2025
Opportunities and Challenges in Using Electronic Health Record Systems to Study Postacute Sequelae of SARS-CoV-2 Infection: Insights From the NIH RECOVER Initiative
Mandel H, Shah S, Bailey L, Carton T, Chen Y, Esquenazi-Karonika S, Haendel M, Hornig M, Kaushal R, Oliveira C, Perlowski A, Pfaff E, Rao S, Razzaghi H, Seibert E, Thomas G, Weiner M, Thorpe L, Divers J, Cohort R. Opportunities and Challenges in Using Electronic Health Record Systems to Study Postacute Sequelae of SARS-CoV-2 Infection: Insights From the NIH RECOVER Initiative. Journal Of Medical Internet Research 2025, 27: e59217. PMID: 40053748, PMCID: PMC11923460, DOI: 10.2196/59217.Peer-Reviewed Original ResearchMeSH KeywordsCOVID-19Electronic Health RecordsHumansNational Institutes of Health (U.S.)Post-Acute COVID-19 SyndromeSARS-CoV-2United StatesConceptsElectronic health recordsElectronic health record dataPostacute sequelae of SARS-CoV-2 infectionSequelae of SARS-CoV-2 infectionElectronic health record systemsHealth record systemsLong COVIDHealth recordsEHR dataEpidemiological researchRecording systemPostacute sequelaeMultifaceted conditionNational InstituteSARS-CoV-2 infectionData sourcesCOVID researchCOVIDInitiationNationalOpportunitiesDataImproving topic modeling performance on social media through semantic relationships within biomedical terminology
Xin Y, Grabowska M, Gangireddy S, Krantz M, Kerchberger V, Dickson A, Feng Q, Yin Z, Wei W. Improving topic modeling performance on social media through semantic relationships within biomedical terminology. PLOS ONE 2025, 20: e0318702. PMID: 39982945, PMCID: PMC11845042, DOI: 10.1371/journal.pone.0318702.Peer-Reviewed Original ResearchMeSH KeywordsElectronic Health RecordsHumansSemanticsSocial MediaSystematized Nomenclature of MedicineTerminology as TopicConceptsSocial media textsTopic modelsSocial mediaHealth-related topicsAnalyze social mediaSemantic relationshipsBiomedical terminologiesMedical conceptsSemantic typesRecord validationModeling pipelineMedia textsUnsupervised machineExpert evaluationHealthcare researchModel performanceOnline discussionsTextTopicsPipelineUsersMachineModeling approachModelTechnique's potentialDistributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals
Kuo T, Gabriel R, Koola J, Schooley R, Ohno-Machado L. Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals. Nature Communications 2025, 16: 1371. PMID: 39910076, PMCID: PMC11799213, DOI: 10.1038/s41467-025-56510-9.Peer-Reviewed Original ResearchConceptsHeart disease dataParts of informationLearning counterpartsCentralized solutionVertical scenariosPatient privacyPredictive analyticsFederated modelSynchronization timePrivacyUC San DiegoPatient-level recordsDisease dataPatient dataPrediction modelPatient careHealthcare centersUniversity of CaliforniaCalifornia hospitalsHealthcare systemQuality improvementPatient recordsEvaluating the Bias, type I error and statistical power of the prior Knowledge-Guided integrated likelihood estimation (PIE) for bias reduction in EHR based association studies
Jing N, Lu Y, Tong J, Weaver J, Ryan P, Xu H, Chen Y. Evaluating the Bias, type I error and statistical power of the prior Knowledge-Guided integrated likelihood estimation (PIE) for bias reduction in EHR based association studies. Journal Of Biomedical Informatics 2025, 163: 104787. PMID: 39904407, DOI: 10.1016/j.jbi.2025.104787.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsBiasComputer SimulationCOVID-19Electronic Health RecordsHumansLikelihood FunctionsPhenotypeSARS-CoV-2ConceptsType I errorIntegrated likelihood estimatorsElectronic health recordsUse-case analysisLikelihood estimationLow prevalence outcomesUse-casesBias reductionNaive methodEffect sizeSynthetic dataPhenotyping algorithmsEstimation biasReal-world scenariosStatistical inferenceSimulation studyAssociation effect sizesAccurate prior informationBinary outcomesPoint estimatesAssociation estimatesStatistical powerHealth recordsKnowledge-guidedOutcome prevalenceEvaluation of a Machine Learning-Guided Strategy for Elevated Lipoprotein(a) Screening in Health Systems
