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Impact of Physician Screening in the Emergency Department on Patient Flow
The Journal of Emergency Medicine, Vol. 43, No. 3, pp. 509–515, 2012 Copyright Ó 2012 Elsevier Inc. Printed in the USA. All rights reserved 0736-4679/$ - see front matter
doi:10.1016/j.jemermed.2012.01.025
Administration of Emergency Medicine
IMPACT OF PHYSICIAN SCREENING IN THE EMERGENCY DEPARTMENT ON PATIENT FLOW Olanrewaju A. Soremekun, MD, MBA,* Roberta Capp, MD,† Paul D. Biddinger, MD,‡ Benjamin A. White, MD,‡ Yuchiao Chang, PHD,§ Sarah B. Carignan, MBA,‡ and David F. M. Brown, MD‡ *Department of Emergency Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, †Harvard Affiliated Emergency Medicine Residency Program, Brigham & Women’s Hospital and Massachusetts General Hospital, Boston, Massachusetts, ‡Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, and §Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts Reprint Address: Olanrewaju A. Soremekun, MD, MBA, Department of Emergency Medicine, Hospital of University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104
, Abstract—Background: Physician triage is one of many front-end interventions being implemented to improve emergency department (ED) efficiency. Study Objective: We aim to determine the impact of this intervention on some key components of ED patient flow, including time to physician evaluation, treatment order entry, diagnostic order entry, and disposition time for admitted patients. Methods: We conducted a 2-year before–after analysis of a physician triage system at an urban tertiary academic center with 90,000 annual visits. The goal of the physician in triage was to arrange safe disposition of straightforward patients as well as to initiate work-ups. All medium-acuity patients arriving during the hours of the intervention were impacted and thus included in the analysis. Our primary outcome was the time to disposition decision. In addition to before–after analysis, comparison was made with highacuity patients, a group not impacted by this intervention. Patient flow data were extracted from the ED information system. Outcomes were summarized with medians and interquartiles. Multivariable regression analysis was performed to investigate the intervention effect controlling for potential confounding variables. Results: The median time to disposition decision decreased by 6 min, and the time to physician evaluation, analgesia, antiemetic, antibiotic, and radiology order decreased by 16, 70, 66, 36, and 16 min, respectively. These findings were all statistically significant. Similar results were observed from the multivariable
regression models after controlling for potential confounding factors. Conclusions: Physician triage led to earlier evaluation, physician orders, and a decrease in the time to disposition decision. Ó 2012 Elsevier Inc. , Keywords—emergency department; physician triage
INTRODUCTION Prolonged wait times is an issue faced by many emergency departments (EDs) around the United States, and conflicts with one of the key tenets of emergency medicine: to provide rapid evaluation and treatment for patients with urgent and emergent conditions (1). Recent publications have highlighted that the delay in evaluation and treatment affects patients of all acuity, and although the majority of patients presenting to EDs experience longer waits than is generally accepted, medium-acuity patients are disproportionately impacted (2,3). These delays impact the quality of care for patients with serious conditions such as sepsis and myocardial infarction, where prompt evaluation and treatment has been shown to positively impact outcomes (4–6). In addition, prolonged wait time also impacts patient satisfaction, with prolonged wait times correlated to
RECEIVED: 2 August 2011; FINAL SUBMISSION RECEIVED: 25 October 2011; ACCEPTED: 16 January 2012 509
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Figure 1. Schematic of ED patient flow.
lower levels of patient satisfaction and high rates of patients leaving without being seen (LWBS) (7,8). According to the literature, patients expect to be evaluated by a physician in less than an hour and the disposition decision made in < 3 h (9,10). Many EDs are unable to meet this expectation, leading to high rates of dissatisfaction with ED care and LWBS. Physician triage is one of several front-end interventions being implemented in some EDs to decrease time to physician evaluation, diagnostic testing, treatment, and disposition (11). Physician triage is a system where a designated physician is deployed to intervene early in patients’ ED course to guide triage or accelerate the initial evaluation and treatment of patients during a period when patients otherwise would be waiting for a bed space in the ED. While this intervention has been implemented in a nonstandardized format across institutions, the majority have demonstrated a positive impact on efficiency, leading to a reduction in key operational performance measures like time to initial physician evaluation, ED length of stay (LOS), and the number of patients who LWBS (12–15). Whereas some positive operational improvements associated with physician triage have been described in the literature, the impact on specific components of ED patient flow—time to physician evaluation, diagnostic tests, treatment delivery, and disposition—adjusting for confounding variables such as ED census and triage physician experience level has not been well described in the literature. In this study we aim to describe the impact of physician triage on components of the ED care delivery process and the impact on overall patient flow for patients admitted to the hospital or observation unit. METHODS The study was a retrospective review 12 months pre- and post-implementation of a physician triage system. The study was performed in an urban tertiary academic center with approximately 90,000 annual visits and an overall
admission rate of approximately 27%. The center has four adult treatment areas: one high-acuity area, two medium-acuity areas, and one fast-track area. There are also separate dedicated pediatric and acute psychiatric treatment areas. Besides a 4.5% growth in total ED visits and staffing changes directly related to the physician in triage (see below), there were no changes to ED staffing or other major interventions identified in the postimplementation period. A physician triage system (also referred to below as the ‘‘intervention’’) was implemented in our institution in December 2007. The hours of operation of the triage system were 11:00 a.m. to 11:00 p.m., corresponding to peak arrival rates in our ED. Figure 1 is a schematic of ED patient flow. The triage nurse performs the initial evaluation of all patients arriving to the ED, even after implementation of the physician-in-triage system. Although the emergency severity index classification of patients was phased out during the study period, the nursing triage process was unchanged pre- and post-implementation of the intervention. After evaluation by the triage nurse, high- and low-acuity patients are sent directly to the acute care and fast-track area, respectively, bypassing the main ED waiting room and the physician in triage. Postimplementation of the intervention, the attending physician in triage evaluated all the medium-acuity patients in the waiting room with the following goals: 1) screen all medium-acuity patients and identify those patients who may have occult presentations of severe illness where prolonged wait times may lead to worse outcomes; 2) initiate the evaluation and treatment of patients who are deemed able to wait for an ED bed, but for whom no bed is immediately available; and 3) accelerate the disposition of a subset of medium-acuity patients (i.e., arrange for discharge or admission from the ED without use of a treatment area bed in the ED) after initial evaluation and treatment. For patients where disposition can be easily arranged, the physician in triage writes a complete ED evaluation note. For all other patients, the physician in triage writes a screening
