A practical method for predicting frequent use of emergency department care using routinely available electronic registration data

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6 Citations (Scopus)

Abstract

Background: Accurately predicting future frequent emergency department (ED) utilization can support a case management approach and ultimately reduce health care costs. This study assesses the feasibility of using routinely collected registration data to predict future frequent ED visits. Method: Using routinely collected registration data in the state of Indiana, U.S.A., from 2008, we developed multivariable logistic regression models to predict frequent ED visits in the subsequent two years. We assessed the model's accuracy using Receiver Operating Characteristic (ROC) curves, sensitivity, and positive predictive value (PPV). Results: Strong predictors of frequent ED visits included age between 25 and 44years, female gender, close proximity to the ED (less than 5 miles traveling distance), total visits in the baseline year, and respiratory and dental chief complaint syndromes. The area under ROC curve (AUC) ranged from 0.83 to 0.92 for models predicting patients with 8 or more visits to 16 or more visits in the subsequent two years, suggesting acceptable discrimination. With 25% sensitivity, the model predicting frequent ED use as defined as 16 or more visits in 2009 and 2010 had a PPV of 59.5% and specificity of 99.9%. The "adjusted" PPV of this model, which includes patients having 8 or more visits, is 81.9%. Conclusion: We demonstrate a strong association between predictor variables present in registration data and frequent ED use. The algorithm's performance characteristics suggest that it is technically feasible to use routinely collected registration data to predict future frequent ED use.

Original languageEnglish (US)
Article number12
JournalBMC Emergency Medicine
Volume16
Issue number1
DOIs
StatePublished - Feb 9 2016

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Emergency Medical Services
Hospital Emergency Service
ROC Curve
Logistic Models
Case Management
Feasibility Studies
Health Care Costs
Tooth

Keywords

  • Emergency department
  • Frequent use
  • Prediction
  • Registration data

ASJC Scopus subject areas

  • Emergency Medicine

Cite this

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abstract = "Background: Accurately predicting future frequent emergency department (ED) utilization can support a case management approach and ultimately reduce health care costs. This study assesses the feasibility of using routinely collected registration data to predict future frequent ED visits. Method: Using routinely collected registration data in the state of Indiana, U.S.A., from 2008, we developed multivariable logistic regression models to predict frequent ED visits in the subsequent two years. We assessed the model's accuracy using Receiver Operating Characteristic (ROC) curves, sensitivity, and positive predictive value (PPV). Results: Strong predictors of frequent ED visits included age between 25 and 44years, female gender, close proximity to the ED (less than 5 miles traveling distance), total visits in the baseline year, and respiratory and dental chief complaint syndromes. The area under ROC curve (AUC) ranged from 0.83 to 0.92 for models predicting patients with 8 or more visits to 16 or more visits in the subsequent two years, suggesting acceptable discrimination. With 25{\%} sensitivity, the model predicting frequent ED use as defined as 16 or more visits in 2009 and 2010 had a PPV of 59.5{\%} and specificity of 99.9{\%}. The {"}adjusted{"} PPV of this model, which includes patients having 8 or more visits, is 81.9{\%}. Conclusion: We demonstrate a strong association between predictor variables present in registration data and frequent ED use. The algorithm's performance characteristics suggest that it is technically feasible to use routinely collected registration data to predict future frequent ED use.",
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author = "Jianmin Wu and Shaun Grannis and Huiping Xu and John Finnell",
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N2 - Background: Accurately predicting future frequent emergency department (ED) utilization can support a case management approach and ultimately reduce health care costs. This study assesses the feasibility of using routinely collected registration data to predict future frequent ED visits. Method: Using routinely collected registration data in the state of Indiana, U.S.A., from 2008, we developed multivariable logistic regression models to predict frequent ED visits in the subsequent two years. We assessed the model's accuracy using Receiver Operating Characteristic (ROC) curves, sensitivity, and positive predictive value (PPV). Results: Strong predictors of frequent ED visits included age between 25 and 44years, female gender, close proximity to the ED (less than 5 miles traveling distance), total visits in the baseline year, and respiratory and dental chief complaint syndromes. The area under ROC curve (AUC) ranged from 0.83 to 0.92 for models predicting patients with 8 or more visits to 16 or more visits in the subsequent two years, suggesting acceptable discrimination. With 25% sensitivity, the model predicting frequent ED use as defined as 16 or more visits in 2009 and 2010 had a PPV of 59.5% and specificity of 99.9%. The "adjusted" PPV of this model, which includes patients having 8 or more visits, is 81.9%. Conclusion: We demonstrate a strong association between predictor variables present in registration data and frequent ED use. The algorithm's performance characteristics suggest that it is technically feasible to use routinely collected registration data to predict future frequent ED use.

AB - Background: Accurately predicting future frequent emergency department (ED) utilization can support a case management approach and ultimately reduce health care costs. This study assesses the feasibility of using routinely collected registration data to predict future frequent ED visits. Method: Using routinely collected registration data in the state of Indiana, U.S.A., from 2008, we developed multivariable logistic regression models to predict frequent ED visits in the subsequent two years. We assessed the model's accuracy using Receiver Operating Characteristic (ROC) curves, sensitivity, and positive predictive value (PPV). Results: Strong predictors of frequent ED visits included age between 25 and 44years, female gender, close proximity to the ED (less than 5 miles traveling distance), total visits in the baseline year, and respiratory and dental chief complaint syndromes. The area under ROC curve (AUC) ranged from 0.83 to 0.92 for models predicting patients with 8 or more visits to 16 or more visits in the subsequent two years, suggesting acceptable discrimination. With 25% sensitivity, the model predicting frequent ED use as defined as 16 or more visits in 2009 and 2010 had a PPV of 59.5% and specificity of 99.9%. The "adjusted" PPV of this model, which includes patients having 8 or more visits, is 81.9%. Conclusion: We demonstrate a strong association between predictor variables present in registration data and frequent ED use. The algorithm's performance characteristics suggest that it is technically feasible to use routinely collected registration data to predict future frequent ED use.

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