2023
Extracting Pain Care Quality Indicators from U.S. Veterans Health Administration Chiropractic Care Using Natural Language Processing
Coleman B, Finch D, Wang R, Luther S, Heapy A, Brandt C, Lisi A. Extracting Pain Care Quality Indicators from U.S. Veterans Health Administration Chiropractic Care Using Natural Language Processing. Applied Clinical Informatics 2023, 14: 600-608. PMID: 37164327, PMCID: PMC10411229, DOI: 10.1055/a-2091-1162.Peer-Reviewed Original ResearchConceptsCare quality indicatorsVeterans Health AdministrationVisit typePain assessmentChiropractic careConsultation visitCare qualityClinic visit typePain care qualityComprehensive pain assessmentWomen Veterans Cohort StudyCare visitsCohort studyMusculoskeletal painManagement visitsVisit notesHealth AdministrationConsultation notesChiropractic servicesVisitsQuality indicatorsMean numberTreatment planningPatientsDescriptive statisticsUnderstanding headache classification coding within the veterans health administration using ICD-9-CM and ICD-10-CM in fiscal years 2014–2017
Fodeh S, Fenton B, Wang R, Skanderson M, Altalib H, Kuruvilla D, Schindler E, Haskell S, Brandt C, Sico J. Understanding headache classification coding within the veterans health administration using ICD-9-CM and ICD-10-CM in fiscal years 2014–2017. PLOS ONE 2023, 18: e0279163. PMID: 36598881, PMCID: PMC9812322, DOI: 10.1371/journal.pone.0279163.Peer-Reviewed Original ResearchConceptsVeterans Health AdministrationHeadache diagnosisICD-10-CMICD-10-CM diagnosisICD-10-CM codingSpecialty care clinicsSpecific headache diagnosesICD-9-CM codingDifferent headache typesICD-9-CMRace/ethnicityHeadache comorbidityHeadache NOSPatient ageCare clinicsChronic conditionsHeadache typesProvider typeSociodemographic factorsHealth AdministrationDiagnosisTwo yearsHeadachePatientsMigraine
2020
A Prototype Application to Identify LGBT Patients in Clinical Notes
Workman T, Goulet J, Brandt C, Skanderson M, Wang R, Warren A, Eleazer J, Gordon K, Zeng-Treitler Q. A Prototype Application to Identify LGBT Patients in Clinical Notes. 2020, 00: 4270-4275. DOI: 10.1109/bigdata50022.2020.9378109.Peer-Reviewed Original ResearchElectronic health record notesPrototype applicationLGBT patientsRule-based patternLarge data sourcesBinary classification taskRecord notesData scientistsMachine learningClassification taskPositive predictive valueData researchData sourcesTest setClinical relevancePredictive valueHealthcare providersPatientsClinical notesHealth disparitiesLittle workDisproportional burdenApplicationsTaskLearning