Physician Screening Impact on ED Patient Flow
note highlighting key portions of their brief evaluation and justification of their initial work-up. The accelerated disposition of a subset of patients is a key difference between our study center and other physician triage systems in the published literature. This difference requires the practitioner in triage to be an attending physician. In addition, a nurse practitioner and four nurses are also needed for phlebotomy, medication administration, follow-up of test results, and handling the logistics required for safe disposition of patients from the waiting room. The staffing required was incremental to the existing staffing of the ED, leading to an estimated increase in the annual operating expense of $1.86 million. In addition, an estimated $1.2 million in capital resources was needed to redesign the waiting room to achieve the three goals of the intervention. These resources were used to construct four evaluation rooms, physician work area, and pre- and post-screening waiting areas. Besides the staffing changes and waiting room redesign that were directly related to the intervention, no changes were made to the ED care areas. All adult patients arriving during the hours of operation of the intervention (11:00 a.m. to 11:00 p.m.) triaged to the medium-acuity treatment areas during the study period and who were admitted to the hospital (inpatient or observation units) were included in the analysis. We excluded all low-acuity and high-acuity patients, as after their initial nursing triage they are sent directly to fasttrack and high-acuity areas, bypassing the main ED waiting room and the physician in triage. In addition, we compared medium-acuity patients to high-acuity patients (a group that bypassed the physician in triage and thus not impacted by the intervention) during the preto post-intervention period. We focused on admitted medium-acuity patients, as the physician triage system impacted their care process directly. Data were extracted from the computerized order entry system and patient tracking system. Our primary outcome was the time to disposition decision as determined by the time of bed requisition. Other outcomes measured were: time to physician evaluation, time to treatment order (antibiotics, analgesia, and antiemetic), time to first radiology order, and total ED LOS. Treatments were classified according to the drug categorization from PDRÔ (Montvale, NJ). Time to first physician order was used as a proxy for time to physician evaluation. This is an accurate and conservative proxy, as it is common practice for all patients to have orders placed immediately after evaluation by the triage physician. Data Analysis Continuous outcomes (e.g., time to disposition decision, time to treatment) were summarized with medians and
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interquartile ranges. The 95% confidence intervals of the difference in medians between groups and p-values were estimated using the bootstrap re-sampling method. Two-sided p-values # 0.05 were considered statistically significant, and no adjustment was made for the inflation of Type I error from the multiple secondary outcomes. Multivariable regression models were used to determine the impact of physician triage controlling for potential confounding factors, including the experience level of the physician in triage, ED crowding measured by ED census at time of patient arrival, patient arrival time, and patient illness severity measured by billing level. Outcomes were log-transformed in these regression models to improve the normality assumption. We used SAS version 9.2 (SAS Institute Inc., Cary, NC) for all statistical analysis. The institutional review board at the study hospital approved the study protocol. RESULTS The total number of admitted medium-acuity patients arriving to the ED during the hours of 11:00 a.m.– 11:00 p.m. was 20,318 during the 2-year period (9506 pre-implementation and 10,812 post-implementation). The characteristics of admitted patients triaged to the medium-acuity areas pre- and post-implementation of the intervention can be seen in Table 1. Besides a 14% growth in volume of admitted medium-acuity patients and a 4% growth in the overall mean ED census at time of patient arrival in the post-intervention period, the acuity as measured by mode of arrival, disposition bed type, hospital LOS, and billing level distribution was similar in the pre- and post-intervention periods. For our primary outcome—time to disposition decision—we observed a 6-min decrease in the median time to disposition decision from 260 min to 254 min (95% confidence interval [CI] 12 to 0, p value = 0.025). Comparison of our secondary outcome measures, preand post-implementation of physician triage (Table 2), showed that the median time to physician evaluation decreased by 16 min (95% CI 19 to 14, p < 0.001). In addition, the median time to analgesia order decreased by 70 min (95% CI 80 to 60, p < 0.001), median time to antiemetic order decreased by 66 min (95% CI 81 to 50, p < 0.001), median time to antibiotics order decreased by 36 min (95% CI 48 to 24, p < 0.001), and the median time to first radiology order decreased by 16 min (95% CI 19 to 14, p < 0.001). There was also a decrease of 13 min in the total ED LOS for these patients (95% CI 21 to 5, p = 0.001). LWBS rates also decreased by 1.41% (95% CI 2.33–2.55%, p < 0.001), from 3.19% to 1.78%. These improvements in the care of medium-acuity patients arriving during the hours of 11:00 a.m.–11:00 p.m.
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Table 1. Patient Characteristics: Medium-acuity Patients Admitted between 11:00 a.m. and 11:00 p.m. Patient Characteristics Total patients Age, mean (SD) Male, % Arrival by ambulance, % Disposition by bed type, % Observation unit General med/surgical Intensive care unit Billing level, % 99283 99284 99285 99291 ED census at time of patient arrival, mean (SD) Length of hospital stay, median (IQR)
12 Months Pre Physician Screening
12 Months Post Physician Screening
9506 56 (22) 49 34.3
10,812 55 (23) 49.6 35.1
22.8 76.9 0.2
22.7 76.9 0.4
p Value <0.0001 0.44 0.26 0.36
0.15 0.1 0.4 96.3 2.8 80.3 (12.5) 3 (1–6)
0.1 0.5 96.1 2.8 83.2 (16.8) 3 (1–6)
<0.0001 0.009
ED = emergency department; IQR = interquartile range.
occurred despite a growth in ED volume as well as increased levels of crowding, with no change in the acuity level during the intervention period. In the multivariable regression models, the time to disposition decision decreased by 6% post-implementation after controlling for potential confounding factors such as experience level of physician and ED volume (p < 0.001) (Tables 2,3). All secondary outcomes showed significant reduction ranging from 6% to 31% post-implementation (all with p < 0.001). When compared to high-acuity patients, a group not impacted by the physician in triage, improvements in the median times to physician evaluation, treatment, and radiology study seen in admitted medium-acuity patients were significantly higher than those improvements seen in the high-acuity patients, and the time to disposition decision increased in the high-acuity groups (Table 4).
