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Predicting emergency department inpatient admissions to improve same-day patient flow.
Acad Emerg Med. 2012 Sep; 19(9):E1045-54.AE

Abstract

OBJECTIVES

The objectives were to evaluate three models that use information gathered during triage to predict, in real time, the number of emergency department (ED) patients who subsequently will be admitted to a hospital inpatient unit (IU) and to introduce a new methodology for implementing these predictions in the hospital setting.

METHODS

Three simple methods were compared for predicting hospital admission at ED triage: expert opinion, naïve Bayes conditional probability, and a generalized linear regression model with a logit link function (logit-linear). Two months of data were gathered from the Boston VA Healthcare System's 13-bed ED, which receives approximately 1,100 patients per month. Triage nurses were asked to estimate the likelihood that each of 767 triaged patients from that 2-month period would be admitted after their ED treatment, by placing them into one of six categories ranging from low to high likelihood. Logit-linear regression and naïve Bayes models also were developed using retrospective data and used to estimate admission probabilities for each patient who entered the ED within a 2-month time frame, during triage hours (1,160 patients). Predictors considered included patient age, primary complaint, provider, designation (ED or fast track), arrival mode, and urgency level (emergency severity index assigned at triage).

RESULTS

Of the three methods considered, logit-linear regression performed the best in predicting total bed need, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.887, an R(2) of 0.58, an average estimation error of 0.19 beds per day, and on average roughly 3.5 hours before peak demand occurred. Significant predictors were patient age, primary complaint, bed type designation, and arrival mode (p < 0.0001 for all factors). The naïve Bayesian model had similar positive predictive value, with an AUC of 0.841 and an R(2) of 0.58, but with average difference in total bed need of approximately 2.08 per day. Triage nurse expert opinion also had some predictive capability, with an R(2) of 0.52 and an average difference in total bed need of 1.87 per day.

CONCLUSIONS

Simple probability models can reasonably predict ED-to-IU patient volumes based on basic data gathered at triage. This predictive information could be used for improved real-time bed management, patient flow, and discharge processes. Both statistical models were reasonably accurate, using only a minimal number of readily available independent variables.

Authors+Show Affiliations

New England Veterans Engineering Resource Center, Boston, MA, USA. jspeck@mit.eduNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Comparative Study
Evaluation Study
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

22978731

Citation

Peck, Jordan S., et al. "Predicting Emergency Department Inpatient Admissions to Improve Same-day Patient Flow." Academic Emergency Medicine : Official Journal of the Society for Academic Emergency Medicine, vol. 19, no. 9, 2012, pp. E1045-54.
Peck JS, Benneyan JC, Nightingale DJ, et al. Predicting emergency department inpatient admissions to improve same-day patient flow. Acad Emerg Med. 2012;19(9):E1045-54.
Peck, J. S., Benneyan, J. C., Nightingale, D. J., & Gaehde, S. A. (2012). Predicting emergency department inpatient admissions to improve same-day patient flow. Academic Emergency Medicine : Official Journal of the Society for Academic Emergency Medicine, 19(9), E1045-54. https://doi.org/10.1111/j.1553-2712.2012.01435.x
Peck JS, et al. Predicting Emergency Department Inpatient Admissions to Improve Same-day Patient Flow. Acad Emerg Med. 2012;19(9):E1045-54. PubMed PMID: 22978731.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Predicting emergency department inpatient admissions to improve same-day patient flow. AU - Peck,Jordan S, AU - Benneyan,James C, AU - Nightingale,Deborah J, AU - Gaehde,Stephan A, PY - 2012/9/18/entrez PY - 2012/9/18/pubmed PY - 2013/3/7/medline SP - E1045 EP - 54 JF - Academic emergency medicine : official journal of the Society for Academic Emergency Medicine JO - Acad Emerg Med VL - 19 IS - 9 N2 - OBJECTIVES: The objectives were to evaluate three models that use information gathered during triage to predict, in real time, the number of emergency department (ED) patients who subsequently will be admitted to a hospital inpatient unit (IU) and to introduce a new methodology for implementing these predictions in the hospital setting. METHODS: Three simple methods were compared for predicting hospital admission at ED triage: expert opinion, naïve Bayes conditional probability, and a generalized linear regression model with a logit link function (logit-linear). Two months of data were gathered from the Boston VA Healthcare System's 13-bed ED, which receives approximately 1,100 patients per month. Triage nurses were asked to estimate the likelihood that each of 767 triaged patients from that 2-month period would be admitted after their ED treatment, by placing them into one of six categories ranging from low to high likelihood. Logit-linear regression and naïve Bayes models also were developed using retrospective data and used to estimate admission probabilities for each patient who entered the ED within a 2-month time frame, during triage hours (1,160 patients). Predictors considered included patient age, primary complaint, provider, designation (ED or fast track), arrival mode, and urgency level (emergency severity index assigned at triage). RESULTS: Of the three methods considered, logit-linear regression performed the best in predicting total bed need, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.887, an R(2) of 0.58, an average estimation error of 0.19 beds per day, and on average roughly 3.5 hours before peak demand occurred. Significant predictors were patient age, primary complaint, bed type designation, and arrival mode (p < 0.0001 for all factors). The naïve Bayesian model had similar positive predictive value, with an AUC of 0.841 and an R(2) of 0.58, but with average difference in total bed need of approximately 2.08 per day. Triage nurse expert opinion also had some predictive capability, with an R(2) of 0.52 and an average difference in total bed need of 1.87 per day. CONCLUSIONS: Simple probability models can reasonably predict ED-to-IU patient volumes based on basic data gathered at triage. This predictive information could be used for improved real-time bed management, patient flow, and discharge processes. Both statistical models were reasonably accurate, using only a minimal number of readily available independent variables. SN - 1553-2712 UR - https://www.unboundmedicine.com/medline/citation/22978731/Predicting_emergency_department_inpatient_admissions_to_improve_same_day_patient_flow_ L2 - https://doi.org/10.1111/j.1553-2712.2012.01435.x DB - PRIME DP - Unbound Medicine ER -