Abstract— Decreases in available hospital Emergency Departments (EDs) combined with increases in the number of patients visiting the ED are creating significant challenges for hospital EDs nationwide. Increased attention is being placed on patient turnaround time (TAT), as longer patient stays result in higher costs for the ED and lower patient satisfaction, especially when compounded by increasing patient volume. Hospitals around the world are applying a systems engineering approach to resolve these problems. This paper describes an effort to reduce TAT in the ED of a medium-sized hospital. Along with literature review, a number of tools were used to analyze the ED, including a survey questionnaire, interviews, direct observation, and analysis of archival process data. Examples of problem areas identified include handoffs and communication between the ED and other departments and patient flow through the ED. Based on analysis findings, recommendations for improvement have been identified which include a point-of-care Laboratory testing station, a saturation level assessment system, and an in-room Kanban supply replenishment system,. Implementation of these changes is expected to significantly reduce patient TAT and patient costs in the ED.

I. INTRODUCTION n the U.S., 62% of Emergency Departments (EDs) are reported as being at or over operating capacity [1]. Operating over capacity puts strain on patient flow

through the ED and often results in longer-than-desired patient turnaround times (TAT). This issue of high TAT in EDs can be seen in many hospitals throughout the country. With a 23% increase in ED use, coupled with a simultaneous 15% decrease in the number of hospital EDs, it is clear why hospitals are experiencing higher TATs [1]. To see an ED provider in the U.S., patients must wait an average of 46.5 minutes, resulting in a mean length of stay of 3.2 hours [2].

Manuscript received April 5, 2010. This work was supported in part by the Virginia Tech Grado Department of Industrial and Systems Engineering and Lewis Gale Medical Center (HCA).

Mikayla Fieri is a student in the Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061 USA (email: mfieri@vt.edu).

Nathan F. Ranney is a student in the Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061 USA (email: nranney@vt.edu).

Eric B. Schroeder is a student in the Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA 24061 USA (email: schroede@vt.edu.com).

Eileen M. Van Aken is the Associate Head and Associate Professor in the Grado Department of Industrial and Systems Engineering, Blacksburg, VA 24061 USA (email: evanaken@vt.edu).

Amanda H. Stone is the Assistant Director of Emergency Services at Lewis Gale Hospital, Salem, VA 24153 USA (email: amanda.stone@hcahealthcare.com)

While healthcare in the U.S. is highest in cost, it ranks 35th in customer service [3]. In the medical field, long turnaround times can have detrimental effects on lives as well as unnecessary expenses to the hospital. In one case, ED hospital staff were charged and convicted of homicide when a patient waiting two hours for treatment in the waiting area died [4]. News of similar cases has caused several hospitals to seriously consider whether to close waiting rooms as a necessary – albeit incomplete – solution [5].

One area of particular concern in reducing overall TAT is the TAT for Laboratory testing. Holland et al. estimate that between 60-70% of the objective information on the patient’s chart relates to Laboratory tests. Accordingly, it is logical to assume that delays in reporting Laboratory results would also cause delays in patient diagnosis and treatment [6]. Long TAT affects patients and EDs in other ways as well. At one hospital, the number of people who left the ED prior to seeing a physician had increased to 12% of the total patient volume [7]. Furthermore, long TAT at this hospital resulted in fully-saturated inpatient beds and a 6% drop in yearly patient volume [7]. The recent passage of healthcare reform legislation no doubt will have an impact on ED volumes. Although the specific impact is not yet known, process improvement, waste reduction, and decreasing patient length-of-stay (LOS) will all continue to be issues at the forefront of ED operations. For these reasons, the use of lean production tools in healthcare applications is particularly relevant [7].

The targeted hospital in this study is medium-sized, with 521 beds total and a 26-bed ED. The ED sees 3,300-4,500 patients per month, with an average of 100-120 patients per day. Demographically, the majority of daytime patients are elderly (+60 years old), while nighttime patients are mainly 18-24 due to the proximity of several colleges.

