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Grow the Pie: Interdepartmental Cooperation as a Method for Achieving Operational Efficiency in an Emergency Department
The Journal of Emergency Medicine, Vol. -, No. -, pp. 1–9, 2018 Ó 2018 Elsevier Inc. All rights reserved. 0736-4679/$ - see front matter
https://doi.org/10.1016/j.jemermed.2018.04.054
Administration of Emergency Medicine
GROW THE PIE: INTERDEPARTMENTAL COOPERATION AS A METHOD FOR ACHIEVING OPERATIONAL EFFICIENCY IN AN EMERGENCY DEPARTMENT Jesse A. Guittard, MD, MBA,* Gabe Wardi, MD, MPH,* Edward M. Castillo, MPH, PHD,* Blake J. Stock, MBA,† Shannon Heuberger, BS,‡ and Christian A. Tomaszewski, MD, MBA* *Department of Emergency Medicine, UC San Diego Health System, San Diego, California, †Perioperative and Imaging Services, UC San Diego Health System, San Diego, California, and ‡Budgeting and Financial Forecasting, UC San Diego Health System, San Diego, California Reprint Address: Jesse A. Guittard, MD, MBA, Department of Emergency Medicine, UC San Diego Health System, 200 W. Arbor Drive #8676, San Diego, CA 92103
, Abstract—Background: Despite sufficient literature analyzing macroscopic and microscopic methods of addressing emergency department (ED) operations, there is a paucity of studies that analyze methods between these extremes. Objective: We conducted a quasi-experimental study incorporating a pre/post-intervention comparison to determine whether interdepartmental cooperation is effective at improving ED operations by combining microscopic and macroscopic concepts. Methods: We performed an analysis of operational and financial data from a cooperative investment in imaging transport personnel between the emergency and radiology departments. Our primary outcome, order to table time (OTT), measured imaging times by modality (computed tomography [CT], ultrasound [US], magnetic resonance imaging [MRI]). These were compared for statistically significant change before and after the intervention. Our secondary outcome, gross profit, was calculated using the revenue generated from gained outpatient studies minus the associated direct personnel costs. Results: Transporters improved OTTs by decreasing median imaging times from 132 min to 116 min (p < 0.0005). Efficiency improved for CT scans with median time decreasing from 142 min to 114 min (p < 0.0005). Transport hires had adverse effects on US, with an increase in median OTT from 91 min to 99 min (p < 0.018). MRI experienced a similar trend in OTT, as median times worsened from 215 min to 235 min (p < 0.225). The investment in transporters generated a gross profit of $1.03 million for the radiology department over 9 months. Conclusions: Interdepartmental cooperation is a broadly applicable
macroscopic method that is effective at achieving microscopic, site-specific gains in ED efficiency. Transporters provided operational gains for the ED and financial gains for the radiology department. Ó 2018 Elsevier Inc. All rights reserved. , Keywords—radiology; imaging; operations; management
INTRODUCTION The problem of inefficiency in emergency departments (EDs) has been widely recognized with related downsides in care, outcomes, and cost (1–6). Taking broad strokes, there are two study types in the literature that address ED operational efficiency: macroscopic and microscopic. Macroscopic papers apply broad concepts of operations management in order to provide a framework for addressing change (7–10). While very useful in providing a general framework for analysis that accounts for the high degree of diversity with respect to patient populations and resources, these frameworks may be difficult to apply in practice, as they leave the work of problem identification and change implementation to the practitioner. Conversely, microscopic papers detail specific changes that departments have made in order to address inefficiency and overcrowding (11–15). These
RECEIVED: 10 September 2017; FINAL SUBMISSION RECEIVED: 9 March 2018; ACCEPTED: 20 April 2018 1
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papers are powerful in addressing common themes, such as frequent utilizers, triage practices, laboratory turn-around times, information technology, and other operational systems. However, while helpful in identifying areas of change, organizational specifics of the reporting institution may prohibit application to the diverse range of ED practices. Thus, after reviewing the literature, we find there is a large gap with regard to the methods that address common operational themes in the ED using broadly applicable concepts. Given this background, we sought to determine whether interdepartmental cooperation could be a broadly applicable method that is effective at achieving site-specific gains in ED efficiency. To achieve this, we performed a quasi-experimental study incorporating a pre/post-intervention comparison analysis of operational and financial data from a cooperative investment in imaging transport personnel between the emergency and radiology departments. Our hypothesis was that this cooperative endeavor would decrease the time to obtain advanced imaging in the ED, while also increasing profits for the radiology department. METHODS Study Design and Patient Population This study attempts to analyze whether interdepartmental cooperation is effective at increasing operational efficiency in EDs. We performed a quasi-experimental before-and-after analysis of operational and financial data resulting from a joint emergency and radiology intervention in patient flow within ED imaging between November 2012 and July 2014. This analysis was certified as exempt from Institutional Review Board review and was conducted at UC San Diego Health System, an academic urban tertiary care hospital with 42,360 and 43,872 ED visits in 2013 and 2014, respectively, and an admission rate of 21%. Our radiology department serves both inpatient and outpatients with 190,129 total encounters (48,165 ED encounters) in 2013 and 206,006 encounters (54,351 ED encounters) in 2014. Project Description During need-finding surveys, it was estimated that radiology technicians, who operated ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI), spent roughly 20% of their time transporting ED patients to and from the sites, which resulted in marked machine idle time. In addition to slowing the turnover between patients, which hindered ED patient flow, this idle time also resulted in foregone radiology profits, which primarily took the form of foregone outpatient studies. As separate and independent from emergency imaging, these
outpatient studies are billed separately and constitute an independent revenue stream for the radiology department. Of note, although radiographs constitute a major ED imaging modality, these were excluded from the intervention due to ED operational specifics. In our ED, radiographs are obtained within the ED physical plant using portable machines taken to patient rooms or in an on-site suite. Patients do not need to be transported out of the department to obtain these studies, as is the case with US, CT, and MRI. Having identified a mutually beneficial area for improvement, the ED partnered with the radiology department and received approval from the Executive Office to hire two full-time imaging transporters to shuttle patients between the ED and imaging sites: CT, US, and MRI. In theory, this move would decrease equipment downtime by allowing the technician to remain in the imaging suite as transporters moved patients. During the 3 months of implementation time, from August 2013 through October 2013, brief biweekly meetings were held between the two departments in order to smooth processes and address any issues. Transporters were scheduled from 8:00 AM to 4:30 PM and 3:00 PM to 11:30 PM, giving a total coverage of 15.5 h/d, Monday through Friday, and this structure was maintained for the duration of the intervention. Definitions and Operations To determine the effect of the transport hires on ED operations, we evaluated both primary and secondary outcomes. Order to table time (OTT), which measured the time from study order in Epic’s electronic medical record to the time the patient arrived in the imaging suite, was directly influenced by transporter hires and was the primary outcome measured. OTTs were collected in total and aggregated by study modality (CT vs. US vs. MRI) to account for differences in demand, throughput, and capacity between the imaging lines of operation. OTTs were then tracked on a monthly basis for the 9-month pre- and post-implementation periods. In order to evaluate the overall effect on ED operations, we calculated length of stay (LOS) as a secondary outcome. LOS was defined as period of time from patient ‘‘check in’’ in the waiting room to disposition order (admission or discharge), and was aggregated between admitted, discharged, and total patients. Of note, in order to remove the influence of special circumstances from biweekly oversight and operational adjustments, the 3-month implementation period was omitted from comparative analysis. Finance In order to analyze the wider effect of transportation hires on interdepartmental and executive incentives, we chose
