Reliability of electronic recording of waiting times in the emergency department

Gorlicki, Judith; Raynal, Pierre-Alexis; Leleu, Agathe; Riou, Bruno; Ray, Patrick; Freund, Yonathan

doi:10.1097/mej.0000000000000232

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Furthermore, our analysis was based on EMR data that may or may not accurately reflect the actual ED events [ 42 , 43 ]. Therefore, we sought to validate our data by a professional external Institute (Health Care Research Institute AG, Zürich, Switzerland) [ 44 ].…”

Section: Limitationsmentioning

confidence: 99%

Medical Team Evaluation: Effect on Emergency Department Waiting Time and Length of Stay

et al. 2016

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Emergency Departments (ED) are trying to alleviate crowding using various interventions. We assessed the effect of an alternative model of care, the Medical Team Evaluation (MTE) concept, encompassing team triage, quick registration, redesign of triage rooms and electronic medical records (EMR) on door-to-doctor (waiting) time and ED length of stay (LOS). We conducted an observational, before-and-after study at an urban academic tertiary care centre. On July 17th 2014, MTE was initiated from 9:00 a.m. to 10 p.m., 7 days a week. A registered triage nurse was teamed with an additional senior ED physician. Data of the 5-month pre-MTE and the 5-month MTE period were analysed. A matched comparison of waiting times and ED LOS of discharged and admitted patients pertaining to various Emergency Severity Index (ESI) triage categories was performed based on propensity scores. With MTE, the median waiting times improved from 41.2 (24.8–66.6) to 10.2 (5.7–18.1) minutes (min; P < 0.01). Though being beneficial for all strata, the improvement was somewhat greater for discharged, than for admitted patients. With a reduction from 54.3 (34.2–84.7) to 10.5 (5.9–18.4) min (P < 0.01), in terms of waiting times, MTE was most advantageous for ESI4 patients. The overall median ED LOS increased for about 15 min (P < 0.01), increasing from 3.4 (2.1–5.3) to 3.7 (2.3–5.6) hours. A significant increase was observed for all the strata, except for ESI5 patients. Their median ED LOS dropped by 73% from 1.2 (0.8–1.8) to 0.3 (0.2–0.5) hours (P < 0.01). In the same period the total orders for diagnostic radiology increased by 1,178 (11%) from 10,924 to 12,102 orders, with more imaging tests being ordered for ESI 2, 3 and 4 patients. Despite improved waiting times a decrease of ED LOS was only seen in ESI level 5 patients, whereas in all the other strata ED LOS increased. We speculate that this was brought about by the tendency of triage physicians to order more diagnostic radiology, anticipating that it may be better for the downstream physician to have more information rather than less.

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Section: Limitationsmentioning

confidence: 99%

Medical Team Evaluation: Effect on Emergency Department Waiting Time and Length of Stay

et al. 2016

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“…This electronic method has been reported to be accurate in a previous study. 19 We retrieved data regarding WT and other characteristics of the ED visit through our electronic medical record software, which includes previously described predictors of exceeded WT: day of the week, time of the consult, trauma patients, age, departmental occupancy of the day and severity triage.…”

Section: Outcomes Measured and Data Collectionmentioning

confidence: 99%

Effect of emergency physician burnout on patient waiting times

et al. 2017

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Objectives: Burnout is common amongst emergency physicians. This syndrome may negatively affect patient care and alter work productivity. We seek to assess whether burnout of emergency physicians impacts waiting times in the emergency department. Methods:Prospective study in an academic ED. All patients that visited the main ED for a four month period in 2016 were included. Target waiting times are assigned by triage nurse to patients on arrival depending on their severity. The primary endpoint was an exceeded target waiting time for ED patients. All emergency physicians were surveyed by a psychologist to assess their level of burnout using the Maslach Burnout Inventory. We defined the level of burnout of the day in the ED as the mean burnout level of the physicians working that day (8:30 to the 8:30 the next day). A logistic regression model was performed to assess whether burnout level of the day was independently associated with prolonged waiting times, along with previously reported predictors.Results: Target waiting time was exceeded in 7,524 patients (59%). Twenty six emergency physicians were surveyed. Median burnout score was 35 (Interquartile [24-49]). A burnout level of the day higher than 35 was independently associated with an exceeded target waiting time (adjusted odds ratio 1.54, 95% confidence interval 1.39 to 1.70), together with previously reported predictors (i.e. day of the week, time of the day, trauma, age and daily census).

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“…However, both initiatives mainly focus on a macro perspective (e.g., general care situation in a region) and do not aim to identify the quality, and thus improvement potentials, within a specific hospital or ED. Other European initiatives focus on the development of new QIs [4][5][6][7] or calculate specific QIs based on additionally collected data (e.g., from local registers) [8][9][10][11]. Besides, there exist initiatives that focus on the use of routinely collected data like the MIPS system in the United States, for example, which requires clinicians to calculate 268 QIs [12].…”

Section: Introductionmentioning

confidence: 99%

Assessing healthcare service quality using routinely collected data: Linking information systems in emergency care

et al. 2020

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Emergency departments need to continuously calculate quality indicators in order to perform structural improvements, improvements in the daily routine, and ad-hoc improvements in everyday life. However, many different actors across multiple disciplines collaborate to provide emergency care. Hence, patient-related data is stored in several information systems, which in turn makes the calculation of quality indicators more difficult. To address this issue, we aim to link and use routinely collected data of the different actors within the emergency care continuum. In order to assess the feasibility of linking and using routinely collected data for quality indicators and whether this approach adds value to the assessment of emergency care quality, we conducted a single case study in a German academic teaching hospital. We analyzed the available data of the existing information systems in the emergency continuum and linked and pre-processed the data. Based on this, we then calculated four quality indicators (Left Without Been Seen, Unplanned Reattendance, Diagnostic Efficiency, and Overload Closure). Lessons learned from the calculation and results of the discussions with staff members that had multiple years of work experience in the emergency department provide a better understanding of the quality of the emergency department, the related challenges during the calculation, and the added value of linking routinely collected data.

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Reliability of electronic recording of waiting times in the emergency department

Cited by 5 publications

References 9 publications

Medical Team Evaluation: Effect on Emergency Department Waiting Time and Length of Stay

Medical Team Evaluation: Effect on Emergency Department Waiting Time and Length of Stay

Effect of emergency physician burnout on patient waiting times

Assessing healthcare service quality using routinely collected data: Linking information systems in emergency care

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