Overcrowding is a major problem for emergency care centers across the United States.1,2 Over the past decade, the number of patients seeking care at emergency departments has increased by 26 percent.3 With this rapid increase in patient demand, health systems have had limited opportunities to expand structural capacity and optimize patient-flow processes, leading to substantially increased patient wait times.2 Long emergency department wait times have been associated with substantially lower patient satisfaction scores4 and significantly worse health outcomes.5–12 For example, patients with acute ST-segment elevation myocardial infarctions experience a 7.5 percent increase in 1-year mortality rate for every 30 minutes of treatment delay.8 Although the impact of wait times on health outcomes is concerning for the general population, it is particularly alarming for vulnerable populations, such as racial minorities, who use the emergency department more frequently than whites13 and experience disproportionately longer wait times.14–16
To date, substantial effort has been dedicated to understanding the impact of emergency department wait times in the management of fatal traumatic injuries.14–16 However, little is known about their impact on the management of nonfatal injuries, such as traumatic digit amputations. Considered one of the most serious hand injuries, traumatic digit amputations afflict upward of 45,000 people annually.17 Although not fatal, digit amputations can substantially impact patients’ function, aesthetics, quality of life, and occupation.18 Furthermore, children and individuals in their prime, income-earning years suffer a disproportionate number of these injuries.18 Consequently, choosing the most appropriate treatment can have significant and longstanding economic effects on the well-being of trauma patients. Little is known about inequality in emergency department wait times for digit amputation injuries and the associations between emergency department wait times and the type of treatment received because of their nonfatal nature.
Applying the conceptual framework of quality of care described by Donabedian19 and using the 2007 to 2012 National Trauma Data Bank20 developed and managed by the American College of Surgeons,21 we addressed the intersection of hospital quality, defined by emergency department wait times, and performance of digit (finger or thumb) replantation. Emergency department wait times were merely used as a quality measure and not as an indicator to determine whether replantation was possible or not. Given the lack of nationalized data related to hospital quality metrics, hand/plastic surgeon staffing/availability, and treatment received for patients with traumatic digit amputation, we specifically chose emergency department wait times as a proxy for hospital-level efficiency and availability of hand/plastic surgeons with microsurgical skills. As such, our study aimed to examine any associations between hospital systems of care and the treatment received. We hypothesized that patients with traumatic digit amputation injuries treated at hospitals with overall lower emergency department wait times would have higher odds of undergoing replantation.
PATIENTS AND METHODS
We used the 2007 to 2012 National Trauma Data Bank, which is the largest collection of trauma data optionally reported by a growing number of trauma centers across the United States.22 Participation is entirely voluntary, with a yearly call for data, and no outside incentive is given. Over the study period, of roughly 1200 trauma centers in the United States, the number of annually reported trauma centers in the National Trauma Data Bank grew from 43523 in 2007 to 80524 in 2012.22
In this study, we examined the association between emergency department wait times (i.e., the time between acceptance to the emergency department and surgery or discharge) and the treatment received. We used International Classification of Diseases, Ninth Revision codes. International Classification of Diseases, Ninth Revision codes are used to define the injury diagnosis and procedure(s) undertaken. We used diagnostic codes 885.0, 885.1, 886.0, and 886.1 to classify traumatic digit amputations. Procedure codes 84.21 and 84.22 define replantation of the amputated finger and procedure codes 84.01 and 84.02 define revision amputations.
