Article

Increased door to admission time is associated with prolonged throughput for ED patients discharged home

a b s t r a c t

Background: Emergency Department (ED) service evaluations are typically based on surveys of discharged pa- tients. Physicians/administrators benefit from data that quantifies system-based factors that adversely impact the experience of those who represent the survey cohort.

Objective: While investigators have established that admitted Patient boarding impacts overall ED throughput times, we sought to specifically quantify the relationship between throughput times for patients admitted (EDLOS) versus discharged home from the ED (DCLOS).

Methods: We performed a Prospective analysis of consecutive patient encounters at an inner-city ED. Variables collected: median daily DCLOS for ED patients, ED Daily census, Left without being seen , median door to doctor, median room to doctor, and daily number admitted. Admitted patients divided into 2 groups based on daily median EDLOS for admits (b 6 hours, >=6 hours). Continuous variables analyzed by t-tests. Multivariate regression utilized to identify independent effects of the co-variants on median daily DCLOS. Results: We analyzed 24,127 patient visits. ED patient DCLOS was longer for patients seen on days with prolonged EDLOS (193.7 minutes, 95%CI 186.7-200.7 vs. 152.8, 144.9-160.5, Pb .0001). Variables that were associated with increased daily median EDLOS for admits included: daily admits (P= 0.01), room to doctor time (Pb .01), number of patients that left without being seen (Pb .01). When controlling for the covariate daily census, differences in DCLOS remained significant for the >= 6 hours group (189.4 minutes, 95%CI 184.1-194.7 vs. 164.8, 155.7-173.9 (Pb .0001).

Conclusion: Prolonged ED stays for admitted patients were associated with prolonged throughput times for pa- tients discharged home from the ED.

(C) 2016

Introduction

The emergency department has become the entry portal for the hos- pital as well as the final safety net for all patients in the United States healthcare system. As a consequence of these roles, EDs are becoming increasingly crowded (or overcrowded) with adverse consequences. McCusker linked administrative databases of over 670,000 patients and found that a 10% increase in emergency bed relative occupancy ratio was associated with a 3% increase of the following 30-day out- comes: deaths (for admitted and discharged patients), one or more re- turn ED visits (among discharged patients), and hospital admission at first return visit with a “strong correlation between bed crowding and mortality among large emergency departments” [1]. Numerous other studies have shown similar negative effects on patient outcomes [2-10].

? Financial Disclosures/Conflicts: None.

* Corresponding author at: Department of Emergency Medicine, Texas A&M Health Sci- ence Center/Christus Spohn, Corpus Christi, TX 78405. Tel.: +1 361 902 6762.

E-mail address: [email protected] (P.B. Richman).

In response to these concerns, the Institute of Medicine has recom- mended that hospitals reduce ED overcrowding and utilize tools such as queuing theory on admission process and 23 hour ED Observation Units for improving hospital efficiency [11]. The Centers for Medicare and Medicaid Services and The Joint Commission (TJC) have begun to regulate the process with the following performance measure-TJC element LD.04.03.11 that states “the hospital measures and sets goals for mitigating and managing the boarding (the practice of holding patients in the ED or a temporary location for four or more hours after the decision to admit or transfer has been made) of patients who come through the emergency department” [12].

Researchers studying the area of ED patient flow have demonstrated that a variety of internal processes, external factors and the ED depart- ment size can impact the ED throughput [13-45]. Such studies of inter- nal inputs have addressed department process changes, including the use of “fast track” areas and redesigning the location of nursing staff and physicians throughout the ED [21-28]. Investigators have also eval- uated the effect of optimizing laboratory efficiencies including point of care testing as a means to reduce emergency department length of stay [29-34]. The other major factors in ED flow are hospital occupancy

http://dx.doi.org/10.1016/j.ajem.2016.06.002

0735-6757/(C) 2016

and boarding of in-patients in the ED, and, as previously noted, these have been shown to have an adverse impact on emergency department length of stay and outcomes for patients who are ultimately admitted to the hospital [16,17,20,35-39].

The ED is a complex system due to the uncontrolled nature of inputs. Patient factors that influence ED flow include variation in acuity, types of chief complaints and unpredictable surges in patient volume throughout a given day. Flow is also significantly influenced by hospital-based factors outside the direct domain of ED control. As cus- tomer service surveys for ED patients focus on patients who are discharged home, it is also important to understand the impact of pa- tient boarding on flow for this large segment of the ED census. We con- ducted a prospective, observational study to test the hypothesis that extended EDLOS for admitted patients (boarding) would be associated with increased length of stay for those patients ultimately discharged from the ED (DCLOS), and quantify that effect in our institution.

