About

Dr. Yujie Hu is an Associate Professor and Graduate Coordinator in the Department of Geography at the University of Florida. His research and teaching interests are centered on urban transportation, human mobility, and accessibility. His current research focuses on the relationships between people's mobility within cities—including commuting, healthcare-seeking, and crime—and the urban built environment. He also investigates accessibility to opportunities such as jobs, healthcare, food, and transportation infrastructure, particularly how natural hazards impact these factors. Additionally, Dr. Hu specializes in network flow analysis and the optimization of travel patterns related to commuting, bike sharing, healthcare, and food delivery. Dr. Hu employs GIS, network analysis, and machine learning/AI techniques to analyze big geospatial data, revealing patterns of individual and group behaviors from point patterns and network data. His goal is to convert data into knowledge that can inform and evaluate place-based policies focused on transportation, land use, public health, and community safety. He has contributed to the academic field through teaching courses such as Urban/Business Geography, Transportation Geography, and Spatial Networks, and has been involved in funded projects related to geospatial modeling and risk mitigation for human movement under hurricane threats. His educational background includes a PhD in Geography from Louisiana State University, a Master’s in Cartography and GIS from East China Normal University, and a Bachelor’s in GIS from North China University of Water Resources and Electric Power.

Research topics

• Medicine
• Emergency medicine
• Anesthesia
• Intensive care medicine
• Internal medicine

Selected publications

• Postoperative Overtriage to an Intensive Care Unit Is Associated With Low Value of Care

  Annals of Surgery · 2022 · 15 citations

  • Medicine
  • Emergency medicine
  • Intensive care medicine

  OBJECTIVE: We test the hypothesis that for low-acuity surgical patients, postoperative intensive care unit (ICU) admission is associated with lower value of care compared with ward admission. BACKGROUND: Overtriaging low-acuity patients to ICU consumes valuable resources and may not confer better patient outcomes. Associations among postoperative overtriage, patient outcomes, costs, and value of care have not been previously reported. METHODS: In this longitudinal cohort study, postoperative ICU admissions were classified as overtriaged or appropriately triaged according to machine learning-based patient acuity assessments and requirements for immediate postoperative mechanical ventilation or vasopressor support. The nearest neighbors algorithm identified risk-matched control ward admissions. The primary outcome was value of care, calculated as inverse observed-to-expected mortality ratios divided by total costs. RESULTS: Acuity assessments had an area under the receiver operating characteristic curve of 0.92 in generating predictions for triage classifications. Of 8592 postoperative ICU admissions, 423 (4.9%) were overtriaged. These were matched with 2155 control ward admissions with similar comorbidities, incidence of emergent surgery, immediate postoperative vital signs, and do not resuscitate order placement and rescindment patterns. Compared with controls, overtraiged admissions did not have a lower incidence of any measured complications. Total costs for admission were $16.4K for overtriage and $15.9K for controls ( P =0.03). Value of care was lower for overtriaged admissions [2.9 (2.0-4.0)] compared with controls [24.2 (14.1-34.5), P <0.001]. CONCLUSIONS: Low-acuity postoperative patients who were overtriaged to ICUs had increased total costs, no improvements in outcomes, and received low-value care.

  DOI

• Overtriage, Undertriage, and Value of Care after Major Surgery: An Automated, Explainable Deep Learning-Enabled Classification System

  Journal of the American College of Surgeons · 2022 · 17 citations

  • Medicine
  • Emergency medicine
  • Internal medicine

  BACKGROUND: In single-institution studies, overtriaging low-risk postoperative patients to ICUs has been associated with a low value of care; undertriaging high-risk postoperative patients to general wards has been associated with increased mortality and morbidity. This study tested the reproducibility of an automated postoperative triage classification system to generating an actionable, explainable decision support system. STUDY DESIGN: This longitudinal cohort study included adults undergoing inpatient surgery at two university hospitals. Triage classifications were generated by an explainable deep learning model using preoperative and intraoperative electronic health record features. Nearest neighbor algorithms identified risk-matched controls. Primary outcomes were mortality, morbidity, and value of care (inverted risk-adjusted mortality/total direct costs). RESULTS: Among 4,669 ICU admissions, 237 (5.1%) were overtriaged. Compared with 1,021 control ward admissions, overtriaged admissions had similar outcomes but higher costs ($15.9K [interquartile range $9.8K to $22.3K] vs $10.7K [$7.0K to $17.6K], p < 0.001) and lower value of care (0.2 [0.1 to 0.3] vs 1.5 [0.9 to 2.2], p < 0.001). Among 8,594 ward admissions, 1,029 (12.0%) were undertriaged. Compared with 2,498 control ICU admissions, undertriaged admissions had longer hospital length-of-stays (6.4 [3.4 to 12.4] vs 5.4 [2.6 to 10.4] days, p < 0.001); greater incidence of hospital mortality (1.7% vs 0.7%, p = 0.03), cardiac arrest (1.4% vs 0.5%, p = 0.04), and persistent acute kidney injury without renal recovery (5.2% vs 2.8%, p = 0.002); similar costs ($21.8K [$13.3K to $34.9K] vs $21.9K [$13.1K to $36.3K]); and lower value of care (0.8 [0.5 to 1.3] vs 1.2 [0.7 to 2.0], p < 0.001). CONCLUSIONS: Overtriage was associated with low value of care; undertriage was associated with both low value of care and increased mortality and morbidity. The proposed framework for generating automated postoperative triage classifications is reproducible.

  DOI

Frequent coauthors

• Tyler J. Loftus

  University of Florida

  25 shared

• Jeremy A. Balch

  National Institute of General Medical Sciences

  23 shared

• Matthew M. Ruppert

  University of Central Florida

  18 shared

• Philip A. Efron

  University of Florida

  17 shared

• Azra Bihorac

  16 shared

• Tezcan Ozrazgat‐Baslanti

  University of Florida

  16 shared

• Gilbert R. Upchurch

  University of Florida

  15 shared

• Benjamin Shickel

  University of Florida

  14 shared

Labs

• Geography Lab of Dr. Yujie HuPI

Education

• PhD, Geography & Anthropology

  Louisiana State University

  2016

Similar researchers at University of Florida

• Steven T DeKoskyh-162
• Matthew Farrerh-133
• George Christouh-133
• Mark A Atkinsonh-129
• Christiaan Leeuwenburghh-125