II. BACKGROUND Significant interest in hospital operations improvement is

evident in the vast body of research and improvements that have been suggested and implemented. A small part of this work is summarized in this section. The Robert Wood Johnson Foundation is a private healthcare organization that has funded an initiative called Urgent Matters in an effort to reduce the turnaround time of patients in the ED. Ten hospitals participated in the initiative, and as a result, have seen decreased ED wait times as well as improved patient flow. By manually tracking patients and breaking down the steps of patient flow, the

Analysis and Improvement of Patient Turnaround Time in an Emergency Department

Mikayla Fieri, Nathan F. Ranney, Eric B. Schroeder, Eileen M. Van Aken, and Amanda H. Stone

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Proceedings of the 2010 IEEESystems and Information Engineering Design SymposiumUniversity of Virginia, Charlottesville, VA, USA, April 23, 2010

FPM2Risk.2

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hospitals helped ensure that patients are treated and discharged in less time [1]. In another case, the use of lean production tools decreased patient LOS by 60-66% while increasing nurse and physician productivity by 75-100%. Several tools were utilized to establish standard work, 5S, inventory management (pull systems), rapid changeover, root cause analysis, and continuous flow cells [7]. Using the 5S approach, one hospital was able to reduce nurse travel time by 41% [8]. The use of lean tools such as process mapping and value stream mapping is particularly applicable to the healthcare industry [4]. In the utilization of this type of mapping, Martin suggests developing Metrics-Based Process Maps (MBPM) as a visual process analysis tool to assist in identifying disconnects, wastes, and delays in a process at a micro-level. This includes documenting and numbering all activities, identifying a Timeline Critical Path – the longest lead time required for a process unless a “dead-end” step is reached – determining summary performance metrics, and identifying value-adding and necessary non-value-adding activities [9].

One facility experienced success by establishing a team of nurses whose sole focus was specifically on discharges, admissions, and transfers into and out of the hospital [10]. In an overview of best practices, Carroll recommends implementing immediate bedding for patients, the use of a 5-level triage system for gauging acuity, establishing fast-track areas for low-acuity patients, assigning a “navigator” nurse to expedite patient flow, 1+1 admission policies (bridge orders), and the utilization of ED saturation levels [11]. Saturation levels in particular can be used to implement protocols and actions such as immediate bed control meetings, ambulance diversions, and having additional staff report to the ED from other hospital departments until the saturation level is reduced [12].

III. METHODOLOGY In order to analyze the ED in the hospital targeted in this

work and to develop design solutions to reduce TAT, various data collection and analysis tools were utilized, in addition to literature review. These included direct process observation, shadowing (nurses, physicians, and patients), spaghetti diagrams, process flowcharting, Pareto analysis, survey questionnaire, and benchmarking. EDs should be (re)designed with an understanding of all the flows (patients, visitors, physicians, material, etc.), as well as an accurate understanding of current performance against quantifiable metrics [13]. This describes how each of these was used in the TAT reduction effort.

A. Survey Questionnaire A survey questionnaire was developed and administered

to all ED staff. The purpose of the survey was to assess ED processes from the perspective of staff members in order to identify areas for improvement with regard to patient TAT. The survey was distributed to the approximately 100 ED employees, and 21 were returned, for a 21% response rate. The survey included 31 quantitative questions (using a Likert-type scale) and six open-ended questions designed to

elicit qualitative data in the respondents’ own words. This allowed the respondent to provide information that may not have been addressed by questions in other sections of the survey. Quantitative questions were rated using the following 6-point scale: 1=strongly disagree, 2=disagree, 3=somewhat disagree, 4=somewhat agree, 5=agree, and 6=strongly agree. Each question addressed one of the following five categories: Delays, Staffing, Information Validity, Handoffs, or Work Environment – thus, each of these categories were assessed using multiple questions, which enhances the validity and reliability of the survey measures.

B. Spaghetti Diagramming The target ED has a central area, “The Desk,” where most

of the administrative work is performed, and the patient rooms surround the area on all four sides. At “The Desk” is the main supply room, computers, printers, nurse stations, physician desks, etc. In order to create spaghetti diagrams of this area, the team obtained a floor layout from a hospital administrator.