Methods

Study design

We performed a prospective, observational study to assess the rela- tionship between throughput of patients discharged from the ED and boarding times for patients admitted to the hospital through the ED.

Study setting

We conducted this study at an urban county hospital facility. The hospital is a designated level-2 trauma center by the American College of Surgeons covering a 12-county region. Physicians at our facility see 48,000 ED patients annually, with an admission rate of 17.5%. Twenty- five percent of those admissions are admitted to the intensive care units. physician staffing in the main part of the ED consists of an attend- ing physician and two emergency medicine residents (of variable levels of training) for a 24-hour period, an additional fast track area staffed 20 hours by an advanced practice practitioner. The radiology department is located directly behind the ED and is equipped with CT, ultrasound, as well as MRI capabilities. The Christus Spohn Institutional Review Board designated our study as exempt status prior to the initiation of data collection.

Population

Consecutive patient encounters during the period from March 2013 to November 2013 were identified through query of our electronic med- ical record (Meditech Information Technologies, Inc, Westwood, MA).

Study process

to the hospital for further treatment (EDLOS for admits), or discharged home with discharge instructions for care at home (DCLOS).

Method of measurement

Median daily time interval data was collected from the ED Tracking system, Meditech(C). This is a dynamic tracking system that requires the staff to time mark the following events: when the patient received a room (room), when the physician assessed the patient (doctor), and when the patient was discharged from the ED (dismiss). The variables collected included classic ED measures as defined by Welch et al [41] (Table 1). We chose those variables as they represented the ED mea- sures with separate event points entered for every visit, and represent- ed distinct ED measures in the flow of a patient through the ED.

Primary data analysis

The data analysis includes summary statistics for the intervals ana- lyzed. The interval data was divided into 2 groups based on the median daily EDLOS for admits. 6 hours was chosen as the dividing point to re- flect 2 hours for the physician to make a decision to admit the patient and the additional 4 hours from the TJC element LD.04.03.11 for maxi- mum acceptable hold interval in the emergency department. Mean in- tervals of the 2 groups, divided based on the daily median interval EDLOS for admits, were compared by t-tests. Subsequently, we utilized regression analysis to control for confounding variables. Our multiple regression model contained the following variables: DCLOS (depen- dent), EDLOS for admit group (b 6 hours, N = 6 hours), daily census, LWBS (count), median daily room to doctor, median daily door to MD, and number of admissions. The number of admissions and median door to MD variables that were highly correlated were excluded to avoid multicollinearity.

Results

Data were collated from 24,127 patient visits. There was an admis- sion rate of 17.3% during the study period. Patient characteristics are presented in Table 2. Our emergency department has visits primarily by adults with only 2.66% of the census comprised of pediatric patients. The Emergency Severity Index level 3 and 4 categorization of pa- tients was similar, but 21.36% of the visits did not receive the ESI desig- nation in the electronic medical record. The department has a high

Table 1

Emergency department (ED) metrics and definitions [36].

The ED had an evaluation process during the study period that is typ- ical of most EDs. All walk-in patients checked in at the triage window,

ED Interval Meditech

Interval

Definitions

which was staffed by an ED technician. The patient signed in on a triage complaint form with their name, time of arrival, and chief complaint (time of arrival started the ED intervals). Then, a registered nurse eval- uated the patient. A vast majority of the patients received an evaluation using the Emergency Severity Index index based on the stability of the patient as well as need for evaluation [40]. If their complaint had po- tential severity, and/or if a room were available, the patient would be immediately assigned to an ED room. If no rooms were available, the tri- age nurse asked the patient to wait in the lobby until a room was avail-

Room to doctor (doctor includes physician, resident or allied health professional)

ED LOS for discharged patients (DCLOS)

ED LOS for admitted patients (EDLOS for admits)

Room to provider

Received to dismiss

Received to dismiss (Admitted patients)

Time it takes for a physician to see the patient in the room after the patient is placed in the room

The time interval in minutes between arrival time to physical discharge time

The time interval in minutes between arrival time and physical departure of the patient from the ED treatment area

able for further evaluation. Most patients that arrived by ambulance were immediately taken to a room for evaluation (in that situation, time of nurses initial triage started the ED intervals). The emergency physician, or advanced practice practitioner then assessed the patient to determine if further evaluation or consultation was necessary. The emergency physician determined if the patient needed to be admitted

Total patients per day (Daily New arrivals Total number of patients that Census) signed up for triage that day.

Admit ADMIT All patients that are given a bed in the hospital (includes admissions and Observation status)

Left without being seen (LWBS) Any patient who leaves the ED before initiation of the MSE.