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to analyze investment profit as an additional secondary outcome. We conducted a simple financial analysis using revenue and cost to determine profitability. Cost was calculated using the associated total cost of the transport hires. Once associated benefits were added, estimated by the budget office to be 39% of salary, the total monthly cost for two full-time transporters was $7654.40 in fiscal year (FY)14 and $9222.00 in FY15. To calculate revenue, we used historical data to estimate volume and reimbursement values for outpatient imaging studies. Baseline volumes per modality were derived using the average monthly volumes of outpatient studies for the prior year, with CT comprising 658 studies/month, US 627 studies/month, and MRI 396 studies/month. This yearlong timeframe was chosen because an additional CT scanner had been added in early 2012, and it was necessary to ensure baseline financial estimates and pre-implementation conditions regarding the number of imaging devices remained comparable with the postimplementation period. After project implementation, monthly outpatient study volumes were compared to baseline historical volumes. This ‘‘volume variance’’ represented the imaging studies that were gained or lost in association with hired transporters. The financial impact of the volume variance was then quantified using an ‘‘average contribution margin.’’ This margin was calculated using historical reimbursement and cost data for outpatient studies during FY2013, the nearest fiscal year, and aggregated by imaging modality as well as payer classes (Table 1). Under this analysis, CT provided an average contribution margin of $449.40 per study, US
provided $117.56 per study, and MRI provided $523.92 per study. Monthly revenue was calculated by multiplying the monthly volume variance for each of the three imaging modalities by their respective contribution margin, and then adding the totals. Monthly cost was then subtracted from the monthly revenue to provide the total profit per month for the project. Costs comprised all direct costs from the transporter hires, including salaries, benefits, supplies, equipment, and purchased services. Of note, the analysis for the transporters did not include any indirect/overhead costs. Statistical Analysis Once gathered, OTT median, quartile, and count values were calculated by month and similar months were compared between years to account for seasonal variability. Given the nonparametric data, a Mann-Whitney U test with Wilcoxon signed rank was used to determine statistically significant change in OTT between the pre and post-implementation periods. For those modalities that showed statistically significant improvement in OTT, total ED LOS was gathered for the affected patients and analyzed using the same methods to determine whether there was a significant change in LOS. Data analysis was performed using Excel 2011, version 14.7 (Microsoft Corp., Redmond, WA) and IBM SPSS Statistics software package, version 23.0 (IBM Corp., Armonk, NY). A p value < 0.05 was considered significant. Lastly, we should note that no other administrative projects took place during the study period, thereby limiting the number of confounding factors. RESULTS
Table 1. Fiscal Year 2013 Average Payer Mix Variable Computed tomography Payer Commercial/HMO/PPO Medicaid Medicare Uninsured Total Magnetic resonance imaging Payer Commercial/HMO/PPO Medicaid Medicare Uninsured Total Ultrasound Payer Commercial/HMO/PPO Medicaid Medicare Uninsured Total
% of Total
22.8 40.1 36.4 0.8 100 32.0 41.5 26.1 0.4 100 29.6 49.1 20.5 0.7 100
HMO = health maintenance organization; PPO = preferred provider organization.
Distribution The majority of ED imaging studies were CT scans, which comprised 69.0% (monthly range 64.3%–72.5%) of all ED studies ordered during our observation period. US was the second most utilized ED imaging study during the period, comprising 26.1% (monthly range 22.1%–30.5%) of all studies. MRI constituted the imaging study with the lowest volume during this time, comprising 4.8% (monthly range 2.0%–7.9%) of all ED studies. Efficiency Gains Overall, hiring transporters created a statistically significant improvement in total OTTs (CT, US and MRI). This was demonstrated by a decrease in median OTT from 132 min in the pre-implementation phase to 116 min in the post-implementation phase (p < 0.0005) (Table 2). For CT scans, the majority of all studies, OTTs exhibited a significant decrease from a median
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Financial Gains
Table 2. Order to Table Time Median by Period Pre-Implementation Post-Implementation November 2012–July November Modality 2013, min 2013–July 2014, min p Value* Overall CT US MRI
132 142 91 215
116 114 99 235
<0.0005 <0.0005 0.018 0.225
CT = computed tomography; MRI = magnetic resonance imaging; US = ultrasound. Values rounded to nearest minute. * Statistically significant at p < 0.05.
of 142 min in the pre-implementation phase to 114 min in the post-implementation phase (p < 0.0005) (Table 2). Percent decrease in the monthly OTT when compared to the prior year ranged between a 0.5% (32 s) decrease in May 2014, to as much as a 41.2% (1 h, 44 s) decrease in December 2013 (Figure 1A). Given the statistically significant improvement in CT OTTs after transport hires, the analysis was expanded to total LOS for those patients who received a CT scan during their ED visit (Table 3). Median LOS decreased significantly from 489 to 461 min for these patients between the pre- and post-implementation periods (p < 0.001). Furthermore, of those patients who were discharged (pre: 60.6%, post: 59.4% of total), median LOS also decreased from 453 to 418 min, which was also significant (p < 0.001). LOS also decreased from 551 to 549 min for admitted patients, although this was not significant (p = 0.334). Improved OTTs were not realized for every month of the observation period for either US or MRI, and OTT actually increased for these studies. For US, the second most utilized imaging study from the ED, median times worsened from 91 min in the pre-implementation phase to 99 min in the post-implementation phase, with a significant p < 0.018 (Table 2). Seven of 9 months in the steady state experienced an increase in OTT, ranging from a 1.7% increase (1 min, 13 s) in July 2014 to a 29.4% increase (15 min, 38 s) in November 2013 (Figure 1B). MRI experienced a similar trend in OTT, as median times worsened from 215 min in the preimplementation phase to 235 min in the postimplementation phase, though this was not statistically significant (p < 0.225) (Table 2). For MRI, 6 of 9 months in the steady state experienced an increase in OTT, ranging from a 0.3% increase (0 min, 25 s) in December 2013, to as much as a 138.9% increase (2 h, 11 min, 36 s) in November 2013 (Figure 1C). Furthermore, MRI experienced increased volatility after implementation, with the interquartile range widening over the majority of months. Thus, MRI was adversely affected by the change, with a net increase in OTTs as well as increased volatility in the steady-state period.
