Max Rady College of Medicine

Concept: Overview of the Emergency Department (ED) Data

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Concept Description

Last Updated: 2018-04-16


    This concept provides an overview of the Emergency Department (ED) data held in the Manitoba Population Research Data Repository at MCHP. The main focus is on the most recent data from the Emergency Department Information System (EDIS). The content for this concept is based on information from the MCHP deliverable Factors Affecting Emergency Department Waiting Room Times in Winnipeg by Doupe et al. (2017) and a presentation on the ED data (internal access only) from an MCHP Data Analyst.

    The purpose of this concept is to provide researchers and analysts with information about the ED / EDIS data. To do this, the concept is structured as follows:

    • a brief description of the two different ED databases available in the Repository, with links to additional on-line information about each database;
    • a list and description of the key fields in the EDIS data;
    • a discussion on the EDIS Data Quality and Strengths and Weaknesses of the data; and
    • recommendations from the Deliverable for improvements to EDIS.

Emergency Department Data in the Repository

    There are two different datasets in the Repository that contain emergency department data:

    • Emergency - Admissions, Discharge & Transfer (ADT) and E-Triage data; and
    • Emergency Department Information System (EDIS) data.

1. Emergency - Admissions, Discharge & Transfer (ADT) and E-Triage Data

    Prior to 2009, emergency department episodes are captured using a combination of the Emergency - ADT (Admissions, Discharge and Transfer) and E-triage data systems. ADT provided information on patient arrival and discharge status, while E-triage recorded the computer-generated Canadian Triage & Acuity Scale (CTAS) scores for each visit. ADT/E-Triage contains data for almost all ED visits in Winnipeg from 1999/2000 to 2010/11, including:

    • ADT contains adult ED episodes from April, 1999 to December, 2010 and children ED episodes from June, 2006 to March, 2010; and
    • E-Triage contains adult and children episodes from April 1, 2004 to March 31, 2012.

    ADT/E-Triage data for this period includes details on the patient's arrival (e.g.: date, time, and method), discharge date and time, disposition (discharge) status, triage code and total duration (time) of the ED visit. However, no information is available on the ED wait time, the care or treatment provided, boarding times, type of provider who saw the patient, diagnosis or type(s) of procedures / test conducted during the visit.

    For more information on the ADT/E-Triage data, see the Emergency - Admissions, Discharge and Transfer (ADT) and E-Triage Data Description.

2. Emergency Department Information System (EDIS) Data

    In comparison to the ADT/E-Triage system, EDIS contains more detailed information about individual ED visits in Winnipeg. Implemented in 2009/10, EDIS contains a more structured collection of the individual activities involved throughout a patient’s entire emergency department visit; from their first point of entry at the triage desk and all the way through to their discharge.

    EDIS contains data from the years 2007/08 to our most recent acquisition. EDIS captures up to 100 data elements for approximately 285,000 ER visit records per year from EDs within the Winnipeg Regional Health Authority (WRHA). This includes the following Winnipeg hospitals: Concordia Hospital, Grace Hospital, Health Science Centre (HSC) Adult, HSC Children’s, Seven Oaks General Hospital, St. Boniface General Hospital, and Victoria General Hospital. As of 2014, data is available for Selkirk General Hospital, Brandon Regional Health Centre, and the Misericordia Urgent Care Centre.

    Patients registered in the ADT system upon entering the ED are automatically entered in EDIS with a patient record. Consequently, any ED information entered into EDIS is immediately accessible to care providers as a record of what is happening to the patient, including initial entry date and time, triage information, wait times, patient history in the ED (to assist with treatment), test results and orders, discharge status, and reassessment needs.

    For more detailed information on the EDIS data, see the Emergency Department Information System (EDIS) Data Description .

Key Data Fields in EDIS

    The following list identifies some of the key data fields / variables available in the EDIS data, as reported in the Deliverable by Doupe et al. (2017). A more complete listing of the data available in EDIS can be found in the MCHP Metadata Repository - Emergency Department Information System entry - (Internal access only).

    1. Arrival Status:

        Similar to the ADT and E-triage system, each EDIS visit captures basic patient demographics, the ED site (location), date and time, and mode of arrival. Arrival status data is entered in EDIS as free text, and thus standard additional arrival status options (e.g. police escort) are not available.

    2. Canadian Triage & Acuity Scale (CTAS) Code:

        The CTAS score is part of EDIS, and has been used ‘pre-EDIS’ in Winnipeg since 2004/05. CTAS relies on a standard set of questions asked at the time of triage to help ensure CTAS levels are comparable across ED sites. CTAS allocates patients into one of five categories based on urgency of need:

        1. Resuscitation (Level I)
        2. Emergent (Level II)
        3. Urgent (Level III)
        4. Less Urgent (Level IV):
        5. Non Urgent (Level V)

        For more detailed description of CTAS levels, see the Canadian Triage & Acuity Scale (CTAS) - Emergency Department glossary term.

