Crowding in emergency rooms (ERs) has been an increasing problem in the developed world for the last few decades, especially in the United States. However, the political and medical arenas are not appropriately addressing this problem as from 1995 to 2009 annual ER visits in the U.S. increased by 41% (96.5 million to 136.1 million), but the number of hospital ERs have decreased by 27% (2,446 to 1,779)1-3 Among U.S. ERs in 2010 a mere 31% achieved their triage targets and only 48% were able to admit patients within 6 hours of registration.4 One of the immediate problems with this overcrowding problem is that it has become a normal occurrence. How could ERs effectively respond to outbreaks of highly contagious pathogens, industrial accidents, terrorist attacks, etc. if currently over half of the non-critical patients have to wait 6+ hours before receiving treatment? Apart from disasters ER crowding increases patient mortality, reduces quality of overall care, impaired transport access and increases financial losses and stresses. Also note that ER crowding is not a unique problem to market economics, but also affects countries with universal systems of medicine like Canada, Australia, New Zealand, etc., thus the passage of the Affordable Care Act will not systematically result in a reduction in crowding.
ER operations have numerous metrics to measure the effectiveness of operations, but typically the most commonly used ones are length of stay (LOS), % of patients who leave without being seen (LWBS), wait time (WT), and ambulance diversion (AD).5,6 However, while these metrics are commonly used, they should not be utilized in a vacuum because some ERs do not even have the ability to divert ambulances and patient wait metrics like LOS and WT are influenced by case complexity. Another concern about these metrics is that most of them are rarely made public nor are there set standards regarding quality, thus it is difficult to have common up-to-date information to determine whether or not a given community is receiving adequate medical care in both absolute and relative terms.
Opposite the fast-paced ambulatory delivery of a critical patient into an emergency room who is immediately admitted, the general operation of an emergency room from the perspective of an individual who enters outside an immediately apparent life threatening condition is as followed:
First, the attending nurse (rarely a physician) conducts a basic triage. Triage itself typically adheres to the Emergency Severity Index (ESI), which is a 3 or 5-tier categorization that combines urgency with an estimate of the resources required to treat the condition.7-9 In the original, now less common 3-tier system the three groups are: immediate treatment required (emergent); urgent, but not currently life or permanent health threatening; or minor condition that can be addressed in time (non-urgent); obviously these categorizations are required to ensure the best and most appropriate care for all potential patients.
In the 5-tier system an additional two groups are added: resuscitation and less urgent making the whole tier structure – 1) resuscitation; 2) emergent; 3) urgent; 4) less urgent; 5) non-urgent.8,9 Realistically the addition of these two new tiers seems rather unnecessary because resuscitation is an obvious choice for immediate treatment not requiring its own category and the difference between less urgent, urgent and non-urgent is marginal. However, it seems to work and does not appear to create significant complications with its seeming unnecessary redundancy.
Clearly individuals with urgent conditions should be seen by physicians before individuals with minor conditions even if the individual with a minor condition arrived first. Triage typically involves acquiring major vital signs (temperature, pulse, respiratory rate, blood pressure, etc.) and a short interview to assess what the patient is feeling and the major details regarding medical and medication history. Depending on the type of classification the new patient will be placed in a certain position on a waiting list.