We included all trauma patients with valid International Classification of Diseases, Ninth Revision codes who underwent a revision amputation or replantation after a digit amputation injury. Between 2007 and 2012, based on International Classification of Diseases, Ninth Revision diagnosis and procedure codes, there were 13,337 reported cases of traumatic digit amputation. Because treatment of older patients may be related to many other confounding issues, our study cohort included patients aged 64 years or younger. Trauma level (I, II, or III) is designated by state officials and is verified by the American College of Surgeons.25 The National Trauma Data Bank contains a separate record, labeled with a patient number, for each diagnosis a patient receives. To perform our analyses at a patient level, we grouped the injuries by patient number. Between 2007 and 2012, there were 12,577 patients younger than 65 years with traumatic digit (i.e., finger and thumb) amputations. Our final sample, after excluding patients with missing values, included 12,126 patients with traumatic digit amputations treated at 598 centers; among them, 409 patients experienced complicated thumb amputation injuries (International Classification of Diseases, Ninth Revision code 885.1). Because the decision to attempt replantation is less debatable for complicated thumb amputation injuries (International Classification of Diseases, Ninth Revision code 885.1), we analyzed the association between decision to undergo replantation and emergency department wait times for (1) all traumatic digit amputation patients and (2) complicated thumb amputation injury patients only. We verified the completeness of our cohort by matching the total number of digit amputation injuries (12,577) with the sum of revision amputations (10,409) and replantations (2168). (See Figure, Supplemental Digital Content 1, which presents the schematic flow diagram of our study population, detailing the selection of patients in the National Trauma Data Bank. Source: 2007 to 2012 National Trauma Data Bank. ICD-9, International Classification of Diseases, Ninth Revision, http://links.lww.com/PRS/C19.) More than 5 percent of patients had missing values for race, insurance status, and emergency department wait times. Because of its heterogenous assignment, ethnicity as defined by being Hispanic or not usually has extensive missing values in administrative and medical data. By creating a separate, “unspecified” category for each of these three variables, we included them in our regression models to avoid selection bias.26,27
Explanatory and Outcome Variables
The two main outcomes of interest in this study were emergency department wait times and incidence of replantation. At the patient level, our analysis included race, age, sex, Injury Severity Score, presence of thumb or other finger amputation, presence of multiple- or single-digit amputations, presence and number of certain chronic conditions, being a smoker, having an alcohol problem, and insurance status. Age is represented by the patient’s age at the time of injury and categorized into three mutually exclusive sets of younger than 18, between 18 and 44, and between 45 and 64 years. Based on patient-reported race, we generated three distinct categories of white, nonwhite, and race not specified (race missing). Insurance was measured using five distinct categories: private (i.e., commercial insurance, Blue Cross Blue Shield, and worker’s compensation), public (i.e., Medicaid or Medicare), other (i.e., no-fault and other insurance), uninsured (i.e., self-pay), and unspecified.
Injury Severity Score was used to control for the severity of the injury.28 At the hospital level, we controlled for teaching status, for-profit status, number of hospital beds, trauma level, and geographic region of hospital. Hospitals were categorized as either for-profit or public and nonacademic or academic. We used four categories for the number of beds: less than 200, between 200 and 400, between 401 and 600, and more than 600, with less than 200 beds serving as the reference category. We categorized the hospital trauma level into four mutually exclusive groups of Level I, II, III or higher, and hospitals without a trauma level designation. Finally, the geographic location of each hospital was categorized by region into Midwest, Northeast, South, or West.
First, we looked at unadjusted characteristics of patients stratified by four quartiles based on the time (in minutes) that each patient spent in the emergency department. To determine the statistical significance between the lowest quartile and other quartiles, we used the chi-square test for categorical variables and the t test for continuous variables (Table 1). In addition, we used the chi-square test to show the statistical significance of the unadjusted time spent in the emergency department, stratified by insurance status (Fig. 1). We used the clustered variance estimation method to adjust for hospitals with repeated measures over time (Table 2). We then ran our second regression model to estimate the association between average emergency department wait times at the hospital level, inclusive of patients with and without traumatic digit amputation, and the treatment option undertaken (replantation versus revision amputation) for all traumatic digit amputations and for complicated thumb amputation injuries (Table 3). Finally, based on the regression models described in Table 3, we estimated the adjusted predictive probability of undergoing replantation for each quartile of time spent at the emergency department (Fig. 2).
Table 1 presents the characteristics of patients younger than 65 years with traumatic digit amputation. We stratified patients based on their emergency department wait times. Older patients spent more time in the emergency department with 4 (p = 0.010), 7 (p < 0.001), and 2 percent (p = 0.091) more adults between ages 44 and 64 years with wait times in the second (93 to 147 minutes), third (148 to 231 minutes), and fourth quartiles (>231 minutes), than in the first quartile (<93 minutes), respectively. There were 9 and 7 percent more nonwhites among patients in the third and fourth quartiles, respectively (p < 0.050). Five percent fewer people with private insurance were in the fourth quartile compared with the first quartile. The average Injury Severity Score in the first quartile was 5.22 and was significantly higher than the Injury Severity Score in other quartiles (p < 0.001 for all). In addition, substantially more patients with multiple-digit and thumb amputations were in the first quartile (p < 0.001 for all).
Figure 1 shows the unadjusted average of wait times in the emergency department based on health insurance. Compared with individuals with private insurance, those with public insurance, no insurance, or other insurance, on average, spent 15, 31, and 83 minutes longer in the emergency department, respectively.