Spaghetti diagrams of nurse and physician paths during patient care were created to assess travel patterns. By following a nurse’s typical path, the team was able to observe what stations the nurse typically traveled between, where higher traffic areas might be, and how often and frequently he/she had to travel out of his/her way to accomplish certain tasks. In order to create spaghetti diagrams to assess travel patterns of nurses, the study team observed 7 nurses on 2 different visits to the ED.

C. Pareto Analysis The excess time a patient stays in the ED past the

physician’s disposition is referred to as “holding hours” in this hospital. Once the holding hour time reaches 60 minutes, the nurse in charge of the patient must enter a reason for the delay. The team analyzed these data for all patients from January-August 2009 and performed a Pareto analysis to determine the most frequent delay reasons. Combining these data with cost data (i.e., the cost per patient per hold hour) enabled the identification of which delays cost the most. A similar analysis was performed according to the physician’s disposition (whether the patient is discharged, admitted to the hospital, etc.). Thus, dispositions representing the highest cost for the ED were identified.

D. Benchmarking The study team visited three other hospitals within the

same corporation for the purpose of benchmarking ED operations, processes, and best practices. Following the identification of specific areas for improvement in the targeted hospital, other hospitals within the same division in the corporation were identified that were known to have experienced some success or improvement in these areas. A benchmarking interview guide was developed that addressed not only the specific areas in question (e.g., in-room supply replenishment systems), but also overall ED operations and practices.

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At each of the three benchmarking locations, the study team was given a tour of the ED by a hospital ED administrator, directly observed processes, and interviewed several ED staff members. The interview guide was used to elicit specific information throughout the visit, however, additional areas were probed based on what was learned in the tour and interviews. Each member of the study team assumed an area of focus within the ED. For example, one team member ensured that all questions regarding the admittance process were addressed, another focused exclusively on supply replenishment, etc.

There was a fair degree of variability between the three hospitals benchmarked regarding size and demographics The EDs ranged between 18- and 45-bed facilities, seeing anywhere from 80 to more than 200 patients per day. One hospital served a more rural area, while the remaining two hospitals served more urban areas. Observing ED procedures and practices applied across this range of ED size and patient base was helpful in identifying specific practices that appeared to be successful across all three benchmarked hospitals, representing potential “best practices.”

E. Observations, Shadowing, Interviews, and Literature Review

Additional tools were used for analyzing current ED operations and identifying potential design solutions in the targeted hospital. Additional data collection and analysis tools were direct observations of the ED, shadowing, and informal interviews of ED staff. Systematic note-taking and digital photographs were used to document process details. Interviews were conducted with staff in other departments involved in handoffs with the ED, including the directors of the Laboratory, Radiology and Hospitalist departments. More than 90% of ED patients require interaction with these departments, thus, observation and clear understanding of the ED and its internal supplier departments was essential. To increase familiarity with hospital systems from a systems engineering perspective, the study team also conducted a literature review on various process improvement projects, particularly those with healthcare applications. This literature review, summarized earlier in the paper, was used to provide context information for the organization targeted (i.e., a hospital ED) and to identify potential best practices as documented in the literature that represented an additional source of input for design solutions.

IV. RESULTS

A. Survey Questionnaire The ED survey provided insight into key strengths and

problem areas from the perspective of staff. Analysis of the quantitative data (i.e., responses to questions with the six-point Likert-type scale) revealed that the most negative perceptions were related to Staffing which was assessed using 3 questions (mean for the category = 2.77, standard deviation 0.85). Recent changes to improve Staffing have taken place since the survey was conducted, including expanding the current nursing staff by 15%. The most

variability in responses was observed in the Handoffs category, assessed using 3 questions (mean = 3.50, standard deviation = 0.92). Figure 1 presents boxplots of survey responses, portraying both the level and variability of responses. Open-ended questions produced a total of 116 unique “comment segments” which were subjected to content analysis such that segments from each question were grouped into themes. Staffing was the most frequently-reported problem area (with 9 of 30 comment segments for the question relating to “understaffing”) and was also the most commonly-suggested change (15 of 30 comment segments). In general, Handoffs were considered a problem and specifically, handoffs with the Lab, Radiology and the Hospitalist were equally identified as problematic.