The triage system typically functions under a scoring system to evaluate the condition of the individual. In addition to physical scoring, physiological scoring is also used to address urgency for treatment. Utilized scoring systems include APACHE II (which is also the most common ICU system to measure prospective mortality), SAPS II, MODS, PRM and GCS (becoming more popular due to its simplicity, sensitivity and specificity).10-14 Scoring systems have also demonstrated that ER care is significantly more important than follow-up care in the ICU showing significant drops in predicted mortality for proper ER care.15 In addition to the older tests, a newer test, the Mortality in Emergency Department Sepsis Score (MEDS), was recently been developed to predict the probability that ER patient contract an infection that could increase complications and/or mortality.16
While tests like APACHE II, SAPS II and MODS are important analysis elements, the development of new ER specific scoring systems like MEDS is important because the older systems were designed to measure illness severity and mortality risk probabilities in a less time dependent nature within the confines of an ICU whereas the ER environment is fast-paced and more time dependent creating a lead-time bias.10,15 Factors that are considered important for ER based scoring systems include: 1) variables that reflect pre-hospital illness severity; 2) illnesses that can be contracted from the ER; 3) ability to be incorporated into a multi-center database with sufficient size and power to validate the model’s accuracy; 4) analytical ability for the relationship between the predictive variables and actual patient outcome for calibration and reliability measurement; 5) secondary predictive effects beyond simple mortality to measure LOS, WT and return visits; 6) use of time-indexed variables to reflect treatment response during care.10,17,18
While a nurse typically governs triage, some studies have suggested that when a physician is in charge of triage instead of a nurse various performance metrics like LOS, LWBS and AD all decrease.19-21 Of course the trade-off for this potential improvement is an increase in cost due to hiring another physician. Otherwise in-room care for patients that have moved from the ER waiting room to an exam or operating room will suffer because of the lack of a physician or one being stretched between exams and triage.
Second, individuals who do not require immediate treatment enter the registration process where the patient officially registers as a patient, which involves filling out all of the relevant paperwork familiar to any first-time patient in a general practitioners office. This step is relevant to consolidate all relevant information including a more detailed medical history and payment information (insurance, etc.). These details are important to create a single medical record that can be referenced during the patient’s stay in the ER. It is important to note that a number of people incorrectly believe that an uninsured individual receives free medical care when going to an ER. This is not correct. The Emergency Medical Treatment and Active Labor Act of 1986 only obligates ERs to care for individuals regardless of ability to pay. Uninsured individuals that receive care from an ER still receive a bill for the services rendered. If they are unable to pay the bill then their credit score is negatively affected and if the hospital/physician so desires they can be sued for the amount. This billing is why uninsured individuals in the past did not go to the ER for every little thing that may be wrong with them.
Afterwards the ER visit proceeds similar to an standard physician visit where when it is an individual’s turn the individual enters an exam room where a nurse reassesses blood pressure and temperature, and if necessary draws blood and/or collects a urine sample for lab testing purposes. Next a physician visits the patient and after a brief discussion makes a differential diagnosis. After the diagnosis for conditions that are not immediately critical the patient is prescribed a treatment and sent home.
One of the major reasons critics cite for continued difficulty in transforming ERs to better manage their patient flow is their tradition/culture. As described above the standard operation of an ER is one person – one task with little intra-staff interaction, a methodology that in the era of computers and multi-tasking is viewed as inefficient and costly. A significant amount of this inefficiency comes from having different doctors and nurses repeat information gathering due to lost or “mistranslated” previous attempts. This problem is augmented by poor coordination among providers, which are typically highly fragmented encompassing multiple emergency medical service agencies with different standards and different practices to the point where agencies in different, but adjacent jurisdictions have difficulty communicating. This coordination is difficult due to turf wars and because transport options are limited.
To maximize the effectiveness of reform interventions dramatic improvement in intra and inter-hospital coordination will be required including standardization of procedures and practice. Incorporation of electronic health records would help in managing this concern, but applying electronic health records for an ER is significantly more difficult than a standard physician office due to the required pacing and lack of consistency in the repeat visitation of patients. Unfortunately in addition to the incorporation of electronic health records, the expanded coordination discussed above has always been the go-to solution and the general dream of individuals trying to address crowding problems, but this coordination is very slow to developed despite the desire to produce it.
