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How artificial intelligence could transform emergency department operations

  • Yosef Berlyand
    Affiliations
    Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States
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  • Ali S. Raja
    Affiliations
    Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States

    Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States

    Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
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  • Stephen C. Dorner
    Affiliations
    Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States

    Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States

    Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States

    Harvard Affiliated Emergency Medicine Residency Program, 5 Emerson Place, Suite 101, Boston, MA 02114, United States
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  • Anand M. Prabhakar
    Affiliations
    Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States

    Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States

    Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
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  • Jonathan D. Sonis
    Affiliations
    Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States

    Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States

    Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
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  • Ravi V. Gottumukkala
    Affiliations
    Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States

    Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States

    Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
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  • Marc David Succi
    Affiliations
    Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States

    Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States

    Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States

    Medically Engineered Solutions in Healthcare (MESH) Incubator, Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
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  • Brian J. Yun
    Correspondence
    Corresponding author at: Department of Emergency Medicine, Massachusetts General Hospital, 0 Emerson Place, Suite 3B, Boston, MA 02114, United States.
    Affiliations
    Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, United States

    Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States

    Center for Research in Emergency Department Operations (CREDO), Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, United States
    Search for articles by this author
Published:January 04, 2018DOI:https://doi.org/10.1016/j.ajem.2018.01.017
      Artificial intelligence (AI) is the study of computer systems capable of performing tasks that traditionally require human intelligence, and machine learning (ML) is one mechanism through which an AI system can be developed by creating algorithms that modify themselves in response to patterns and make inferences when applied to new data [
      • Jordan M.I.
      • Mitchell T.M.
      Machine learning: Trends, perspectives, and prospects.
      ,
      • Obermeyer Z.
      • Emanuel E.J.
      Predicting the future — big data, machine learning, and clinical medicine.
      ,
      • Bala J.W.
      • Michalski R.S.
      • Ryszard S.
      • Tecuci G.
      ,
      • Nilsson N.J.
      Principles of Artificial Intelligence.
      ]. AI has already proven itself to be useful in several fields of medicine, including radiology, neurosurgery, dermatology, and ophthalmology, in which AI has either matched, or, in some cases, exceeded the ability of physicians [
      • Senders J.T.
      • Arnaout O.
      • Karhade A.V.
      • Dasenbrock H.H.
      • Gormley W.B.
      • Broekman M.L.
      • et al.
      Natural and artificial intelligence in neurosurgery: a systemic review.
      ,
      • Deo R.C.
      Machine learning in medicine.
      ,
      • Chen M.C.
      • Ball R.L.
      • Yang L.
      • Moradzadeh N.
      • Chapman B.E.
      • Larson D.B.
      • et al.
      Deep learning to classify radiology free-text reports.
      ,
      • Walton O.B.
      • Garoon R.B.
      • Weng C.Y.
      • Gross J.
      • Young A.K.
      • Camero K.A.
      • et al.
      Evaluation of automated teleretinal screening program for diabetic retinopathy.
      ,
      • Esteva A.
      • Kuprel B.
      • Novoa R.A.
      • Ko J.
      • Swetter S.M.
      • Blau H.M.
      • et al.
      Dermatologist-level classification of skin cancer with deep neural networks.
      ,
      • Zheng B.
      • Yoon S.W.
      • Lam S.S.
      Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms.
      ]. Rapidly interpreting clinical data to classify patients and predict outcomes is paramount to emergency department (ED) operations, with direct impacts on cost, efficiency, and quality of care. As such, there exists significant potential for improvement in ED operations through the application of AI.

      Keywords

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