When analyzed from a financial perspective, the investment was profitable for the radiology department throughout all months that were tracked (Figure 2). Overall, there was a gross profit of $1,025,181 during the 9-month post-implementation period. Profits from additional outpatient studies ranged from a low of $9286 in November 2013 to a high of $204,447 in June 2014. On average, the radiology department was able to add an additional 318 outpatient scans per month in the steady-state period, with the following distribution: 199 CT (+30.3% baseline), 12 MRI (+3.0% baseline), and 107 US (+17.0% baseline). DISCUSSION Operations and Finance Based on our findings, interdepartmental cooperation is a broadly applicable method that is effective at achieving site-specific gains in ED efficiency. Our intervention provided significant primary and secondary efficiency gains in patient flow for the ED and also produced significant financial gains for the radiology department. These mutually beneficial outcomes constitute a significant incentive for departments to partner with the ED in order to identify and address operational issues. Most importantly, this is a novel finding about ED operations and has never been demonstrated in previous literature on the subject. Improved OTTs were variable but demonstrated a net benefit. This net benefit was demonstrated by the statistically significant improvement in total OTTs after transport personnel were hired. We hypothesize that this was primarily due to decreased OTTs from CT scans, the most utilized imaging modality. At an average of 69.0% of all measured imaging, CT scans comprised more than two-thirds of the total studies and demonstrated consistent decreases in median OTTs during all months in the steady state. We believe that CTs benefited from the transport hires due to the original hypothesis that transporters decrease equipment downtime by allowing the technician to remain in the imaging suite, thereby increasing CT availability and turn-around time per patient scanned. CT technology is relatively suited for the type of efficiency gains created by the transporters. CT procedures are fairly regimented, requiring input of initial study parameters and protocol, depending on study type, patient body habitus, and condition, while the actual scan is relatively short once these values are selected. This lack of variability per scan allowed for this modality to benefit from decreased downtimes. In addition, we found that patient’s undergoing CT scans during their ED visit also experienced a significant
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Figure 1. Monthly interquartile ranges by imaging modality with associated median values. Trend lines of first quartile, median, and third quartile during pre and post-implementation periods demonstrate change over time by imaging modality.
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Table 3. Median Length of Stay for Patients With a Computed Tomography Scan Pre-Implementation Post-Implementation November 2012–July November 2013–July Disposition 2013, min 2014, min p Value* Overall Discharges Admissions
489 453 551
461 418 549
<0.001 <0.001 0.348
Length of stay calculated as end of triage to emergency department departure. * Statistically significant at p < 0.05.
decrease in total ED LOS; a secondary outcome for the EDs overall operational efficiency regarding patient flow. We were unable to isolate OTT in order to provide proof of a causal relationship between the two trends. However, the 26-min decrease in median CT OTT between the periods is similar to the 28-min decrease in median LOS for patients who underwent CT scans. It seems very likely that the efficiency gains from decreased CT OTTs directly translated into an overall improvement in ED patient flow efficiency. US actually demonstrated a statistically significant inefficiency after transporter hires, with 7 of 9 months in the steady state experiencing an increase in OTT. However, with US comprising an average 26.1% of total imaging, these effects were less pronounced. We hypothesize that several factors could have contributed to the increase in OTT. First, US scans are operator dependent regarding the time to achieve quality images that are adequate for radiology review. While this may not explain an increase
in OTT, it may suggest that this process is more insulated from transportation gains, as the rate-limiting step in the process is operator efficacy and not transportation efficiency. In addition, this operator-dependent, rate-limiting step could have been vulnerable to adverse effects of increased imaging volumes that were seen across all studies between the pre and post-implementation periods. Also, the inclusion of portable US studies may have further insulated the OTT from transporter gains, as US technicians are required to transport the machine to the patient in these cases. Although these portable scans were only around 10% of total US scans, this may have been significant enough to affect the outcome. Lastly, there could be an interaction between the transporters and technicians to explain this, as transporters were shared among CT, MRI, and US. As a result, high demands from one modality, namely CT, could have had an adverse effect on the others. Similarly, MRI also demonstrated decreased efficiency after the hires, with 7 of 10 months in the steady state experiencing an increase in OTT, though this was not a significant change. This does not account for as much of a logistical effect on patient flow due to the fact that MRI only comprises 4.8% of the total scans. We hypothesize that this effect resulted from higher utilization of MRI outpatient imaging, where volume increased and downtime decreased as a result of transporter hires. In all likelihood, these conditions made it more difficult for STAT ED MRI scans to fall into an open space in the queue, thereby delaying their effective OTT. In addition, the singular MRI suite at the facility makes it inherently
Figure 2. Monthly radiology department profits as a result of additional outpatient imaging made possible by hiring two full-time transport personnel. Gross profit defined as the additional revenue from the increase in baseline outpatient imaging capacity minus the monthly cost of the personnel (all direct costs from the transporter hires including salaries, benefits, supplies, equipment, and purchased services).
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prone to operational volatility. As is the case with US, an interaction between the transporters and technicians could also explain this decreased efficiency. As demonstrated by the profit range of $9286 in November 2013 to $204,447 in June 2014, as well as a gross profit of $1.03 million during the postimplementation period, the interdepartmental investment proved financially beneficial for the radiology department. While we cannot conclude that increased profits are directly related to the hiring of transporters, this is a promising association. The cost of transporter hires was easily offset by increased revenue. In addition, we averaged roughly 318 extra outpatient scans per month after transport hires. These trends suggest that our original hypothesis may be correct; as it appears that decreased equipment downtime from transport hires allowed for more outpatient scans to be completed. Outpatient imaging studies were dissimilar to those in the ED in that they were limited by defined operating hours and scheduling. Thus, while increased imaging demand in the ED resulted in increased study volumes due to STAT protocoling, increase in outpatient scan demand could only translate into increased volumes by creating more room for these routine studies during scheduled operating hours. Our positive volume variance in each post-implementation month indicates that outpatient demand for routine imaging studies may have translated into an increased volume of these studies as a result of transporter hires, an effect that could enable the radiology department to capture more profit as a result of the hires. It is worth noting that the ED did not benefit financially from the investment. However, it was not subjected to the inherent risks of investment. Assuming EDs are risk averse, this neutral financial risk profile provides an added benefit in the pre-investment process. Limitations We should note that the origin of our project was tied to the work of professional external consultants that were hired in a hospital-wide effort to identify areas for cost savings and efficiency gains. As a result, we cannot discount the problem of project identification and implementation. The Table 4. Patient Visits With Multiple Computed Tomography (CT) Scans Pre-Implementation November 2012–July 2013 No. of CT Scans 1 >1 Total
Post-Implementation November 2013–July 2014
n
%
n
%
2364 1197 3561
66.4 33.6 100.0
2986 1206 4192
71.2 28.8 100.0
concept of interdepartmental cooperation is a valuable one, but these need-finding origins may continue to be a significant hurdle for the inexperienced practitioner. A further limitation of the study is based on the nature of transport with respect to multiple imaging studies. Each imaging study carried a unique OTT value and, as a result, some studies shared a single transport. For instance, if a patient receives a combination CT head and CT cervical spine (C-spine) during one transportation to the CT suite, the two independent data points would carry a nearly identical OTT value because they were ordered and completed at the same time. This is in comparison to another situation where a patient receives a CT head and, later, a CT C-spine during two separate transportations to the suite, creating two different OTTs for a similar set of studies. In order to account for this limitation, the percentage of patients who underwent multiple studies in a given modality were tracked between the preand post-transporter periods and analyzed for statistical significance using a c2 test. Although it seems probable that the percentage of patients undergoing multiple CT scans would be constant between the pre- and postimplementation periods, we found a statistically significant decrease in multiple OTTs from 33.6% of patients in the pre-implementation period and 28.8% in the post-implementation period (p < 0.001) (Table 4). Because OTTs for CT scans were the largest and only statistically significant portion of the data, this constitutes a limitation of these findings. However, we feel that the limitation from OTT inclusions is less significant than the alternative of filtering multiple OTTs using an inclusion criteria based on time difference between studies. This alternative would leave our methods open to the criticism of data manipulation, as one would be able to change the inclusion cutoff to suit the needs of statistical significance. Thus, we have elected to preserve all OTTs and clearly demonstrate their effects and limitations. Ultimately, efficiency gains in ED operations attempt to address the growing wait times and problems of overcrowding in today’s health care environment. In our institution, we were discussing process changes before the intervention, which likely contributed to the decrease in time noted for CT scans in the pre-intervention period. These initial gains would have been difficult to sustain without the intervention. In addition, this study only looks at LOS as a secondary outcome for statistically significant OTTs and does not directly isolate the effect of imaging efficiency gains on wait times or times related to patient disposition. Thus, we can make limited assumptions that decreased OTTs would serve as an advantage to these parameters and their downstream effects regarding patient care. Attempts to measure a direct effect may be difficult to conduct based on confounding factors, but may be productive areas for future study.