One strategy to increase coordination is to increase multi-tasking. However, while some cite some limited studies about the improved efficiency born from multi-tasking there is concern about expanding this strategy for other studies suggest that demonstrated reduced cognitive efficiency in individuals who engage in multi-tasking versus focusing on a single task and then moving on to a secondary task.22 Reduced cognitive efficiency would increase the probability of medical errors and increase the probability of detrimental medical outcomes including death. In addition the demographic of ER patients and the seriousness and complexity of their conditions are changing with more older patients with chronic conditions and multiple co-morbidities with younger patients having fewer non-urgent and more semi-urgent and urgent visits.23 Increase the level of complexity in condition and diagnosis while decreasing the attentiveness and focus will further increase the probability of negative outcomes.
One of the past arguments rationalizing ER crowding was that too many uninsured individuals used the ER as a primary care physician because the lack of insurance dramatically reduced their ability to schedule appointments with general practitioners. Individuals who frequent the ER constantly are referred to as “frequent flyers” and typically make up 8-14% of ER patients and were thought to include large numbers of uninsured individuals.24 Therefore, one solution was increasing the probability that these individuals get insurance so instead of going to the ER they would go to a general practitioner to receive general medical care. Unfortunately this solution, while sound in theory, has not followed theory in reality. Both the expansion of insurance availability in Massachusetts in 2006 and various other states through the ACA have resulted in increases in ER patients with state based insurance (Medicare, Medicaid).25 So why is reality apparently trending contradictory to theory?
Two principle reasons jump to mind. First, most common analysis overestimated the number of individuals with no insurance who were using the ER for basic and principle medical care. While frequently flyers make up anywhere from 8-14% of the total patients during the day, most of these individuals have insurance. Recall that the ER is only bound to treat individuals regardless of ability to pay ensuring that they will receive treatment. However, that treatment is not free. Therefore, in the past individuals without insurance who received medical care from an ER would still have to pay for those services. It stands to reason that these individuals would not attend the ER constant because if they could not afford insurance, then they would not have consistent levels of disposable income to cover numerous ER visits for every nick and scrap.
This rationality hints at the second reason for why ER patients have increased. The primary assumption was that uninsured individuals would stop attending the ER when they received insurance. However, what appears to be happening is that previously uninsured individuals are actually attending the ER more often. The reason behind this behavior is probably derived from the fact that government sponsored insurance has significantly increased the number of individuals with insurance while the number of available general practitioners that are able to service these newly insured patients as well as past/current insured patients has increased at a much slower rate. Therefore, there are significant shortages between insured patients and available doctors to see them via appointment. With the lack of consistency in acquiring an appointment with a general practitioner, the consistency of an ER is appealing. The only real ways to resolve this behavior is train and certify more general practitioners, something that will not happen in the immediate future.
Interestingly enough this premise of ER crowding due to uninsured individuals using the ER for basic medical care in the past is not supported by research. Research suggests that while it was initially reported that this input factor was meaningful26,27 that initial interpretation was probably incorrect. ED crowding is more influenced by sickly and chronic patients who are admitted to the hospital than individuals who have minor injuries and are sent home after routine care/check-ups.28-32 Not surprisingly hospital occupancy (i.e. the number of available beds) versus the number of patients, which leads to boarding, is the strongest element correlated to length of stay in the ER and overall wait times.31,32 Other smaller factors leading to crowding are inappropriate ambulance diversion and direction33 and recently discharged inpatients looking for additional care under various motivations.34 However, as mentioned above boarding due to a lack of bed is the chief element responsible for ER crowding.
The most important consideration when identifying possible solutions to ER crowding is to create a standardized evaluation system to determine which solutions are effective, which are not effective and which are mediated by unique environmental conditions (i.e. effective for one particular hospital, but not for another). Developing this evaluation system would also make it much easier to assign accountability and measure overall and sector specific performance to create effective strategies to correct any problems. In addressing “quality” the Institute of Medicine (IOM) defined quality as “the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge” and described six dimensions of quality care: a care that is safe, effective, patient centered, timely, efficient, and equitable.35
Not surprisingly various individuals have suggested that to measure the true value of a system an ER must be evaluated on the application of evidence-based medicine. While this solution should be effective it is sometimes difficult to coordinate the necessary information to ER doctors who typically have little downtime and do not want to spend it reading the latest meta-study. Ideally the practice of extensive evidence-based medicine is one of the dreams of incorporating technology into hospitals to the point where a physician can simply type a condition into a computer and the most effective treatments (as defined by existing evidence) with their corresponding caveats would appear. Unfortunately this reality has not arrived, but a less efficient substitution strategy involves conducting frequent physician meetings for brief reviews of the newest treatment strategies.