DISCUSSION Physician triage is one of the many front-end interventions being implemented to improve ED efficiency and tackle the prolonged wait times for evaluation and treatment (11). Although the majority of institutions implementing this intervention have observed reductions in LWBS and ED LOS, Russ et al. actually noted an increase in ED LOS after implementation of physician in triage (12–16). In our study, we looked at the impact of physician triage on the components of flow and the overall time to disposition for admitted patients. When combined, these components of flow are in line with a key goal of emergency care: to provide rapid evaluation, treatment, testing, and appropriate disposition of patients. Logically speaking, an intervention where a physician is deployed to intervene early in the ED course to accelerate the initial evaluation and treatment of patients
Table 2. Quality Measures for Admitted Medium Acuity Patients
Median time to admit decision, min (IQR) Median time to physician evaluation (IQR) Median time to ED discharge (IQR) Median time to ED treatment,† min (IQR) Antibiotics Analgesia Antiemetic Median time to ED radiology, min (IQR)
12 Months Pre-Physician Screening n = 9056
12 Months Post-Physician Screening n = 10,812
Difference (95% CI)*
p-Value*
260 (157–398) 76 (34–183) 467 (330–680)
254 (150–394) 60 (32–105) 454 (314–664)
6 (12 to 0) 16 (19 to 14) 13 (21 to 5)
0.025 <0.001 0.001
n = 2499 265 (154–417) n = 3998 243 (115–423) n = 1643 228 (92–412) n = 5004 92 (43–188)
n = 3741 229 (131–373) n = 5993 173 (75–330) n = 2550 162 (63–338) n = 8802 76 (40–140)
36 (48 to 24)
<0.001
70 (80 to 60)
<0.001
66 (81 to 50)
<0.001
16 (19 to 12)
<0.001
CI = confidence interval; ED = emergency department; IQR = interquartile range. * The 95% CI of differences and p values were estimated using bootstrap resampling method. † Sample size varied due to different types of treatment received.
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Table 3. Multivariable Regression Model – Impact of Variables on Time to Disposition Parameter
Estimate
Standard Error
t-Value
Pr > jtj
Intercept Post period Pre-intervention Physician experience < 3 years Physician experience > 3 years Overall ED volume Dependent variables Time to disposition
0.603334806 1.905375951 0 0.002963041 0 0.007406379
0.30585694 0.01762547 – 0.02040744 – 0.00058227
1.97 108.1 – 0.15 – 12.72
0.0486 <0.0001 – 0.8846 – <0.0001
0.06
<0.0001
ED = emergency department.
during a period when patients would otherwise have been waiting for an ED bed, leads to decrease in the time to initial evaluation, testing, and treatment. These gains should translate to a decrease in the time to disposition decision. Our findings show that although there were significant decreases in the time to initial evaluation, treatment order, and radiology study order, we were able to achieve only a 6-min decrease in the time to disposition decision for admitted patients. This improvement, although small, occurred in the face of increase in ED volume and crowding. As we expected, the high-acuity area that was not impacted by the intervention but also faced increases in crowding had a significant increase in the time to disposition. Our prior work estimating the financial impact of this intervention showed that although the initial investment of $1.2 million spent to redesign the waiting room and annual incremental operating costs of $1.86 million were significant, the overall net present value that takes into consideration a reduction in LWBS rates and incremental ED capacity was an estimated $2.8 million (17). A key observation in our study was that although there were significant time savings early in the process, we were able to achieve only a 6-min decrease in time to disposition. In essence, the time saved early on in the evaluation of patients is not carried forward all the way through to the final step, the disposition decision. This finding highlights a key concept in operations research, the bottleneck. In a multi-step service or manufacturing process, the bottleneck is the slowest and thus, rate-limiting step. In a complex service delivery
environment like the ED, identifying the bottlenecks is a complicated process for several reasons. These include a combination of diverse patient needs, numerous steps in the care process, and changing cycle time of each step depending on factors such as resource limitations, patient mix, or staffing. These factors lead to shifting bottlenecks, which might vary for each provider, clinical encounter, or time of day. Although our study demonstrates that earlier physician evaluation and orders did impact the time to disposition for admitted patients, our findings may be based on our institution-specific bottlenecks, explaining why our findings may be different from findings by Russ et al. (16). Finally, although early evaluation and treatment may lead to improved quality and safety of critically ill patients, the impact of rapid evaluation and disposition of medium-acuity patients on outcome has not been published in the literature. Further studies are needed to understand the impact of this intervention on quality, triage, resource utilization, patient satisfaction, and resident education. Nonetheless, this article and our prior work has shown that this intervention, when implemented appropriately, can lead to improved operational outcomes in the setting of fixed ED physical plant space and increases to ED census and crowding. Limitations The retrospective study design, lack of randomization, and single study site impacts the generalizability of our
Table 4. Difference in Medians between Pre and Post Intervention: Admitted Medium-acuity vs. Admitted High-acuity Patients
Time to admit decision, min Time to physician evaluation Time to ED discharge Time to ED treatment order Antibiotics Analgesia Antiemetic Time to ED radiology
Medium Acuity (95% CI)
High Acuity (95% CI)
Difference (95% CI)*
p-Value*
6 (12, to 0) 16 (19 to 14) 13 (21 to 5)
3 (1 to 7) 3 (4 to 2) 2 (11 to 6)
9 (16 to 2) 13 (16 to 11) 11 (22 to 1)
0.010 <0.001 0.038
36 (48 to 24) 70 (80 to 60) 66 (81 to 50) 16 (19 to 12)
16 (31 to 0) 32 (41 to 21) 38 (62 to 17) 1 (2 to 0)
20 (39 to 1) 39 (53 to 24) 27 (56 to 2) 15 (19 to 11)
0.021 <0.001 0.034 <0.001
CI = confidence interval; ED = emergency department. * The 95% CI of differences and p values were estimated using bootstrap resampling method.
