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International Journal for Quality in Health Care, 2017, 29(5), 722–727
doi: 10.1093/intqhc/mzx097
Advance Access Publication Date: 14 September 2017
Article
Article
The role of patient perception of crowding
in the determination of real-time patient
satisfaction at Emergency Department
HAO WANG1, JEFFREY A. KLINE2, BRADFORD E. JACKSON3,
RICHARD D. ROBINSON1, MATTHEW SULLIVAN1, MARCUS HOLMES1,
KATHERINE A. WATSON1, CHAD D. COWDEN1,
JESSICA LAUREANO PHILLIPS3, CHET D. SCHRADER1, JOANNA LEUCK1,
and NESTOR R. ZENAROSA1
1
Department of Emergency Medicine, Integrative Emergency Services, John Peter Smith Health Network, 1500S.
Main St., Fort Worth, TX 76104, USA, 2Department of Emergency Medicine, Indiana University School of Medicine,
640 Eskenazi Ave, Indianapolis, IN 46202, USA, and 3Center for Outcomes Research, John Peter Smith Health
Network, 1500S. Main St., Fort Worth, TX 76104, USA
Address reprint requests to: Hao Wang. Tel: +1-817-702-8696; Fax: +1-817-702-1143; E-mail: hwang01@jpshealth.org
Editorial Decision 3 July 2017; Accepted 4 July 2017
Abstract
Objective: To evaluate the associations between real-time overall patient satisfaction and
Emergency Department (ED) crowding as determined by patient percepton and crowding estimation tool score in a high-volume ED.
Design: A prospective observational study.
Setting: A tertiary acute hospital ED and a Level 1 trauma center.
Participants: ED patients.
Intervention(s): Crowding status was measured by two crowding tools [National Emergency
Department Overcrowding Scale (NEDOCS) and Severely overcrowded–Overcrowded–Not overcrowded Estimation Tool (SONET)] and patient perception of crowding surveys administered at
discharge.
Main outcome measure(s): ED crowding and patient real-time satisfaction.
Results: From 29 November 2015 through 11 January 2016, we enrolled 1345 participants. We
observed considerable agreement between the NEDOCS and SONET assessment of ED crowding
(bias = 0.22; 95% limits of agreement (LOAs): −1.67, 2.12). However, agreement was more variable
between patient perceptions of ED crowding with NEDOCS (bias = 0.62; 95% LOA: −5.85, 7.09)
and SONET (bias = 0.40; 95% LOA: −5.81, 6.61). Compared to not overcrowded, there were overall
inverse associations between ED overcrowding and patient satisfaction (Patient perception OR =
0.49, 95% confidence limit (CL): 0.38, 0.63; NEDOCS OR = 0.78, 95% CL: 0.65, 0.95; SONET OR =
0.82, 95% CL: 0.69, 0.98).
Conclusions: While heterogeneity exists in the degree of agreement between objective and patient
perceived assessments of ED crowding, in our study we observed that higher degrees of ED
crowding at admission might be associated with lower real-time patient satisfaction.
Key words: real-time patient satisfaction survey, Emergency Department, patient perception, tool, crowding
© The Author 2017. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved.
For permissions, please e-mail: journals.permissions@oup.com
722
ED crowding and patient satisfaction • Patient Satisfaction
Introduction
Emergency Department (ED) overcrowding degrades the quality of
emergency care by increased ambulance diversion, increased rate of
patients left without being seen, prolonged patient total length of
stay, decreased patient satisfaction, etc. [1–3]. Several ED crowding
estimation tools have been developed and deployed to evaluate relative ED crowding [4–6]. Among these tools, the National
Emergency Department Overcrowding Scale (NEDOCS) was validated in previous studies and is widely used in the USA [4], while
Severely overcrowded–Overcrowded–Not overcrowded Estimation
Tool (SONET) was specifically derived at an extremely high-volume
ED setting and has since been externally validated [6]. Both tools
reported worsening of the patient care outcomes with increased
levels of ED crowding and both were derived based on providers’
perceptions of ED crowding [6, 7]. However, these perceptions
determine crowding by viewing an ED as an entire entity and accuracy of the reports from different studies are quite controversial [8–10].