Table 2

Department characteristics

Census age distribution

Adult: 23,485 (97.33%)

Pediatric: 642 (2.66%)

ESI triage categorization:

ESI Level 1

15 (0.06%)

ESI Level 2

948 (3.93%)

ESI Level 3

7968 (33.03%)

ESI Level 4

7909 (32.78%)

ESI Level 5

651 (2.70%)

ESI MISC.

562 (2.33%)

ESI (LNS or AMA)

920 (3.81%)

ESI not designated (or other)

5154 (21.36%)

Admission types Psyche

966 (22.10%)

ICU

793 (18.14%)

Med/Surg

1522 (34.82%)

Telemetry

532 (12.17%)

Observation

558 (12.76)

percentage of ICU admissions, and the institution does also function as a major psychiatric facility for the area.

For the entire 6-month period the median daily census was 132 (95% CI, 128-133) and median daily number of admissions were 24 (95% CI, 23-24). The mean of the daily emergency department discharged pa- tient median length of stay (ED DCLOS) was 182 minutes (95% CI, 176-189) and the mean daily emergency department median length of stay for admits (EDLOS for admits) was 404 minutes (95% CI, 393-414). Patient encounters were divided into 2 groups. Group one in- cluded patients with EDLOS for admit daily median less then 6 hours (6229 visits); group 2 included patients with median greater then 6 hours ED admit length of stay (17,898 visits).

With respect to outcome parameters, ED DCLOS was 42 minutes lon- ger for patients seen on days with prolonged EDLOS for admits (194 mi- nutes; 95% CI, 187-201 vs 153, 145-160; Pb .0001). (see Table 3). Other variables that were associated with increased EDLOS for admits includ- ed: daily admits (P= .01), door to doctor (Pb .01) room to doctor (Pb

.01), LWBS (Pb .01). The daily census averaged 10.6 more patients seen per day in the N 6 hour study group (134, 95% CI 131-137 vs. 123, 118-129; Pb .0001). Given the increased daily census in N 6 hour group we used multiple regression (R^2 was 0.49, overall Pb .001) to control for the covariate daily census and see if EDLOS for admits contin- ued to affect ED DCLOS. The differences in DCLOS still remained signifi- cant at 24 minutes for the >= 6 hours. Group (189, minutes 95%CI 184-195 vs. 165, 156-174; (Pb .0001), see Figure for comparison of be- fore and after regression.

Discussion

The Institute of Medicine long declared that EDs are “overcrowded”, and in their report “Hospital-Based Emergency Care: At the Breaking Point” (2006) they described the adverse effects of ED overcrowding, in- cluding Ambulance diversion and patient boarding in ED hallways while waiting for inpatient beds. They noted that this problem was exacerbat- ed by the increase in ED visits nationally with a concomitant decrease in

Table 3

Outcome Metrics.

Interval:

ADMIT

LOS b6 hours

ADMIT

LOS N 6 hours.

P

Difference

ED DCLOS

152

193.7

Pb .01

41.7

Total ED patients per day

123.4

134

Pb .01

10.6

Door to doctor

23.5

30.6

Pb .01

7.1

Room to doctor

11.2

15.1

P= .01

3.9

LWBS

2.9

5.2

Pb .01

2

Admits (%)

21.7 (17.9%)

24 (18.1%)

P= .01

2.3 (0.2%)

Figure. Comparison of DCLOS for admits, before and after covariant daily census control by multiple regression analysis.

overall inpatient beds [11]. While bringing attention to the issue and highlighting the consequences nearly a decade ago, the IOM did/has not provided significant metrics for quantifying overcrowding.

As research in the area of ED overcrowding has grown, researchers

have been able to rely on validated definitions and models of over- crowding in order to turn their attention towards quantifying/identify- ing bottlenecks in the chain of ED throughput [37,41,42]. Using Computer simulation, Asaro et al analyzed 166,854 ED patient visits at a single center to demonstrate the respective effects of ED patient arrival rate, ED admission percentage, and inpatient bed utilization on crowding within the ED [37]. They found that there was a 12 minute in- crease in Waiting times and a 15 minute increase in ED LOS (DCLOS) when the admission rate Percentage changed from 21.7% to 27%.