Some have suggested that patients define whether or not the quality metrics have been met through evaluations. However, patient evaluation is troublesome because patients may regard elements or instances of discomfort through their own personal lens without understanding or acknowledging the bigger picture. For example a patient may want a glass of water, but due to nurse/physician preoccupation in other more pressing tasks this individual waits a long time before getting the water and possibly develop a slight case of dehydration while waiting. For the patient such an event could easily be worth a quality demerit, but from the perspective of the hospital such an event is irrelevant. Similarly patients are not aware of a significant amount of “behind the scenes” actions relative to their treatment, thus have incomplete information regarding overall treatments and may mischaracterize certain outcomes as poor or negative. This is not to say that patients should not have the ability to make evaluations of their care, but it must be understood that there is high probability that those evaluation cannot be viewed as accurate inside the vacuum of the patient’s own opinion.
Another idea would be to create a small group of government based auditors who would periodically visit ERs and after observation and various informal interviews these individuals would evaluate ER performance and quality based on a series of standardized evaluation metrics. Under this system the bias of patients can be neutralized by an individual who has an understanding of a bigger picture and the bias of the ER authority will be eliminated by a neutral un-invested individual as well as dramatically reduce the time requirements that would be required for mandatory employee based evaluations. The one major drawback to this method would involve producing additional money to fund these government-sponsored auditors.
As mentioned above creating an effective evaluation system will increase the ability to produce quality solutions. Currently one of the most obvious solutions to addressing ER crowding is to reduce boarding. Boarding is the official term to describe when a patient who cannot be moved into an inpatient unit due to a lack of beds remains in the general ER area and receives periodic treatment within. During normal operating hours boarding represents anywhere from 20-40% of the total ER patient population.36 Boarding levels are also significantly influenced by financial decisions in effort to maximize hospital revenues. Not surprisingly average revenue per patient is higher for non-ER admissions than for ER admissions,37 thus hospitals favor giving beds and rooms to those higher value patients leaving ER patients waiting for a bed. The easiest method to reduce boarding is to increase the number of beds available in a hospital. However, this method costs significant amounts of money not only for the beds, but also for hospital expansion to place the beds. Hospitals have already attempted to increase bed number by placing more beds in single rooms, but this strategy can reduce patient welfare being counterproductive.
Some argue that how the bed is utilized also needs to be considered. There are two major types of beds: observation and inpatient. Observation beds are less costly to construct and staff due to building code requirements and upkeep relative to the patients that utilize them.38 In addition in Certificate of Need states constructing additional observation beds do not require the approval of a state agency unlike constructing additional inpatient beds.38 However, when constructing beds in general it must be understood that there are diminishing returns based on changes in patient inflow and medical requirements. Roemer’s Law is frequently cited when considering bed expansions because if one expands bed capacity one is expected to need it and use it. In some context similar to the psychology behind the Jevons paradox if beds are constantly used then the perception for more beds typically results. Basically there appears to be a positive feedback between bed capacity and number of beds used, which may create an inverse relationship where increased capacity increases demand rather than addresses it.39 Thus characteristics behind bed addition must be carefully analyzed before it occurs.