Fig. 1. Boxplots of survey categories. From left to right: Delays, Staffing,

Information validity, Handoffs, and Work Environment.

B. Spaghetti Diagramming Spaghetti diagrams provided insight into a potential time

loss due to frequent in-room supply replenishment from the central ED supply room. While the majority of nurse paths were between the nurses’ station, patient room and pharmaceutical room, several trips per hour were recorded between the patient room and the supply room. These trips typically lasted between 1-3 minutes.

C. Pareto Analysis From data provided by the ED monitoring system

(Meditech), Pareto analysis of the most common delay causes were determined. Over an eight-month period from January-August 2009, the top delays were determined and are depicted in Figure 2. Figure 3 presents the relative frequency of the top five delay occurrences (as identified in Figure 2) over time. Figure 4 shows the relative cost for each delay based on the holding hour contribution of each.

Fig. 2. Pareto chart of most frequent delay codes entered by nurses for

holding hour delays. Values are monthly averages.

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Fig. 3. Delay frequency for the top five delay reasons, January-August

2009.

Fig. 4. Relative contributions to costs for the top five delay reasons.

D. Benchmarking Best practices observed during benchmarking included

standardization of processes and supplies, including use-specific carts for medical supplies, in-room supply lists, standing orders for Laboratory and radiology tests, staging of equipment for common procedures and the use of bar codes and scanners to control inventory and ensure it is properly allocated during use with patients. Additionally, a point-of-care (POC) Lab testing station at one of the hospitals allowed for 50% of ED Lab testing requests to be performed at the POC station by trained, full-time Lab technicians. Finally, two of the hospitals observed during benchmarking made significant use of ED saturation levels announced at pre-defined time intervals during the day. Saturation level algorithms were based on the number of licensed beds, hall beds, occupied beds, lobby capacity, number of patients by acuity level, number of patients en route via ambulance, patients currently in holding, patients requiring one-on-one care, average wait time of all patients in waiting area, and the number of unfilled nurse shifts. Data on these factors are used to determine a saturation level, using a 1-3 integer scale, where 1 indicates low/routine capacity and 3 indicates that the ED is over maximum capacity and needs assistance from other hospital departments. Specific protocols were in place for each saturation level prescribing actions to be taken by ED and

non-ED personnel to alleviate the excessive strain that saturation levels 2 or 3 represent. Part of the success of the saturation level system in the benchmarked hospitals was due to consistency of use. Saturation levels were announced throughout the hospital on a scheduled basis to accustom staff members to utilizing and attending to the system, regardless of whether a change in status occurred. To be effective, this type of consistent use and communication is essential.

E. Interviews Interviews with several internal supplier departments

having frequent handoffs with the ED – specifically the directors of the Lab, Radiology and Hospitalist departments – also provided insight into problem areas. Estimates of up to 20% of patient Lab samples were routinely discovered to be unusable due to poor collection methods or excessive sample sitting times. While some POC testing had been implemented, full-scale usage by dedicated personnel was not in effect. Moreover, Radiology reported patients not being ready 80% of the time when technicians would come to take them for tests. Reasons for unpreparedness included the patient being inappropriately dressed or not having used the bathroom, waiting for Lab samples to be collected, and messes caused by accidents. Some evidence also existed that waiting could occur due to no clear priority structure between Lab and Radiology tests that were ordered simultaneously, although this was difficult to quantify.

F. Literature Review Review of available literature revealed additional insights

into practices aimed to reduce patient TAT. Holland et al. assert that improving Lab performance in terms of both quality and TAT has a direct influence in decreasing ED LOS [6]. Morgan suggests setting up a tabletop Lab in fast-track areas to run high-volume tests such as urinalysis, pregnancy, flu screens, etc. Furthermore, POC cardiac markers in many cases deliver results within 15 minutes [14]. Carroll asserts that the use of immediate bedding, five-level triage systems, the use of fast-track areas, navigator nurses, 1+1 admission policies and the use of saturation levels can further reduce ED overcrowding by reducing patient LOS [11].

