Huan Li, MSHI
About
Research
Publications
2023
Quantifying EHR and Policy Factors Associated with the Gender Productivity Gap in Ambulatory, General Internal Medicine
Li H, Rotenstein L, Jeffery M, Paek H, Nath B, Williams B, McLean R, Goldstein R, Nuckols T, Hoq L, Melnick E. Quantifying EHR and Policy Factors Associated with the Gender Productivity Gap in Ambulatory, General Internal Medicine. Journal Of General Internal Medicine 2023, 39: 557-565. PMID: 37843702, PMCID: PMC10973284, DOI: 10.1007/s11606-023-08428-5.Peer-Reviewed Original ResearchElectronic health recordsWork relative value unitsPhysician genderPractice characteristicsWomen physiciansMen physiciansGeneral internal medicine physiciansEHR useInternal medicine physiciansPhysician productivityGeneral internal medicineMultivariable adjustmentPatient counselingCare discussionsPhysician ageClinical activityMedicine physiciansPredicting physician departure with machine learning on EHR use patterns: A longitudinal cohort from a large multi-specialty ambulatory practice
Lopez K, Li H, Paek H, Williams B, Nath B, Melnick E, Loza A. Predicting physician departure with machine learning on EHR use patterns: A longitudinal cohort from a large multi-specialty ambulatory practice. PLOS ONE 2023, 18: e0280251. PMID: 36724149, PMCID: PMC9891518, DOI: 10.1371/journal.pone.0280251.Peer-Reviewed Original ResearchConceptsElectronic health recordsEHR use patternsHealthcare industryPhysician departureSHAP valuesHealth recordsPhysician characteristicsLongitudinal cohortPhysician ageRisk physiciansAmbulatory practiceTargeted interventionsAppropriate interventionsPhysiciansTop variablesDocumentation timePhysician turnoverPredictive modelHeavy burdenInterventionInboxPhysician demandMachineValidatingPatientsDeep learning prediction of hospital readmissions for asthma and COPD
Lopez K, Li H, Lipkin-Moore Z, Kay S, Rajeevan H, Davis J, Wilson F, Rochester C, Gomez J. Deep learning prediction of hospital readmissions for asthma and COPD. Respiratory Research 2023, 24: 311. PMID: 38093373, PMCID: PMC10720134, DOI: 10.1186/s12931-023-02628-7.Peer-Reviewed Original Research
2021
Emergency care and the patient experience: Using sentiment analysis and topic modeling to understand the impact of the COVID-19 pandemic
Chekijian S, Li H, Fodeh S. Emergency care and the patient experience: Using sentiment analysis and topic modeling to understand the impact of the COVID-19 pandemic. Health And Technology 2021, 11: 1073-1082. PMID: 34414063, PMCID: PMC8363088, DOI: 10.1007/s12553-021-00585-z.Peer-Reviewed Original Research