While the metric behind the need to increase bed occupancy is not standard, some research has suggested that a consistent level of 85% during measurements taken at midnight is the minimum level required before beds should be added.40 Note that the average “midnight census” typically calculates the minimum level of occupancy in a given day. The principle reason for this characteristic is the process of the “23-hour patient”. These types of patients are admitted in the morning and discharged in the late evening as a means to allow for evaluation of patients, yet avoiding unnecessary hospitalization. While estimating the difference between the midnight census and the actual occupancy is not universally deterministic most estimate a 5-15 absolute percentage point increase from the midnight census percentage value.40 However, it must be noted that the “23-hour patient” was a popular strategy in managed care, with the ebb and flow in the popularity of managed care it is difficult to estimate how significant this strategy will have in the future.
85% occupancy is the target more cited by professionals and in research, but this figure is typically applied universally not considering the size of the hospital and the number of people that seek medical services. Due to a lack of economies of scale and usage flows, smaller hospitals should have smaller target levels because they will typically have a smaller number of beds creating a greater sense of urgencies when facing greater than average patient visitation. For example if hospital A has 100 beds, an 85% occupancy utilizes 85 beds leaving 15 free; however, hospital B may only have 35 beds, an 85% occupancy utilizes 30 beds leaving only 5 free placing them in more danger for exceeding capacity on an above average admittance day.
Also there are some elements that must be considered including the difference between certified beds and staffed beds. Certified beds are those that are approved by authorities for use on a permanent basis and have been deemed to have sufficient staff to support its use where staffed beds are those that designated only for inpatient or day case care. One commonly suggested improvement to manage bed use is to establish a management program run by a “bed team” who would operate discharge, facilitate rapid turnaround of newly vacated beds, initiate ambulance diversion, and assign waiting patients to an inpatient bed.35 Unfortunately for most hospitals increasing the number of beds is not a viable option without a significant increase in funding, a result that is not forthcoming from state or Federal legislatures.
Another popular method that has been explored to improve ER crowding is the “fast track”. Broadly stated “fast track” is a system designed to process lower acuity patient quicker in order to increase bed turnover and reduce boarding.41 Individuals with injuries like superficial wounds, minor allergic reactions, small bone fractures and minor burns are typical fast track candidates. Interestingly enough fast tracking patient with minor injuries is not new and has been utilized by a number of ERs since the late 1980s.41 Due to this significant penetration fast tracking has been studied the most of any ER reform strategy and has demonstrated reductions in LOS and WT,42-44 yet almost all of this study has focused on LOS and WT and not whether or not patient safety outcomes are improved. One of the concerns with evaluating the efficacy of fast track is that there really is no standard evaluation protocol instead many hospitals have their own rules and criteria. While fast track proponents sing its praises, the overall ability to expand fast tracking is limited because most studies estimate fast tracking only encompasses 10-30% of the total patients seen in an ER and any gains seen when applying a fast track strategy only occur during peak hours.43,45,46
Unfortunately benefits from fast track only emerge when patients are discharged, not streamed through hospital admission.46 Also fast track benefits may be negatively impacted in the future because it largely depends on eliminating technological diagnostic procedures (blood tests, x-rays, CT scans, etc.) versus physical cues that can be evaluated by physicians. The need for diagnostic procedures will more than likely increase in the future as the number of elderly patients with more extensive health histories continue to increase in the future. This demographic change in ER population will not eliminate the advantages of fast track, but should reduce its rate of use limiting its usefulness. This additional testing will add to the already 60-70% of individuals who require laboratory tests when visiting an ER.47
While some strategies have been introduced to reduce testing time like pre-defined test panels for specific symptoms, faster laboratory transportation and early ordering,48 realistically testing takes time and little can be done about it. Some believe that the most useful strategy may be point-of-care testing (POCT) which involves moving laboratory analysis and tests to the ER. As expected undertaking a POCT strategy reduces WT and LOS through a reduction in turn-around time in the laboratory.48 However, a POCT strategy typically involves either large capital expenditure for hospital expansion or giving up space in other areas of the ER which may increase inefficiency and/or boarding for patients with more severe conditions due to a reduction in beds. The potential loss of some beds will be detrimental, but with reasonable expectation in the future for the expansion of primary care from general practitioners (when they eventually start to expand) and the increased need for laboratory services for elderly patients, currently preparation for and utilization of a POCT strategy seems beneficial overall.