        It should be noted that triage nurses have the ability to over-ride computer-generated CTAS scores. Past studies (Doupe M. et al., 2008) indicate this occurs during about 5% of ED visits in Winnipeg.

    3. Disposition (Discharge) Status:

        Similar to ADT, EDIS defines a patient’s disposition (discharge) status as:

        • Left without being seen - patients who left the ED prior to seeing a physician.
        • Left against medical advice - patients whose treatment was started by an ED provider but who left before this treatment was completed.
        • Admitted into hospital
        • Transferred to another ED
        • Died during the ED visit
        • Sent home

        For more information, see the Disposition Status glossary term.

    4. Time:

        EDIS collects the time that "key" events occur during an ED visit, including the time of:

        • registration and triage;
        • transfer from the waiting room to an internal ED treatment area;
        • treatment initiation by the main care provider;
        • provider "sign-off" on the patient (e.g.: the end of active treatment with a disposition status decision); and
        • patient departure from the ED

        Event time information can be used to divide the total ED visit duration into identifiable components, such as waiting room time (from registration to transfer to an internal treatment area), treatment area waiting time (patient is on an internal treatment area but has not yet been seen by a physician), treatment duration, and post-treatment time.

        For a schematic of these time components, see Figure 3.1 - Components of an ED Visit Captured in EDIS in Doupe et al. (2017).

    5. Patient Location:

        EDIS records the physical location (e.g.: treatment area ID) of each patient once inside the ED. These data can be used to identify the number of regularly used ED locations for patient care, and to measure operating capacity (number of patients on site relative to the number of treatment areas available) and patient turnover (number of daily visits per treatment area).

    6. Chief Complaint:

        During triage, the chief complaint for each patient is collected and classified into one specific category.

        Doupe et al's. (2017) analysis combined some of these categories, or grouped as them ‘other’ due to small numbers, resulting in the following 13 chief complaint categories: Cardiovascular, ENT (ear; nose; throat/mouth/neck), Genitourinary, Gastrointestinal, Mental Health, Neurologic, Obstetrical/Gynecological, Orthopedic, Respiratory, Skin, Substance Misuse, Trauma, and Other (e.g.: abdominal pain, cough, eye pain, general and minor issues, headache).

    7. Diagnostic tests:

        Diagnostic test information from EDIS examined in Doupe et al. (2017) include:

        • x-rays,
        • urine tests,
        • ultrasound tests,
        • nuclear medicine tests,
        • computed tomography (CT) scans,
        • cerebral spinal fluid tests,
        • magnetic resonance imaging (MRI), and
        • cardiovascular tests.

          The latter consist mainly of angioplasty, angiograms, and catheterizations. NOTE: The authors consulted Advisory Group members and found echocardiograms, stress tests and electrocardiograms are ordered but not performed during ED visits.

        Diagnostic test data was found to be marked by performance times only, with no reliable ‘order time’ data available. Where order data time was present at some sites, a follow-up analysis by Doupe et al. (2017) showed order and performance times to be identical in many instances. This implies that order times are either system or laboratory technician generated, and should not be used to denote when providers requested the test.

        EDIS also records diagnostic tests (e.g. MRI) which require multiple scans separately. Doupe et al. (2017) collapsed diagnostic tests repeated on the same patient within 30 minutes of each other into one test to account for this.

    8. Blood Tests:

        All blood tests are time stamped in EDIS, with each component of a given blood test (e.g.: red and white blood cell counts for a complete blood count) recorded separately. As these can be linked back to the overall test, it is feasible to analyze blood test data at various levels.

        Doupe et al. (2017) defined patients as having some or no blood work performed, except in the case of troponin blood tests which were measured separately (this test typically requires repeat testing to determine if patients have had a heart attack, which can potentially lengthen visit durations).

    9. ED Providers:

        EDIS can identify the number and type of providers who cared for a given patient. Provider types include physicians, resident (training) physicians, plus nurse practitioners and physician assistants (collapsed into one group for the purposes of this research).

        While key research questions can be asked with these data (e.g., What impact do nurse practitioners have on patient flow?), select improvements are needed to optimize their value. Refer to the Recommendations section below for more information about improving the collection of ED provider information.

Emergency Department Information System Data Quality, Strengths and Weaknesses

    EDIS Data Quality:

      Doupe et al. (2017) determined EDIS was of high quality for research and evaluation, and is a much improved data system compared to the previous systems used in Winnipeg. In most instances the data are accurate and complete, and provide a rich supply of information for defining how throughput and output factors impact ED wait times to better inform reform for care practice and policy.

      EDIS data quality was examined through cross-tabulation tests to determine accuracy of key EDIS measures. This was done according to STROBE (Benchimol et al., 2015) and RECORD (Nicholls et al., 2015) guidelines for conducting research using observational data. In general, these comparisons demonstrated the high quality of the EDIS data where key measures in EDIS mostly "agree with" each other. For instance, visit duration components and patient acuity aligned as expected: higher acuity patients had very short wait times followed by longer care times, and the reverse was true for lower acuity patients. For more information on results of cross-tabulation procedures, see Table 3.1-3.3 and Table 3.6 in Doupe et al. (2017).

