2022
Mental Health Diagnoses are Not Associated With Indicators of Lower Quality Pain Care in Electronic Health Records of a National Sample of Veterans Treated in Veterans Health Administration Primary Care Settings
Dobscha SK, Luther SL, Kerns RD, Finch DK, Goulet JL, Brandt CA, Skanderson M, Bathulapalli H, Fodeh SJ, Hahm B, Bouayad L, Lee A, Han L. Mental Health Diagnoses are Not Associated With Indicators of Lower Quality Pain Care in Electronic Health Records of a National Sample of Veterans Treated in Veterans Health Administration Primary Care Settings. Journal Of Pain 2022, 24: 273-281. PMID: 36167230, PMCID: PMC9898089, DOI: 10.1016/j.jpain.2022.08.009.Peer-Reviewed Original ResearchMeSH KeywordsChronic PainElectronic Health RecordsHumansMental HealthPrimary Health CareQuality of Health CareRetrospective StudiesUnited StatesUnited States Department of Veterans AffairsVeteransVeterans HealthConceptsPain care qualityQuality pain careMental health conditionsPrimary care cliniciansVeterans Health AdministrationPain carePCQ scoresHealth conditionsCare cliniciansUse disordersCare qualitySevere musculoskeletal painRetrospective cohort studyPrimary care visitsGeneral medical carePrimary care settingElectronic health record dataFinal adjusted modelMental health diagnosesEquation Poisson modelsHealth record dataBipolar disorder diagnosisSubstance use disordersAlcohol use disorderPost-traumatic stress disorder
2021
Measuring pain care quality in the Veterans Health Administration primary care setting
Luther SL, Finch DK, Bouayad L, McCart J, Han L, Dobscha SK, Skanderson M, Fodeh SJ, Hahm B, Lee A, Goulet JL, Brandt CA, Kerns RD. Measuring pain care quality in the Veterans Health Administration primary care setting. Pain 2021, 163: e715-e724. PMID: 34724683, PMCID: PMC8920945, DOI: 10.1097/j.pain.0000000000002477.Peer-Reviewed Original ResearchMeSH KeywordsHumansPainPrimary Health CareQuality of Health CareReproducibility of ResultsUnited StatesUnited States Department of Veterans AffairsVeteransVeterans HealthConceptsPain care qualityPattern of documentationSevere pain intensityFrequency of documentationPresence of painSite of painPrimary care providersPrimary care settingCare quality indicatorsQuality improvement initiativesTotal PCQ scoresPatient characteristicsPain intensityPain carePain impactPCQ scoresCare settingsCare providersMusculoskeletal disordersFurther evaluationPainCare qualityHealthcare facilitiesImprovement initiativesUnique visits
2020
Serious Falls in Middle‐Aged Veterans: Development and Validation of a Predictive Risk Model
Womack JA, Murphy TE, Bathulapalli H, Smith A, Bates J, Jarad S, Redeker NS, Luther SL, Gill TM, Brandt CA, Justice AC. Serious Falls in Middle‐Aged Veterans: Development and Validation of a Predictive Risk Model. Journal Of The American Geriatrics Society 2020, 68: 2847-2854. PMID: 32860222, PMCID: PMC7744431, DOI: 10.1111/jgs.16773.Peer-Reviewed Original ResearchMeSH KeywordsAccidental FallsAlgorithmsBody Mass IndexCohort StudiesComorbidityFemaleHumansMaleMiddle AgedPolypharmacyQuality of LifeReproducibility of ResultsRisk AssessmentSex FactorsSubstance-Related DisordersUnited StatesUnited States Department of Veterans AffairsVeteransConceptsMiddle-aged veteransVeterans Health AdministrationOpioid useSerious fallsAlcohol Use Disorders Identification Test-Consumption scoresCategory-free net reclassification improvementIllicit substance use disordersMental health comorbiditiesPrescription opioid useMultivariable logistic regressionNet reclassification improvementSubstance use disordersQuality of lifeHazardous alcohol usePredictive risk modelChronic medicationsCohort studyHealth comorbiditiesNinth RevisionReclassification improvementGeriatric healthInjury codesHazardous alcoholInternational ClassificationUse disorders
