Association of Emergency Department Waiting Times With Patient Experience in Admitted and Discharged Patients (2025)

Demographic Variables

We collected the following variables: age, gender, race, admission status to the hospital, hospital length of stay (LOS, if applicable), and insurance status.

Emergency Department Throughput, Definitions, and ED Times

Our ED throughput and waiting times are measured using our Electronic health Record, Epic. Patients entering the ED through the walk-in entrance are preregistered, then undergo a “quick look” triage where the patient is assigned an Emergency Severity Index with levels 1 to 5 by the triage nurse. Patients entering the ED via emergency medical service are processed in a similar manner by a separate triage nurse. At times, patients may be registered and triaged at the bedside. Upon completion of this process, the patient is placed on the “needs attending” list of the ED track board once the patient is in an ED care space.

The time from ED registration until the patient is seen by a provider is termed “time to first provider.” This can be a resident, attending physician, or advanced practice provider. The time from ED registration until the patient is seen by a licensed independent provider (attending physician or advanced practice provider) is termed “door to doctor” (if a resident in not involved in the care of the patient, the door-to-provider and door-to-doctor time are identical). The time from an independent provider seeing the patient until a disposition decision is made is termed “doctor to disposition.” The time between the disposition decision and the patient leaving the ED is termed “disposition to departure.” For patients admitted to the hospital, this interval time of disposition decision to leaving the ED represents “boarding time” in the ED. The “total ED time” is the total time that a patient spent in the ED from registration to departure. All these times were measured in minutes.

We defined prolonged waiting times as greater than the median.

Patient Experience Questionnaires

Our institution uses Press Ganey to assist in collecting data for patient experience. For patients who were in the ED and subsequently admitted to the hospital, we collected the Hospital Consumer Patient Assessment of Healthcare provider and systems (HCAPS) questionnaire (15) and for those who were discharged from the ED: the Press Ganey ED survey (see Supplementary material).

From the HCAPS questionnaire, we extracted the answers to the following questions as outcomes of interest: “Rate the Hospital,” “Recommend the Hospital,” “Communication with Nurses,” and “Communication with Doctors.” For the HCAHPS survey, the questions used are answered on either a 4-point or 11-point Likert scale: Rate the Hospital is answered on an 11-point Likert scale (0-10), Recommend the Hospital is answered on a 4-point Likert scale (definitely no, probably no, probably yes, definitely yes), and Physician and Nurse Communication are answered on a 4-point scale (never, sometimes, usually, always).

All responses that give the highest rating on any Likert scale: 9 and 10 (definitely yes, always) are considered to be “TopBox” responses. This is a binary scoring methodology where all TopBox responses are equated to a numerical value of 100 whereas all the other responses are equated to a numerical value of 0. The TopBox methodology is recommended by CMS (16).

For the ED satisfaction survey, we recorded the answers to the following domains as the outcomes of interest: “the nursing domain,” “the care provider domain,” and “overall assessment.” All questions are answered on a 5-point Likert scale (very poor, poor, fair, good, very good). Individual responses are then converted into “mean scores “using the formula (x × 25) − 25,” where x is the numerical Likert response (ie, 1 for very poor and 5 for very good). Using the mean score methodology is recommended by Press Ganey.

We then dichotomized the scores into optimal (100% score) or suboptimal (any other value). The TopBox score determines the percentile ranking of institutions when they are compared among each other. Therefore we used a similar methodology to replicate what happens in real life.

Statistical Analysis

The patient population was divided in 2 based on subsequent hospitalization status: discharged from ED versus admitted to the hospital. Categorical variables are presented as percentages, continuous variables are presented as median and interquartile range.

We performed χ² analysis, Mann-Whitney analysis, Fleiss κ calculation, and multivariate regression analysis. For the latter, we entered the variables, such as age, gender, race, insurance status, door to first provider, door to doctor, doctor to disposition, disposition to departure, and total ED time. We used a forward conditional methodology and considered variables to have significant association with the outcomes of interest if P < .05. For patients who were admitted to the hospital, we also entered the variable hospital LOS. We performed 2 sets of multivariate regression analyses. In the first set, all the ED times were entered as continuous variables. In the second set, the ED waiting times were dichotomized to their medians and prolonged waiting times were considered as > median. In both sets of analyses, gender, race, and insurance status were entered as categorical variables. All analyses were done using SPSS, IBM 25.0 software.

