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Daniel Buckland

Assistant Professor of Emergency Medicine
Emergency Medicine
DUMC Box 3096, 2301 Erwin Road, Durham, NC 27710
2301 Erwin Road, DUMC Box 3096, Durham, NC 27710

Overview


Dr. Buckland is an Attending Physician at Duke University Hospital Emergency Department. He is the Director of the Duke Acute Care Technology Lab where he leads research in developing technology for the diagnosis and treatment of acute disease in data science and robotics projects by managing collaborative research projects between clinicians and engineers. His work at involves studying how advancements in autonomy impact safety critical systems, including the healthcare system. As part of his focus on autonomy, he is the Medical Director of the the Laboratory for Transformational Administration (LTA) an Operational Data Science group in the Duke Department of Surgery.
In addition, Dr. Buckland is the Deputy Chair of the Human System Risk Board of the Office of the Chief Health and Medical Officer via an Intergovernmental Personnel Act agreement with NASA, where he determines the human system risk of spaceflight and how standards, countermeasures, and mission design can mitigate risk.

Office Hours


For patient or clinical care related questions please call the Duke Emergency Department at: 919-684-2413

Current Appointments & Affiliations


Assistant Professor of Emergency Medicine · 2021 - Present Emergency Medicine, Clinical Science Departments
Assistant Professor in Surgery · 2024 - Present Surgery, Surgical Sciences, Surgery

In the News


Published November 14, 2024
Pratt Researchers Imagine Robots of Tomorrow, From the Surgical Table to Outer Space
Published July 23, 2024
Expiring Medications Could Pose Challenge for Astronauts on Long Space Missions
Published June 27, 2023
Algorithm Improves Accuracy of Scheduling Surgeries

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Recent Publications


Ambulatory Surgery Ensemble: Predicting Adult and Pediatric Same-Day Surgery Cases Across Specialties.

Journal Article Ann Surg Open · March 2025 OBJECTIVE: To develop an ensemble model using case-posting data to predict which patients could be discharged on the day of surgery. BACKGROUND: Few models have predicted which surgeries are appropriate for day cases. Increasing the ratio of ambulatory sur ... Full text Link to item Cite

Development of Multiservice Machine Learning Models to Predict Postsurgical Length of Stay and Discharge Disposition at the Time of Case Posting.

Journal Article Ann Surg Open · March 2025 OBJECTIVE: Develop machine learning (ML) models to predict postsurgical length of stay (LOS) and discharge disposition (DD) for multiple services with only the data available at the time of case posting. BACKGROUND: Surgeries are scheduled largely based on ... Full text Link to item Cite
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Recent Grants


Medical Scientist Training Program

Inst. Training Prgm or CMEPreceptor · Awarded by National Institute of General Medical Sciences · 2022 - 2027

Title: NRT-FW-HTF: NSF Traineeship in the Advancement of Surgical Technologies

Inst. Training Prgm or CMEParticipants · Awarded by National Science Foundation · 2021 - 2026

Medical Scientist Training Program

Inst. Training Prgm or CMEMentor · Awarded by National Institutes of Health · 1997 - 2022

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Education, Training & Certifications


Harvard Medical School · 2014 M.D.
Massachusetts Institute of Technology · 2011 Ph.D.
Massachusetts Institute of Technology · 2006 M.S.