A consideration for the increasing elderly population must be made in the scope of ER reform for all signs point to this increase continuing and accelerating. It is projected that demographically elderly patients will increase from approximately 15% to 25-35% of ER visits over the next 30 years.19 As previously mentioned elderly individuals typically require more time and resources for their medical care both on a logistics level (greater medical history) and a biological level (higher probability something can go wrong). Unfortunately there is also a side concern with the elderly. Typically seniors have fewer travel options than younger individuals and may have difficulty attending routine physical examinations (from general practitioners or the ER) even if appointments can be made. Therefore, this lack of option for travel can increase the probability that these elderly individuals put off medical care until it becomes critical creating a problem from nothing.
Another issue with the elderly is that nearly 25% of nursing home residents visit the ER at least once per year.49 Unfortunately a number of nursing homes tend not to promote good health, but instead attempt to simply keep their residence alive, thus those who are suffering from deteriorating health continue to have failing health. This “strategy” produces ER patients that are typically in poorer health than those elderly individuals who live on their own, about 67% of nursing home ER patients have cognitive impairment50 that complicate medical history collection and the nursing home records are rarely helpful. In fact 10% of nursing home ER patients arrive without any written medical documentation and 90% have significant gaps in their histories.51-53 Thus there is little coordination between ERs and nursing homes, largely because it appears that nursing homes do not care enough to apply the effort. However, ERs do need to be more diligent in ensuring that elderly patients across the board receive more clearly written instructions regarding their outpatient care.
Addressing current and future crowding in the ER will first require the development of a standard definition for quality and measurable components that encompass that definition because it is difficult to identify and classify problems when those problems cannot be identified. Independent government sponsored auditors, to ensure effective care and root out any problems quickly, should periodically evaluate these quality metrics. ERs should develop strategies to better manage beds through understanding real average occupancy values, not those taken from overnight values, to determine where there are excess beds and where/when bed demand is greatest. Finally it should be useful to study strategies that will increase the ability to manage elderly patients due to the logical expectation that their ER demographic will increase in the future. It stands to reason that areas with large elderly populations and quality ER service should have some effective strategies that can be applied to other ERs. Overall there are solutions that can be applied to the problem of ER crowding, but it is important that individuals ask the right questions and appreciate the change in future trends rather than declare simplistic panaceas like the incorporation of electronic health records.
1. Johnson, K, and Winkelman, C. “The effect of emergency department crowding on patient outcomes: a literature review.” Advanced Emergency Nursing Journal. 2011. 33(1):39–54.
2. Bullard, M, et Al. “The role of a rapid assessment zone/pod on reducing overcrowding in emergency departments: a systematic review.” EmergencyMedicine Journal. 2012. 29(5):372–378.
3. Bell, M, and Parisi, J. “ED slashes average wait time by more than an hour.” ED Management. 2009. 21(3):30-31.
4. Wiler, J, et Al. “Optimizing emergency department front-end operations,” Annals of Emergency Medicine. 2010. 55(2):142-160.
5. Welch, S, et Al. “Emergency Department Performance Measures and Benchmarking Summit.” Acad. Emerg. Med. 2006. 13(10):1074-1080.
6. Welch, S, et Al. “Emergency Department Operational Metrics, Measures and Definitions: Results of the Second Performance Measures and Benchmarking Summit.” Ann. Emerg. Med. 2011. 58(1):33-40.
7. Fernandes, C, et Al. “Five-Level Triage: A Report from the ACEP/ENA Five-Level Triage Task Force.” J. Emerg. Nurs. 2005. 31(1):39-50.
8. Chonde, S, et Al. “Model comparison in Emergency Severity Index level prediction.” Expert Syst. Appl. 2013. 40:6901-6909.