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Imaging volume increased for all imaging studies between pre and post-implementation periods and was tracked. However, while it is expected that increased volume would hinder efficiency gains, efficiency actually increased overall. This is particularly true for CT scans that increased in volume during the study period, with a mean of 602.8 scans/month in the pre-implementation period and 655.8 scans/month in the post-implementation period. Given this, it is important to note that that radiology did not increase their employees on duty besides the transporters during this time. Hired transporters only worked 15.5 h during weekdays, leaving weekends and 8.5 h of night-time without coverage. This lessened the potential impact of the transporters, however, as transporter coverage includes both cost and revenue, it is unknown whether additional coverage during nights and weekends would have resulted in a loss or gain in profit. As it stands, the coverage time studied resulted in overall profit gain. An additional study of changing coverage times could address this variable and further optimize operational and financial gains. Lastly, we should note that this is a retrospective study. As such, all findings are associations and not causations. CONCLUSIONS Our study demonstrates a novel finding, that interdepartmental cooperation is a broadly applicable method that is effective for achieving site-specific gains in ED operational efficiency. The imaging transporter project was a success for the ED. It demonstrated statistically significant improvement in OTTs, our primary outcome, as well as the secondary outcomes of LOS. Furthermore, these efficiency gains were made in the setting of an 8.7% increase in scan volumes between the pre and postimplementation periods. Although US and MRI showed slightly increased OTTs, these effects are minimal and are outweighed by the gains in CT during the same period, as demonstrated by the overall statistically significant improvement in OTTs. Furthermore, transport hires were a significant financial success for the radiology department, which gained gross profit of $1.03 million during the 9month post-intervention period. These mutually beneficial outcomes constitute a significant incentive for departments
to partner with the ED in order to identify and address operational issues. Proof of the program’s overall success lies in the executive office’s decision to retain the imaging transport hires, who continue to operate and generate revenue for the hospital system. Larger prospective studies of interdepartmental interventions would be required to assess for correlation and durability of our findings.
REFERENCES 1. Eitel DR, Rudkin SE, Malvehy MA, et al. Improving service quality by understanding emergency department flow: a white paper and position statement prepared for the AAEM. J Emerg Med 2010;38:70–9. 2. Sun BC, Hsia RY, Weiss RE, et al. Effect of emergency department crowding on outcomes of admitted patients. Ann Emerg Med 2013; 61:605–11. 3. Gabayan GZ, Derose SF, Chiu VY, et al. Emergency department crowding and outcomes after emergency department discharge. Ann Emerg Med 2015;66:483–4925. 4. Cowan RM, Treciak S. Emergency department crowding and the potential impact on the critically ill. Crit Care 2005;9:291–5. 5. Schull MJ, Vermeulen M, Slaughter G, et al. Emergency department overcrowding and thrombolysis delays in acute myocardial infarction. Ann Emerg Med 2004;44:577–85. 6. Hoot NR, Zhou C, Jones I, et al. measuring and forecasting emergency department crowding in real time. Ann Emerg Med 2007; 49:747–55. 7. Dickson EW, Singh S, Cheung DS, et al. Application of lean manufacturing techniques in the emergency department. J Emerg Med 2009;37:177–82. 8. Soremekun OA, Terwiesch C, Pines JM. Emergency medicine, an operations management view. Acad Emerg Med 2011;18:1262–8. 9. Holden RJ. lean thinking in emergency departments: a critical review. Ann Emerg Med 2011;57:265–78. 10. Ward MJ, Ferrand YB, Pines JM, et al. The nature and necessity of operational flexibility in the emergency department. Ann Emerg Med 2015;65:156–61. 11. Storrow AB, Zhou C, Gaddis G, et al. Decreasing lab turnaround time improves emergency department throughput and decreases emergency medical services diversion: a simulation model. Acad Emerg Med 2008;15:1130–5. 12. Bishop RO, Patrick J, Besiso A. Efficiency achievements from a user-developed real-time modifiable clinical information system. Ann Emerg Med 2015;65:133–42. 13. Laker LF, Froehle CM, Lindsell CJ, et al. The flex track: flexible partitioning between low- and high-acuity areas of an emergency department. Ann Emerg Med 2014;64:591–603. 14. Arya R, Wei G, McCoy JV, et al. Decreasing length of stay in the emergency department with a split emergency severity index 3 patient flow model. Acad Emerg Med 2013;20:1171–9. 15. Moe J, Kirkland S, Rawe E, et al. Effectiveness of interventions to decrease emergency department visits by adult frequent users: a systematic review. Acad Emerg Med 2017;24:40–52.
A Cooperative Approach to ED Operations
ARTICLE SUMMARY 1. Why is this topic important? Operational efficiency is essential to safe and effective patient care in the emergency department (ED). 2. What does this study attempt to show? Interdepartmental cooperation is a broadly applicable method that is effective for achieving site-specific gains in ED operational efficiency. 3. What are the key findings? A joint investment in imaging transport personnel resulted in statistically significant reduction in median imaging times from 132 min to 116 min for the ED while generating $1.03 million in 9 months for the radiology department. Patients had a statistically significant improvement in median ED length of stay from 489 min to 461 min with imaging modalities that had improved imaging times. 4. How is patient care impacted? In our department, interdepartmental cooperation resulted in faster imaging times for patients. Patients with faster imaging times also experienced a decrease in total ED length of stay.
