2024
Validation of an Electronic Health Record–Based Machine Learning Model Compared With Clinical Risk Scores for Gastrointestinal Bleeding
Shung D, Chan C, You K, Nakamura S, Saarinen T, Zheng N, Simonov M, Li D, Tsay C, Kawamura Y, Shen M, Hsiao A, Sekhon J, Laine L. Validation of an Electronic Health Record–Based Machine Learning Model Compared With Clinical Risk Scores for Gastrointestinal Bleeding. Gastroenterology 2024, 167: 1198-1212. PMID: 38971198, PMCID: PMC11493512, DOI: 10.1053/j.gastro.2024.06.030.Peer-Reviewed Original ResearchElectronic health recordsGlasgow-Blatchford scoreEmergency departmentVery-low-risk patientsRisk scoreOakland scoreMachine learning modelsStructured data fieldsClinical risk scoreGastrointestinal bleedingAll-cause mortalityHealth recordsLearning modelsManual data entrySecondary analysisRisk stratification scoresAssess proportionRed blood-cell transfusionPrimary outcomeProportion of patientsData entryOvert gastrointestinal bleedingPrimary analysisReceiver-operating-characteristic curveVery-low-risk
2021
Disparities in Accessing and Reading Open Notes in the Emergency Department Upon Implementation of the 21st Century CURES Act
Sangal RB, Powers E, Rothenberg C, Ndumele C, Ulrich A, Hsiao A, Venkatesh AK. Disparities in Accessing and Reading Open Notes in the Emergency Department Upon Implementation of the 21st Century CURES Act. Annals Of Emergency Medicine 2021, 78: 593-598. PMID: 34353651, DOI: 10.1016/j.annemergmed.2021.06.014.Peer-Reviewed Original ResearchConceptsProportion of patientsPatient portal accessEmergency departmentOpen notesClinical notesPortal accessPublic insuranceUrgent care centersCentury Cures ActDifferent patient demographicsSingle health systemPatient demographicsPrimary outcomeCures ActPatient utilizationPatient visitsCare centerObservational studyPatientsDigital health toolsAge 18Health systemHealth toolsUnique barriersNon-English speakers