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conclusions and our ability to fully exclude all potential confounding. These are key limitations to the study, and we attempt to address them in two ways. First, we use the high-acuity patients, a group that would be similarly affected by overall ED efficiency improvements and not impacted by the intervention, as a comparison group. Second, by using regression analysis, we attempt to identify possible confounders that may impact our conclusions. The limitations in our study are indeed common in the ED operational research literature, and although some of these limitations may be addressed by these techniques, they do not fully address the underlying limitations of the study design. An additional limitation is the use of physician computer order time as a proxy for time to physician evaluation. Although it is very uncommon for patients to be seen by a physician without subsequent orders, the order entry may occur several minutes after evaluation. However, this delay would serve to falsely decrease the effect size, in that the actual time to physician evaluation would be lower than the calculated time. Also, although the physician order is the first step in the order process, these orders are, on occasion, given verbally, and there are several steps from physician order to actual care delivery. CONCLUSION Physician triage that focuses on both the disposition of straightforward patients and initiating work-ups, leads to a modest improvement in the time to disposition decision for admitted patients and more marked improvements in the times to evaluation, diagnostic testing, and initiation of critical treatments. These improvements are in line with the key tenets of emergency medicine and patient expectations. REFERENCES 1. Institute of Medicine. Hospital-based emergency care: at the breaking point. Washington, DC: The National Academies Press; 2007.
2. Horwitz LI, Green J, Bradley EH. US emergency department performance on wait time and length of visit. Ann Emerg Med 2010; 55:133–41. 3. McCarthy ML, Zeger SL, Ding R, et al. Crowding delays treatment and lengthens emergency department length of stay, even among high-acuity patients. Ann Emerg Med 2009;54:492–503.e4. 4. Bernstein SL, Aronsky D, Duseja R, et al. Society for Academic Emergency Medicine, Emergency Department Crowding Task Force. The effect of emergency department crowding on clinically oriented outcomes. Acad Emerg Med 2009;16:1–10. 5. Pines JM, Pollack CV Jr, Diercks DB, Chang AM, Shofer FS, Hollander JE. The association between emergency department crowding and adverse cardiovascular outcomes in patients with chest pain. Acad Emerg Med 2009;16:617–25. 6. Pines JM, Localio AR, Hollander JE, et al. The impact of emergency department crowding measures on time to antibiotics for patients with community-acquired pneumonia. Ann Emerg Med 2007; 50:510–6. 7. Pines JM. Timing of antibiotics for acute, severe infections. Emerg Med Clin North Am 2008;26:245–57, vii. 8. Boudreaux ED, O’Hea EL. Patient satisfaction in the Emergency Department: a review of the literature and implications for practice. J Emerg Med 2004;26:13–26. 9. Fernandes CM, Daya MR, Barry S, Palmer N. Emergency department patients who leave without seeing a physician: the Toronto Hospital experience. Ann Emerg Med 1994;24:1092–6. 10. Cassidy-Smith TN, Baumann BM, Boudreaux ED. The disconfirmation paradigm: throughput times and emergency department patient satisfaction. J Emerg Med 2007;32:7–13. 11. Wiler JL, Gentle C, Halfpenny JM, et al. Optimizing emergency department front-end operations. Ann Emerg Med 2010;55: 142–60.e1. 12. White BA, Brown DF, Sinclair J, et al. Supplemented Triage and Rapid Treatment (START) improves performance measures in the emergency department. J Emerg Med 2010 Jun 14. [Epub ahead of print]. 13. Chan TC, Killeen JP, Kelly D, Guss DA. Impact of rapid entry and accelerated care at triage on reducing emergency department patient wait times, lengths of stay, and rate of left without being seen. Ann Emerg Med 2005;46:491–7. 14. Han JH, France DJ, Levin SR, Jones ID, Storrow AB, Aronsky D. The effect of physician triage on emergency department length of stay. J Emerg Med 2010;39:227–33. 15. Holroyd BR, Bullard MJ, Latoszek K, et al. Impact of a triage liaison physician on emergency department overcrowding and throughput: a randomized controlled trial. Acad Emerg Med 2007;14: 702–8. 16. Russ S, Jones I, Aronsky D, Dittus RS, Slovis CM. Placing physician orders at triage: the effect on length of stay. Ann Emerg Med 2010;56:27–33. 17. Soremekun OA, Biddinger PD, White BA, et al. Operational and financial impact of physician screening in the ED. Am J Emerg Med 2011 Mar 17. [Epub ahead of print].
Physician Screening Impact on ED Patient Flow
ARTICLE SUMMARY 1. Why is this topic important? Placing a physician in triage is one of several front-end interventions that have been proposed to reduce crowding and increase patient safety in the waiting room. Thus, it is important to understand the impact of this intervention on patient flow and potential quality measures. 2. What does this study attempt to show? The design of physician in triage in the published literature has varied, with mixed results obtained on the impact. This study attempts to show the impact of a properly resourced and implemented physician-intriage program on patient flow. 3. What are the key findings? The physician-in-triage intervention led to a reduction in time to physician evaluation, time to disposition, and total length of stay despite an increase in overall ED volume. There was also significant improvement in time to analgesia and antiemetic orders. 4. How is patient care impacted? The physician-in-triage program improved overall patient flow, time to evaluation, and time to key orders of medium acuity patients.
