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DeBehnke et al. • RESIDENT WORK PRODUCTIVITY
BRIEF REPORTS Emergency Medicine Resident Work Productivity in an Academic Emergency Department In EDs with emergency medicine (EM) residencies, patients are typically seen by a mixture of EM residents, off-service residents, and rotating medical students. In many of these academic EDs, the majority of patients are treated by EM residents under the supervision of EM faculty. The clinical productivity of EM residents while working in an academic ED is important for both educational and operational reasons. Program directors can use this information to evaluate resident clinical exposure and clinical efficiency as residents progress through their training. Medical directors, departmental chairs, and program directors can use this information to better plan ED workforce needs and staffing patterns. However, published data on EM resident clinical productivity are lacking. Only one study from 1990 has investigated the clinical productivity of EM residents.1 The goal of our study was to investigate the number of patients seen per hour, length of stay per patient seen, and triage code distribution for each EM class in an academic ED.
METHODS Study Design. This retrospective study investigated the number of patients seen per hour, length of stay per patient seen, and triage code distribution per EM class in an academic ED. Because this study used archival data, it was considered exempt from informed consent.
Study Setting and Population. This study was performed in an academic ED during calendar year 1997. During this time period, 40,394 adult (age ⱖ18 years) patients were evaluated in the ED. The hospital has 469 beds and is an American College of Surgeons (ACS)-verified Level 1 trauma center. Eighteen percent of the ED pa-
A related commentary appears on page 72.
tients are admitted to the hospital. The hospital is affiliated with the local medical school and serves as both a community hospital for the surrounding community and a tertiary care referral center for southeastern Wisconsin. All major specialties are represented at the hospital and accessible for consultation to the ED if necessary. Specialties taking in-house call (residents) include internal medicine, cardiology, trauma surgery, anesthesia, radiology, neurosurgery, orthopedic surgery, obstetrics/gynecology, and neurology. During the study period, there were no major unusual operational obstacles that delayed patient care. Patients in the ED are triaged according to a four-point scale, with triage code 1 being the most ill and triage code 4 being least ill. During the study period, the EM residency format was a PGY-2–4 (EM 1–3) format with eight residents per year. Thirteen full-time equivalents (FTEs) of board-certified EM faculty physicians attended the ED 24 hours a day. Faculty shifts are 7:00 AM –3:00 PM, 3:00 PM –11:00 PM, and 11:00 PM –7:00 AM daily, and a double coverage shift of 1:00 PM –10:00 PM Monday–Saturday. The ED is staffed 24 hours per day by EM-1, EM-2, and EM-3 residents. The EM1s work nine-hour shifts (7:00 AM – 4:00 PM, 3:00 PM –12:00 PM, 10:00 PM –7:00 AM) Monday through Friday from January through March and 12-hour shifts. (7:00 AM –7:00 PM, 7:00 PM –7:00 AM) on the weekends January through March and every day from April through December. The EM-2s and 3s work 12hour shifts (7:00 AM –7:00 PM, 7:00 PM –7:00 AM) for the entire study period. There is also variable staffing by rotating interns and senior medical students. The EM-1 and EM-2 residents are expected to evaluate patients and staff with the EM attending on duty. The EM-1 and EM2 residents do not staff with the EM3 resident. The EM-3 residents primarily staff rotating interns and medical students, and see patients
primarily while staffing with the EM attending on duty. The EM attending physicians also staff rotating interns and students, and see patients primarily. Residents are expected to sign out patients to their respective class counterpart (EM-1 to EM-1, etc.) at the conclusion of their shift. There is no cross-class sign out. In our patient care system, the EM-2 residents are assigned to the resuscitation area and are the primary resident caregiver for most acutely ill or injured patient. The EM-3s stay in the ED proper and staff rotating residents and students.
Measurements. Patient
demographic data are captured at the time of admission through the registration process and are stored in the hospital computer system. The time the patient first appeared at the triage desk is used as the initial anchor point for length of stay and is entered by the registration clerks. Further patient information (time of discharge, triage code, transport mode, admission location, and ED provider) is entered into the hospital computer system by the ED unit coordinator at the time of patient discharge from the ED. Each hospital physician (attending and resident) has a unique five-digit identifying code that is entered into the hospital computer system. The information system accepts two provider codes (resident and attending fields). All patients are staffed with an attending physician and an attending provider number is always entered into the attending provider field. The resident provider field is occupied by an EM resident number if the patient was seen by an EM resident (EM-3 number used if the EM-3 staffed a rotating intern or medical student). The resident provider field is occupied by the rotating resident provider number if seen by a rotating resident and staffed with the faculty. If a medical student evaluated the patient and staffed with the ED attending faculty, the resident provider field is occupied by a zero. The resident provider field is left blank if the patient was seen only by the attending faculty. Since all provider data are entered at the time of discharge and are based on resident and/or faculty signature, mis-assignment of patients and providers does
ACADEMIC EMERGENCY MEDICINE • January 2000, Volume 7, Number 1
not occur. Also, since only one resident provider number is allowed per patient, charts with two signatures were coded as the first resident who evaluated the patient and signed the chart. Since there is no sign-out across classes, multiple signatures did not affect class productivity numbers. For the purposes of this study, patient data were obtained from patients who registered from midnight on January 1, 1997, to 11:59 PM on December 31, 1997. Length of stay was defined as the elapsed time from presentation at triage to discharge from the ED. The number of hours worked per resident was obtained from the master resident schedule. All patient data (demographics triage code, length of stay, etc.) were downloaded from the hospital computer system to a standard database for query. The patient’s visit was classified by the type of provider who cared for the patient (EM-1, EM-2, or EM-3). For the purpose of this study, only data from patients seen by EM residents were analyzed.