V. DESIGN SOLUTIONS The design solutions described below represent different

ways of addressing the problem of high TAT. These alternatives are not mutually exclusive but rather, represent potential multi-faceted approaches to reducing TAT.

A. Point-of-Care Station The first design solution is a countertop POC Laboratory in the ED. This recommended POC station will contain devices capable of performing simple, high-volume tests that are frequently ordered in the ED. The devices used at the POC station will be able to perform a multitude of tests such as complete blood count (CBC), urinalysis, basic and complete metabolic count (BMP/CMP), and cardiac test.

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Without a POC station in the ED, nurses must send samples to the Lab in another part of the hospital where in many cases, Lab technicians wait for a batch of samples before running a test. This process can take between 30 minutes and an hour to complete for tests that may take as little as 2-3 minutes. Additionally, the general hospital Lab must process orders from every other department in the hospital as well as the ED. Analysis of Lab TAT outliers by Holland et al. showed a correlation between patient LOS and Lab TAT. By reducing the percentage of outliers in Lab TAT, overall patient LOS has been reported in the literature to decrease [6]. Thus, reducing the percentage of outliers in Lab TAT should decrease the overall Lab TAT; by extension, a POC station in the ED will reduce the travel and wait time required to get Lab results for a patient, thereby also reducing patient LOS. It is expected that about 50% of the tests needed during ED patient care would be able to be performed at this ED POC station. Due to the fact that this station will have high utilization, the POC station should have a designated Lab technician assigned to it full-time. This technician will be certified in handling the devices at the station and collecting samples and will be responsible for running the tests ordered. This reassignment may save the hospital the costs in hiring and training a new staff member to run the station.

The POC station will be the single, centralized location for the Lab testing devices in the ED. There may be exceptions to this depending on the specialties of the ED, such as the need to run cardiac tests in a chest pain center (CPC), if applicable. In this case, the device in the CPC would be used solely for the testing needs of patients in the CPC. By placing the devices at a staffed, designated station, nurses will be able to drop off samples and continue caring for their patients while the Lab technician runs the tests. A single location will also help reduce the chances of human error that could occur, which might include patient samples being misplaced, samples being tested and forgotten, and patient samples being mixed. With a designated Lab technician running a single POC station, all the samples will be in a single location and under observation at all times to prevent mix-ups. In summary, the POC station is expected to reduce patient TAT by delivering Lab results much more quickly for about half of the Lab tests needed in the ED.

B. Saturation Level System A second design solution is a saturation level system. This

system was observed during benchmarking and is designed to provide assistance to the ED when patient volumes are extremely high. This system will be based on an algorithm that determines an integer saturation level value between 1 and 3. Saturation level 1 would signify the ED is adequately staffed and able to handle the current patient load. Saturation level 2 would indicate an increase in the number of patients who are not being treated due to the ED’s current capacity such as patients in the lobby or waiting area, or patients en route in an ambulance. At this point, a possible protocol may include implementing bridge orders more frequently. Bridge

orders allow a patient that will be admitted to be transferred to the appropriate floor in the hospital prior to being seen by the hospitalist or admitting physician. The hospitalist will then be able to see the patient on the hospital floor rather than having to come to the ED before admitting the patient. This will help reduce patient TAT due to the fact that waiting for a hospitalist to see a patient in the ED has been cited as the main reason for patient TAT delays in the ED (refer to Fig. 1 and Figure 4). At saturation level 3, possible protocols may include sending any available nurses or technicians in the hospital to the ED to help move patients through the ED. Bridge orders may still be used more frequently than at level 1; additionally, the ED may be permitted to transport patients to appropriate floors with less notification time given. The saturation level will be announced to the entire hospital through the overhead paging system in place. In order to ensure that this system is adopted and used properly, the saturation level will be announced over consistent time intervals, regardless of whether or not the level has changed. The intervals will be spaced such that sudden changes in the ED saturation levels can be addressed appropriately. This is to ensure that staff members are aware of the system and to help prepare staff for the event that the saturation level does become elevated.