Conversely, patient perception of ED crowding is considered to view
crowding specifically at the individual-level with impressions reflecting
the patient experience. To date, no study has investigated the potential
agreement of ED crowding determined by either estimation tool or
patients’ perceptions,
Prior research suggests that prolonged ED wait times, as perceived by patients, is associated with decreased patient satisfaction [11]. Similarly, validated ED crowding metrics are predictive
of failure to meet patient satisfaction goals [12] and lower overall
ED satisfaction [12]. However, most of these studies were retrospective and patient satisfaction results were obtained through remote
survey data gathering and analysis done weeks after the index visit (e.g.
Press-Ganey, NRC Picker). Such survey results are typically not available in real-time and, therefore, do not reflect perceptions of ED crowding during the index visit. Although it would seem obvious that
crowding worsens patient satisfaction, little literature has been published
to document this effect. One previous study showed ED crowding—
especially prolonged waiting times as perceived by patients—was associated with compromised emergency care. Uncertainty with respect to
direct evidence of a link between patient satisfaction and ED crowding
was noted due to variabilities among patients, nurses and physicians
in a setting yielding relatively poor survey participation (<20%) in the
form of perceptions of patients [13]. Another study conducted a realtime ED patient satisfaction survey among high acuity patients in a
Pakistan tertiary hospital [14]. Investigators described several justifications favoring the use of real-time patient satisfaction surveys over
mailed surveys as it is easier to provide quality assured service, easier
to capture ED patients and easier to reflect the direct response
between patient satisfaction and ED service provided. Real-time
patient satisfaction evaluation has been reported as advantageous in
terms of rendering instant feedback for quality improvement, higher
completion rates and ease of use [15–17]. Prospective studies of current state real-time ED patient satisfaction as a function of relative ED
crowding should yield improved understanding of this dynamic and
guide development of quality projects that deliver an enhanced future
state [18].
The primary aim of this study was to evaluate the direct association between ED crowding and real-time patient satisfaction. Since
there is no gold standard to determine ED crowding and current
measurements are subjective, we sought to evaluate the association
between ED crowding and real-time patient satisfaction using different ED crowding measurements (tools vs. patient perceptions) despite
their accuracy. In addition to this, we assessed potential consistencies
723
in associations when ED crowding was determined by patient perception, as well as, two objective estimation tools. We hypothesized that
overcrowding worsened patient satisfaction despite different measurements used.
Methods
Study design and protocol
This was a single-center prospective observational study conducted
at an academic ED (John Peter Smith Health Network, Fort Worth,
TX) with an annual volume of over 118 000 patients. Each ED
crowding tool utilized in this study were initially derived from provider perceptions of crowding. This study sought to assess ED
crowding as determined by provider perception captured using two
validated estimation tools and patient perceptions as attributed
using a real-time patient satisfaction survey. The initial step was to
assign an objective ED crowding score to each patient encounter
using two ED crowding estimation tools (NEDOCS and SONET)
upon patient arrival and registration at ED [4, 6]. The second step
was to simultaneously collect patient satisfaction from real-time
patient surveys upon their individual dispositions (e.g. upon patient
discharge, transferred to other facilities, admitted, etc.).
Study participants
All ED-registered patients during the period of 29 November 2015
through 11 January 2016 and were subsequently dispositioned (e.g.
discharged home or admitted to hospital) from ED were considered
eligible for enrollment into this study. Exclusion criteria were: (i)
patients who refused to participate in the real-time patient satisfaction survey; (ii) patients who did not participate in the satisfaction
survey (e.g. patients who left without being seen or eloped); (iii)
patients whose surveyors entered incorrect information (e.g. answers
not chosen from predetermined options [menu] or answers unrelated
to the questions); (iv) patients who completed <20% of the survey;
(v) patients not assigned an initial ED crowding score upon registration and/or (vi) patients not assigned an ED crowding score due to
incomplete data.