Table 2 represents the results of our generalized linear model regression analysis. Being younger than 18 years was associated with shorter wait times in the emergency department by a factor of 0.82 (beta = −0.20; 95 percent CI, −0.30 to −0.10). Being nonwhite was associated with longer wait times by a factor of 1.13 (beta = 0.06; 95 percent CI, 0.00 to 0.11). Higher Injury Severity Score and having a thumb amputation were associated with shorter wait times by factors of 0.99 (beta = −0.01; 95 percent CI, −0.02 to 0.00) and 0.84 (beta = −0.17; 95 percent CI, −0.23 to −0.10), respectively. Compared with private insurance, having public or no insurance was associated with longer wait times by factors of 1.11 (beta = 0.10; 95 percent CI, 0.00 to 0.20) and 1.13 (beta = 0.12; 95 percent CI, 0.05 to 0.20). At the hospital level, being a Level I trauma center was associated with longer wait times by a factor of 1.32 (beta = 0.28; 95 percent CI, 0.11 to 0.45) compared with hospitals with full capacity but without trauma level designation. In addition, compared with academic hospitals, being a nonacademic center was associated with longer wait times by a factor of 1.22 (beta = 0.20; 95 percent CI, 0.01 to 0.39).
Table 3 shows that for all digit amputation injuries and for complicated thumb amputation injuries only, hospital level emergency department wait times were associated with replantation. For example, odds of replantation for all digit amputation injuries that were treated in hospitals with more than 231 minutes’ emergency department wait times (fourth quartile) compared with those being treated in hospitals with less than 93 minutes’ emergency department wait times (first quartile) was as follows: OR, 0.36 (95 percent CI, 0.30 to 0.42). For complicated thumb amputation injuries only, the odds ratio of replantation if treated in hospitals with emergency department wait times at the fourth quartile compared with being treated at hospitals within the first quartile was 0.26 (95 percent CI, 0.10 to 0.69).
Figure 2 shows the adjusted predicted probability of replantation stratified by wait times in the emergency department. For all digit amputation injuries, compared with fewer than 93 minutes (first quartile), the probability of replantation decreased by 5, 12, and 15 percent in second, third, and fourth emergency department wait time quartiles, respectively (p < 0.001 for all). For complicated thumb amputation injuries, compared with fewer than 93 minutes (first quartile), the probability of replantation decreased by 10, 8, and 20 percent in the second, third, and fourth emergency department wait time quartiles, respectively (p < 0.001 for all).
We used emergency department wait times as a proxy for hospital structure and availability of on-call hand or plastic surgeons with microsurgical skills. For both patient groups (patients with simple and complicated finger and thumb amputation injuries and patients with complicated thumb amputation injuries only), longer emergency department wait times were associated with lower odds of undergoing a replantation. Our evaluation yielded three additional findings. First, being minority and having no insurance were associated with longer emergency department wait times. Second, academic hospitals were associated with shorter emergency department wait times. Finally, for patients with complicated thumb amputation injuries only, compared with those with all types of digit amputation injuries, there were no associations between patients’ minority or insurance status and replantation.
Our study supports earlier research showing that vulnerable populations, such as minority groups and uninsured individuals, experience disproportionately delayed access to care in the emergency department. A 2008 evaluation of emergency department quality and efficiency found that white patients with acute myocardial infarction waited a median of 24 minutes to see an emergency medicine physician, whereas African Americans and Hispanics waited median times of 31 and 33 minutes, respectively.11 Similarly, studies have shown that wait times for uninsured patients can be as high as 33 percent longer than wait times for patients with insurance.29 There are several possible explanations for the race- and insurance-related disparities in emergency department wait times. First, efficient and effective emergency departments (higher quality emergency departments) may be less accessible to vulnerable populations because minority patients tend to reside closer to low-quality hospitals, as defined by mortality rate, relative to their white counterparts.30 Given that many patients seek care through nearby facilities,31 minority or uninsured patients may disproportionately seek treatment at low-quality hospitals close to their place of residence.32
Second, an unfavorable payer mix at hospitals serving predominantly minority patients may severely impact patient wait times. Haider et al. showed that hospitals serving predominantly minority populations can have nearly double the percentage of uninsured patients compared with hospitals serving predominantly majority or mixed populations.33 The increased proportion of uninsured patients may drive higher patient volumes and longer wait times. Moreover, lower quality hospitals may not have the resources to (1) staff hand/plastic surgeons with microsurgical skills at all times,34 (2) maintain a strong plastic and hand surgery residency/fellowship training program,35 or (3) invest in process improvement tools that could reduce patient wait times.36
Regarding the impact of hospital-level characteristics on timely access to care in the emergency department, our analysis revealed that being an academic hospital was associated with decreased wait times for patients seeking treatment for traumatic digit amputations. These findings substantiate earlier studies, which found that teaching status impacts quality-of-care processes and patient outcomes. For example, patients with acute myocardial infarction who are treated at teaching hospitals are more likely to receive guideline-recommended treatment modalities, such as aspirin, angiotensin-converting enzyme inhibitors, and beta-blockers, compared with those treated at nonteaching hospitals.37 With respect to complex surgical procedures, research shows that teaching hospitals have lower operative mortality rates for hepatic, pancreatic, and esophageal resections compared with nonteaching hospitals.38 The superior surgical treatment at academic hospitals may be attributable to a higher volume of complex operations and the lower risk of adverse events associated with high-volume hospitals.39
