Natural Language Processing System

Automated, machine learning-based alerts increase epilepsy surgery referrals: A randomized controlled trial.

Citation
Wissel BD, Greiner HM, Glauser TA, Mangano FT, Holland-Bouley KD, Zhang N, Szczesniak RD, Santel D, Pestian JP, Dexheimer JW. Automated, machine learning-based alerts increase epilepsy surgery referrals: A randomized controlled trial. Epilepsia. 2023 Jul;64(7):1791-1799. doi: 10.1111/epi.17629. Epub 2023 May 27. PMID: 37102995; PMCID: PMC10524622.
Principal Investigator
Dexheimer, Judith W.
Project Name
Optimal Methods for Notifying Clinicians About Epilepsy Surgery Patients

Using Large Language Models to Identify Social Determinants of Health to Enhance Healthcare Services and Equity

Description

This research explores using natural language processing and generative AI to capture and structure social determinants of health from patient narratives, aiming to improve data completeness and quality, enhance clinical decision support, and reduce the manual burden on clinical staff in routine care.

Grant Number
R21 HS029991
Principal Investigator(s)
Goss, Foster R.
Zhou, Li

Estimation of racial and language disparities in pediatric emergency department triage using statistical modeling and natural language processing.

Citation
Lee SJ, Alzeen M, Ahmed A. Estimation of racial and language disparities in pediatric emergency department triage using statistical modeling and natural language processing. J Am Med Inform Assoc. 2024 Apr 3;31(4):958-967. doi: 10.1093/jamia/ocae018. PMID: 38349846; PMCID: PMC10990499.
Principal Investigator
Ahmed, Abdulaziz
Ozaydin, Bunyamin
Project Name
Building and Implementing a Predictive Decision Support System Based on a Proactive Full Capacity Protocol to Mitigate Emergency Department Overcrowding Problems

Improving Patient Safety and Clinician Cognitive Support Through eMAR Redesign – Final Report

Citation
Ratwani R., Hettinger A. Improving Patient Safety and Clinician Cognitive Support Through eMAR Redesign – Final Report. (Prepared by MedStar Health Research Institute under Grant No. R01 HS025136). Rockville, MD: Agency for Healthcare Research and Quality, 2024.
Principal Investigator
Ratwani, Raj M.
Hettinger, Aaron Zachary
Project Name
Improving Patient Safety and Clinician Cognitive Support Through eMAR Redesign

An Electronic Health Record-Based Screening Tool to Support Safe Discharges of COVID-19 Patients in the Emergency Department – Final Report

Citation
Galarraga J. An Electronic Health Record-Based Screening Tool to Support Safe Discharges of COVID-19 Patients in the Emergency Department – Final Report. (Prepared by MedStar Health Research Institute under Grant No. R21 HS028563). Rockville, MD: Agency for Healthcare Research and Quality, 2023.
Principal Investigator
Galarraga, Jessica E.
Project Name
An Electronic Health Record-Based Screening Tool to Support Safe Discharges of COVID-19 Patients in the Emergency Department

Generalizability and portability of natural language processing system to extract individual social risk factors.

Citation
Magoc T, Allen KS, McDonnell C, Russo JP, Cummins J, Vest JR, Harle CA. Generalizability and portability of natural language processing system to extract individual social risk factors. Int J Med Inform. 2023 Jun 5;177:105115. doi: 10.1016/j.ijmedinf.2023.105115. Epub ahead of print. PMID: 37302362.
Principal Investigator
Vest, Joshua Ryan
Project Name
Computable Social Factor Phenotyping Using EHR and HIE Data

Methods for identifying and reconciling allergy information in the electronic health record.

Citation
Ortega C, Lo Y, Blackley SV, Vallamkonda S, Chang F, James OD, Yerneni S, Wang, L, Seger D, Blumenthal KG, Goss FR, Wickner PG, Zhou L. Methods for identifying and reconciling allergy information in the electronic health record. Presented at the 2020 AAAAI Annual Meeting. Journal of Allergy and Clinical Immunology. 2021 Feb 1;147(2):AB168. https://doi.org/10.1016/j.jaci.2020.12.597.
Principal Investigator
Zhou, Li
Project Name
Improving Allergy Documentation and Clinical Decision Support in the Electronic Health Record