While Asaro et al’s study established the adverse impact of ED ad- mission and hospital patient volume on ED flow at a large hospital, such data does not specifically provide metrics upon which process im- provement may be quantified. Rather, it is more instructive to examine how longer ED LOS for admitted patients may impact the LOS for those ultimately discharged to home from the ED. White et al divided the ED Admit LOS (boarder burden) into quartiles and showed a 16 minute in- crease in ED LOS for discharged patients between the first quartile and the forth quartile, and when looking at the groups between 11:00 AM to 11:00 PM the throughput time increased by 57 minutes [39]. Our in- vestigation is similar to White’s attempts to quantify the association be- tween longer times to receive Hospital beds for admitted patients and the throughput time of those patients ultimately discharged from the ED [1-10,16-34,39]. We found after controlling for the covariate daily census, that there were significant differences in the length of stay for patients discharged home (DCLOS) when the length of ED stay for ad- mitted patients was prolonged for more than 6 hours vs. shorter ED holding periods (189.4 minutes; 95%CI 184.1-194.7 vs. 164.8 minutes,

95%CI 155.7-173.9; (Pb .0001.

Not surprisingly, prior research from ED and ambulatory centers has revealed that prolonged wait and throughput times can have an adverse impact not only on patients’ satisfaction ratings with their experience but also on their perception of the quality of care that they receive [46,47]. Thus, emergency physicians frequently start their patient en- counter with a “leg down” that has the potential to negatively influence their performance evaluations and even place them at risk of adverse medical-legal consequences. hospital administrators typically hold ED medical directors and groups accountable for various patient through- put metrics and core measures of ED service focus on discharged pa- tients length of stay. Our results suggest with respect to efforts to improve ED performance and service metrics, that leadership needs to focus equally, if not primarily, on improving the door to hospital bed time for patients admitted to the hospital through the ED.

Limitations

Our study has several limitations that warrant discussion. While it is intuitive that the findings of our results would be generalizable to many other settings, i.e. prolonged ED stays for admitted patients would be as- sociated with slower throughput for all patients, we conducted the study only at a single institution. Our facility represents an inner-city type setting with typical resource problems, including inadequate space in the ED and in-patient wards as well as RN Staffing shortages throughout. The extent of the potential effect of patient holding is likely going to differ across different ED census sizes, admission rates, ED space, RN staffing patterns, and hospital size. We do have an unusually high rate of ICU admissions (25%), however, this largely reflects a lower threshold to admit patients of moderate acuity to the ICU setting based on nurse staffing/training patterns within our facility.

Another limitation of our study is that we utilized daily medians in

the analysis, which does not allow for analysis of the effect of triage surges (ED arrival variation) or daily complaint variation on throughput. We utilized daily medians for our various metrics so as to analyze the ef- fects of our independent variables on a 24-hour period. In turn, this led to smoothing the impact of ED arrival surges and daily complaint varia- tion on DCLOS. If we had utilized discrete patient data points, we could have evaluated data from smaller time segments (eg, 1- to 2-hour inter- vals) as well as assessing the influence of specific chief complaint varia- tion on our outcome parameters. This limitation affects analysis of what happens with a surge over the smaller time intervals, ie, 1 to 2 hours and the downstream effect, while using daily medians (24 hours periods) al- lows for a demonstration of trends.

Another area of concern that may influence the generalizability of our results is the degree to which an institution cares for and boards Psychiatric patients. Psychiatric patients represent an important subset of ED patients nationally that have prolonged EDLOS and occupy a sig- nificant number of ED beds while awaiting inpatient treatment. At our institution, we do have delays in getting psychiatric patients transferred to inpatient treatment areas and they represent 9% of our ED patient population, with a 3.8% admission rate. As the process for psychiatric evaluations may be significantly prolonged for both admitted and discharged paitents, it is unclear how our results were impacted by this characteristics of our overall census.

Another limitation of our study is the selection of variable inputs into our regression model and the potential to not account for other signifi- cant factors that may have influenced our results. We found that there was highly correlation for such metrics as door to MD and daily census. One of the variables not highly correlated was room to MD and while this is a segment in the sequence of ED Patient throughput it is a very small segment and the interval varies with Acuity of patients in the available ED rooms at a point in time and frequently does not track with door to doctor and/or ED daily census which track with difficulties in ED flow.

Our results generate ideas for several areas of future investigation.

Similar studies should be conducted to validate our results in other set- tings. We believe it is important for hospital administrators and ED medical directors alike to better understand which inputs for ED flow are outside the realm of ED physician influence. Going forward, re- searchers should also consider interventional studies to improve the flow of admitted patients while assessing its impact on throughput for those that are discharged home from the ED.

Conclusions

Prolonged ED stays for patients admitted to the hospital are associat- ed with prolonged throughput times for patients discharged home from the ED. Core measures of ED service typically emphasize the length of stay for discharged patients. Our results suggest the need to focus equal- ly, if not primarily on improving the door to hospital bed time for pa- tients admitted to the hospital through the ED.

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