Aminorroaya A, Dhingra L, Oikonomou E, Khera R. Evaluation of a Machine Learning-Guided Strategy for Elevated Lipoprotein(a) Screening in Health Systems. Circulation Genomic And Precision Medicine 2025, 18: e004632. PMID: 39846171, PMCID: PMC11835527, DOI: 10.1161/circgen.124.004632.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAtherosclerosisElectronic Health RecordsFemaleHumansLipoprotein(a)Machine LearningMaleMass ScreeningMiddle AgedRisk FactorsConceptsYale New Haven Health SystemHealth systemVanderbilt University Medical CenterHealth system electronic health recordUniversity Medical CenterCoronary Artery Risk DevelopmentMulti-Ethnic Study of AtherosclerosisElectronic health recordsMedical CenterUS health systemHealth system patientsAssociated with significantly higher oddsMulti-Ethnic StudyUS-based cohortStudy of AtherosclerosisSignificantly higher oddsHealth recordsUK BiobankAtherosclerosis RiskRisk DevelopmentHigher oddsElevated Lp(aUniversal screeningSystem patientsStudy cohortClassifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study
Cardamone N, Olfson M, Schmutte T, Ungar L, Liu T, Cullen S, Williams N, Marcus S. Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study. JMIR Medical Informatics 2025, 13: e65454. PMID: 39864953, PMCID: PMC11884378, DOI: 10.2196/65454.Peer-Reviewed Original ResearchMeSH KeywordsElectronic Health RecordsEmergency Service, HospitalHumansMental DisordersMental HealthNatural Language ProcessingUnited StatesConceptsElectronic health recordsMental health termsHealth termsClinical expertsEmergency departmentHealth recordsPhysical healthMental healthElectronic health record systemsHealth care provider organizationsMental health-related problemsElectronic health record data setsCare provider organizationsMental health cliniciansMental health disordersHealth-related problemsED episodesHealth cliniciansRecords of individualsState-of-the-artDiagnostic codesHealth disordersHospital readmissionProvider organizationsMortality riskUtility of Candidate Genes From an Algorithm Designed to Predict Genetic Risk for Opioid Use Disorder
Davis C, Jinwala Z, Hatoum A, Toikumo S, Agrawal A, Rentsch C, Edenberg H, Baurley J, Hartwell E, Crist R, Gray J, Justice A, Gelernter J, Kember R, Kranzler H, Muralidhar S, Moser J, Deen J, Tsao P, Gaziano J, Hauser E, Kilbourne A, Matheny M, Oslin D, Churby L, Whitbourne S, Brewer J, Shayan S, Selva L, Pyarajan S, Cho K, DuVall S, Brophy M, Stephens B, Connor T, Argyres D, Assimes T, Hung A, Kranzler H, Aguayo S, Ahuja S, Alexander K, Androulakis X, Balasubramanian P, Ballas Z, Beckham J, Bhushan S, Boyko E, Cohen D, Dellitalia L, Faulk L, Fayad J, Fujii D, Gappy S, Gesek F, Greco J, Godschalk M, Gress T, Gupta S, Gutierrez S, Harley J, Hamner M, Hurley R, Iruvanti P, Jacono F, Jhala D, Kinlay S, Landry M, Liang P, Liangpunsakul S, Lichy J, Mahan C, Marrache R, Mastorides S, Mattocks K, Meyer P, Moorman J, Morgan T, Murdoch M, Norton J, Okusaga O, Oursler K, Poon S, Rauchman M, Servatius R, Sharma S, Smith R, Sriram P, Strollo P, Tandon N, Villareal G, Walsh J, Wells J, Whittle J, Whooley M, Wilson P, Xu J, Yeh S, Bast E, Dryden G, Hogan D, Joshi S, Lo T, Morales P, Naik E, Ong M, Petrakis I, Rai A, Yen A. Utility of Candidate Genes From an Algorithm Designed to Predict Genetic Risk for Opioid Use Disorder. JAMA Network Open 2025, 8: e2453913. PMID: 39786773, PMCID: PMC11718552, DOI: 10.1001/jamanetworkopen.2024.53913.Peer-Reviewed Original ResearchConceptsOpioid use disorder riskElectronic health record dataHealth record dataInternational Classification of DiseasesOpioid use disorderClassification of DiseasesGenetic variantsInternational ClassificationGenetic riskRecord dataRisk of opioid use disorderMillion Veteran ProgramOpioid use disorder diagnosisUse disorderCase-control studyVeteran ProgramMain OutcomesDiagnostic codesClinical careOpioid exposurePharmacy recordsLogistic regressionRisk allelesNagelkerke R2Clinically useful modelWhom Should We Regard as Responsible for Health Record Inaccuracies That Hinder Population-Based Fact Finding?