Definition of ED crowding
Three measures of ED crowding were assessed for this study, two
objective ED crowding estimation tools (NEDOCS and SONET)
derived from provider perception and patient perception of overcrowding. NEDOCS has been externally validated and is nationally
recognized as a tool to measure ED crowding [4, 8, 19]. SONET
was derived directly from the study ED and determined to be more
accurate than NEDOCS for crowding estimation in an extremely
high-volume ED setting (>100 000 annual visits) [20]. ED crowding
was categorized into three different levels by both NEDOCS and
SONET (not overcrowded ≤100, overcrowded >100 to ≤140 and
severely overcrowded >140). During the study period, a question
regarding ED crowding status was included in the satisfaction survey whereby the patient was asked to rate their perception of relative ED crowding on a Likert scale of 1–10 with 1 being the least
and 10 being the worst crowded. To align ED crowding scores as
calculated by the crowding estimation tools, patient perceived ED
crowding scores were further categorized into the same three levels
used to derive SONET as previously reported (i.e. not overcrowded =
1–5, overcrowded = 6–7 and severely overcrowded = 8–10) (i.e. multiples of a constant of 20) [6].
Wang et al.
724
Patient satisfaction survey
A real-time patient satisfaction survey currently available in market
(Qualitick, Clearwater, FL) was conducted at the end of the ED
encounter, prior to patient moving out of ED (Supplemental Table 1).
A computer tablet housing the confidential survey was provided to
patients and/or their designees in a private setting, away from healthcare providers. The survey only allows the patient to view one question at a time and does not give the option to return to the previous
question once the ‘turn page’ button is hit. Expected time for survey
completion was <5 min. This brief survey included a priori questions
addressing established risks affecting patient satisfaction as published
in the literature (i.e. patient satisfaction with provider(s), patient satisfaction with nurse(s), perception of pain control, etc.) [21, 22]. Patient
perception of ED crowding was the last question of the survey. The
primary outcome of this study, patient satisfaction, was assessed from
the following, ‘Overall, how satisfied were you with your visit today?
(Scale of 1–10, 1: very dissatisfied, 10: very satisfied).’
reported ED as overcrowded more than NEDOCS (bias = 0.22; 95%
LOA: −1.67, 2.12). We found similar differences in agreement between
patient perception and both NEDOCS and SONET. Patient’s perceptions of crowding were emphasized at the extremes of the crowding
Data analysis
Descriptive statistics are presented as frequencies and percentages for
categorical variables and median and interquartile ranges (IQRs) for
continuous variables. Agreement between ED crowding measures
(patient perception, NEDOCS and SONET) were assessed by the
between-measure mean difference (bias) and corresponding 95% limits
of agreement (LOAs) and presented graphically using Bland–Altman
plots [23]. We used three separate fractional logistic regression models
with robust variance to estimate odds ratios (ORs) and corresponding
95% confidence limits (CLs) for the association between ED crowding
and patient satisfaction with adjustment for a minimal sufficient set of
covariates to reduce confounding bias. This set of covariates was identified using directed acyclic graphs and included age, sex, race/ethnicity
and acuity level [24]. Patient satisfaction scores were transformed
from the original scale of 1 to 10, to a continuous 0 to 1 data element
in order to facilitate statistical modeling. Furthermore, for each measure of ED crowding, we estimated the expected overall patient satisfaction across levels of ED crowding. More pragmatically in the
management of ED flow, ED crowding scores were categorized into
three levels of crowding (not overcrowded, overcrowded or severely
overcrowded). Observations with missing values for relevant covariates were excluded from the analysis. All analysis was performed using
Stata 14.0 (College Station, TX). This study was approved by the local
Institutional Review Board.
Results
A total of 13 196 patients were registered in the ED during the study
period (29 November 2015 through 11 January 2016), of which,
1746 (13%) were eligible to participate and enrolled in the study
(Fig. 1). Of those enrolled, 161 patients were excluded due to incorrect information, 135 for less than 20% survey completion and 105
due to no ED crowding score group assignment upon registration.
