Emergency medicine physicians' ability to predict hospital admission at the time of triage

被引:10
作者
Vlodaver, Zlata K. [1 ]
Anderson, Jeffrey P. [2 ]
Brown, Brittney E. [2 ]
Zwank, Michael D. [1 ]
机构
[1] Reg Hosp, Emergency Dept, St Paul, MN 55101 USA
[2] Hlth Partners, Hlth Partners Inst, St Paul, MN USA
关键词
Triage; Hospital admission; Prediction; Patient flow;
D O I
10.1016/j.ajem.2018.06.023
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
Background: We seek to determine if experienced emergency medicine physicians can accurately predict the likelihood of admission for patients at the time of triage. Such predictions, if proven to be accurate, could decrease the time spent in the ED for patients who will ultimately be admitted by hastening downstream workflow. Methods: This is a prospective cohort study of experienced physicians at a large urban hospital. Physicians were asked to predict the likelihood of admission for patients based only on information available in the EMR at the time of triage. Physicians also predicted the service to which the patients would be admitted. Physicians provided a confidence level of their prediction. Measures of predictive accuracy were calculated, including sensitivity, specificity, and area under the receiver operating characteristic curve. Results: 35 physicians evaluated 398 patient charts and made predictions. Sensitivity of determining admission for the entire cohort was 51.8%. The specificity was 89.1%. For those predictions made with a confidence level of >90%, sensitivity was 61.5% and specificity was 95.7%. Among physicians correctly predicting admission, the admitting service was predicted accurately 88.6% of the time. Conclusion: Physicians performed poorly at predicting which patients would be admitted at the time of triage, even when they were confident in their predictions. Conversely, physicians accurately predicted who would be discharged. Physicians predicted with reasonable accuracy the service to which patients were ultimately admitted. More research and operational assessment needs to be performed to determine if these predictions can help improve ED efficiency. (C) 2018 Elsevier inc. All rights reserved.
引用
收藏
页码:478 / 481
页数:4
相关论文
共 10 条
  • [1] The association between a prolonged stay in the emergency department and adverse events in older patients admitted to hospital: a retrospective cohort study
    Ackroyd-Stolarz, S.
    Guernsey, J. Read
    MacKinnon, N. J.
    Kovacs, G.
    [J]. BMJ QUALITY & SAFETY, 2011, 20 (07) : 564 - 569
  • [2] Allan Cameron, 2014, EMERGENCY MED J EMER, V32, P174
  • [3] Kate Bradman, 2012, J PAEDIAT CHILD HLTH, V50
  • [4] Predicting admission of patients by their presentation to the emergency department
    Kim, Susan W.
    Li, Jordan Y.
    Hakendorf, Paul
    Teubner, David J. O.
    Ben-Tovim, David I.
    Thompson, Campbell H.
    [J]. EMERGENCY MEDICINE AUSTRALASIA, 2014, 26 (04) : 361 - 367
  • [5] Can emergency department triage nurses predict patients' dispositions?
    Kosowsky, JM
    Shindel, S
    Liu, TP
    Hamilton, C
    Pancioli, AM
    [J]. AMERICAN JOURNAL OF EMERGENCY MEDICINE, 2001, 19 (01) : 10 - 14
  • [6] How well do paramedics predict admission to the hospital? A prospective study
    Levine, SD
    Colwell, CB
    Pons, PT
    Gravitz, C
    Haukoos, JS
    McVaney, KE
    [J]. JOURNAL OF EMERGENCY MEDICINE, 2006, 31 (01) : 1 - 5
  • [7] Peck Jordan S, 2012, ACAD EMERG MED, V19
  • [8] Predicting Hospital Admissions at Emergency Department Triage Using Routine Administrative Data
    Sun, Yan
    Heng, Bee Hoon
    Tay, Seow Yian
    Seow, Eillyne
    [J]. ACADEMIC EMERGENCY MEDICINE, 2011, 18 (08) : 844 - 850
  • [9] Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks
    Zhang, Xingyu
    Kim, Joyce
    Patzer, Rachel E.
    Pitts, Stephen R.
    Patzer, Aaron
    Schrager, Justin D.
    [J]. METHODS OF INFORMATION IN MEDICINE, 2017, 56 (05) : 377 - 389
  • [10] Zlotnik A, 2016, CIN-COMPUT INFORM NU, V34, P224, DOI 10.1097/CIN.0000000000000230