2025
In the time of COVID-19: challenges, successes, and lessons learned from studies in cancer patients
Mack P, Crawford J, Chang A, Yin A, Klein S, Shea P, Hirsch F, Zidar D, Simon V, Gleason C, McBride R, Cordon-Cardo C, VanOudenhove J, Halene S, Lee F, Mantis N, Kushi L, Weiskopf D, Merchant A, Reckamp K, Skarbinski J, Figueiredo J. In the time of COVID-19: challenges, successes, and lessons learned from studies in cancer patients. Journal Of The National Cancer Institute 2025, djaf073. PMID: 40127178, DOI: 10.1093/jnci/djaf073.Peer-Reviewed Original ResearchChanging public health landscapePublic health landscapeEnrollment of participantsSARS-CoV-2National Cancer InstituteRisk of SARS-CoV-2 infectionU.S. National Cancer InstituteNational Institute of AllergyOverall healthHealth landscapeSelf-ReportCohort studyStudy designBiospecimen collectionInstitute of AllergySARS-CoV-2 infectionCancer InstituteStandard serological testsData elementsStudy immune responsesHematologic malignanciesClinicopathological dataNational InstituteCancer patientsCOVID-19
2024
The effect of a Veterans Affairs rapid rehousing and homelessness prevention program on long‐term housing instability
Chapman A, Scharfstein D, Byrne T, Montgomery A, Suo Y, Effiong A, Shorter C, Huebler S, Greene T, Tsai J, Gelberg L, Kertesz S, Nelson R. The effect of a Veterans Affairs rapid rehousing and homelessness prevention program on long‐term housing instability. Health Services Research 2024, 60: e14428. PMID: 39739599, PMCID: PMC12052498, DOI: 10.1111/1475-6773.14428.Peer-Reviewed Original ResearchSupportive Services for Veteran FamiliesElectronic health recordsHousing instabilityPrevention programsVA Corporate Data WarehouseStructured data elementsTarget trial emulation frameworkHousing outcomesCorporate Data WarehouseHomeless-experienced individualsRisk of housing instabilityHomelessness prevention programImprove housing outcomesHealth recordsVeteran familiesHousing statusRisk differenceEligibility criteriaUnique patientsVisit timeVeteransData elementsProgram designAnalyzed dataEligibilityThe Academic Community Early Psychosis Intervention Network: Toward building a novel learning health system across six US states
Vohs J, Srihari V, Vinson A, Lapidos A, Cahill J, Taylor S, Heckers S, Weiss A, Chaudhry S, Silverstein S, Tso I, Breitborde N, Breier A. The Academic Community Early Psychosis Intervention Network: Toward building a novel learning health system across six US states. Learning Health Systems 2024, 9: e10471. PMID: 40247900, PMCID: PMC12000763, DOI: 10.1002/lrh2.10471.Peer-Reviewed Original ResearchEarly Psychosis Intervention NetworkIntervention NetworkFirst-episode psychosisRegional clinical networksLearning health systemQuality improvement projectQuality improvement effortsCulture of continuous learningData elementsPrimary diagnosis of schizophreniaConsecutive 6-month intervalsFirst-episode psychosis clinicUsual careSuperior patient outcomesSpecialty servicesHealth systemClinical teamImplementation meetingsQuality assurance effortsImprovement projectClinical networksHealthcare systemPractice-based researchImprovement effortsQuality improvementDevelopment of a Uniform Apheresis Case Report Form for Standardized Collection of Apheresis Data
Johnson A, Szczepiorkowski Z, Balogun R, Karam O, Nellis M, Schneiderman J, Schwartz J, Winters J, Wu Y, Armendariz T, Burgstaler E, Collins L, Geile K, Pavenski K, Sanchez A, Witt V, Muthusamy A, Pederson T, Ramesh V, Thao M, Chlebeck T, Zantek N. Development of a Uniform Apheresis Case Report Form for Standardized Collection of Apheresis Data. Journal Of Clinical Apheresis 2024, 39: e22146. PMID: 39420527, PMCID: PMC11523286, DOI: 10.1002/jca.22146.Peer-Reviewed Original ResearchConceptsElectronic case report formsCase report formsReport formsData elementsConsensus panelImprove patient careRelevant data elementsOnline survey toolPatient careSurvey toolQuality InitiativeBlood cell collectionLaboratory parametersPatient demographicsDraft documentTool's abilityStandardized collectionApheresisCell collectionDatabase developmentVascular accessTherapeutic apheresisOnline conferencingUsabilitySubject eligibilityCorrection: Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Goals-of-Care and Family/Surrogate Decision-Maker Data