The final 1345 patients were analyzed. Table 1 presents the general
characteristics of the enrolled and nonenrolled participants during the
study period. In brief, enrolled patients were predominately female,
younger (median age 41 years vs. 47 years) and had lower Emergency
Severity Index (ESI) scores. The majority of patients were assigned a
mid-acuity triage level (ESI-3) and were ultimately discharged.
Figure 2 presents the assessments of agreement between the three
measures of ED crowding using Bland–Altman plots. There was consistent agreement between SONET and NEDOCS where SONET
Figure 1 Study patient enrollment flow diagram.
Table 1 Characteristics of enrolled and nonenrolled patients
Enrolled
Nonenrolled
Patients, n (%)
1345 (10.2)
11 851 (89.8)
Age (year), median (IQR)
41 (28–54)
47 (33–57)
Sex (male), yes, n (%)
595 (44)
5834 (49)
Race, n (%)
NH White
533 (40)
4451 (38)
NH Black
476 (35)
4069 (34)
Hispanic
335 (25)
3179 (27)
ESI, n (%)
ESI-1
17 (1.3)
296 (2.5)
ESI-2
379 (28)
2694 (23)
ESI-3
849 (63)
6586 (56)
ESI-4
87 (6.5)
1974 (17)
ESI-5
11 (0.8)
241 (2)
Unknown
2 (0.2)
60 (0.5)
ED disposition, n (%)
Admit
234 (17)
2371 (20)
Discharge
1039 (77)
7427 (63)
72 (5)
2053 (17)
Othersa
Total ED length of stay, minutes
274 (189–381)
230 (144–339)
(median, IQR)
ED crowding levels determined by NEDOCS, n (%)b
Not overcrowded
718 (53)
5748 (49)
Overcrowded
386 (29)
3716/ (32)
Severely overcrowded
241 (18)
2282 (19)
ED crowding levels determined by SONET, n (%)b
Not overcrowded
763 (57)
6151 (52)
Overcrowded
486 (36)
4804 (41)
Severely overcrowded
96 (7)
791 (7)
NH, non-Hispanic.
a
Others refer to other ED dispositions including ED transfer to other facilities, ED sent to Labor & Delivery, ED send to Operating Room or Left without being seen (only applied to nonenrolled patients). Percentage adding up
does not equal to 100 due to number rounding up.
b
ED crowding levels (e.g. not overcrowded, overcrowded and severely overcrowded) were determined upon each patient arriving to and registering at ED.
ED crowding and patient satisfaction • Patient Satisfaction
spectrum, with more frequent perceptions of ED as less crowded compared to each objective measure. The mean differences in assessment
methods were close to 0 with wide limits of agreement for patient perception and both NEDOCS and SONET bias = 0.62; 95% LOA:
−5.85, 7.09 and bias = 0.40; 95% LOA: −5.81, 6.61, respectively.
The adjusted ORs and 95% CLs for the association between ED
crowding and real-time patient satisfaction are presented in Table 2.
In terms of the objective measures of overcrowding, ED overcrowded was associated with lower odds of patient satisfaction compared to not overcrowded (NEDOCS = 0.78; 95% CL: 0.65, 0.95;
SONET = 0.82; 95% CL: 0.69, 0.98). Moreover, severely
725
overcrowded for objective measures was also associated with lower
odds of patient satisfaction (NEDOCS = 0.79; 95% CL: 0.61, 1.01;
SONET = 0.78; 95% CL: 0.51, 1.18). Patient perceptions of overcrowded was associated with lower odds of patient satisfaction. The
magnitude of the association was greater with the perception of
overcrowded (OR = 0.49; 95% CL: 0.38, 0.63) than severely overcrowded (OR = 0.73; 95% CL: 0.56, 0.97). Figure 3 further illustrates the relation between ED crowding and patient satisfaction
resulting in an overall downward trend across levels of crowding
and patient perceptions exhibiting a nonlinear pattern compared to
the objective measures of ED crowding.