Akgün K, Feder S. Whom Should We Regard as Responsible for Health Record Inaccuracies That Hinder Population-Based Fact Finding? The AMA Journal Of Ethic 2025, 27: e6-13. PMID: 39745909, DOI: 10.1001/amajethics.2025.6.Peer-Reviewed Original ResearchConceptsElectronic health recordsPatient-level dataHealth recordsFrontline cliniciansCommunity partnersEHR dataHealth dataPatient reportsAnalysis of patient-level dataData entryPopulation-level analysisHealthReview processEpidemiological purposesScience expertsCliniciansFrontlineData inquiryDataCommentaryCommunityStakeholdersPatientsPartnersRecords
2024
Increasing patient viewership of complex imaging reports: The paradox of the Cures Act
Amin K, Davis M, Naderi A, Forman H. Increasing patient viewership of complex imaging reports: The paradox of the Cures Act. Clinical Imaging 2024, 119: 110398. PMID: 39756146, DOI: 10.1016/j.clinimag.2024.110398.Peer-Reviewed Original ResearchMeSH KeywordsDiagnostic ImagingElectronic Health RecordsFemaleHumansMaleRadiology Information SystemsRetrospective StudiesUnited StatesConceptsElectronic health recordsCentury Cures ActCures ActImprove patient experienceRadiology reportsReading grade levelPatient experienceHealth recordsHealth informationRecommended levelsRetrospective observational studyObservational studyPatientsViewing probabilityRadiologyOddsHealthYearsIncrease viewershipReportsGrade levelModalitiesProvisionEnhancing patient representation learning with inferred family pedigrees improves disease risk prediction
Huang X, Arora J, Erzurumluoglu A, Stanhope S, Lam D, Arora J, Erzurumluoglu A, Lam D, Khoueiry P, Jensen J, Cai J, Lawless N, Kriegl J, Ding Z, de Jong J, Zhao H, Ding Z, Wang Z, de Jong J. Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction. Journal Of The American Medical Informatics Association 2024, 32: 435-446. PMID: 39723811, PMCID: PMC11833479, DOI: 10.1093/jamia/ocae297.Peer-Reviewed Original ResearchMeSH KeywordsColitis, UlcerativeCrohn DiseaseDeep LearningElectronic Health RecordsFamily RelationsGenetic Predisposition to DiseaseHumansMachine LearningPedigreeRisk AssessmentConceptsElectronic health recordsDisease risk predictionElectronic health record researchFamily health historyGenetic aspects of diseaseRisk predictionInflammatory bowel disease subtypeHealth recordsHealth historyAspects of diseaseFamily relationsHealthcare researchPatient's disease riskInfluence of geneticsDisease riskDiagnosis dataFamily pedigreeEnvironmental exposuresRisk factorsDisease dependencyPatient representation learningClinical profileFamilyDisease subtypesRiskAccuracy of Electronic Health Record Phenotypes to Detect Recognition of Hypertension in Pediatric Primary Care
Nugent J, Cueto V, Tong C, Sharifi M. Accuracy of Electronic Health Record Phenotypes to Detect Recognition of Hypertension in Pediatric Primary Care. Academic Pediatrics 2024, 25: 102629. PMID: 39732164, PMCID: PMC11893226, DOI: 10.1016/j.acap.2024.102629.Peer-Reviewed Original ResearchConceptsPediatric primary careIncident hypertensionHypertensive BPHypertension recognitionPrimary careRecognition of hypertensionCross-sectional study of children aged 3Diagnosis codesElectronic health record phenotypingClinician recognitionClinician decision supportGuideline-recommended careElectronic health recordsInternational Classification of DiseasesChart reviewDocumentation of hypertensionClassification of DiseasesCross-sectional studyChildren aged 3Problem list entriesWellness visitsHealth recordsEHR phenotypesInternational ClassificationICD-10Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure
Adejumo P, Thangaraj P, Dhingra L, Aminorroaya A, Zhou X, Brandt C, Xu H, Krumholz H, Khera R. Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure. JAMA Network Open 2024, 7: e2443925. PMID: 39509128, PMCID: PMC11544492, DOI: 10.1001/jamanetworkopen.2024.43925.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overConnecticutDeep LearningDocumentationElectronic Health RecordsFemaleFunctional StatusHeart FailureHumansMaleMiddle AgedNatural Language ProcessingROC CurveConceptsFunctional status assessmentArea under the receiver operating characteristic curveClinical documentationElectronic health record dataHF symptomsOptimal care deliveryHealth record dataAssess functional statusStatus assessmentClinical trial participationProcessing of clinical documentsFunctional status groupCare deliveryOutpatient careMain OutcomesMedical notesTrial participantsNew York Heart AssociationFunctional statusQuality improvementRecord dataHeart failureClinical notesDiagnostic studiesStatus groupsA Validated Algorithm to Identify Hepatic Decompensation in the Veterans Health Administration Electronic Health Record System
