Assessing electronic health record systems in emergency departments: Using a decision analytic Bayesian model

Health Informatics J. 2016 Sep;22(3):712-29. doi: 10.1177/1460458215584203. Epub 2015 Jun 1.


In the last decade, health providers have implemented information systems to improve accuracy in medical diagnosis and decision-making. This article evaluates the impact of an electronic health record on emergency department physicians' diagnosis and admission decisions. A decision analytic approach using a decision tree was constructed to model the admission decision process to assess the added value of medical information retrieved from the electronic health record. Using a Bayesian statistical model, this method was evaluated on two coronary artery disease scenarios. The results show that the cases of coronary artery disease were better diagnosed when the electronic health record was consulted and led to more informed admission decisions. Furthermore, the value of medical information required for a specific admission decision in emergency departments could be quantified. The findings support the notion that physicians and patient healthcare can benefit from implementing electronic health record systems in emergency departments.

Keywords: Bayesian statistics; decision support systems; decision trees; electronic health records; emergency department; medical decision-making.

MeSH terms

  • Bayes Theorem*
  • Coronary Artery Disease / diagnosis
  • Decision Support Systems, Clinical / statistics & numerical data*
  • Electronic Health Records / statistics & numerical data*
  • Emergency Service, Hospital*
  • Humans
  • Patient Admission
  • Physicians*