Figure 2 Assessments of agreement between NEDOCS, SONET and patient perception of ED crowding using Bland–Altman plots.
Table 2 Adjusted OR and 95% CLs for the association between ED crowding and real-time patient satisfaction across different assessment
modes
Emergency department crowding assessments
ED crowding
Not overcrowded
Overcrowded
Severely overcrowded
Age
Sex (male vs. female)
Race/ethnicity
NH black vs. NH white
Hispanic vs. NH white
Acuity level
ESI 1
ESI 2
ESI 3
ESI 4
ESI 5
Patient perception
AOR (LCL, UCL)
NEDOCS
AOR (LCL, UCL)
SONET
AOR (LCL, UCL)
Reference
0.49 (0.38, 0.63)
0.73 (0.56, 0.97)
1.01 (1.00, 1.01)
1.10 (0.93, 1.30)
Reference
0.78 (0.65, 0.95)
0.79 (0.61, 1.01)
1.01 (1.00, 1.01)
1.06 (0.89, 1.25)
Reference
0.82 (0.69, 0.98)
0.78 (0.51, 1.18)
1.01 (1.00, 1.01)
1.05 (0.89, 1.25)
1.33 (1.09, 1.63)
1.07 (0.87, 1.31)
1.32 (1.08, 1.62)
1.04 (0.84, 1.29)
1.32 (1.08, 1.61)
1.04 (0.84, 1.28)
Reference
1.00 (0.47, 2.13)
1.04 (0.49, 2.21)
1.08 (0.48, 2.43)
0.81 (0.29, 2.26)
Reference
0.88 (0.41, 1.87)
0.92 (0.42, 1.92)
0.92 (0.39, 2.01)
0.73 (0.25, 2.03)
Reference
0.90 (0.41, 1.93)
0.94 (0.44, 1.99)
0.95 (0.42, 2.14)
0.77 (0.27, 2.18)
AOR, adjusted odds ratio; LCL, 95% lower confidence limit; UCL, 95% upper confidence limit.
Wang et al.
726
Figure 3 Conditional means of patient satisfaction across NEDOCS, SONET and patient perception of ED crowding.
Discussion
Our results suggest that ED crowding, regardless of determination
method (e.g. patient perceptions, NEDOCS or SONET), was associated with patient satisfaction even after adjustment for potential
confounders. There was heterogeneity between the associations of
crowding and patient satisfaction, where patient perception of
crowding seemed to result in more pronounced impact on overall
patient satisfaction than that noted with either objective scores
(NEDOCS and SONET). We observed varying degrees of agreement
between the three measures of crowding, in which the two objective
measures exhibited the strongest agreement.
One of the most notable findings of this study was that patient
perception of ED crowding was associated with varying levels of realtime patient satisfaction. Given that no standard measurement has
been developed to accurately measure ED crowding, we included
patient perceptions of crowding as a study modality. Discrepancies
were noted when comparing crowding estimation tools (NEDOCS
and SONET) and patient perceptions of ED crowding. This suggests
that patient perception of ED crowding may not be a reliable marker,
although relative overcrowding as perceived by patients, and families,
may negatively impact overall patient satisfaction.
To date, no studies have determined whether a direct link exists
between patient perceptions of ED crowding and their overall realtime ED satisfaction. Ours is the first to investigate the heterogeneity
of different ED crowding assessment modalities. Our findings support the literature in terms of improved understanding of potential
associations between ED crowding and real-time patient satisfaction
that differs from other traditional satisfaction surveys currently on
the market. We believe that interface or integration of real-time
patient satisfaction surveys to the Electronic Health Record (EHR)
can provide meaningful feedback regarding improved delivery of
quality health care.