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Administration of Emergency Medicine
GROW THE PIE: INTERDEPARTMENTAL COOPERATION AS A METHOD FOR ACHIEVING OPERATIONAL EFFICIENCY IN AN EMERGENCY DEPARTMENT Jesse A. Guittard, MD, MBA,* Gabe Wardi, MD, MPH,* Edward M. Castillo, MPH, PHD,* Blake J. Stock, MBA,† Shannon Heuberger, BS,‡ and Christian A. Tomaszewski, MD, MBA* *Department of Emergency Medicine, UC San Diego Health System, San Diego, California, †Perioperative and Imaging Services, UC San Diego Health System, San Diego, California, and ‡Budgeting and Financial Forecasting, UC San Diego Health System, San Diego, California Reprint Address: Jesse A. Guittard, MD, MBA, Department of Emergency Medicine, UC San Diego Health System, 200 W. Arbor Drive #8676, San Diego, CA 92103
, Abstract—Background: Despite sufficient literature analyzing macroscopic and microscopic methods of addressing emergency department (ED) operations, there is a paucity of studies that analyze methods between these extremes. Objective: We conducted a quasi-experimental study incorporating a pre/post-intervention comparison to determine whether interdepartmental cooperation is effective at improving ED operations by combining microscopic and macroscopic concepts. Methods: We performed an analysis of operational and financial data from a cooperative investment in imaging transport personnel between the emergency and radiology departments. Our primary outcome, order to table time (OTT), measured imaging times by modality (computed tomography [CT], ultrasound [US], magnetic resonance imaging [MRI]). These were compared for statistically significant change before and after the intervention. Our secondary outcome, gross profit, was calculated using the revenue generated from gained outpatient studies minus the associated direct personnel costs. Results: Transporters improved OTTs by decreasing median imaging times from 132 min to 116 min (p < 0.0005). Efficiency improved for CT scans with median time decreasing from 142 min to 114 min (p < 0.0005). Transport hires had adverse effects on US, with an increase in median OTT from 91 min to 99 min (p < 0.018). MRI experienced a similar trend in OTT, as median times worsened from 215 min to 235 min (p < 0.225). The investment in transporters generated a gross profit of $1.03 million for the radiology department over 9 months. Conclusions: Interdepartmental cooperation is a broadly applicable
macroscopic method that is effective at achieving microscopic, site-specific gains in ED efficiency. Transporters provided operational gains for the ED and financial gains for the radiology department. Ó 2018 Elsevier Inc. All rights reserved. , Keywords—radiology; imaging; operations; management
INTRODUCTION The problem of inefficiency in emergency departments (EDs) has been widely recognized with related downsides in care, outcomes, and cost (1–6). Taking broad strokes, there are two study types in the literature that address ED operational efficiency: macroscopic and microscopic. Macroscopic papers apply broad concepts of operations management in order to provide a framework for addressing change (7–10). While very useful in providing a general framework for analysis that accounts for the high degree of diversity with respect to patient populations and resources, these frameworks may be difficult to apply in practice, as they leave the work of problem identification and change implementation to the practitioner. Conversely, microscopic papers detail specific changes that departments have made in order to address inefficiency and overcrowding (11–15). These
RECEIVED: 10 September 2017; FINAL SUBMISSION RECEIVED: 9 March 2018; ACCEPTED: 20 April 2018 1
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papers are powerful in addressing common themes, such as frequent utilizers, triage practices, laboratory turn-around times, information technology, and other operational systems. However, while helpful in identifying areas of change, organizational specifics of the reporting institution may prohibit application to the diverse range of ED practices. Thus, after reviewing the literature, we find there is a large gap with regard to the methods that address common operational themes in the ED using broadly applicable concepts. Given this background, we sought to determine whether interdepartmental cooperation could be a broadly applicable method that is effective at achieving site-specific gains in ED efficiency. To achieve this, we performed a quasi-experimental study incorporating a pre/post-intervention comparison analysis of operational and financial data from a cooperative investment in imaging transport personnel between the emergency and radiology departments. Our hypothesis was that this cooperative endeavor would decrease the time to obtain advanced imaging in the ED, while also increasing profits for the radiology department. METHODS Study Design and Patient Population This study attempts to analyze whether interdepartmental cooperation is effective at increasing operational efficiency in EDs. We performed a quasi-experimental before-and-after analysis of operational and financial data resulting from a joint emergency and radiology intervention in patient flow within ED imaging between November 2012 and July 2014. This analysis was certified as exempt from Institutional Review Board review and was conducted at UC San Diego Health System, an academic urban tertiary care hospital with 42,360 and 43,872 ED visits in 2013 and 2014, respectively, and an admission rate of 21%. Our radiology department serves both inpatient and outpatients with 190,129 total encounters (48,165 ED encounters) in 2013 and 206,006 encounters (54,351 ED encounters) in 2014. Project Description During need-finding surveys, it was estimated that radiology technicians, who operated ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI), spent roughly 20% of their time transporting ED patients to and from the sites, which resulted in marked machine idle time. In addition to slowing the turnover between patients, which hindered ED patient flow, this idle time also resulted in foregone radiology profits, which primarily took the form of foregone outpatient studies. As separate and independent from emergency imaging, these
outpatient studies are billed separately and constitute an independent revenue stream for the radiology department. Of note, although radiographs constitute a major ED imaging modality, these were excluded from the intervention due to ED operational specifics. In our ED, radiographs are obtained within the ED physical plant using portable machines taken to patient rooms or in an on-site suite. Patients do not need to be transported out of the department to obtain these studies, as is the case with US, CT, and MRI. Having identified a mutually beneficial area for improvement, the ED partnered with the radiology department and received approval from the Executive Office to hire two full-time imaging transporters to shuttle patients between the ED and imaging sites: CT, US, and MRI. In theory, this move would decrease equipment downtime by allowing the technician to remain in the imaging suite as transporters moved patients. During the 3 months of implementation time, from August 2013 through October 2013, brief biweekly meetings were held between the two departments in order to smooth processes and address any issues. Transporters were scheduled from 8:00 AM to 4:30 PM and 3:00 PM to 11:30 PM, giving a total coverage of 15.5 h/d, Monday through Friday, and this structure was maintained for the duration of the intervention. Definitions and Operations To determine the effect of the transport hires on ED operations, we evaluated both primary and secondary outcomes. Order to table time (OTT), which measured the time from study order in Epic’s electronic medical record to the time the patient arrived in the imaging suite, was directly influenced by transporter hires and was the primary outcome measured. OTTs were collected in total and aggregated by study modality (CT vs. US vs. MRI) to account for differences in demand, throughput, and capacity between the imaging lines of operation. OTTs were then tracked on a monthly basis for the 9-month pre- and post-implementation periods. In order to evaluate the overall effect on ED operations, we calculated length of stay (LOS) as a secondary outcome. LOS was defined as period of time from patient ‘‘check in’’ in the waiting room to disposition order (admission or discharge), and was aggregated between admitted, discharged, and total patients. Of note, in order to remove the influence of special circumstances from biweekly oversight and operational adjustments, the 3-month implementation period was omitted from comparative analysis. Finance In order to analyze the wider effect of transportation hires on interdepartmental and executive incentives, we chose