      Key fields in EDIS were also found to "agree with" other Repository files. For instance, patients identified in EDIS as being hospitalized were found in the hospital abstract file, and almost all patients reported in EIDS to have died were also reported as deceased in other Repository files. For examples in Doupe et al. (2017), see:

      • Table 3.4 for information on EDIS disposition (discharge) status compared to same-day hospitalization in the Hospital Abstract data.
      • Table 3.5 for information on EDIS disposition (discharge) status in EDIS compared to same-day death in the Health Insurance Registry data.

    EDIS Data Strengths:

      EDIS data strengths noted in Doupe et al. (2017) include:

      • clearly demarked patient transition times used to partition ED visit durations into specific sub-components (i.e. waiting room, care and post-treatment times);
      • the ability to identify diagnostic tests and blood work procedures conducted during the visit; and
      • the ability to link ED providers to the patient.

    EDIS Data Weaknesses:

      After a review of 2012/13 data, the EDIS data weaknesses noted in Doupe et al. (2017) include:

      • the absence of diagnostic and blood test order times (needed to understand how long patients wait for these tests);
      • the lack of reliable consult data (needed to understand how long it takes to receive care from specialists); and
      • the absence of data reflecting nursing care provided.

      Data improvements in each of these areas are recommended to better understand the complex nature of wait times and care provided in emergency departments.


    The following recommendations for improvement of EDIS data were made by Doupe et al. (2017):

    1. Patient Arrival Status: a more reliable and complete description of status of patients upon arrival would help to define patients who arrived at EDs by ambulance, police escort, and other means.

    2. Diagnostic test order times: these are currently not available in EDIS, making it impossible to know how long people wait for different diagnostic tests (a key throughput factor in waiting times).

    3. Medical consults: these are not consistently captured by EDIS, and a key waiting time factor as they are shown to influence ED patient flow.

    4. ED Providers:

      • EDIS does not always differentiate ED physicians from other physicians (e.g., hospital consultants) who may have had input into caring for patients. To avoid over-counting, measuring ED physician supply requires input from Winnipeg stakeholders.
      • ED providers can be linked to each patient but not to diagnostic or blood tests. This can be achieved indirectly only when there is one provider assigned to a patient. Doupe et al. (2017) found multiple physicians were linked to a patient during 17.7% of all ED visits in 2012/13 EDIS data. For the remainder of visits, the frequency with which diagnostic and blood tests were ordered could not be compared across physician providers.
      • The frequency and nature of nursing care is captured poorly in EDIS. Similarly, medical consults (e.g., for gastrointestinal problems, mental illness) are not captured in EDIS. Doupe et al. (2017) note their Advisory Group reports that these data are captured at the St. Boniface site, but without input from stakeholders (i.e., going through the list of provider names individually), it is not feasible to differentiate ED physicians from medical consultants. All other types of consultants (e.g., for physiotherapy, home care) are not recorded in EDIS.

    5. International Classification of Disease (ICD) Codes: While chief complaint data are collected at triage and captured in EDIS, ICD codes are not provided to summarize the physicians’ diagnosis. These data would help to clarify the primary reason for ED visits. In order to collect ICD codes, ED records would have to go through an abstracting process similar to hospital discharges.

Data Cautions

    On suggestions from MCHP, if you are planning to use both ADT/E-Triage and EDIS data in your research, use the following guide:

    • use ADT/E-Triage data from 2001/02 to 2008/09; and
    • use EDIS data from 2009/10 forward.

Related concepts 

Related terms 



  • Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, Sorensen HT, von Elm E, Langan SM, RECORD Working Committee. The REporting of studies conducted using observational routinely-collected health data (RECORD) statement. PLoS Med 2015;12(10):e1001885. [Abstract] (View)
  • Doupe M, Kozyrskyj A, Soodeen R, Derksen S, Burchill C, Huq S. An Initial Analysis of Emergency Departments and Urgent Care in Winnipeg. Winnipeg, MB: Manitoba Centre for Health Policy, 2008. [Report] [Summary] [Additional Materials] (View)
  • Doupe M, Chateau D, Derksen S, Sarkar J, Lobato de Faria R, Strome T, Soodeen R-A, McCulloch S, Dahl M. Factors Affecting Emergency Department Waiting Room Times in Winnipeg. Winnipeg, MB: Manitoba Centre for Health Policy, 2017. [Report] [Summary] [Additional Materials] (View)
  • Nicholls SG, Quach P, von Elm E, Guttmann A, Moher D, Petersen I, Sorensen HY, Smeeth L, Langan SM, Benchimol EI. The REporting of studies conducted using observational routinely-collected health data (RECORD) statement: Methods for arriving at consensus and developing reporting guidelines. PLoS One 2015;10(5):e0125620. [Abstract] (View)

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