2024
Identification of Severe Acute Exacerbations of Chronic Obstructive Pulmonary Disease Subgroups by Machine Learning Implementation in Electronic Health Records.
Li H, Huston J, Zielonka J, Kay S, Sauler M, Gomez J. Identification of Severe Acute Exacerbations of Chronic Obstructive Pulmonary Disease Subgroups by Machine Learning Implementation in Electronic Health Records. Chronic Obstructive Pulmonary Diseases Journal Of The COPD Foundation 2024, 11: 611-623. PMID: 39423339, PMCID: PMC11703024, DOI: 10.15326/jcopdf.2024.0556.Peer-Reviewed Original ResearchElectronic health recordsMachine learningAcute exacerbation of COPDAdoption of electronic health recordsHealth recordsMachine learning implementationElectronic health record dataImplementation of MLEHR-derived dataML implementationChronic obstructive pulmonary disease subgroupCOVID-19 cohortSevere acute exacerbation of COPDClinical careExacerbation of COPDAcute exacerbationRetrospective cohort of patientsLearning implementationSeverity of acute exacerbations of COPDMachineRelevant subgroupsRetrospective cohortImplementationCohortCohort of patientsAssessing Real-World Data From Electronic Health Records for Health Technology Assessment: The SUITABILITY Checklist: A Good Practices Report of an ISPOR Task Force
Fleurence R, Kent S, Adamson B, Tcheng J, Balicer R, Ross J, Haynes K, Muller P, Campbell J, Bouée-Benhamiche E, García Martí S, Ramsey S. Assessing Real-World Data From Electronic Health Records for Health Technology Assessment: The SUITABILITY Checklist: A Good Practices Report of an ISPOR Task Force. Value In Health 2024, 27: 692-701. PMID: 38871437, PMCID: PMC11182651, DOI: 10.1016/j.jval.2024.01.019.Peer-Reviewed Original ResearchConceptsElectronic health recordsHealth technology assessmentElectronic health record dataEHR-derived dataHealth record dataTechnology assessmentHealth technology assessment agenciesHealth recordsGenerative artificial intelligenceData provenanceLanguage modelEvidence gapsPractice reportArtificial intelligenceRecord dataISPOR Task ForceData characteristicsData governanceReal worldEvidence developmentTask ForceReal World DataISPORChecklistRelevant items
2023
Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory.
Boyd A, Gonzalez-Guarda R, Lawrence K, Patil C, Ezenwa M, O'Brien E, Paek H, Braciszewski J, Adeyemi O, Cuthel A, Darby J, Zigler C, Ho P, Faurot K, Staman K, Leigh J, Dailey D, Cheville A, Del Fiol G, Knisely M, Grudzen C, Marsolo K, Richesson R, Schlaeger J. Potential bias and lack of generalizability in electronic health record data: reflections on health equity from the National Institutes of Health Pragmatic Trials Collaboratory. Journal Of The American Medical Informatics Association 2023, 30: 1561-1566. PMID: 37364017, PMCID: PMC10436149, DOI: 10.1093/jamia/ocad115.Peer-Reviewed Original ResearchConceptsElectronic health record dataPragmatic clinical trialsHealth record dataPopulation health problemElectronic health record systemsClinical trialsEHR-derived dataHealth record systemsHealth problemsSocial determinantsHealth equityRecord dataVulnerable populationsEHR dataHealthcare systemNational InstituteRecord systemLack of generalizabilityHealthDifferent subsetsGeneralizable researchEPCTsPopulationPotential biasTrials
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