Predicting hospital admission at the emergency department triage: A novel prediction model

Published:October 29, 2018DOI:



      Emergency department (ED) overcrowding is a growing international patient safety issue. A major contributor to overcrowding is long wait times for inpatient hospital admission. The objective of this study is to create a model that can predict a patient's need for hospital admission at the time of triage.


      Retrospective observational study of electronic clinical records of all ED visits over ten years to a large urban hospital in Singapore. The data was randomly divided into a derivation set and a validation set. We used the derivation set to develop a logistic regression model that predicts probability of hospital admission for patients presenting to the ED. We tested the model on the validation set and evaluated the performance with receiver operating characteristic (ROC) curve analysis.


      A total of 1,232,016 visits were included for final analysis, of which 38.7% were admitted. Eight variables were included in the final model: age group, race, postal code, day of week, time of day, triage category, mode of arrival, and fever status. The model performed well on the validation set with an area under the curve of 0.825 (95% CI 0.824–0.827). Increasing age, increasing triage acuity, and mode of arrival via private patient transport were most predictive of the need for admission.


      We developed a model that accurately predicts admission for patients presenting to the ED using demographic, administrative, and clinical data routinely collected at triage. Implementation of the model into the electronic health record could help reduce the burden of overcrowding.


      To read this article in full you will need to make a payment
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to The American Journal of Emergency Medicine
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Fatovich D.M.
        Emergency medicine.
        BMJ. 2002; 324: 958-962
        • Lowthian J.A.
        • Curtis A.J.
        • Cameron P.A.
        • Stoelwinder J.U.
        • Cooke M.W.
        • McNeil J.J.
        Systematic review of trends in emergency department attendances: an Australian perspective.
        Emerg Med J. 2011; 28: 373-377
        • Fatovich D.M.
        • Nagree Y.
        • Sprivulis P.
        Access block causes emergency department overcrowding and ambulance diversion in Perth, Western Australia.
        Emerg Med J. 2005; 22: 351-354
        • Bernstein S.L.
        • Aronsky D.
        • Duseja R.
        • et al.
        The effect of emergency department crowding on clinically oriented outcomes.
        Acad Emerg Med. 2009; 16: 1-10
        • Sun B.C.
        • Hsia R.Y.
        • Weiss R.E.
        • et al.
        Effect of emergency department crowding on outcomes of admitted patients.
        YMEM. 2012; 61605-611.e6
        • Sun B.C.
        • Adams J.
        • Orav E.J.
        • et al.
        Determinants of patient satisfaction and willingness to return with emergency care.
        Ann Emerg Med. 2000; 35: 426-434
        • Derlet R.W.
        • Richards JR.
        Overcrowding in the nation's emergency departments: complex causes and disturbing effects.
        Ann Emerg Med. 2000; 35: 63-68
        • Sun Y.
        • Heng B.H.
        • Tay S.Y.
        • Seow E.
        Predicting hospital admissions at emergency department triage using routine administrative data.
        Acad Emerg Med. 2011; 18: 844-850
        • Peck J.
        • Benneyan J
        • Gaehde S.
        • Nightingale D.
        Models for using predictions to facilitate hospital patient flow.
        in: Healthcare Systems Process Improvement Conference. 2012
        • Schull M.J.
        • Lazier K.
        • Vermeulen M.
        • Mawhinney S.
        • Morrison L.J.
        Emergency department contributors to ambulance diversion: a quantitative analysis.
        Ann Emerg Med. 2003; 41: 467-476
        • LaMantia M.A.
        • Platts-Mills T.F.
        • Biese K.
        • et al.
        Predicting hospital admission and returns to the emergency department for elderly patients.
        Acad Emerg Med. 2010; 17: 252-259
        • Leegon J.
        • Jones I.
        • Lanaghan K.
        • Aronsky D.
        Predicting hospital admission in a pediatric emergency department using an artificial neural network. AMIA.
        Annu Symp proceedings AMIA Symp. 2006; 2006: 1004
        • Zlotnik A.
        • Alfaro M.C.
        • Pérez M.C.P.
        • Gallardo-Antolín A.
        • Martínez J.M.M.
        Building a decision support system for inpatient admission prediction with the Manchester triage system and administrative check-in variables.
        Comput Inform Nurs. 2016; 34: 224-230
        • Storm-Versloot M.N.
        • Ubbink D.T.
        • Kappelhof J.
        • Luitse J.S.K.
        Comparison of an informally structured triage system, the emergency severity index, and the Manchester triage system to distinguish patient priority in the emergency department.
        Acad Emerg Med. 2011; 18: 822-829
        • Cameron A.
        • Ireland A.J.
        • Mckay G.A.
        • Stark A.
        • Lowe D.J.
        Predicting Admission at Triage: Are Nurses Better Than a Simple Objective Score?.
        Emerg Med J. 2017; 34: 2-7
        • Qiu S.
        • Babu Chinnam R.
        • Murat A.
        • Batarse B.
        • Neemuchwala H.
        • Jordan W.
        A cost sensitive inpatient bed reservation approach to reduce emergency department boarding times.
        Health Care Manag Sci. 2015; 18: 67-85
        • Cameron A.
        • Rodgers K.
        • Ireland A.
        • Jamdar R.
        • Mckay G.A.
        A simple tool to predict admission at the time of triage.
        Emerg Med J. 2015; 32: 174-179
        • Baumann M.R.
        • Strout T.D.
        riage of Geriatric Patients in the Emergency Department: Validity and Survival With the Emergency Severity Index.
        Ann Emerg Med. 2007; 49: 234-240
        • Peck J.S.
        • Gaehde S.A.
        • Nightingale D.J.
        • et al.
        Generalizability of a simple approach for predicting hospital admission from an emergency department.
        Acad Emerg Med. 2013; 20: 1156-1163
        • Xie B.
        International journal of statistics in medical research.
        Lifescience Global, 2013
        • Peck J.S.
        • Benneyan J.C.
        • Nightingale D.J.
        • Gaehde S.A.
        Predicting emergency department inpatient admissions to improve same-day patient flow.
        Acad Emerg Med. 2012; 19: E1045-E1054
        • Lucini F.R.
        • Fogliatto F.S.
        • da Silveira G.J.
        • et al.
        Text mining approach to predict hospital admissions using early medical records from the emergency department.
        Int J Med Inform. 2017; 100: 1-8
        • Cameron A.
        • Jones D.
        • Logan E.
        • O'keeffe C.A.
        • Mason S.M.
        • Lowe D.J.
        Comparison of Glasgow admission prediction score and Amb score in predicting need for inpatient care.
        Emerg Med J. 2018; 0: 1-5
        • RStudio Team
        RStudio: integrated development for R. RStudio, Inc.
        • Royal College of Physicians of London
        National early warning score (NEWS): standardising the assessment of acute-illness severity in the NHS—report of a working party.
        • Kim S.W.
        • Li J.Y.
        • Hakendorf P.
        • Teubner D.J.O.
        • Ben-Tovim D.I.
        • Thompson C.H.
        Predicting admission of patients by their presentation to the emergency department.
        Emerg Med Australas. 2014; 26: 361-367