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to analyze investment profit as an additional secondary outcome. We conducted a simple financial analysis using revenue and cost to determine profitability. Cost was calculated using the associated total cost of the transport hires. Once associated benefits were added, estimated by the budget office to be 39% of salary, the total monthly cost for two full-time transporters was $7654.40 in fiscal year (FY)14 and $9222.00 in FY15. To calculate revenue, we used historical data to estimate volume and reimbursement values for outpatient imaging studies. Baseline volumes per modality were derived using the average monthly volumes of outpatient studies for the prior year, with CT comprising 658 studies/month, US 627 studies/month, and MRI 396 studies/month. This yearlong timeframe was chosen because an additional CT scanner had been added in early 2012, and it was necessary to ensure baseline financial estimates and pre-implementation conditions regarding the number of imaging devices remained comparable with the postimplementation period. After project implementation, monthly outpatient study volumes were compared to baseline historical volumes. This ‘‘volume variance’’ represented the imaging studies that were gained or lost in association with hired transporters. The financial impact of the volume variance was then quantified using an ‘‘average contribution margin.’’ This margin was calculated using historical reimbursement and cost data for outpatient studies during FY2013, the nearest fiscal year, and aggregated by imaging modality as well as payer classes (Table 1). Under this analysis, CT provided an average contribution margin of $449.40 per study, US
provided $117.56 per study, and MRI provided $523.92 per study. Monthly revenue was calculated by multiplying the monthly volume variance for each of the three imaging modalities by their respective contribution margin, and then adding the totals. Monthly cost was then subtracted from the monthly revenue to provide the total profit per month for the project. Costs comprised all direct costs from the transporter hires, including salaries, benefits, supplies, equipment, and purchased services. Of note, the analysis for the transporters did not include any indirect/overhead costs. Statistical Analysis Once gathered, OTT median, quartile, and count values were calculated by month and similar months were compared between years to account for seasonal variability. Given the nonparametric data, a Mann-Whitney U test with Wilcoxon signed rank was used to determine statistically significant change in OTT between the pre and post-implementation periods. For those modalities that showed statistically significant improvement in OTT, total ED LOS was gathered for the affected patients and analyzed using the same methods to determine whether there was a significant change in LOS. Data analysis was performed using Excel 2011, version 14.7 (Microsoft Corp., Redmond, WA) and IBM SPSS Statistics software package, version 23.0 (IBM Corp., Armonk, NY). A p value < 0.05 was considered significant. Lastly, we should note that no other administrative projects took place during the study period, thereby limiting the number of confounding factors. RESULTS
Table 1. Fiscal Year 2013 Average Payer Mix Variable Computed tomography Payer Commercial/HMO/PPO Medicaid Medicare Uninsured Total Magnetic resonance imaging Payer Commercial/HMO/PPO Medicaid Medicare Uninsured Total Ultrasound Payer Commercial/HMO/PPO Medicaid Medicare Uninsured Total
% of Total
22.8 40.1 36.4 0.8 100 32.0 41.5 26.1 0.4 100 29.6 49.1 20.5 0.7 100
HMO = health maintenance organization; PPO = preferred provider organization.
Distribution The majority of ED imaging studies were CT scans, which comprised 69.0% (monthly range 64.3%–72.5%) of all ED studies ordered during our observation period. US was the second most utilized ED imaging study during the period, comprising 26.1% (monthly range 22.1%–30.5%) of all studies. MRI constituted the imaging study with the lowest volume during this time, comprising 4.8% (monthly range 2.0%–7.9%) of all ED studies. Efficiency Gains Overall, hiring transporters created a statistically significant improvement in total OTTs (CT, US and MRI). This was demonstrated by a decrease in median OTT from 132 min in the pre-implementation phase to 116 min in the post-implementation phase (p < 0.0005) (Table 2). For CT scans, the majority of all studies, OTTs exhibited a significant decrease from a median
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Financial Gains
Table 2. Order to Table Time Median by Period Pre-Implementation Post-Implementation November 2012–July November Modality 2013, min 2013–July 2014, min p Value* Overall CT US MRI
132 142 91 215
116 114 99 235
<0.0005 <0.0005 0.018 0.225
CT = computed tomography; MRI = magnetic resonance imaging; US = ultrasound. Values rounded to nearest minute. * Statistically significant at p < 0.05.
of 142 min in the pre-implementation phase to 114 min in the post-implementation phase (p < 0.0005) (Table 2). Percent decrease in the monthly OTT when compared to the prior year ranged between a 0.5% (32 s) decrease in May 2014, to as much as a 41.2% (1 h, 44 s) decrease in December 2013 (Figure 1A). Given the statistically significant improvement in CT OTTs after transport hires, the analysis was expanded to total LOS for those patients who received a CT scan during their ED visit (Table 3). Median LOS decreased significantly from 489 to 461 min for these patients between the pre- and post-implementation periods (p < 0.001). Furthermore, of those patients who were discharged (pre: 60.6%, post: 59.4% of total), median LOS also decreased from 453 to 418 min, which was also significant (p < 0.001). LOS also decreased from 551 to 549 min for admitted patients, although this was not significant (p = 0.334). Improved OTTs were not realized for every month of the observation period for either US or MRI, and OTT actually increased for these studies. For US, the second most utilized imaging study from the ED, median times worsened from 91 min in the pre-implementation phase to 99 min in the post-implementation phase, with a significant p < 0.018 (Table 2). Seven of 9 months in the steady state experienced an increase in OTT, ranging from a 1.7% increase (1 min, 13 s) in July 2014 to a 29.4% increase (15 min, 38 s) in November 2013 (Figure 1B). MRI experienced a similar trend in OTT, as median times worsened from 215 min in the preimplementation phase to 235 min in the postimplementation phase, though this was not statistically significant (p < 0.225) (Table 2). For MRI, 6 of 9 months in the steady state experienced an increase in OTT, ranging from a 0.3% increase (0 min, 25 s) in December 2013, to as much as a 138.9% increase (2 h, 11 min, 36 s) in November 2013 (Figure 1C). Furthermore, MRI experienced increased volatility after implementation, with the interquartile range widening over the majority of months. Thus, MRI was adversely affected by the change, with a net increase in OTTs as well as increased volatility in the steady-state period.
