy surgery candidates: a multicenter, machine learning study.
Acta Neurol Scand 2021 Jul;114(1):41-50. doi: 10.1111/ane.13418..
Keywords: Neurological Disorders, Surgery, Health Information Technology (HIT)
Wissel BD, Greiner TA, Holland-Bouley KD
Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.
Delay to resective epilepsy surgery results in avoidable disease burden and increased risk of mortality. The objective of this study was to prospectively validate a natural language processing (NLP) application that uses provider notes to assign epilepsy surgery candidacy scores. The authors suggest that an electronic health record-integrated NLP application can accurately assign surgical candidacy scores to patients in a clinical setting.
AHRQ-funded; HS024977.
Citation: Wissel BD, Greiner TA, Holland-Bouley KD .
Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.
Epilepsia 2020 Jan;61(1):39-48. doi: 10.1111/epi.16398..
Keywords: Neurological Disorders, Surgery, Health Information Technology (HIT), Clinical Decision Support (CDS), Decision Making
Wissel BD, Greiner HM, Glauser TA
Investigation of bias in an epilepsy machine learning algorithm trained on physician notes.
Racial disparities in the utilization of epilepsy surgery are well documented, but it is unknown whether a natural language processing (NLP) algorithm trained on physician notes would produce biased recommendations for epilepsy presurgical evaluations. To assess this, an NLP algorithm was trained to identify potential surgical candidates using 1097 notes from 175 epilepsy patients with a history of resective epilepsy surgery and 268 patients who achieved seizure freedom without surgery (total N = 443 patients).
AHRQ-funded; HS024977.
Citation: Wissel BD, Greiner HM, Glauser TA .
Investigation of bias in an epilepsy machine learning algorithm trained on physician notes.
Epilepsia 2019 Sep;60(9):e93-e98. doi: 10.1111/epi.16320..
Keywords: Neurological Disorders, Surgery, Clinical Decision Support (CDS), Healthcare Utilization, Health Information Technology (HIT), Decision Making