Jaffa M, Kirsch H, Creutzfeldt C, Guanci M, Hwang D, LeTavec D, Mahanes D, Natarajan G, Steinberg A, Zahuranec D, Muehlschlegel S. Correction: Common Data Elements for Disorders of Consciousness: Recommendations from the Working Group on Goals-of-Care and Family/Surrogate Decision-Maker Data. Neurocritical Care 2024, 42: 1130-1131. PMID: 39407076, DOI: 10.1007/s12028-024-02098-9.Peer-Reviewed Original ResearchSEETrials: Leveraging large language models for safety and efficacy extraction in oncology clinical trials
Lee K, Paek H, Huang L, Hilton C, Datta S, Higashi J, Ofoegbu N, Wang J, Rubinstein S, Cowan A, Kwok M, Warner J, Xu H, Wang X. SEETrials: Leveraging large language models for safety and efficacy extraction in oncology clinical trials. Informatics In Medicine Unlocked 2024, 50: 101589. PMID: 39493413, PMCID: PMC11530223, DOI: 10.1016/j.imu.2024.101589.Peer-Reviewed Original ResearchAntibody-drug conjugatesOverall response rateMultiple myelomaF1 scoreCAR-TComplete responseBispecific antibodiesComparative performance analysisClinical trial studyClinical trial outcomesLanguage modelAccurate data extractionTherapy subgroupFine granularityOncology clinical trialsAdverse eventsClinical decision-makingPerformance analysisClinical trialsInnovative therapiesDiverse therapiesClinical trial abstractsCancer domainData elementsTherapyEvaluation of completeness of commonly used data elements for clinical trial eligibility criteria using a registry-enhanced data collection process: Results from patients with non-small cell lung cancer (NSCLC) and colorectal cancer (CRC) at three community oncology practices.
Campbell Fontaine A, McAneny B, Tucker V, Koontz M, Trotter C, Paulson J, McMurdie P, Tezcan A, Peguero J, Peguero J, Campos L. Evaluation of completeness of commonly used data elements for clinical trial eligibility criteria using a registry-enhanced data collection process: Results from patients with non-small cell lung cancer (NSCLC) and colorectal cancer (CRC) at three community oncology practices. Journal Of Clinical Oncology 2024, 20: 346-346. DOI: 10.1200/op.2024.20.10_suppl.346.Peer-Reviewed Original ResearchNon-small cell lung cancerCommunity oncology practicesColorectal cancerDiagnosis dateOncology practiceCancer diagnosisData elementsEligibility assessmentColorectal cancer patientsCompletion ratesGenomic test resultsEssential data elementsLung cancerClinical trialsRemote staffMale to female ratioSystemic anti-cancer treatmentTrial eligibility criteriaPatient identificationECOG performance statusICD10 codesPatient RegistryYears of ageLung cancer diagnosisCell lung cancerExercise and Nutrition to Improve Cancer Treatment-Related Outcomes (ENICTO)
Schmitz K, Brown J, Irwin M, Robien K, Scott J, Berger N, Caan B, Cercek A, Crane T, Evans S, Ligibel J, Meyerhardt J, Agurs-Collins T, Basen-Engquist K, Bea J, Cai S, Cartmel B, Chinchilli V, Demark-Wahnefried W, Dieli-Conwright C, DiPietro L, Doerksen S, Edelstein S, Elena J, Evans W, Ferrucci L, Foldi J, Freylersythe S, Furberg H, Jones L, Levine R, Moskowitz C, Owusu C, Penedo F, Rabin B, Ratner E, Rosenzweig M, Salz T, Sanft T, Schlumbrecht M, Spielmann G, Thomson C, Tjaden A, Weiser M, Yang S, Yu A, Perna F, Caan B, Anderson S, Bahia H, Castillo A, Feliciano E, Johnson K, Ross M, Weltzein E, Brown J, Albarado B, Compton S, Green T, Nash R, Nauta P, Welch M, Yang S, Meyerhardt J, Dieli-Conwright C, Nguyen D, Pena A, Spielmann G, Kim