The Impact of Input and Output Factors on Emergency Department Throughput Phillip V. Asaro, MD, Lawrence M. Lewis, MD, Stuart B. Boxerman, DSc
Abstract Objectives: To quantify the impact of input and output factors on emergency department (ED) process outcomes while controlling for patient-level variables. Methods: Using patient- and system-level data from multiple sources, multivariate linear regression models were constructed with length of stay (LOS), wait time, treatment time, and boarding time as dependent variables. The products of the 20th to 80th percentile ranges of the input and output factor variables and their regression coefficients demonstrate the actual impact (in minutes) of each of these factors on throughput outcomes. Results: An increase from the 20th to the 80th percentile in ED arrivals resulted in increases of 42 minutes in wait time, 49 minutes in LOS (admitted patients), and 24 minutes in ED boarding time (admitted patients). For admit percentage (20th to 80th percentile), the increases were 12 minutes in wait time, 15 minutes in LOS, and 1 minute in boarding time. For inpatient bed utilization as of 7 AM (20th to 80th percentile), the increases were 4 minutes in wait time, 19 minutes in LOS, and 16 minutes in boarding time. For admitted patients boarded in the ED as of 7 AM (20th to 80th percentile), the increases were 35 minutes in wait time, 94 minutes in LOS, and 75 minutes in boarding time. Conclusions: Achieving significant improvement in ED throughput is unlikely without determining the most important factors on process outcomes and taking measures to address variations in ED input and bottlenecks in the ED output stream. ACADEMIC EMERGENCY MEDICINE 2007; 14:235–242 ª 2007 by the Society for Academic Emergency Medicine Keywords: crowding, hospital emergency services, bed occupancy, patient care, regression analysis
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mergency department (ED) overcrowding, especially in urban academic EDs, is a nationally recognized problem that continues to threaten the health care safety net.1–3 Reasons for overcrowding are complex and many,4,5 but increasing volume and lack of inpatient beds are clearly important factors.6–8 It is also clear that the causes of overcrowding interact and must be addressed within an overall conceptual framework if progress is to be made, giving rise to conceptual models such as the input-throughput-output model,9 the Fields chaos model,10 and the ED Cardiac Analogy Model.11 Patient care process outcomes (e.g., wait time to be seen, ED length of stay [LOS]) are affected by individual patient and disease characteristics as well as system-level factors. In a study of surgical critical care patients, ED
From the Emergency Medicine Division (PVA, LML), Health Administration Program (SBB), Washington University School of Medicine, St. Louis, MO. Received March 2, 2006; revisions received August 14, 2006, and September 26, 2006; accepted October 30, 2006. Contact for correspondence and reprints: Phillip V. Asaro, MD; e-mail:
[email protected].
ª 2007 by the Society for Academic Emergency Medicine doi: 10.1197/j.aem.2006.10.104
LOS was most significantly affected by diagnostic evaluation such as computed tomographic scans and special procedures performed in the ED.12 System-level factors relate to the general environment of care, including patient care processes within the ED and the hospital as a whole (e.g., ED staffing, ED arrivals, and hospital census). The throughput and output components of the input-throughput-output model relate to these ED and hospital environmental and process issues. Efforts at consensus on a usable definition of overcrowding have been under way for several years.13 Much of this work has revolved around establishing meaningful and usable measures of busyness and crowding. Progress has been made, with several published proposals for measures and indices.14–16 Recently, the National ED Overcrowding Study demonstrated a simple measure of ED overcrowding across multiple institutions.2,17 A better understanding of the relative contribution of various system-level factors to measurable patient care process outcomes may provide further direction to this avenue of research. Our study objective was to quantify the impact of ED input factors and output factors on ED process outcomes while controlling for patient-level variables.