Haque L, Tate J, Chew M, Caniglia E, Taddei T, Re V. A Validated Algorithm to Identify Hepatic Decompensation in the Veterans Health Administration Electronic Health Record System. Pharmacoepidemiology And Drug Safety 2024, 33: e70024. PMID: 39477692, PMCID: PMC11631147, DOI: 10.1002/pds.70024.Peer-Reviewed Original ResearchConceptsVeterans Health Administration dataHealth administrative dataAdministrative dataElectronic health record systemsHealth record systemsInternational Classification of DiseasesCoding algorithmOutpatient International Classification of DiseasesPositive predictive valueClassification of DiseasesHepatic decompensationDiagnosis codesPharmacoepidemiologic researchMedical recordsVeteransRecording systemValidation algorithmAlgorithmChronic liver diseaseDecompensationLiver diseasePredictive valueRecordsDiagnosisCriteriaMapper: establishing the automatic identification of clinical trial cohorts from electronic health records by matching normalized eligibility criteria and patient clinical characteristics
Lee K, Mai Y, Liu Z, Raja K, Jun T, Ma M, Wang T, Ai L, Calay E, Oh W, Schadt E, Wang X. CriteriaMapper: establishing the automatic identification of clinical trial cohorts from electronic health records by matching normalized eligibility criteria and patient clinical characteristics. Scientific Reports 2024, 14: 25387. PMID: 39455879, PMCID: PMC11511882, DOI: 10.1038/s41598-024-77447-x.Peer-Reviewed Original ResearchMeSH KeywordsClinical Trials as TopicDatabases, FactualElectronic Health RecordsEligibility DeterminationFemaleHumansMalePatient SelectionConceptsElectronic health recordsCell lung cancerEligibility criteriaClinical characteristicsLung cancerHealth recordsNon-small cell lung cancerSmall cell lung cancerPatient clinical characteristicsClinical trial cohortPatients' electronic health recordsIdentification of patientsClinical trial criteriaIdentification of eligible patientsSickle cell anemiaNon-alcoholic steatohepatitisStandardized terminologyProstate cancerMultiple myelomaTrial eligibility criteriaPatient selectionTrial cohortBreast cancerEligible patientsTrial criteriaCombining electronic health records data from a clinical research network with registry data to examine long-term outcomes of interventions and devices: an observational cohort study
Mao J, Matheny M, Smolderen K, Mena-Hurtado C, Sedrakyan A, Goodney P. Combining electronic health records data from a clinical research network with registry data to examine long-term outcomes of interventions and devices: an observational cohort study. BMJ Open 2024, 14: e085806. PMID: 39327057, PMCID: PMC11429269, DOI: 10.1136/bmjopen-2024-085806.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overAmputation, SurgicalCohort StudiesElectronic Health RecordsFeasibility StudiesFemaleHumansMaleMiddle AgedNew York CityRegistriesUnited StatesConceptsElectronic health recordsElectronic health record dataClinical Research NetworkAmputation-free survivalPeripheral vascular interventionsResearch NetworkINSIGHT Clinical Research NetworkNew York CityHealth record dataIncreased riskLong-term outcomes of interventionsSecondary outcome measuresOutcomes of interventionsIncreased risk of deathYork CityRisk of deathObservational cohort studyHealth recordsRegistry dataClinical registryVascular Quality Initiative registryCohort studyOutcome assessmentRecord dataLong-term outcomesUse of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks
Lu Y, Keeley E, Barrette E, Cooper-DeHoff R, Dhruva S, Gaffney J, Gamble G, Handke B, Huang C, Krumholz H, McDonough C, Schulz W, Shaw K, Smith M, Woodard J, Young P, Ervin K, Ross J. Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks. BMC Cardiovascular Disorders 2024, 24: 497. PMID: 39289597, PMCID: PMC11409735, DOI: 10.1186/s12872-024-04161-x.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedAntihypertensive AgentsBlood PressureElectronic Health RecordsFemaleHumansHypertensionMaleMiddle AgedRetrospective StudiesTime FactorsTreatment OutcomeUnited StatesConceptsElectronic health recordsHealth recordsHealth systemUncontrolled hypertensionUse of electronic health recordsHypertension managementElectronic health record systemsOneFlorida Clinical Research ConsortiumElectronic health record dataYale New Haven Health SystemBP measurementsICD-10-CM codesHealth system networkPublic health priorityICD-10-CMIncidence rate of deathElevated BP measurementsElevated blood pressure measurementsHealthcare visitsAmbulatory careHealth priorityRetrospective cohort studyEHR dataOneFloridaBlood pressure measurementsHarnessing Natural Language Processing to Assess Quality of End-of-Life Care for Children With Cancer