Demographics and ED Waiting Times by Discharge Status

Over the study period, we surveyed 31 759 inpatients and 3940 completed surveys were returned (response rate of 12.4%), of which 1680 were from patients admitted from the ED. In the same period, we also sent surveyed 44 988 of the patients discharged from the ED and 1477 completed surveys (response rate of 3.3%) were returned.

There were 3278 patients included in the study: 1477 patients were discharged from the ED and 1680 were admitted to the hospital. Demographic data are presented in Table 1. Patients who were admitted to the hospital were significantly older and were proportional more male. In the hospitalized group, more patients defined their race as white and had Medicare insurance. The ED waiting times are shown and compared for both groups in Table 1. Patients who were discharged had a higher door-to-first provider and door-to-doctor time, but a lower doctor to disposition, disposition to departure, and total ED time when compared to admitted patients (Table 1). The median LOS for the inpatient group was 4 days.

Responses to Patient Experience Questionnaires

The inpatient cohort and the ED-discharged cohort responses are presented in Table 2. The Fleiss κ was .371 for the inpatient cohort and .338 for the ED-discharged cohort, indicating a fair agreement between the responses.

Abbreviation: ED, emergency department. Emergency Department Waiting Times by Experience Outcome

We compared the waiting times for patients and stratified the 2 cohorts (hospitalized and ED discharged) between the patients whose experience was an optimal (score 100) and suboptimal (<100).

For the inpatient cohort overall, there were significant differences, as summarized in Table 3. In particular, door-to-doctor time was significantly different for all the outcome groups, whereas disposition to departure was not significantly different in any group (Table 3).

We then analyzed a subset of the inpatient cohort: patients with LOS ≤ 4 days (median of the sample) due to the immediacy factor. Door-to-first provider and door-to-doctor time were significantly different for all outcomes, and disposition to departure was also not different in any subgroup (Table 3).

Finally, for the patients discharged from the ED, the waiting times: door to first provider, door-to-doctor, and total ED times were significantly different between all subgroups (P < .001). There was no significant difference for the doctor-to-disposition time between groups (Table 3).

Multivariate Analysis

We performed a multivariate regression analysis as described in section “Methods” for 3 groups: inpatient cohort with an LOS ≤ 4 days, inpatient cohort with LOS > 4 days, and ED-discharged patients.

In this first set of analyses, the waiting times were ran as continuous variables.

For patients with LOS less than or equal to 4 days, door to doctor was associated with all 4 outcome variables, doctor to disposition was associated with recommend the hospital outcome only. Door-to-first provider time, disposition to departure, and total ED time were not associated with any outcome. For the patients with LOS of more than 4 days, no waiting times were associated with patient experience scores.

For the group that was discharged from the ED, the door-to-doctor variable was significantly associated with lower likelihood of getting an optimal experience score for the physician and nursing domain outcomes. Door to first provider was associated with the overall assessment domain. Disposition to depart was associated with recommend the hospital. Physician domain and total ED time were associated with overall rating. Nursing domain and doctor to disposition were not associated with any outcome (Supplemental table).

For the second set of analyses, we dichotomized the waiting times to their medians and entered them into the model as categorical variables.

For the patients with LOS less than or equal to 4 days, the variable prolonged door-to-doctor time was found to be associated with a higher likelihood of suboptimal experience for all the outcomes. Prolonged doctor to disposition had a higher likelihood of suboptimal experience for the outcomes of rate the doctors and recommend the hospital.

For the patients with LOS of more than 4 days, no waiting times were associated with patient experience scores.

For the patients who were discharged from the ED, both a prolonged door-to-doctor and prolonged total ED time were associated with suboptimal experience for all outcomes (Table 4).

Limitations

This is a retrospective study with all the limitations that come with it. There could be other factors that impacted patient experience that we did not record. We were not able to control for patient-specific outcomes such as complications or need for surgery or testing. Also, in our ED, patients can be assigned to different “regions” of the ED depending on their acuity of illness. We were not able to control for this. We were not able to adjust for discharging service.