Y, Evans W, Bea J, Blew R, Crane T, Bhatti A, Clavon R, Erlandsen S, Freylersythe S, Hollander K, Lopez-Pentecost M, Penedo F, Rolle L, Rossi P, Schlumbrecht M, Wheeler M, Irwin M, Cao A, Cartmel B, Ferrucci L, Gottlieb L, Harrigan M, Li F, McGowan C, Puklin L, Ratner E, Sanft T, Zupa M, Berger N, Cerne S, Mills C, Conochan S, Hundal J, Owusu C, Ligibel J, Campbell N, DiGuglielmo K, Kemp W, Maples-Campbell C, Nguyen T, Oppenheim J, Tanasijevic A, Thomson C, Yung A, Basen-Engquist K, Loomba P, Chinchilli V, Schmitz K, Binder J, Doerksen S, Foldi J, Garrett S, Scalise R, Sobolewski M, White L, Scott J, Cercek A, Cai S, Cao S, Furberg H, Harrison J, Jones L, Lee C, Levine R, Michalski M, Moskowitz C, Novo R, Rabazzi J, Stoeckel K, Salz T, Weiser M, Yu A, Demark-Wahnefried W, Robien K, Evans S, DiPietro L, Duong B, Edelstein S, Helmchen L, Le D, McCleary C, Tjaden A, Wopat H, Rabin B, Perna F, Agurs-Collins T, Czajkowski S, Elena J, Nebeling L, Norton W. Exercise and Nutrition to Improve Cancer Treatment-Related Outcomes (ENICTO). Journal Of The National Cancer Institute 2024, 117: 9-19. PMID: 39118255, PMCID: PMC11717426, DOI: 10.1093/jnci/djae177.Peer-Reviewed Original ResearchSelf-reported physical functionTreatment-related outcomesTreatment-related side effectsRisk of suboptimal outcomesExercise interventionOncology carePhysical functionNational Cancer InstituteNutrition ProgramIntervention effectsCommon data elementsExerciseCancer InstituteCommunity opportunitiesData elementsStandard of careInterventionCareCancer patientsNutritionRelative dose intensityOutcomesSuboptimal outcomesCancer treatmentChemotherapy relative dose intensityMapping Study Variables to Common Data Elements Using GPT for Sheets: Towards Standardized Data Collection and Sharing
Ram P, Hong N, Xu H, Jiang X. Mapping Study Variables to Common Data Elements Using GPT for Sheets: Towards Standardized Data Collection and Sharing. 2024, 00: 320-325. DOI: 10.1109/ichi61247.2024.00048.Peer-Reviewed Original ResearchSHEA position statement on pandemic preparedness for policymakers: pandemic data collection, maintenance, and release
Branch-Elliman W, Banach D, Batshon L, Dumyati G, Haessler S, Hsu V, Jump R, Malani A, Mathew T, Murthy R, Pergam S, Shenoy E, Weber D. SHEA position statement on pandemic preparedness for policymakers: pandemic data collection, maintenance, and release. Infection Control And Hospital Epidemiology 2024, 45: 821-825. PMID: 38835230, DOI: 10.1017/ice.2024.65.Peer-Reviewed Original ResearchNursing Home to Emergency Care Transition Form Has Limited Uptake But Improves Documentation
Serina P, Stavrand A, Lind M, Gettel C, Southerland L, Goldberg E. Nursing Home to Emergency Care Transition Form Has Limited Uptake But Improves Documentation. Journal Of The American Medical Directors Association 2024, 25: 105056. PMID: 38843872, PMCID: PMC11403945, DOI: 10.1016/j.jamda.2024.105056.Peer-Reviewed Original ResearchDocumentation completenessAcute care transfersPre-implementation dataOdds of admissionRandom cross-sectional sampleQuality of information exchangeCross-sectional sampleNursing homesCare transfersCoC standardsImproved documentationPost-implementationTransfer documentsImplementation evaluationDecreased oddsEncounter dataEvaluate adoptionHospital admissionLogistic regressionSignificant information gapCognitive baselineNursesData elementsInformation gapFunctional baselineCommon Data Elements and Databases Essential for the Study of Musculoskeletal Injuries in Military Personnel