Lindsay M, de Oliveira S, Sciacca K, Lindvall C, Ananth P. Harnessing Natural Language Processing to Assess Quality of End-of-Life Care for Children With Cancer. JCO Clinical Cancer Informatics 2024, 8: e2400134. PMID: 39265122, PMCID: PMC11407740, DOI: 10.1200/cci.24.00134.Peer-Reviewed Original ResearchConceptsEnd-of-life carePalliative care consultationGoals of careLocation of deathProportion of decedentsDocumented discussionCare consultationEvidence-based quality measuresMeasure quality of careGold standard manual chart reviewQuality measuresQuality of careEnd of lifeContent of clinical notesLife-sustaining treatmentEnd-of-lifeManual chart reviewCancer decedentsEfficient quality measureCohort of childrenAssess qualityMulti-center researchQuality improvementMeasure qualityCareLeveraging an Electronic Health Record Patient Portal to Help Patients Formulate Their Health Care Goals: Mixed Methods Evaluation of Pilot Interventions
Naimark J, Tinetti M, Delbanco T, Dong Z, Harcourt K, Esterson J, Charpentier P, Walker J. Leveraging an Electronic Health Record Patient Portal to Help Patients Formulate Their Health Care Goals: Mixed Methods Evaluation of Pilot Interventions. JMIR Formative Research 2024, 8: e56332. PMID: 39207829, PMCID: PMC11393498, DOI: 10.2196/56332.Peer-Reviewed Original ResearchMeSH KeywordsAgedAged, 80 and overElectronic Health RecordsFemaleFocus GroupsHumansMalePatient PortalsPilot ProjectsSurveys and QuestionnairesConceptsPatient portalsPatient prioritiesElectronic health record patient portalEnd-of-life carePatient Priorities CareFamily medicine practiceMixed methods evaluationHealth care goalsComplex medication regimensEnd-of-lifeMedian completion timeMedian session timePrevisit questionnaireCare goalsInvited patientsPilot interventionPriority careSession timeHealth priorityFocus groupsPhone interviewsYears of ageMedication regimensMedicine practiceEPIC questionnaireFeasibility of using real-world data to emulate substance use disorder clinical trials: a cross-sectional study
Janda G, Jeffery M, Ramachandran R, Ross J, Wallach J. Feasibility of using real-world data to emulate substance use disorder clinical trials: a cross-sectional study. BMC Medical Research Methodology 2024, 24: 187. PMID: 39198727, PMCID: PMC11351457, DOI: 10.1186/s12874-024-02307-1.Peer-Reviewed Original ResearchMeSH KeywordsClinical Trials as TopicCross-Sectional StudiesElectronic Health RecordsFeasibility StudiesHumansSubstance-Related DisordersConceptsElectronic health record dataElectronic health recordsSubstance use disordersCross-sectional studyAdministrative claimsEligibility criteriaTrials evaluating treatmentPublic health systemClinical trialsIntroductionReal-world evidenceSafety of medical productsHealth recordsHealth systemReal-world dataTrial emulationPrimary end pointInsurance claimsPhase 2Placebo comparatorActive comparatorInterventionEligibilityEnd pointsTrialsExternal Validation of an Electronic Phenotyping Algorithm Detecting Attention to High Body Mass Index in Pediatric Primary Care
Barron A, Fenick A, Maciejewski K, Turer C, Sharifi M. External Validation of an Electronic Phenotyping Algorithm Detecting Attention to High Body Mass Index in Pediatric Primary Care. Applied Clinical Informatics 2024, 15: 700-708. PMID: 39197473, PMCID: PMC11387092, DOI: 10.1055/s-0044-1787975.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsBody Mass IndexChildComorbidityElectronic Health RecordsFemaleHumansMalePhenotypePrimary Health CareConceptsElectronic health recordsBody mass indexPediatric primary careElevated body mass indexWeight-related comorbiditiesPrimary carePediatric primary care practicesElectronic health record dataBody mass index categoriesMass indexImprove obesity managementPrimary care practicesWell-child visitsHigher body mass indexChart reviewLikelihood of classificationElectronic phenotyping algorithmsFree-text componentsClinician typeCare practicesHealth recordsClinician behaviorLaboratory ordersProgress notesObesity management
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