2016
Estimating healthcare mobility in the Veterans Affairs Healthcare System
Wang KH, Goulet JL, Carroll CM, Skanderson M, Fodeh S, Erdos J, Womack JA, Abel EA, Bathulapalli H, Justice AC, Nunez-Smith M, Brandt CA. Estimating healthcare mobility in the Veterans Affairs Healthcare System. BMC Health Services Research 2016, 16: 609. PMID: 27769221, PMCID: PMC5075153, DOI: 10.1186/s12913-016-1841-4.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overDelivery of Health CareElectronic Health RecordsEmigration and ImmigrationFemaleHospitals, VeteransHumansMaleMental DisordersMiddle AgedPatient Acceptance of Health CareRetrospective StudiesUnited StatesUnited States Department of Veterans AffairsVeteransVeterans HealthYoung AdultConceptsHealthcare systemVeterans Health Administration electronic health recordsVeterans Affairs Healthcare SystemHealthcare mobilityRetrospective cohort studyHepatitis C virusOutcomes of careDifferent healthcare systemsDistinct healthcare systemsElectronic health recordsClinical characteristicsCohort studyHealthcare utilizationC virusSpecialty carePsychiatric disordersYounger veteransDisease preventionYounger agePopulation healthHealth recordsVeteransStatus changesCareYear period
2015
Classification of radiology reports for falls in an HIV study cohort
Bates J, Fodeh SJ, Brandt CA, Womack JA. Classification of radiology reports for falls in an HIV study cohort. Journal Of The American Medical Informatics Association 2015, 23: e113-e117. PMID: 26567329, PMCID: PMC4954638, DOI: 10.1093/jamia/ocv155.Peer-Reviewed Original ResearchMeSH KeywordsAccidental FallsArea Under CurveCohort StudiesElectronic Health RecordsHIV InfectionsHumansRadiology Information SystemsSupport Vector MachineUnified Medical Language SystemUnited StatesUnited States Department of Veterans AffairsVeteransConceptsFeature selectionMutual informationSVM classifierUnified Medical Language System (UMLS) conceptsSupport vector machine classifierRadiology reportsFeature selection approachStructured electronic health record dataFeature selection methodVector machine classifierMachine learningNumber of featuresSupervised machineDiscriminative featuresFeature setsMachine classifierVACS-VCClassifier performanceStudy cohortClassifierSelection approachElectronic health record dataCurve scoreVeterans Aging Cohort Study Virtual CohortSelection method
2013
Validating a natural language processing tool to exclude psychogenic nonepileptic seizures in electronic medical record-based epilepsy research
Hamid H, Fodeh SJ, Lizama AG, Czlapinski R, Pugh MJ, LaFrance WC, Brandt CA. Validating a natural language processing tool to exclude psychogenic nonepileptic seizures in electronic medical record-based epilepsy research. Epilepsy & Behavior 2013, 29: 578-580. PMID: 24135384, DOI: 10.1016/j.yebeh.2013.09.025.Peer-Reviewed Original ResearchMeSH KeywordsAfghan Campaign 2001-Biomedical ResearchElectronic Health RecordsEpilepsyFemaleHumansIraq War, 2003-2011MaleNatural Language ProcessingReproducibility of ResultsUnited StatesUnited States Department of Veterans AffairsConceptsPsychogenic nonepileptic seizuresPositive predictive valueNonepileptic seizuresDefinite PNESAfghanistan veteransEpilepsy researchNational Clinical DatabaseVideo electroencephalograph monitoringDiagnosis of epilepsySeizure disorderDefinitive diagnosisElectronic health record systemsEpidemiologic dataHealth record systemsPredictive valueClinical databaseElectroencephalograph monitoringPatientsEpilepsyVEEGEpidemiologic researchVeteransRecord systemSeizuresDiagnosis