2024
Assessing readiness to use electronic health record data for outcome ascertainment in clinical trials – A case study
Esserman D, Greene E, Latham N, Kane M, Lu C, Peduzzi P, Gill T, Ganz D. Assessing readiness to use electronic health record data for outcome ascertainment in clinical trials – A case study. Contemporary Clinical Trials 2024, 142: 107572. PMID: 38740298, DOI: 10.1016/j.cct.2024.107572.Peer-Reviewed Original ResearchElectronic health record dataElectronic health recordsOutcome ascertainmentDevelop Confidence in EldersElectronic health record platformsClinical sitesPrimary care practicesHealth record dataMulti-site trialMulti-site clinical trialCare practicesHealth recordsAssess readinessAcute clinical outcomesHealthcare systemRecord dataClinical trialsReduce injuriesData qualityData comprehensionChecklistStudy dataClinical trial sitesVariable data qualityAscertainmentValidation of a Rule-Based ICD-10-CM Algorithm to Detect Fall Injuries in Medicare Data
Ganz D, Esserman D, Latham N, Kane M, Min L, Gill T, Reuben D, Peduzzi P, Greene E. Validation of a Rule-Based ICD-10-CM Algorithm to Detect Fall Injuries in Medicare Data. The Journals Of Gerontology Series A 2024, 79: glae096. PMID: 38566617, PMCID: PMC11167485, DOI: 10.1093/gerona/glae096.Peer-Reviewed Original ResearchFee-for-serviceFall injuriesMedicare AdvantageMedicare dataTrial armsHealthcare systemDevelop Confidence in EldersArea under the receiver operating characteristic curveMedicare fee-for-serviceStratified resultsMedicareReduce injuriesMedical attentionObservational studyStrideReceiver operating characteristic curveCalendar monthMA dataInjuryData sourcesHealthcareArmReference standardTrialsWindow size
2023
A compressed large language model embedding dataset of ICD 10 CM descriptions
Kane M, King C, Esserman D, Latham N, Greene E, Ganz D. A compressed large language model embedding dataset of ICD 10 CM descriptions. BMC Bioinformatics 2023, 24: 482. PMID: 38105180, PMCID: PMC10726612, DOI: 10.1186/s12859-023-05597-2.Peer-Reviewed Original Research