Lara Lusa1,2, David Bukovšek3. 1. Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia. 2. Institute for Biostatistics and Medical Informatics, Medical Faculty, University of Ljubljana, Ljubljana, Slovenia. 3. Graduate of the Applied Statistics Programme, University of Ljubljana, Ljubljana, Slovenia.
Abstract
RATIONALE, AIMS, AND OBJECTIVES: Many hospitals provide some type of real time publicly available information about the crowding of the emergency departments (EDs). For example, at the time of writing, the number of patients waiting and in treatment is publicly available for EDs that cover more than half of the Italian population. However, usually, these data do not provide the key information that low-priority patients are seeking, which is the time they might expect to wait before being seen by a doctor. METHODS: In this paper, we reanalyze the prospectively collected public data from the two major EDs of the Friuli Venezia Giulia Italian region, which maintains a web page that provides a real time estimate of the average waiting time of current ED patients. The paper discusses the usefulness of the estimate from the patients' prospective. RESULTS: We point out a flow of the methodology used on web page of the Friuli Venezia Giulia region, and further show that the waiting time of current patients reflects poorly the expected time to treatment of new patients. DISCUSSION: The results suggest that available data should be used to develop and validate ED-specific waiting time prediction models of low-priority patients. The same data could also be used to estimate quantities that might be of interest for EDs resource management.
RATIONALE, AIMS, AND OBJECTIVES: Many hospitals provide some type of real time publicly available information about the crowding of the emergency departments (EDs). For example, at the time of writing, the number of patients waiting and in treatment is publicly available for EDs that cover more than half of the Italian population. However, usually, these data do not provide the key information that low-priority patients are seeking, which is the time they might expect to wait before being seen by a doctor. METHODS: In this paper, we reanalyze the prospectively collected public data from the two major EDs of the Friuli Venezia Giulia Italian region, which maintains a web page that provides a real time estimate of the average waiting time of current ED patients. The paper discusses the usefulness of the estimate from the patients' prospective. RESULTS: We point out a flow of the methodology used on web page of the Friuli Venezia Giulia region, and further show that the waiting time of current patients reflects poorly the expected time to treatment of new patients. DISCUSSION: The results suggest that available data should be used to develop and validate ED-specific waiting time prediction models of low-priority patients. The same data could also be used to estimate quantities that might be of interest for EDs resource management.