Early Prediction Model of Patient Hospitalization From the Pediatric Emergency Department

Using a continuously updating prediction model of patient disposition, this study suggests a new design for ED workflow that could shorten waiting times. BACKGROUND AND OBJECTIVES: Emergency departments (EDs) in the United States are overcrowded and nearing a breaking point. Alongside ever-increasing demand, one of the leading causes of ED overcrowding is the boarding of hospitalized patients in the ED as they await bed placement. We sought to develop a model for early prediction of hospitalizations, thus enabling an earlier start for the placement process and shorter boarding times. METHODS: We conducted a retrospective cohort analysis of all visits to the Boston Children’s Hospital ED from July 1, 2014 to June 30, 2015. We used 50% of the data for model derivation and the remaining 50% for validation. We built the predictive model by using a mixed method approach, running a logistic regression model on results generated by a naive Bayes classifier. We performed sensitivity analyses to evaluate the impact of the model on overall resource utilization. RESULTS: Our analysis comprised 59 033 patient visits, of which 11 975 were hospitalized (cases) and 47 058 were discharged (controls). Using data available within the first 30 minutes from presentation, our model identified 73.4% of the hospitalizations with 90% specificity and 35.4% of hospitalizations with 99.5% specificity (area under the curve = 0.91). Applying this model in a real-time setting could potentially save the ED 5917 hours per year or 30 minutes per hospitalization. CONCLUSIONS: This approach can accurately predict patient hospitalization early in the ED encounter by using data commonly available in most electronic medical records. Such early identification can be used to advance patient placement processes and shorten ED boarding times.

[1]  M. van Veen,et al.  Alarming signs in the Manchester triage system: a tool to identify febrile children at risk of hospitalization. , 2013, The Journal of pediatrics.

[2]  Christian Terwiesch,et al.  The financial consequences of lost demand and reducing boarding in hospital emergency departments. , 2011, Annals of emergency medicine.

[3]  P. Stergiannis,et al.  The Impact of ED Boarding Time, Severity of Illness, and Discharge Destination on Outcomes of Critically Ill ED Patients , 2012, Advanced emergency nursing journal.

[4]  S. Mason,et al.  Can emergency medical service staff predict the disposition of patients they are transporting? , 2008, Emergency Medicine Journal.

[5]  J. Chamberlain,et al.  The Pediatric Risk of Hospital Admission Score: A Second-Generation Severity-of-Illness Score for Pediatric Emergency Patients , 2005, Pediatrics.

[6]  K. Gauvreau,et al.  Validation of the Cardiac Children's Hospital Early Warning Score: an early warning scoring tool to prevent cardiopulmonary arrests in children with heart disease. , 2014, Congenital heart disease.

[7]  Asthma Vital Signs at Triage: Home or Admission (ASTHmA) , 2013, Pediatric emergency care.

[8]  Jin Tian,et al.  A Hybrid Generative/Discriminative Bayesian Classifier , 2006, FLAIRS Conference.

[9]  T. Falvo,et al.  The opportunity loss of boarding admitted patients in the emergency department. , 2007, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[10]  John R. Richards,et al.  Ten Solutions for Emergency Department Crowding , 2008, The western journal of emergency medicine.

[11]  Ahmad Abulaban,et al.  The impact of `admit no bed` and long boarding times in the emergency department on stroke outcome. , 2014, Saudi medical journal.

[12]  Dominik Aronsky,et al.  Predicting Hospital Admission for Emergency Department Patients using a Bayesian Network , 2005, AMIA.

[13]  Dominik Aronsky,et al.  Predicting Hospital Admission in a Pediatric Emergency Department using an Artificial Neural Network , 2006, AMIA.

[14]  William K. Mallon,et al.  Financial Impact of Emergency Department Crowding , 2011, The western journal of emergency medicine.

[15]  Jesse M Pines,et al.  The association between length of emergency department boarding and mortality. , 2011, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[16]  Amardeep Thind,et al.  The impact of delays to admission from the emergency department on inpatient outcomes , 2010, BMC emergency medicine.

[17]  Li-Jung Liang,et al.  Effect of emergency department crowding on outcomes of admitted patients. , 2013, Annals of emergency medicine.

[18]  Nils F. H. Olsen,et al.  Measuring the Opportunity Loss of Time Spent Boarding Admitted Patients in the Emergency Department: A Multihospital Analysis , 2009, Journal of healthcare management / American College of Healthcare Executives.

[19]  Rajat Raina,et al.  Classification with Hybrid Generative/Discriminative Models , 2003, NIPS.

[20]  Jordan S Peck,et al.  Predicting emergency department inpatient admissions to improve same-day patient flow. , 2012, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[21]  Madhu C. Reddy,et al.  Challenges to inter-departmental coordination of patient transfers: A workflow perspective , 2010, Int. J. Medical Informatics.

[22]  J. Shults,et al.  Revised Pediatric Emergency Assessment Tool (RePEAT): a severity index for pediatric emergency care. , 2007, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[23]  Jesse M Pines,et al.  The effect of emergency department crowding on patient satisfaction for admitted patients. , 2008, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.