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doi:10.1016/j.jemermed.2012.01.025
Administration of Emergency Medicine
IMPACT OF PHYSICIAN SCREENING IN THE EMERGENCY DEPARTMENT ON PATIENT FLOW Olanrewaju A. Soremekun, MD, MBA,* Roberta Capp, MD,† Paul D. Biddinger, MD,‡ Benjamin A. White, MD,‡ Yuchiao Chang, PHD,§ Sarah B. Carignan, MBA,‡ and David F. M. Brown, MD‡ *Department of Emergency Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, †Harvard Affiliated Emergency Medicine Residency Program, Brigham & Women’s Hospital and Massachusetts General Hospital, Boston, Massachusetts, ‡Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, and §Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts Reprint Address: Olanrewaju A. Soremekun, MD, MBA, Department of Emergency Medicine, Hospital of University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104
, Abstract—Background: Physician triage is one of many front-end interventions being implemented to improve emergency department (ED) efficiency. Study Objective: We aim to determine the impact of this intervention on some key components of ED patient flow, including time to physician evaluation, treatment order entry, diagnostic order entry, and disposition time for admitted patients. Methods: We conducted a 2-year before–after analysis of a physician triage system at an urban tertiary academic center with 90,000 annual visits. The goal of the physician in triage was to arrange safe disposition of straightforward patients as well as to initiate work-ups. All medium-acuity patients arriving during the hours of the intervention were impacted and thus included in the analysis. Our primary outcome was the time to disposition decision. In addition to before–after analysis, comparison was made with highacuity patients, a group not impacted by this intervention. Patient flow data were extracted from the ED information system. Outcomes were summarized with medians and interquartiles. Multivariable regression analysis was performed to investigate the intervention effect controlling for potential confounding variables. Results: The median time to disposition decision decreased by 6 min, and the time to physician evaluation, analgesia, antiemetic, antibiotic, and radiology order decreased by 16, 70, 66, 36, and 16 min, respectively. These findings were all statistically significant. Similar results were observed from the multivariable
regression models after controlling for potential confounding factors. Conclusions: Physician triage led to earlier evaluation, physician orders, and a decrease in the time to disposition decision. Ó 2012 Elsevier Inc. , Keywords—emergency department; physician triage
INTRODUCTION Prolonged wait times is an issue faced by many emergency departments (EDs) around the United States, and conflicts with one of the key tenets of emergency medicine: to provide rapid evaluation and treatment for patients with urgent and emergent conditions (1). Recent publications have highlighted that the delay in evaluation and treatment affects patients of all acuity, and although the majority of patients presenting to EDs experience longer waits than is generally accepted, medium-acuity patients are disproportionately impacted (2,3). These delays impact the quality of care for patients with serious conditions such as sepsis and myocardial infarction, where prompt evaluation and treatment has been shown to positively impact outcomes (4–6). In addition, prolonged wait time also impacts patient satisfaction, with prolonged wait times correlated to
RECEIVED: 2 August 2011; FINAL SUBMISSION RECEIVED: 25 October 2011; ACCEPTED: 16 January 2012 509
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O. A. Soremekun et al.
Figure 1. Schematic of ED patient flow.
lower levels of patient satisfaction and high rates of patients leaving without being seen (LWBS) (7,8). According to the literature, patients expect to be evaluated by a physician in less than an hour and the disposition decision made in < 3 h (9,10). Many EDs are unable to meet this expectation, leading to high rates of dissatisfaction with ED care and LWBS. Physician triage is one of several front-end interventions being implemented in some EDs to decrease time to physician evaluation, diagnostic testing, treatment, and disposition (11). Physician triage is a system where a designated physician is deployed to intervene early in patients’ ED course to guide triage or accelerate the initial evaluation and treatment of patients during a period when patients otherwise would be waiting for a bed space in the ED. While this intervention has been implemented in a nonstandardized format across institutions, the majority have demonstrated a positive impact on efficiency, leading to a reduction in key operational performance measures like time to initial physician evaluation, ED length of stay (LOS), and the number of patients who LWBS (12–15). Whereas some positive operational improvements associated with physician triage have been described in the literature, the impact on specific components of ED patient flow—time to physician evaluation, diagnostic tests, treatment delivery, and disposition—adjusting for confounding variables such as ED census and triage physician experience level has not been well described in the literature. In this study we aim to describe the impact of physician triage on components of the ED care delivery process and the impact on overall patient flow for patients admitted to the hospital or observation unit. METHODS The study was a retrospective review 12 months pre- and post-implementation of a physician triage system. The study was performed in an urban tertiary academic center with approximately 90,000 annual visits and an overall
admission rate of approximately 27%. The center has four adult treatment areas: one high-acuity area, two medium-acuity areas, and one fast-track area. There are also separate dedicated pediatric and acute psychiatric treatment areas. Besides a 4.5% growth in total ED visits and staffing changes directly related to the physician in triage (see below), there were no changes to ED staffing or other major interventions identified in the postimplementation period. A physician triage system (also referred to below as the ‘‘intervention’’) was implemented in our institution in December 2007. The hours of operation of the triage system were 11:00 a.m. to 11:00 p.m., corresponding to peak arrival rates in our ED. Figure 1 is a schematic of ED patient flow. The triage nurse performs the initial evaluation of all patients arriving to the ED, even after implementation of the physician-in-triage system. Although the emergency severity index classification of patients was phased out during the study period, the nursing triage process was unchanged pre- and post-implementation of the intervention. After evaluation by the triage nurse, high- and low-acuity patients are sent directly to the acute care and fast-track area, respectively, bypassing the main ED waiting room and the physician in triage. Postimplementation of the intervention, the attending physician in triage evaluated all the medium-acuity patients in the waiting room with the following goals: 1) screen all medium-acuity patients and identify those patients who may have occult presentations of severe illness where prolonged wait times may lead to worse outcomes; 2) initiate the evaluation and treatment of patients who are deemed able to wait for an ED bed, but for whom no bed is immediately available; and 3) accelerate the disposition of a subset of medium-acuity patients (i.e., arrange for discharge or admission from the ED without use of a treatment area bed in the ED) after initial evaluation and treatment. For patients where disposition can be easily arranged, the physician in triage writes a complete ED evaluation note. For all other patients, the physician in triage writes a screening