The average LOS in our institution is 5.6 days; thus, patients who responded may not be completely representative of the admitted patient population. Additionally, we had a relatively low number of surveys received compared to the number of patient who were seen in the ED and those subsequently admitted to the hospital (18,19). This could have introduced bias in our findings. This could be partly due to the fact that not all patients are eligible for the ED survey, in particular those with a recent ED visit (within 3 months). Furthermore, patients who fill out the surveys could have been those with prolonged waiting times that may be more upset, therefore giving us a skewed sample. Finally, waiting time can be subjective and can be highly dependent on the presenting symptom. Unfortunately, we did not have access to chart-level data to make more analyses; thus, we used the “greater than median” variable for all patients.

Supplemental Material, sj-docx-1-jpx-10.1177_23743735211011404 - Association of Emergency Department Waiting Times With Patient Experience in Admitted and Discharged Patients:

Supplemental Material, sj-docx-1-jpx-10.1177_23743735211011404 for Association of Emergency Department Waiting Times With Patient Experience in Admitted and Discharged Patients by Andrew Nyce, Snehal Gandhi, Brian Freeze, Joshua Bosire, Terry Ricca, Eric Kupersmith, Anthony Mazzarelli and Jean-Sebastien Rachoin in Journal of Patient Experience

Supplemental Material, sj-pdf-1-jpx-10.1177_23743735211011404 - Association of Emergency Department Waiting Times With Patient Experience in Admitted and Discharged Patients:

Supplemental Material, sj-pdf-1-jpx-10.1177_23743735211011404 for Association of Emergency Department Waiting Times With Patient Experience in Admitted and Discharged Patients by Andrew Nyce, Snehal Gandhi, Brian Freeze, Joshua Bosire, Terry Ricca, Eric Kupersmith, Anthony Mazzarelli and Jean-Sebastien Rachoin in Journal of Patient Experience

Andrew Nyce is an Associate Professor of Emergency Medicine at Cooper Medical School of Rowan University in Camden New Jersey. His research interests include ED operations, Patient Experience and Medical Education. He currently serves as Vice Chairman of Emergency Medicine at Cooper University Hospital.

Snehal Gandhi is an Assistant Professor of Medicine at Cooper Medical School of Rowan University. He currently serves as the Chief of Medical Informatics at Cooper University Hospital.

Brian Freeze is an assistant medical director for the Emergency Department at Cooper University Hospital, and a member of the Department of Clinical Informatics and Innovative Care delivery. His passion in medicine lies in the cross section between clinical practice and leveraging technology to make myself and others better clinicians, by making them work more efficiently, have better access to up to date accurate and relevant information that is important to the care of patients.

Joshua Bosire is an assistant vice president of Patient Experience and Access at Cooper University Health Care.

Terry Ricca is the Senior Vice president of Patient Experience and Access at Cooper University Health Care.

Eric Kupersmith is an Associate professor of Clinical Medicine at Cooper Medical School of Rowan University. He is the Chief physician Executive of Cooper University Health Care.

Anthony Mazzarelli is the Co-President and CEO of Cooper University Health Care and Associate Dean for Clinical Affairs as well as an Associate Professor of Emergency Medicine at Cooper Medical School of Rowan University. His chief area of research focus is compassion science and health care administration/operations. He is the co-author of the book, Compassionomics: The Revolutionary Scientific Evidence That Caring Makes a Difference.

Jean-Sebastien Rachoin is an Associate Professor of Medicine at Cooper Medical School of Rowan University in Camden New Jersey. His research interests include study of Sepsis, Renal failure, systems of Care and health disparities. He currently serves as the Head of the Center of Hospital-Based Servicer and Critical Care Services at Cooper University Healthcare.

Authors’ Note: This research project was approved by the institution review board of the Cooper University Healthcare (IRB # 19-101). Patient’s consent was waived.

Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD: Jean-Sebastien Rachoin, MD https://orcid.org/0000-0002-6322-092X

Supplemental Material: Supplemental material for this article is available online.

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