Juman L, Schneider E, Clifton D, Koehlmoos T. Common Data Elements and Databases Essential for the Study of Musculoskeletal Injuries in Military Personnel. Military Medicine 2024, 189: e2146-e2152. PMID: 38771112, DOI: 10.1093/milmed/usae241.Peer-Reviewed Original ResearchMilitary Health SystemMusculoskeletal injuriesService membersData elementsMedical encountersMilitary Health System Data RepositoryInjury statisticsPrevent musculoskeletal injuriesMilitary personnelBranch of serviceInjury preventionHealth systemCommon data elementsCore data elementsCommon Procedural Terminology codesForce readinessMedical assessmentEnvironmental scanSnowball samplingProcedural Terminology codesEvidence-informed policy decisionsMedical conditionsDisease burdenDeployment statusActive engagementDevelopment and Validation of Case-Finding Algorithms to Identify Pancreatic Cancer in the Veterans Health Administration
Mezzacappa C, Larki N, Skanderson M, Park L, Brandt C, Hauser R, Justice A, Yang Y, Wang L. Development and Validation of Case-Finding Algorithms to Identify Pancreatic Cancer in the Veterans Health Administration. Digestive Diseases And Sciences 2024, 69: 1507-1513. PMID: 38453743, DOI: 10.1007/s10620-024-08324-w.Peer-Reviewed Original ResearchElectronic health recordsVeterans Health AdministrationHealth AdministrationElectronic health records data elementsElectronic health record dataDiagnosis of exocrine pancreatic cancerNational Cancer RegistryCancer RegistryHealth recordsExocrine pancreatic cancerOncology settingOutpatient encountersInpatient encountersData elementsExpert adjudicationPancreatic ductal adenocarcinomaEpidemiological studiesRandom sampleInterquartile rangeIdentification of patientsRange of patientsPancreatic cancerVeteransLate diagnosisExcellent PPVComparison of brain imaging and physical health between research and clinical neuroimaging cohorts of ageing
Mossa-Basha M, Andre J, Yuh E, Hunt D, LaPiana N, Howlett B, Krakauer C, Crane P, Nelson J, DeZelar M, Meyers K, Larson E, Ralston J, Mac Donald C. Comparison of brain imaging and physical health between research and clinical neuroimaging cohorts of ageing. British Journal Of Radiology 2024, 97: 614-621. PMID: 38303547, PMCID: PMC11027291, DOI: 10.1093/bjr/tqae004.Peer-Reviewed Original ResearchConceptsCommon data elementsMRI groupThought (ACTClinical health measuresResearch MRIAging populationWhite matter hyperintensityPrevalence of hypertensionBrain MRI measuresMRI scansNeuroimaging cohortMRI participantsAdult ChangesPhysical healthHealth measuresOverall healthOutcome measuresResearch cohortBrain imagingWMH burdenCongestive heart failureData elementsBrainMRI measurementsMRI metricsThe Clinical Emergency Data Registry: Structure, Use, and Limitations for Research
Lin M, Sharma D, Venkatesh A, Epstein S, Janke A, Genes N, Mehrotra A, Augustine J, Malcolm B, Goyal P, Griffey R. The Clinical Emergency Data Registry: Structure, Use, and Limitations for Research. Annals Of Emergency Medicine 2024, 83: 467-476. PMID: 38276937, PMCID: PMC11627304, DOI: 10.1016/j.annemergmed.2023.12.014.Peer-Reviewed Original ResearchConceptsEmergency departmentData elementsAmerican College of Emergency PhysiciansData registryParticipating emergency departmentsElectronic health recordsClinical data registryEmergency careHealth recordsEmergency medicineEmergency physiciansDe-identifiedBilling dataAmerican CollegeImprove data qualityCompletion of data elementsRegistryMultiple data elementsDemographic dataResearch usePatient demographicsReporting purposesData collectionCentral databaseClinical characteristics
2023
Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record
Dhingra L, Shen M, Mangla A, Khera R. Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record. The American Journal Of Cardiology 2023, 203: 136-148. PMID: 37499593, PMCID: PMC10865722, DOI: 10.1016/j.amjcard.2023.06.104.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsElectronic health recordsData elementsUnstructured data streamsUnstructured data elementsNatural language processingCommon data modelHealth recordsStructured data elementsComputer visionUnstructured dataData streamsHeterogeneity challengesSeamless deliveryData modelLanguage processingData storageFree textClinical narrativesComputational phenotypesOngoing workPatient informationRapid innovationSpecific expertiseConfidentialityOngoing innovationAssessing longitudinal housing status using Electronic Health Record data: a comparison of natural language processing, structured data, and patient-reported history