9. Gilboy, N, et Al. “Emergency Severity Index (ESI). A triage tool for emergency department care. Version 4. November 2011. AHRQ publication #12-0014.
10. Hargrove, J, and Nguyen, B. “Bench-to-bedside review: outcome predictions for critically ill patients in the emergency department.” Critical Care. 2005. 9(4):376-383.
11. Knaus, W, et Al. “APACHE II: a severity of disease classification system.” Crit Care Med. 1985. 13:818-829.
12. Le Gall, J, Lemeshow, S, and Saulnier, F. “A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multi-center study.” JAMA. 1993. 270:2957-2963.
13. Marshall, J, et Al. “Multiple organ dysfunction score: a reliable descriptor of a complex clinical outcome.” Crit Care Med. 1995. 23:1638-1652.
14. Gill, M, Reiley, D, and Green, S. “Interrater reliability of Glasgow Coma Scale scores in the emergency department.” Ann Emerg Med. 2004. 43:215-23.
15. Nguyen, H, et Al. “Critical care in the emergency department: a physiologic assessment and outcome evaluation.” Acad Emerg Med. 2000. 7:1354-1361.
16. Shapiro, N, et Al. “Mortality in Emergency Department Sepsis (MEDS) score: a
prospectively derived and validated clinical prediction rule.” Crit Care Med. 2003. 31:670-675.
17. Knaus, W, et Al. “A comparison of intensive care in the U.S.A. and France.” Lancet. 1982. 2:642-646.
18. Knaus, W, Wagner, D, and Lynn, J. “Short-term mortality predictions for critically ill hospitalized adults: science and ethics.” Science. 1991. 254:389-394.
19. Partovi, S, et Al. “Faculty Triage Shortens Emergency Department Length of Stay.” Acad. Emerg. Med. 2001. 8(10):990-995.
20. Russ, S, et Al. “Placing Physician Orders at Triage: The Effect on Length of Stay.” Ann. Emerg. Med. 2010. 56(1):27-33.
21. Han, J, et Al. “The Effect of Physician Triage on Emergency Department Length of Stay.” J. Emerg. Med. 2010. 39(2):227-233.
22. Poolton, J, et Al. “A comparison of evaluation, time pressure and multitasking as stressors of psychomotor surgical performance.” Surgery. 2011. doi:10.1016/j.surg.2010.12.005
23. Pitts, S, Niska, R, and Burt, C. “National Ambulatory Medical Care Survey: 2006 Emergency Department Summary.” Natl Health Stat Report. 2008. 6:1-38.
24. Huang, J, et Al. “Factors associated with frequent use of emergency services in a medical center.” J. Formos. Med. Assoc. 2003. 102(4):345-353.
25. Moskop, J. “Emergency Department Crowding, Part 1—Concept, Causes, and Moral Consequences.” Annals of Emergency Medicine. 2009. 53(5):605-611.
26. Gallagher, E, and Lynn, S. “The etiology of medical gridlock: causes of emergency department overcrowding in New York City.” J Emerg Med. 1990. 8:785-790.
27. United States General Accounting Office (GAO). “Emergency departments: unevenly affected by growth and change in patient use.” Report to the Chairman, Subcommittee on Health for Families and the Uninsured, Committee on Finance, US Senate, January 1993.
28. Olshaker, J, and Rathlev, N. “Emergency department overcrowding and ambulance diversion: the impact and potential solutions of extended boarding of admitted patients in the emergency department.” J Emerg Med. 2006. 30:351-356.
29. Espinosa, G, et Al. “Effects of external and internal factors on emergency department overcrowding [letter].” Ann Emerg Med. 2002. 39:693-695.
30. Schull, M, Kiss, A, and Szalai, J-P. “The effect of low-complexity patients on emergency department waiting times.” Ann Emerg Med. 2007. 49:257-264.