Several factors could influence the measurement of patient satisfaction, including the manner in which and/or timing whereby the satisfaction survey is administered (e.g. real-time vs. after-care timing;
phone vs. Internet vs. mail survey methods; third party vs. healthcare
personnel administrator, etc.). Different survey techniques have their
advantages and disadvantages. Real-time satisfaction surveys can
reduce issues with recall bias and have higher completion rates [25],
two factors which might produce lower reported satisfaction scores.
On the other hand, some other risks could result in relatively higher
real-time satisfaction scores including fear that the survey may not
truly be anonymous; or concerns that after-care arrangements and
management may be negatively affected by overly critical survey
responses. Consideration of such risks, though not substantially validated, may, in part, explain the overall high satisfaction scores noted in
this study.
This study is not without limitation. First, this was a prospective
observational single-center study in which patients were not randomly selected for participation. The nonrandomization of study
participants may have introduced confounders not previously
accounted for. Second, we anticipated enrolling all patients during
the study period. However, only approximately 10% of ED patients
were included in the evaluable sample yielding a smaller sample
than originally expected. This was due to numerous factors including: patient refusal to participate; inability to initiate the survey due
to less efficient patient flow (e.g. elevated real-time ED census,
admitted patents holding in ED, etc.); unavailability of unit clerk or
study coordinator; and patient status (i.e. severity of illness). If the
perceptions of crowding and patient satisfaction among patients
who did not complete the survey differed from our study sample
then our estimates would be subject to greater bias. Though selfadministered, mailed surveys are a common methodology to assess
patient satisfaction, one may argue that this modality presents challenges, such as obtaining accurate mailing addresses [26]. The study
institution serves a unique patient population, consisting primarily
of high transit, homeless, under- and uninsured patients, resulting in
incomplete EHRs and unreliable patient contact information for
address and/or telephone number which make our real-time survey
response rate relatively higher than the traditional mailed out satisfaction surveys. Given that the current study is the first of its kind,
examining the potential association between ED crowding and real-
ED crowding and patient satisfaction • Patient Satisfaction
time patient satisfaction, we feel the low response rate is acceptable.
However, future research is warranted to externally validate the
real-time survey tool and its association with ED crowding. Third,
the crowding definition in the patient satisfaction survey is not
explained in detail and could further bias the study results. The
magnitude and direction of this bias is unclear due to the unknown
variability of patient responses; however, we believe that our study
does provide useful information to help support future research
which may examine associations between patient perceptions and
patient reported outcomes. Fourth, no standard ED crowding measurement currently exists. Our study addresses agreement between
measurements, not measurement accuracy. Additionally, patient satisfaction results were not compared between real-time and traditional surveys; other factors that might affect overall patient
satisfaction (e.g. patients with psychosocial risks) were not investigated in this study; and, we were unable to address different perceptions of crowding relative to varying severity of patient illness and
assigned level of acuity. We are still uncertain as to whether a
change in the crowding level during an ED visit via planned interventions influences patient perceptions of crowding or if it could
subsequently affect patient satisfaction scores. Fifth, because the
concept of utilizing real-time ED patient satisfaction surveys as a
metric for describing perceived ED crowding is novel, we did not
employ a validated real-time survey tool. Sixth, to ensure we complied with copy right laws afforded to conventional patient satisfaction survey instruments and because data from these tools are not
collected in real-time (e.g. Press-Ganey and NRC Picker), a modified
version was employed. Future research, aimed at large-scale, multisite collaboration is needed to validate the use of a modified, realtime patient satisfaction survey to describe perceived ED crowding
among ED patients.
In summary, ED overcrowding at admission might be associated
with lower real-time patient satisfaction scores. While there may be
varying degrees of agreement between objective and patient perceived assessments of crowding, we observed inverse associations
with patient satisfaction throughout. Future studies would benefit
by focusing on investigation of the (i) association between dynamic
changes in ED crowding levels, interventions to minimize ED overcrowding and overall patient satisfaction and (ii) direction of crowding change and its influence on patient perception of ED crowding
with and/or without interventions and overall patient satisfaction.
Supplementary material
Supplementary material is available at International Journal for Quality in
Health Care online.
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