ISSN 1069-6563 PII ISSN 1069-6563583
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METHODS Study Design This was a descriptive retrospective study using existing data from multiple sources. The study was approved by the Human Studies Committee of Washington University with waiver of informed consent. Study Setting and Population The study was performed on data from a large urban, academic medical center ED with more than 78,000 visits per year. Patients seen in either the main area of the ED (excluding the trauma and critical care unit) or in the urgent care (fast track) area of the ED from January 2004 to March 2006 are the study population for this analysis. We collected data from three separate sources: our ED information system (Healthmatics ED; Allscripts, Chicago, IL), hospital inpatient census logs, and ED staffing logs. Visit records with missing data or apparent time stamp and interval calculation errors were excluded. Exclusions for missing data included patients with missing age, gender, acuity assignment, and chief complaint. Time stamp–related exclusions were for missing or negative patient care process intervals, wait time greater than eight hours, treatment time less than 10 minutes, LOS less than 15 minutes, and LOS greater than 20 hours (36 hours for admitted patients boarded in the ED and observation patients). We also excluded patients seen during relatively rare ED information system downtimes, because patient care process times are unreliable in this situation. Study Protocol, Measures, and Data Analysis The time stamps used for calculation of process times came from the ED information system tracking board. Arrival time is generated in the system upon first contact in the triage process. Disposition time is obtained from discharge or admission orders placed by physicians in the tracking system. The end of the ED stay is the time at which the patient was released from the tracking board. The time of transition from waiting to a treatment bed is obtained from bed assignment in the tracking system. We constructed a set of queries that constitute a filtering algorithm to ignore bed assignments that are changed after a brief time, as sometimes happens when a room assignment for a waiting patient is superseded by the urgent need for a room for a patient arriving by ambulance. Despite the filtering algorithm, there are some time stamp and interval calculation errors that our exclusion criteria attempted to filter out. Multivariate linear regression models were constructed with dependent variables of LOS (arrival to end of ED stay), wait time (arrival to treatment bed placement), treatment time (treatment bed placement to admission or discharge order), and boarding time (admission order until transfer to an inpatient bed). SPSS version 14.0 (SPSS Inc., Chicago, IL) was used to construct the linear regression models. We constructed system-level variables to represent ED input factors and inpatient hospital census factors. Aggregation of patient-level data from the ED information system yielded ED arrivals (patient arrivals for the day: midnight to midnight) and admit percentage for the
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same 24-hour period. From hospital census logs (data as of 7 AM), we constructed the variables of inpatient bed utilization (the sum of occupied inpatient beds and reserved inpatient beds) and ED holding patients (patients in the ED awaiting inpatient bed placement). Emergency department staffing logs were used to construct registered nurse (RN) and patient care technician (PCT) staffing variables. These variables, RN percent and PCT percent, represent staffing levels for the 24hour day (6 AM to 6 AM) as a percentage of the median staffing level for the study period. Patient-level independent variables in the regression models include age, gender, triage acuity, chief complaint, and time of day of arrival. Acuity was determined at triage by a trained RN as 1 (highest acuity) to 5 (lowest acuity). During most of the study period, acuity was assessed using the Emergency Severity Index, previously shown to be reliable and valid.18,19 During the first eight months of our study period, acuity was assessed using the Canadian Triage and Acuity Scale, which has also been shown to be reliable.20 Chief complaints were assessed at triage by an RN and grouped into 53 categories for analysis. Time of day of patient arrival was categorized into six four-hour blocks: the first block from midnight to 4 AM, the next from 4 AM to 8 AM, and so on. For urgent care patients, we added variables indicating whether laboratory tests, radiology tests, and specialty consultations were performed during the visit. The conceptual approach to construction of our regression model views independent variables as either patient level or system level. Patient-level variables are generally considered unmanageable and unpredictable. Some system-level variables can be potentially managed as they relate to ED or hospital resources (e.g., RN staffing and scheduled inpatient admissions), others are not manageable but are somewhat predictable (e.g., daily ED volume), and some are neither manageable nor predictable (e.g., admit percentage). Construction of the models was guided by the intent to determine relative contribution to outcomes (dependent variables) by manageable and predictable factors (independent variables) while controlling for unmanageable, unpredictable factors (also independent variables). Linear regression coefficients, as reported throughout this study, represent the change in the dependent variable (in minutes) per unit change in the independent variable. To understand the relative impact of various factors, we have calculated the product of the regression coefficient of each independent variable and its ‘‘common’’ range, defined as the difference between the 20th and 80th percentile values for each variable. This product reflects the variability in the outcome (dependent variable) due to that factor (independent variable). Comparing these products across factors provides an intuitive measure of the impact that changes in each factor have on the outcome measure. Our analysis focuses on the patients seen in our 32-bed main ED or our 12-bed urgent care (fast track) area using separate regression models. These areas have separate waiting queues and separate staffing. The models include variables reflecting staffing of the specific area for that day. Many of the patients seen in the separate six-bed trauma-critical area are very ill, arrive by ambulance, never enter a waiting
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queue, are prioritized in the allocation of resources such as radiology testing, and are often admitted promptly to an intensive care unit or the operating room. The process times for these patients are therefore less subject to many of the factors of primary interest in this study. However, the distribution of ED and hospital resources to these patients is a part of the system we are studying, and this competitive demand for resources is represented in the models through the system variables of ED arrivals and admit percentage, which include all ED patients. A small number of patients were seen primarily in the ED observation unit and were excluded from analysis; this typically occurs on busy days when many of the ED patient rooms are occupied by boarded patients. The Durbin–Watson statistic was computed to determine
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the presence of first-order autocorrelation in each of the linear regression models. RESULTS There were a total of 176,110 patient records from the 27-month period (January 2004 to March 2006), 166,854 (95%) of which had fully usable data by our inclusion criteria. Although excluded patient records were for visits with all of the disposition categories, a disproportionate share were for patients who left without being seen or were transferred to other facilities, situations in which incomplete data capture or unusual patient process is more likely. The mean age of the patients in the population was 43.8 years (SD 18.9 years), with 56.2% being female.
Figure 1. Study flow.