Physician Screening Impact on ED Patient Flow
note highlighting key portions of their brief evaluation and justification of their initial work-up. The accelerated disposition of a subset of patients is a key difference between our study center and other physician triage systems in the published literature. This difference requires the practitioner in triage to be an attending physician. In addition, a nurse practitioner and four nurses are also needed for phlebotomy, medication administration, follow-up of test results, and handling the logistics required for safe disposition of patients from the waiting room. The staffing required was incremental to the existing staffing of the ED, leading to an estimated increase in the annual operating expense of $1.86 million. In addition, an estimated $1.2 million in capital resources was needed to redesign the waiting room to achieve the three goals of the intervention. These resources were used to construct four evaluation rooms, physician work area, and pre- and post-screening waiting areas. Besides the staffing changes and waiting room redesign that were directly related to the intervention, no changes were made to the ED care areas. All adult patients arriving during the hours of operation of the intervention (11:00 a.m. to 11:00 p.m.) triaged to the medium-acuity treatment areas during the study period and who were admitted to the hospital (inpatient or observation units) were included in the analysis. We excluded all low-acuity and high-acuity patients, as after their initial nursing triage they are sent directly to fasttrack and high-acuity areas, bypassing the main ED waiting room and the physician in triage. In addition, we compared medium-acuity patients to high-acuity patients (a group that bypassed the physician in triage and thus not impacted by the intervention) during the preto post-intervention period. We focused on admitted medium-acuity patients, as the physician triage system impacted their care process directly. Data were extracted from the computerized order entry system and patient tracking system. Our primary outcome was the time to disposition decision as determined by the time of bed requisition. Other outcomes measured were: time to physician evaluation, time to treatment order (antibiotics, analgesia, and antiemetic), time to first radiology order, and total ED LOS. Treatments were classified according to the drug categorization from PDRÔ (Montvale, NJ). Time to first physician order was used as a proxy for time to physician evaluation. This is an accurate and conservative proxy, as it is common practice for all patients to have orders placed immediately after evaluation by the triage physician. Data Analysis Continuous outcomes (e.g., time to disposition decision, time to treatment) were summarized with medians and
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interquartile ranges. The 95% confidence intervals of the difference in medians between groups and p-values were estimated using the bootstrap re-sampling method. Two-sided p-values # 0.05 were considered statistically significant, and no adjustment was made for the inflation of Type I error from the multiple secondary outcomes. Multivariable regression models were used to determine the impact of physician triage controlling for potential confounding factors, including the experience level of the physician in triage, ED crowding measured by ED census at time of patient arrival, patient arrival time, and patient illness severity measured by billing level. Outcomes were log-transformed in these regression models to improve the normality assumption. We used SAS version 9.2 (SAS Institute Inc., Cary, NC) for all statistical analysis. The institutional review board at the study hospital approved the study protocol. RESULTS The total number of admitted medium-acuity patients arriving to the ED during the hours of 11:00 a.m.– 11:00 p.m. was 20,318 during the 2-year period (9506 pre-implementation and 10,812 post-implementation). The characteristics of admitted patients triaged to the medium-acuity areas pre- and post-implementation of the intervention can be seen in Table 1. Besides a 14% growth in volume of admitted medium-acuity patients and a 4% growth in the overall mean ED census at time of patient arrival in the post-intervention period, the acuity as measured by mode of arrival, disposition bed type, hospital LOS, and billing level distribution was similar in the pre- and post-intervention periods. For our primary outcome—time to disposition decision—we observed a 6-min decrease in the median time to disposition decision from 260 min to 254 min (95% confidence interval [CI] 12 to 0, p value = 0.025). Comparison of our secondary outcome measures, preand post-implementation of physician triage (Table 2), showed that the median time to physician evaluation decreased by 16 min (95% CI 19 to 14, p < 0.001). In addition, the median time to analgesia order decreased by 70 min (95% CI 80 to 60, p < 0.001), median time to antiemetic order decreased by 66 min (95% CI 81 to 50, p < 0.001), median time to antibiotics order decreased by 36 min (95% CI 48 to 24, p < 0.001), and the median time to first radiology order decreased by 16 min (95% CI 19 to 14, p < 0.001). There was also a decrease of 13 min in the total ED LOS for these patients (95% CI 21 to 5, p = 0.001). LWBS rates also decreased by 1.41% (95% CI 2.33–2.55%, p < 0.001), from 3.19% to 1.78%. These improvements in the care of medium-acuity patients arriving during the hours of 11:00 a.m.–11:00 p.m.
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Table 1. Patient Characteristics: Medium-acuity Patients Admitted between 11:00 a.m. and 11:00 p.m. Patient Characteristics Total patients Age, mean (SD) Male, % Arrival by ambulance, % Disposition by bed type, % Observation unit General med/surgical Intensive care unit Billing level, % 99283 99284 99285 99291 ED census at time of patient arrival, mean (SD) Length of hospital stay, median (IQR)
12 Months Pre Physician Screening
12 Months Post Physician Screening
9506 56 (22) 49 34.3
10,812 55 (23) 49.6 35.1
22.8 76.9 0.2
22.7 76.9 0.4
p Value <0.0001 0.44 0.26 0.36
0.15 0.1 0.4 96.3 2.8 80.3 (12.5) 3 (1–6)
0.1 0.5 96.1 2.8 83.2 (16.8) 3 (1–6)
<0.0001 0.009
ED = emergency department; IQR = interquartile range.
occurred despite a growth in ED volume as well as increased levels of crowding, with no change in the acuity level during the intervention period. In the multivariable regression models, the time to disposition decision decreased by 6% post-implementation after controlling for potential confounding factors such as experience level of physician and ED volume (p < 0.001) (Tables 2,3). All secondary outcomes showed significant reduction ranging from 6% to 31% post-implementation (all with p < 0.001). When compared to high-acuity patients, a group not impacted by the physician in triage, improvements in the median times to physician evaluation, treatment, and radiology study seen in admitted medium-acuity patients were significantly higher than those improvements seen in the high-acuity patients, and the time to disposition decision increased in the high-acuity groups (Table 4).