Our results agree with previous findings that process components of hospital quality, such as emergency department wait times, affect quality of patient care and outcomes.12,40 Overcrowding is one of the primary drivers of increased patient wait times and has been shown to directly impact the process of patient care. Studies show that overcrowding is associated with delayed access to antibiotics for treatment of pneumonia,12,41 delayed use of thrombolysis for treatment of acute myocardial infarction,42 and increased time to analgesic administration for patients with severe pain from hip fractures.43 Emergency department process inefficiencies are derived largely from resource and capacity constraints; when the emergency department is operating at or above capacity, delivery of multistep patient care is less efficient.43,44 Overcrowding has also been associated with worse clinical guideline adherence rates.40 Importantly, the problem of delayed access to care has significant consequences for patient outcomes. For example, patients admitted to the emergency department during periods of overcrowding experience 5 percent greater odds of death compared with those admitted during periods with manageable volume and lower patient wait times.45 In cases of complicated thumb amputation injuries, longer emergency department wait times could be attributable to the unavailability of an on-call hand or plastic surgeon with the necessary microsurgical skills.46 Replantation of the thumb is mostly performed in Level I or II trauma centers, and it is the optimal treatment option; thus, race and insurance type were not associated with complicated thumb replantation. Many stakeholders have argued that expansion of insurance coverage under the Affordable Care Act may lead to an increase in emergency department crowding. When there is a shortage of physicians, this argument seems logical. However, in a study published in the New England Journal of Medicine47 examining the Massachusetts’s health care reform, the authors did not find any change in emergency department use because of expansion of health coverage.
Our study had a few limitations. First, the National Trauma Data Bank is not a nationally representative sample of all trauma centers in the United States and, among those centers that report their trauma cases, only approximately 67 percent had associated International Classification of Diseases, Ninth Revision codes. Thus, our findings may not be generalizable. However, our results were in agreement with the related literature from other sources. Second, the National Trauma Data Bank lacks certain information, such as the location of these centers, the number and expertise of surgeons, and the distance from the center to the patients’ residences, making it impossible to further investigate the reasons behind longer wait times. In addition, except for insurance status, there is no socioeconomic information to include in our analytic models. Because the National Trauma Data Bank is not a longitudinal data set, we were unable to follow patients over time to evaluate the rate of replantation success as the ultimate outcome measure. Finally, these discrepancies may be attributable to surgeon characteristics. The wait times may point to a maldistribution of microsurgical expertise in various trauma centers rather than just differences in emergency department process of care. Despite these shortcomings, we were able to evaluate the association between quality of a trauma center, measured by emergency department wait times, and probability of undergoing replantation, an optimal but complex surgical treatment.
Because of the lack of a recommended treatment guideline for digit amputation injuries, overtreatment (attempt to perform replantation when it is not recommended) or undertreatment (revision amputation when replantation is recommended) may occur more often than it should. In 2006, the Institute of Medicine’s Committee on the Future of Emergency Care in the United States created a vision that included a reduction in emergency department overcrowding, fragmentation, and on-call specialist shortages.48 Policy makers and hospital systems have explored a number of solutions to resolve problems in the timeliness of emergency department care. One strategy involved improving hospital efficiency and patient flow by implementing process management tools. These tools, used extensively in the business world, aim to increase patient throughput by reducing delays and improving quality. Queuing theory analytics are mathematical methods for understanding patient volume trends that allow emergency departments to preallocate resources and capacity to meet changing patient demand.49 A study by Alavi-Moghaddam et al. found that adding a senior emergency resident to the floor staff decreased patient wait times by 10 percent.49 Moreover, adding 50 percent more staff to specialist consultations reduced patients’ length of stay by 90 minutes.49
The reallocation of capacity and resources by means of regionalization of trauma care services is a much-needed approach to improving emergency department efficiency and effectiveness. Regionalization was initially recommended by the Institute of Medicine as a way for underequipped hospitals to selectively refer complex or high-risk cases to trauma centers with better resources.50 It is important to note that under the leadership of Scott Levin, M.D., the American Society for Surgery of the Hand initiated a joint task force with the American College of Surgeons to address issues related to regionalization of hand trauma care in the United States.51 A few other possibilities include paying academic centers premiums to care for uninsured or patients of lower socioeconomic classes or requiring plastic surgery board-eligible candidates to perform community service for a period after residency graduation.
This work was supported by a American Foundation for Surgery of the Hand Clinical Grant (to E.M.) and a Midcareer Investigator Award in Patient-Oriented Research (2 K24-AR053120-06) (to K.C.C.).
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