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Table 1 Comparison of Triage Acuity, Disposition, and Process Times for Main ED Visits Versus Urgent Care (Fast Track) Visits Entire ED Main ED Urgent Care Mean age (yr) Acuity (%) 1 2 3 4 5 Disposition (%) Discharge Admit Leave without being seen Other Mean process time (min) Wait time Length of stay Treatment time Boarding time
44
47
36
0.9 28.6 46.4 21.5 2.6
37.9 53.4 7.6 1.1
1.2 37.7 55.5 5.6
66.9 23.5 7.5 2.1
66.3 30.4 1.1 2.2
93.8 3.7 1.3 1.2
81 385 227 249
79 445 268 260
86 265 146
The patient population was 65.1% African American, 31.9% white, 0.6% Hispanic, 0.6% Asian, and 1.8% other or unknown. Of the patient records usable for our analysis (see Figure 1), a total of 136,235 (81.7%) were for patients seen either in the main ED or the urgent care area. Patient characteristics for these two areas are shown in Table 1, along with those of the entire ED for comparison. About 7.5% of patients either left without being seen or were seen and disposition was determined before placement in treatment rooms. All of these patients are reflected in our models through the ED arrival and admit percentage variables. Mean values of each of the system-level factors are provided in Table 2, overall and by day of week. Regression Model Coefficients (Raw Results) The regression coefficients for system factors and time of day of arrival are shown in Tables 3 and 4. Patient-level variable coefficients are available as a Data Supplement (http://www.aemj.org/cgi/content/full/j.aem.2006.10.104/ DC1). Along with the coefficients, we show the significance expressed as p-value and 95% confidence intervals. The regression coefficients represent additional time in minutes attributable to that variable per unit change in that variable. For example, in the main ED
IMPACT OF INPUT AND OUTPUT ON THROUGHPUT
models (Table 3), LOS for admitted patients increased by 1.4 (95% confidence interval = 1.3 to 1.6) minutes per additional ED arrival. The staffing variables PCT percent and RN percent represent the number of additional minutes in the outcome variable attributable to an increase of 1% in staffing time for the day. For example, given the RN percent coefficient of 2.0 for LOS admits, a change from seven to eight RNs on each of two shifts per day (an increase of 14%) would be expected to lower LOS for admitted patients by 28 minutes (2.0 14 = 28). Time of day of arrival is represented as a group of ‘‘dummy variables,’’ using arrival between 4 AM and 8 AM as the reference. For example, for a discharged patient seen in the main ED, an additional 93 minutes in LOS is attributable to arriving between 8 PM and midnight as compared with arriving between 4 AM and 8 AM. The computed Durbin–Watson statistic for all but three of the regression models did not show first-order autocorrelation to be a problem. The statistic did indicate autocorrelation in the models for main ED wait time, urgent care wait time, and urgent care LOS, with Durbin– Watson statistics of 1.41, 0.86, and 1.39, respectively. Summing the Variable Contributions To assist in the interpretation of the results, and to give the reader an idea of how the factors collectively impact the outcomes, we have used the regression model coefficients along with common ranges of the independent variables to illustrate potential resultant overall effects on the outcomes (Table 5). We determined the 20th and 80th percentile values of each independent variable from our data. These values represent the range over the central 60% of day values for each variable, with 20% of days falling above and 20% falling below. This ‘‘common range’’ is then multiplied by the variable coefficients from the relevant model to calculate a potential effect for each variable. These products are then summed to represent the difference between hypothetical overall good-flow and overall bad-flow days. Adjusted R-square for Regression Models Table 6 shows the adjusted R-square of each regression model along with the adjusted R-square of two modifications of that model. One modification includes only system variables and time-of-day variables; the other modification includes system variables. Adjusted Rsquare is a measure of the proportion of variability in the outcome (dependent) variable that is explained by
Table 2 Mean Values for System-level Factors by Day of Week
Inpatient bed utilization Boarded ED admits ED arrivals Admit percent RN percent main ED* PCT percent main ED* RN percent urgent care* PCT percent urgent care*
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
All Days
756 2.7 209 24.1 101 95 102 91
833 3.5 232 24.4 97 93 98 135
902 11.2 225 23.9 101 112 100 131
930 11.4 216 23.4 101 115 100 157
928 11.0 214 24.1 101 106 100 149
912 10.8 213 25.6 95 103 97 144
815 5.9 205 24.2 103 100 102 109
868 8.1 216 24.3 100 104 100 131
PCT = patient care technician; RN = registered nurse. * Staffing level for each day as a percentage of the median value for the study period.
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Table 3 Impact of System-level Factors* and Arrival Timey on Process Times for Admitted and Discharged Patients Seen in the Main ED LOS Admits (n = 26,672) Coefficient (95% CI) Arrival 12 AM to 4
9.7 (4.9, 24.4) 4 AM to 8 AM Reference 8 AM to 12 PM 3.7 (16.1, 8.7) 12 PM to 4 PM 7.6 (4.5, 19.8) 4 PM to 8 PM 4.7 (7.7, 17.2) 8 PM to 12 AM 9.5 (3.7, 22.6) ED arrivals 1.44 (1.28, 1.60) Admit percent 2.68 (1.72, 3.65) Inpatient bed 0.14 utilization (0.09, 0.19) Boarded ED 8.52 admits (7.90, 9.15) PCT percent 0.08 (0.06, 0.22) RN percent 1.98 (2.25, 1.70) AM
p-value 0.193
0.559 0.219 0.455 0.157