31. Forster, A, et Al. “The effect of hospital occupancy on emergency department length of stay and patient disposition.” Acad Emerg Med. 2003. 10;127-133.
32. Rathlev, N, et Al. “Time series analysis of variables associated with daily mean emergency department length of stay.” Ann Emerg Med. 2007. 49:265-272.
33. Richards, J, and Ferall, S. “Inappropriate Use of Emergency Medical Services Transport: Comparison of Provider and Patient Perspectives.” Acad. Emerg. Med. 1999. 6(1):14-20.
34. Baer, R, Pasternack, J, and Zwemmer Jr, F. “Recently Discharged Inpatients as a Source of Emergency Department Overcrowding.” Acad. Emerg. Med. 2001. 8(11):1091-1094.
35. Institute of Medicine. “The future of emergency care in the United States health system.” Ann Emerg Med. 2006. 48:115-20.
36. Schneider, S, et Al. “Emergency Department Crowding: A Point in Time.” Ann. Emerg. Med. 2003. 42(2):167-172.
37. Pines, J, et Al. “The Financial Consequences of Lost Demand and Reducing Boarding in Hospital Emergency Departments.” Ann. Emerg. Med. 2011. 58(4):331-340.
38. Lovejoy, W, and Desmond, J. "Little’s Law Flow Analysis of Observation Unit Impact and Sizing.” Acad. Emerg. Med. 2011. 18:183–189.
39. Roemer, M. “Bed supply and hospital utilization: a natural experiment.” Hospitals. 1961. 35:36–42.
40. Green, L. “Queueing Analysis in Healthcare, in Patient Flow: Reducing Delay in Healthcare Delivery.” 2006. Springer, New York.
41. Welch, S. “Patient Segmentation: Redesigning Flow.” Emerg. Med. News. 2009. 31(8).
42. Cochran, J, and Roche, K. “A multi-class queuing network analysis methodology for improving hospital emergency department performance.” Comput. Oper. Res. 2009. 36(5):1497-1512.
43. O'Brien, D, et Al. “Impact of streaming “fast track" emergency department patients.” AHR. 2009. 30(4):525-532.
44. Considine, J, et Al. “Effect of emergency department fast track on emergency department length of stay: a case-control study.” Emerg. Med. J. 2008. 25:815-819.
45. Rogers, T, Ross, N, and Spooner, D. “Evaluation of a ‘See and Treat’ pilot study introduced to an emergency department.” Accid Emerg Nurs. 2004. 12:24-27.
46. Oredsson, S, et Al. “A systematic review of triage-related interventions to improve patient flow in emergency departments.” Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine. 2011. 19:43-52.
47. Yoon, P, Steiner, I, and Reinhardt, G. “Analysis of factors influencing length of stay in the emergency department.” Can J Emerg Med. 2003. 5:155-61.
48. Schimke, I. “Quality and timeliness in medical laboratory testing.” Anal Bioanal Chem. 2009. 393:1499-504.
49. Bergman, H, and Clarfield, A. “Appropriateness of patient transfer from a nursing home to an acute-care hospital: a study of emergency room visits and hospital admissions.” J Am Geriatr Soc. 1991. 39:1164–1168.
50. Gillick, M, and Steel, K. “Referral of patients from long-term to acute-care facilities.” J Am Geriatr Soc. 1983. 31: 74–78.
51. Jones, J, et Al. “Patient transfer from nursing home to emergency department: outcomes and policy implications.” Acad Emerg Med. 1997. 4:908–915.
52. Wilber, S, et Al. “Geriatric Emergency Medicine and the 2006 Institute of Medicine Reports from the Committee on the Future of Emergency Care in the U.S. Health System.” Acad. Emerg. Med. 2006. 13:1345–1351.
53. Davis, M, Toombs Smith, S, and Tyler, S. “Improving transition and communication between acute care and long-term care: a system for better continuity of care.” Ann Long-Term Care. 2005. 13(5):25–32.