DISCUSSION Physician triage is one of the many front-end interventions being implemented to improve ED efficiency and tackle the prolonged wait times for evaluation and treatment (11). Although the majority of institutions implementing this intervention have observed reductions in LWBS and ED LOS, Russ et al. actually noted an increase in ED LOS after implementation of physician in triage (12–16). In our study, we looked at the impact of physician triage on the components of flow and the overall time to disposition for admitted patients. When combined, these components of flow are in line with a key goal of emergency care: to provide rapid evaluation, treatment, testing, and appropriate disposition of patients. Logically speaking, an intervention where a physician is deployed to intervene early in the ED course to accelerate the initial evaluation and treatment of patients
Table 2. Quality Measures for Admitted Medium Acuity Patients
Median time to admit decision, min (IQR) Median time to physician evaluation (IQR) Median time to ED discharge (IQR) Median time to ED treatment,† min (IQR) Antibiotics Analgesia Antiemetic Median time to ED radiology, min (IQR)
12 Months Pre-Physician Screening n = 9056
12 Months Post-Physician Screening n = 10,812
Difference (95% CI)*
p-Value*
260 (157–398) 76 (34–183) 467 (330–680)
254 (150–394) 60 (32–105) 454 (314–664)
6 (12 to 0) 16 (19 to 14) 13 (21 to 5)
0.025 <0.001 0.001
n = 2499 265 (154–417) n = 3998 243 (115–423) n = 1643 228 (92–412) n = 5004 92 (43–188)
n = 3741 229 (131–373) n = 5993 173 (75–330) n = 2550 162 (63–338) n = 8802 76 (40–140)
36 (48 to 24)
<0.001
70 (80 to 60)
<0.001
66 (81 to 50)
<0.001
16 (19 to 12)
<0.001
CI = confidence interval; ED = emergency department; IQR = interquartile range. * The 95% CI of differences and p values were estimated using bootstrap resampling method. † Sample size varied due to different types of treatment received.
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Table 3. Multivariable Regression Model – Impact of Variables on Time to Disposition Parameter
Estimate
Standard Error
t-Value
Pr > jtj
Intercept Post period Pre-intervention Physician experience < 3 years Physician experience > 3 years Overall ED volume Dependent variables Time to disposition
0.603334806 1.905375951 0 0.002963041 0 0.007406379
0.30585694 0.01762547 – 0.02040744 – 0.00058227
1.97 108.1 – 0.15 – 12.72
0.0486 <0.0001 – 0.8846 – <0.0001
0.06
<0.0001
ED = emergency department.
during a period when patients would otherwise have been waiting for an ED bed, leads to decrease in the time to initial evaluation, testing, and treatment. These gains should translate to a decrease in the time to disposition decision. Our findings show that although there were significant decreases in the time to initial evaluation, treatment order, and radiology study order, we were able to achieve only a 6-min decrease in the time to disposition decision for admitted patients. This improvement, although small, occurred in the face of increase in ED volume and crowding. As we expected, the high-acuity area that was not impacted by the intervention but also faced increases in crowding had a significant increase in the time to disposition. Our prior work estimating the financial impact of this intervention showed that although the initial investment of $1.2 million spent to redesign the waiting room and annual incremental operating costs of $1.86 million were significant, the overall net present value that takes into consideration a reduction in LWBS rates and incremental ED capacity was an estimated $2.8 million (17). A key observation in our study was that although there were significant time savings early in the process, we were able to achieve only a 6-min decrease in time to disposition. In essence, the time saved early on in the evaluation of patients is not carried forward all the way through to the final step, the disposition decision. This finding highlights a key concept in operations research, the bottleneck. In a multi-step service or manufacturing process, the bottleneck is the slowest and thus, rate-limiting step. In a complex service delivery
environment like the ED, identifying the bottlenecks is a complicated process for several reasons. These include a combination of diverse patient needs, numerous steps in the care process, and changing cycle time of each step depending on factors such as resource limitations, patient mix, or staffing. These factors lead to shifting bottlenecks, which might vary for each provider, clinical encounter, or time of day. Although our study demonstrates that earlier physician evaluation and orders did impact the time to disposition for admitted patients, our findings may be based on our institution-specific bottlenecks, explaining why our findings may be different from findings by Russ et al. (16). Finally, although early evaluation and treatment may lead to improved quality and safety of critically ill patients, the impact of rapid evaluation and disposition of medium-acuity patients on outcome has not been published in the literature. Further studies are needed to understand the impact of this intervention on quality, triage, resource utilization, patient satisfaction, and resident education. Nonetheless, this article and our prior work has shown that this intervention, when implemented appropriately, can lead to improved operational outcomes in the setting of fixed ED physical plant space and increases to ED census and crowding. Limitations The retrospective study design, lack of randomization, and single study site impacts the generalizability of our
Table 4. Difference in Medians between Pre and Post Intervention: Admitted Medium-acuity vs. Admitted High-acuity Patients
Time to admit decision, min Time to physician evaluation Time to ED discharge Time to ED treatment order Antibiotics Analgesia Antiemetic Time to ED radiology
Medium Acuity (95% CI)
High Acuity (95% CI)
Difference (95% CI)*
p-Value*
6 (12, to 0) 16 (19 to 14) 13 (21 to 5)
3 (1 to 7) 3 (4 to 2) 2 (11 to 6)
9 (16 to 2) 13 (16 to 11) 11 (22 to 1)
0.010 <0.001 0.038
36 (48 to 24) 70 (80 to 60) 66 (81 to 50) 16 (19 to 12)
16 (31 to 0) 32 (41 to 21) 38 (62 to 17) 1 (2 to 0)
20 (39 to 1) 39 (53 to 24) 27 (56 to 2) 15 (19 to 11)
0.021 <0.001 0.034 <0.001
CI = confidence interval; ED = emergency department. * The 95% CI of differences and p values were estimated using bootstrap resampling method.