When analyzed from a financial perspective, the investment was profitable for the radiology department throughout all months that were tracked (Figure 2). Overall, there was a gross profit of $1,025,181 during the 9-month post-implementation period. Profits from additional outpatient studies ranged from a low of $9286 in November 2013 to a high of $204,447 in June 2014. On average, the radiology department was able to add an additional 318 outpatient scans per month in the steady-state period, with the following distribution: 199 CT (+30.3% baseline), 12 MRI (+3.0% baseline), and 107 US (+17.0% baseline). DISCUSSION Operations and Finance Based on our findings, interdepartmental cooperation is a broadly applicable method that is effective at achieving site-specific gains in ED efficiency. Our intervention provided significant primary and secondary efficiency gains in patient flow for the ED and also produced significant financial gains for the radiology department. These mutually beneficial outcomes constitute a significant incentive for departments to partner with the ED in order to identify and address operational issues. Most importantly, this is a novel finding about ED operations and has never been demonstrated in previous literature on the subject. Improved OTTs were variable but demonstrated a net benefit. This net benefit was demonstrated by the statistically significant improvement in total OTTs after transport personnel were hired. We hypothesize that this was primarily due to decreased OTTs from CT scans, the most utilized imaging modality. At an average of 69.0% of all measured imaging, CT scans comprised more than two-thirds of the total studies and demonstrated consistent decreases in median OTTs during all months in the steady state. We believe that CTs benefited from the transport hires due to the original hypothesis that transporters decrease equipment downtime by allowing the technician to remain in the imaging suite, thereby increasing CT availability and turn-around time per patient scanned. CT technology is relatively suited for the type of efficiency gains created by the transporters. CT procedures are fairly regimented, requiring input of initial study parameters and protocol, depending on study type, patient body habitus, and condition, while the actual scan is relatively short once these values are selected. This lack of variability per scan allowed for this modality to benefit from decreased downtimes. In addition, we found that patient’s undergoing CT scans during their ED visit also experienced a significant
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Figure 1. Monthly interquartile ranges by imaging modality with associated median values. Trend lines of first quartile, median, and third quartile during pre and post-implementation periods demonstrate change over time by imaging modality.
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Table 3. Median Length of Stay for Patients With a Computed Tomography Scan Pre-Implementation Post-Implementation November 2012–July November 2013–July Disposition 2013, min 2014, min p Value* Overall Discharges Admissions
489 453 551
461 418 549
<0.001 <0.001 0.348
Length of stay calculated as end of triage to emergency department departure. * Statistically significant at p < 0.05.
decrease in total ED LOS; a secondary outcome for the EDs overall operational efficiency regarding patient flow. We were unable to isolate OTT in order to provide proof of a causal relationship between the two trends. However, the 26-min decrease in median CT OTT between the periods is similar to the 28-min decrease in median LOS for patients who underwent CT scans. It seems very likely that the efficiency gains from decreased CT OTTs directly translated into an overall improvement in ED patient flow efficiency. US actually demonstrated a statistically significant inefficiency after transporter hires, with 7 of 9 months in the steady state experiencing an increase in OTT. However, with US comprising an average 26.1% of total imaging, these effects were less pronounced. We hypothesize that several factors could have contributed to the increase in OTT. First, US scans are operator dependent regarding the time to achieve quality images that are adequate for radiology review. While this may not explain an increase
in OTT, it may suggest that this process is more insulated from transportation gains, as the rate-limiting step in the process is operator efficacy and not transportation efficiency. In addition, this operator-dependent, rate-limiting step could have been vulnerable to adverse effects of increased imaging volumes that were seen across all studies between the pre and post-implementation periods. Also, the inclusion of portable US studies may have further insulated the OTT from transporter gains, as US technicians are required to transport the machine to the patient in these cases. Although these portable scans were only around 10% of total US scans, this may have been significant enough to affect the outcome. Lastly, there could be an interaction between the transporters and technicians to explain this, as transporters were shared among CT, MRI, and US. As a result, high demands from one modality, namely CT, could have had an adverse effect on the others. Similarly, MRI also demonstrated decreased efficiency after the hires, with 7 of 10 months in the steady state experiencing an increase in OTT, though this was not a significant change. This does not account for as much of a logistical effect on patient flow due to the fact that MRI only comprises 4.8% of the total scans. We hypothesize that this effect resulted from higher utilization of MRI outpatient imaging, where volume increased and downtime decreased as a result of transporter hires. In all likelihood, these conditions made it more difficult for STAT ED MRI scans to fall into an open space in the queue, thereby delaying their effective OTT. In addition, the singular MRI suite at the facility makes it inherently
Figure 2. Monthly radiology department profits as a result of additional outpatient imaging made possible by hiring two full-time transport personnel. Gross profit defined as the additional revenue from the increase in baseline outpatient imaging capacity minus the monthly cost of the personnel (all direct costs from the transporter hires including salaries, benefits, supplies, equipment, and purchased services).
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prone to operational volatility. As is the case with US, an interaction between the transporters and technicians could also explain this decreased efficiency. As demonstrated by the profit range of $9286 in November 2013 to $204,447 in June 2014, as well as a gross profit of $1.03 million during the postimplementation period, the interdepartmental investment proved financially beneficial for the radiology department. While we cannot conclude that increased profits are directly related to the hiring of transporters, this is a promising association. The cost of transporter hires was easily offset by increased revenue. In addition, we averaged roughly 318 extra outpatient scans per month after transport hires. These trends suggest that our original hypothesis may be correct; as it appears that decreased equipment downtime from transport hires allowed for more outpatient scans to be completed. Outpatient imaging studies were dissimilar to those in the ED in that they were limited by defined operating hours and scheduling. Thus, while increased imaging demand in the ED resulted in increased study volumes due to STAT protocoling, increase in outpatient scan demand could only translate into increased volumes by creating more room for these routine studies during scheduled operating hours. Our positive volume variance in each post-implementation month indicates that outpatient demand for routine imaging studies may have translated into an increased volume of these studies as a result of transporter hires, an effect that could enable the radiology department to capture more profit as a result of the hires. It is worth noting that the ED did not benefit financially from the investment. However, it was not subjected to the inherent risks of investment. Assuming EDs are risk averse, this neutral financial risk profile provides an added benefit in the pre-investment process. Limitations We should note that the origin of our project was tied to the work of professional external consultants that were hired in a hospital-wide effort to identify areas for cost savings and efficiency gains. As a result, we cannot discount the problem of project identification and implementation. The Table 4. Patient Visits With Multiple Computed Tomography (CT) Scans Pre-Implementation November 2012–July 2013 No. of CT Scans 1 >1 Total
Post-Implementation November 2013–July 2014
n
%
n
%
2364 1197 3561
66.4 33.6 100.0
2986 1206 4192
71.2 28.8 100.0
concept of interdepartmental cooperation is a valuable one, but these need-finding origins may continue to be a significant hurdle for the inexperienced practitioner. A further limitation of the study is based on the nature of transport with respect to multiple imaging studies. Each imaging study carried a unique OTT value and, as a result, some studies shared a single transport. For instance, if a patient receives a combination CT head and CT cervical spine (C-spine) during one transportation to the CT suite, the two independent data points would carry a nearly identical OTT value because they were ordered and completed at the same time. This is in comparison to another situation where a patient receives a CT head and, later, a CT C-spine during two separate transportations to the suite, creating two different OTTs for a similar set of studies. In order to account for this limitation, the percentage of patients who underwent multiple studies in a given modality were tracked between the preand post-transporter periods and analyzed for statistical significance using a c2 test. Although it seems probable that the percentage of patients undergoing multiple CT scans would be constant between the pre- and postimplementation periods, we found a statistically significant decrease in multiple OTTs from 33.6% of patients in the pre-implementation period and 28.8% in the post-implementation period (p < 0.001) (Table 4). Because OTTs for CT scans were the largest and only statistically significant portion of the data, this constitutes a limitation of these findings. However, we feel that the limitation from OTT inclusions is less significant than the alternative of filtering multiple OTTs using an inclusion criteria based on time difference between studies. This alternative would leave our methods open to the criticism of data manipulation, as one would be able to change the inclusion cutoff to suit the needs of statistical significance. Thus, we have elected to preserve all OTTs and clearly demonstrate their effects and limitations. Ultimately, efficiency gains in ED operations attempt to address the growing wait times and problems of overcrowding in today’s health care environment. In our institution, we were discussing process changes before the intervention, which likely contributed to the decrease in time noted for CT scans in the pre-intervention period. These initial gains would have been difficult to sustain without the intervention. In addition, this study only looks at LOS as a secondary outcome for statistically significant OTTs and does not directly isolate the effect of imaging efficiency gains on wait times or times related to patient disposition. Thus, we can make limited assumptions that decreased OTTs would serve as an advantage to these parameters and their downstream effects regarding patient care. Attempts to measure a direct effect may be difficult to conduct based on confounding factors, but may be productive areas for future study.