The saturation level system is designed to help reduce patient TAT by facilitating the process by which patients are discharged or transferred to another part of the hospital. Personnel support from other departments in the hospital reduce the patient TAT by providing assistance in moving patients to other departments in the hospital or by aiding nurses in tasks when they are busy with other patients. Without this assistance, a patient ready to be transferred out may remain in the ED taking up a vital bed because there is no one available for transportation to another department. Allowing the ED to execute bridge orders more often allows for stable patients to be transported out of the ED prior to being seen by a hospitalist. Without the activation of bridge orders, patients may wait for an extended period of time for a hospitalist to come to the ED and approve a patient’s transfer. Whenever a patient experiences a delay due to a hospitalist or admitting physician, the average wait time is 53.45 minutes (refer to Figure 4). These delays accentuate the significance bridge orders can have in reducing patient TAT because a bridge order would negate any wait time for a patient by allowing the hospitalist to care for the patient on the hall they are designated to.

C. Kanban Supply Replenishment System A third design solution is a Kanban system for in-room

replenishment of supplies. As described in the Results section, nurses were often observed retrieving supplies from the central storage area of the ED due to in-room item stock-outs. These stock-outs occurred because there was no system in place to monitor item quantities in patient rooms. Nurses were asked to restock each room before their shift, but this routine was not enforced. A Kanban card system is suggested to be developed as a visual cue that supplies in a

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given patient room are running low. Cards will be designed for the supplies most commonly-used, that most commonly stock out, and/or that are essential for immediate emergency patient care. When a nurse or technician takes an item from a drawer and notices the item quantity is below a certain re-stocking point, they will take the Kanban card for that item, and place it in an envelope or slot just outside the room. The card will be easily visible and the technician or nurse responsible for resupplying items in patient rooms will take the card, retrieve the appropriate number of the item on the card, and restock the patient room. The nurse or technician responsible for restocking patient rooms will be determined on a shift-by-shift basis and assigned the role by the presiding charge nurse or team leader. It is vital to the process that the staff member responsible for restocking rooms performs a routine sweep of the ED checking for any cards on consistent intervals. In general, supplies to be restocked using a Kanban system, as well as the quantities, depend on the hospital in which the system is implemented (especially size of the hospital), the items most often used, items most often stocked-out, and items deemed necessary to keep adequately stocked. An alternative approach to the physical Kanban card system is an electronic Kanban system. This process would serve the same purpose as the card system with the exception that the inventory quantities will be monitored electronically. Each patient room will be equipped with a basic barcode scanner, and selected items in the room would have unique barcodes. The barcodes could be labeled and placed on the outside of the drawer within which the item resides, or all the barcodes could be organized and listed on a single sheet kept with the supplies. Every time a nurse or technician uses an item, he or she will scan the barcode associated with the item. The implementation of a barcode system is expected to be relatively easy because many hospitals already have a barcode system and scanners in patient rooms for assigning medications to patients. The barcode system will be tracked by the ED computer system such that, when an item falls below a re-stocking point, a technician who monitors the status of the ED via a central control area is notified of the item shortage. If a technician is not already in place in the ED to monitor the overall status of the ED, one may be assigned to monitor the system for item shortage notifications. This technician then notifies the staff member assigned to room replenishment who then proceeds to follow the same replenishment steps as in the physical Kanban card system. Once the patient room has been restocked, the staff member must notify the technician so that the quantity listed in the computer system may be updated.

Both Kanban systems aim to reduce patient TAT by ensuring that ED staff members have the necessary supplies available in patient rooms. This improves TAT by reducing the time a nurse or technician must spend traveling to and from the central storage room and searching for necessary supplies. In addition to reducing patient TAT, the electronic Kanban system also aims to reduce lost revenue through supplies that are not correctly assigned to the patients who use them. By scanning the barcode of the item used, the

nurse or technician can automatically allocate the cost of the item to the correct patient, reducing operator error.

VI. CONCLUSIONS This paper has presented design solutions to reduce

patient TAT in the ED for the hospital targeted in this work: an in-house POC system, ED saturation level system, and a physical or electronic supply replenishment Kanban system. These solutions were designed to address different aspects of ED TAT resulting from the most frequent delays observed in the targeted hospital. These design solutions are intended to enable healthcare specialists to treat more patients in a streamlined manner.

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