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conclusions and our ability to fully exclude all potential confounding. These are key limitations to the study, and we attempt to address them in two ways. First, we use the high-acuity patients, a group that would be similarly affected by overall ED efficiency improvements and not impacted by the intervention, as a comparison group. Second, by using regression analysis, we attempt to identify possible confounders that may impact our conclusions. The limitations in our study are indeed common in the ED operational research literature, and although some of these limitations may be addressed by these techniques, they do not fully address the underlying limitations of the study design. An additional limitation is the use of physician computer order time as a proxy for time to physician evaluation. Although it is very uncommon for patients to be seen by a physician without subsequent orders, the order entry may occur several minutes after evaluation. However, this delay would serve to falsely decrease the effect size, in that the actual time to physician evaluation would be lower than the calculated time. Also, although the physician order is the first step in the order process, these orders are, on occasion, given verbally, and there are several steps from physician order to actual care delivery. CONCLUSION Physician triage that focuses on both the disposition of straightforward patients and initiating work-ups, leads to a modest improvement in the time to disposition decision for admitted patients and more marked improvements in the times to evaluation, diagnostic testing, and initiation of critical treatments. These improvements are in line with the key tenets of emergency medicine and patient expectations. REFERENCES 1. Institute of Medicine. Hospital-based emergency care: at the breaking point. Washington, DC: The National Academies Press; 2007.
2. Horwitz LI, Green J, Bradley EH. US emergency department performance on wait time and length of visit. Ann Emerg Med 2010; 55:133–41. 3. McCarthy ML, Zeger SL, Ding R, et al. Crowding delays treatment and lengthens emergency department length of stay, even among high-acuity patients. Ann Emerg Med 2009;54:492–503.e4. 4. Bernstein SL, Aronsky D, Duseja R, et al. Society for Academic Emergency Medicine, Emergency Department Crowding Task Force. The effect of emergency department crowding on clinically oriented outcomes. Acad Emerg Med 2009;16:1–10. 5. Pines JM, Pollack CV Jr, Diercks DB, Chang AM, Shofer FS, Hollander JE. The association between emergency department crowding and adverse cardiovascular outcomes in patients with chest pain. Acad Emerg Med 2009;16:617–25. 6. Pines JM, Localio AR, Hollander JE, et al. The impact of emergency department crowding measures on time to antibiotics for patients with community-acquired pneumonia. Ann Emerg Med 2007; 50:510–6. 7. Pines JM. Timing of antibiotics for acute, severe infections. Emerg Med Clin North Am 2008;26:245–57, vii. 8. Boudreaux ED, O’Hea EL. Patient satisfaction in the Emergency Department: a review of the literature and implications for practice. J Emerg Med 2004;26:13–26. 9. Fernandes CM, Daya MR, Barry S, Palmer N. Emergency department patients who leave without seeing a physician: the Toronto Hospital experience. Ann Emerg Med 1994;24:1092–6. 10. Cassidy-Smith TN, Baumann BM, Boudreaux ED. The disconfirmation paradigm: throughput times and emergency department patient satisfaction. J Emerg Med 2007;32:7–13. 11. Wiler JL, Gentle C, Halfpenny JM, et al. Optimizing emergency department front-end operations. Ann Emerg Med 2010;55: 142–60.e1. 12. White BA, Brown DF, Sinclair J, et al. Supplemented Triage and Rapid Treatment (START) improves performance measures in the emergency department. J Emerg Med 2010 Jun 14. [Epub ahead of print]. 13. Chan TC, Killeen JP, Kelly D, Guss DA. Impact of rapid entry and accelerated care at triage on reducing emergency department patient wait times, lengths of stay, and rate of left without being seen. Ann Emerg Med 2005;46:491–7. 14. Han JH, France DJ, Levin SR, Jones ID, Storrow AB, Aronsky D. The effect of physician triage on emergency department length of stay. J Emerg Med 2010;39:227–33. 15. Holroyd BR, Bullard MJ, Latoszek K, et al. Impact of a triage liaison physician on emergency department overcrowding and throughput: a randomized controlled trial. Acad Emerg Med 2007;14: 702–8. 16. Russ S, Jones I, Aronsky D, Dittus RS, Slovis CM. Placing physician orders at triage: the effect on length of stay. Ann Emerg Med 2010;56:27–33. 17. Soremekun OA, Biddinger PD, White BA, et al. Operational and financial impact of physician screening in the ED. Am J Emerg Med 2011 Mar 17. [Epub ahead of print].
Physician Screening Impact on ED Patient Flow
ARTICLE SUMMARY 1. Why is this topic important? Placing a physician in triage is one of several front-end interventions that have been proposed to reduce crowding and increase patient safety in the waiting room. Thus, it is important to understand the impact of this intervention on patient flow and potential quality measures. 2. What does this study attempt to show? The design of physician in triage in the published literature has varied, with mixed results obtained on the impact. This study attempts to show the impact of a properly resourced and implemented physician-intriage program on patient flow. 3. What are the key findings? The physician-in-triage intervention led to a reduction in time to physician evaluation, time to disposition, and total length of stay despite an increase in overall ED volume. There was also significant improvement in time to analgesia and antiemetic orders. 4. How is patient care impacted? The physician-in-triage program improved overall patient flow, time to evaluation, and time to key orders of medium acuity patients.
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