Chapman A, Cordasco K, Chassman S, Panadero T, Agans D, Jackson N, Clair K, Nelson R, Montgomery A, Tsai J, Finley E, Gabrielian S. Assessing longitudinal housing status using Electronic Health Record data: a comparison of natural language processing, structured data, and patient-reported history. Frontiers In Artificial Intelligence 2023, 6: 1187501. PMID: 37293237, PMCID: PMC10244644, DOI: 10.3389/frai.2023.1187501.Peer-Reviewed Original ResearchNatural language processingStructured data elementsElectronic health recordsData elementsLanguage processingFree-text clinical narrativesStructured dataMultiple data sourcesClinical narrativesElectronic health record dataPromising performanceVeterans Affairs electronic health recordsHealth recordsData sourcesHealth record dataOptimal performanceVA electronic health recordTraditional methodsClinical notesProcessingRecord dataEvaluation effortsPerformanceCodeServicesNIH HEAL Common Data Elements (CDE) implementation: NIH HEAL Initiative IDEA-CC
Adams M, Hurley R, Siddons A, Topaloglu U, Wandner L, Adams M, Arnsten J, Bao Y, Barry D, Becker W, Fiellin D, Fox A, Ghiroli M, Hanmer J, Horn B, Hurlocker M, Jalal H, Joseph V, Merlin J, Murray-Krezan C, Pearson M, Rogal S, Starrels J, Bachrach R, Witkiewitz K, Vasquez A. NIH HEAL Common Data Elements (CDE) implementation: NIH HEAL Initiative IDEA-CC. Pain Medicine 2023, 24: 743-749. PMID: 36799548, PMCID: PMC10321760, DOI: 10.1093/pm/pnad018.Peer-Reviewed Original ResearchConceptsClinical trialsClinical Trials NetworkCDE programsChronic painSecondary data analysisPainTrials NetworkDisease statesOpioidData standardsGeographical codingClinical researchHealing initiationData elementsNational InstituteTrialsDisordersFederal investmentInterventionStandard processDisorder researchSurveyed librariesCDELeveraging toolsNetwork alignment
2022
A companion to the preclinical common data elements for proteomics, lipidomics, and metabolomics data in rodent epilepsy models. A report of the TASK3‐WG4 omics working group of the ILAE/AES joint translational TASK force
Bindila L, Eid T, Mills JD, Hildebrand MS, Brennan GP, Masino SA, Whittemore V, Perucca P, Reid CA, Patel M, Wang KK, van Vliet E. A companion to the preclinical common data elements for proteomics, lipidomics, and metabolomics data in rodent epilepsy models. A report of the TASK3‐WG4 omics working group of the ILAE/AES joint translational TASK force. Epilepsia Open 2022 PMID: 36259125, DOI: 10.1002/epi4.12662.Peer-Reviewed Original ResearchCase report formsCommon data elementsILAE/AES Joint Translational Task ForceRodent epilepsy modelsPreclinical common data elementsPreclinical epilepsy researchTask ForceEpilepsy modelStudy protocolRodent modelsInternational LeagueEpilepsy researchReport formsData elementsLipidomicsMetabolomics methodologyMachine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review
Khosravi B, Rouzrokh P, Faghani S, Moassefi M, Vahdati S, Mahmoudi E, Chalian H, Erickson B. Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review. Diagnostics 2022, 12: 2512. PMID: 36292201, PMCID: PMC9600598, DOI: 10.3390/diagnostics12102512.Peer-Reviewed Original ResearchDeep learningMachine-learningExtract complex informationMedical domainHidden patternsMachine learningTraining processImage processingML/DLCardiothoracic imagingData elementsInput dataModeling algorithmAlgorithmComplex informationProof-of-conceptImaging fieldLearningImagesEarly adoptersResearch focusDeepMachineApplicationsData
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