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Imaging volume increased for all imaging studies between pre and post-implementation periods and was tracked. However, while it is expected that increased volume would hinder efficiency gains, efficiency actually increased overall. This is particularly true for CT scans that increased in volume during the study period, with a mean of 602.8 scans/month in the pre-implementation period and 655.8 scans/month in the post-implementation period. Given this, it is important to note that that radiology did not increase their employees on duty besides the transporters during this time. Hired transporters only worked 15.5 h during weekdays, leaving weekends and 8.5 h of night-time without coverage. This lessened the potential impact of the transporters, however, as transporter coverage includes both cost and revenue, it is unknown whether additional coverage during nights and weekends would have resulted in a loss or gain in profit. As it stands, the coverage time studied resulted in overall profit gain. An additional study of changing coverage times could address this variable and further optimize operational and financial gains. Lastly, we should note that this is a retrospective study. As such, all findings are associations and not causations. CONCLUSIONS Our study demonstrates a novel finding, that interdepartmental cooperation is a broadly applicable method that is effective for achieving site-specific gains in ED operational efficiency. The imaging transporter project was a success for the ED. It demonstrated statistically significant improvement in OTTs, our primary outcome, as well as the secondary outcomes of LOS. Furthermore, these efficiency gains were made in the setting of an 8.7% increase in scan volumes between the pre and postimplementation periods. Although US and MRI showed slightly increased OTTs, these effects are minimal and are outweighed by the gains in CT during the same period, as demonstrated by the overall statistically significant improvement in OTTs. Furthermore, transport hires were a significant financial success for the radiology department, which gained gross profit of $1.03 million during the 9month post-intervention period. These mutually beneficial outcomes constitute a significant incentive for departments
to partner with the ED in order to identify and address operational issues. Proof of the program’s overall success lies in the executive office’s decision to retain the imaging transport hires, who continue to operate and generate revenue for the hospital system. Larger prospective studies of interdepartmental interventions would be required to assess for correlation and durability of our findings.
REFERENCES 1. Eitel DR, Rudkin SE, Malvehy MA, et al. Improving service quality by understanding emergency department flow: a white paper and position statement prepared for the AAEM. J Emerg Med 2010;38:70–9. 2. Sun BC, Hsia RY, Weiss RE, et al. Effect of emergency department crowding on outcomes of admitted patients. Ann Emerg Med 2013; 61:605–11. 3. Gabayan GZ, Derose SF, Chiu VY, et al. Emergency department crowding and outcomes after emergency department discharge. Ann Emerg Med 2015;66:483–4925. 4. Cowan RM, Treciak S. Emergency department crowding and the potential impact on the critically ill. Crit Care 2005;9:291–5. 5. Schull MJ, Vermeulen M, Slaughter G, et al. Emergency department overcrowding and thrombolysis delays in acute myocardial infarction. Ann Emerg Med 2004;44:577–85. 6. Hoot NR, Zhou C, Jones I, et al. measuring and forecasting emergency department crowding in real time. Ann Emerg Med 2007; 49:747–55. 7. Dickson EW, Singh S, Cheung DS, et al. Application of lean manufacturing techniques in the emergency department. J Emerg Med 2009;37:177–82. 8. Soremekun OA, Terwiesch C, Pines JM. Emergency medicine, an operations management view. Acad Emerg Med 2011;18:1262–8. 9. Holden RJ. lean thinking in emergency departments: a critical review. Ann Emerg Med 2011;57:265–78. 10. Ward MJ, Ferrand YB, Pines JM, et al. The nature and necessity of operational flexibility in the emergency department. Ann Emerg Med 2015;65:156–61. 11. Storrow AB, Zhou C, Gaddis G, et al. Decreasing lab turnaround time improves emergency department throughput and decreases emergency medical services diversion: a simulation model. Acad Emerg Med 2008;15:1130–5. 12. Bishop RO, Patrick J, Besiso A. Efficiency achievements from a user-developed real-time modifiable clinical information system. Ann Emerg Med 2015;65:133–42. 13. Laker LF, Froehle CM, Lindsell CJ, et al. The flex track: flexible partitioning between low- and high-acuity areas of an emergency department. Ann Emerg Med 2014;64:591–603. 14. Arya R, Wei G, McCoy JV, et al. Decreasing length of stay in the emergency department with a split emergency severity index 3 patient flow model. Acad Emerg Med 2013;20:1171–9. 15. Moe J, Kirkland S, Rawe E, et al. Effectiveness of interventions to decrease emergency department visits by adult frequent users: a systematic review. Acad Emerg Med 2017;24:40–52.
A Cooperative Approach to ED Operations
ARTICLE SUMMARY 1. Why is this topic important? Operational efficiency is essential to safe and effective patient care in the emergency department (ED). 2. What does this study attempt to show? Interdepartmental cooperation is a broadly applicable method that is effective for achieving site-specific gains in ED operational efficiency. 3. What are the key findings? A joint investment in imaging transport personnel resulted in statistically significant reduction in median imaging times from 132 min to 116 min for the ED while generating $1.03 million in 9 months for the radiology department. Patients had a statistically significant improvement in median ED length of stay from 489 min to 461 min with imaging modalities that had improved imaging times. 4. How is patient care impacted? In our department, interdepartmental cooperation resulted in faster imaging times for patients. Patients with faster imaging times also experienced a decrease in total ED length of stay.
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