Statistical Analysis. Mean ⫾ standard deviation was determined for the number of patients seen per hour, total length of stay in minutes, and percentage of patients by triage code. Between-class comparisons were made using analysis of variance with a Tukey post-hoc comparison test.
RESULTS During the study period, 40,394 patients were seen in the ED, 16.1% were seen by EM-1s, 28.3% by EM2s, 26.6% by EM-3s, 11.9% by medical students or rotating residents, and 17.1% by faculty alone. Figure 1 shows the mean number of patients seen per hour by provider. The EM-1s saw statistically fewer patients per hour compared with the EM-2s and EM-3s. Figure 2 shows the mean length of stay by provider. Patients cared for by EM-1s had a significantly longer length of stay compared with patients cared for by EM-2s and EM-3s. Figure 3 shows the distribution of patients by acuity level (triage code) per provider. The EM-2s saw the most and EM-3s the least triage
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Figure 1. Mean ⫾ standard deviation for number of patients seen per hour by provider. *p < 0.0001 compared with EM-2 and EM-3 classes. code 1 patients. More triage code 2 patients were seen by EM-1s and EM-2s compared with EM-3s. The EM-3s saw significantly more triage code 3s compared with EM-1s and EM-2s, and EM-2s saw the least number of triage code 3s. More triage code 4 patients were seen by EM-3s compared with EM-1s and EM-2s.
DISCUSSION The results of our study show that in an academic ED, EM residents are involved in the majority (71%) of patient encounters. Our data show that EM-1s see significantly fewer patients per hour than their EM-2 and EM-3 colleagues, and that productivity (number of patients per hour) increases as the residents progress through their training. To the best of our knowledge, the only other study of EM resident productivity showed results similar to ours, with EM-1s seeing 0.73 ⫾ 0.21 patients per hour,
EM-2s 0.85 ⫾ 0.14 patients per hour, and EM-3s 1.19 ⫾ 0.23 patients per hour. Length-of-stay data are one gross measure of ‘‘efficiency’’ in patient care. We showed that EM residents become more efficient as they progress through their EM training. The patients seen by EM-1s had statistically the longest length of stay compared with the patients seen by EM-2s and EM-3s. This may be in part due to the lack of experience in this group of providers, or, more likely, that they were required to staff their patients with the on-duty faculty attending prior to initiating patient workup and disposition. In our system, EM-2s and EM-3s are given more autonomy in initiating patient workups, and this may have caused the improvement in their length-of-stay data. To our knowledge, length-of-stay data for individual EM resident classes have not been published. A recent study by Southall and Harris noted a mean ED length of stay for teaching
Figure 2. Mean ⫾ standard deviation for length of stay (minutes) per provider. *p ⱕ 0.006 compared with EM-2 and EM-3 classes.
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DeBehnke et al. • RESIDENT WORK PRODUCTIVITY
Figure 3. Distribution (%) of patients by triage code (TC) per provider. ( ) = standard deviation, *p < 0.0001 compared with EM-1 and EM-3 classes, # p < 0.0001 compared with EM-1 class, p < 0.0001 compared with EM-1 and EM-2 classes. hospitals ranging from 136–204 minutes.2 Our data are consistent with this report and offer more provider-specific regional information. Our patient distribution by triage code per provider can be explained by our patient care system. Since the EM-2 residents are assigned to the resuscitation area, they see a larger number of triage code 1 and 2 patients. The EM-3s stay in the ED proper and staff the rotating residents and students, hence seeing more triage code 3 and 4 patients compared with those in the other classes.
LIMITATIONS AND FUTURE QUESTIONS It could be argued that our choice of number of patients seen per hour as a measure of productivity, and length of stay as a measure of efficiency, does not accurately reflect true resident productivity and efficiency. There certainly are many variables that affect a resident’s ability to see multiple patients, including time of day, patient complexity, procedures, availability of diagnostic testing, staffing roles and responsibilities, and the number of other patients in the ED. There are also many external factors that impact length of stay, many of them the same factors that affect the number of patients a resident can see in a given time frame. This paper attempted to address triage code
and its impact on productivity and efficiency. Triage code certainly has its limitations in representing patient acuity but was the only available marker for this study. In future studies, perhaps Current Procedural Terminology evaluation and management (CPT E/M) level could be used as a better marker of acuity (although this also has inherent limitations). Other external factors affecting productivity and efficiency were not addressed in this paper; these external factors are realities of EM practice and, despite inherent weaknesses, the number of patients seen/hour and length of stay are currently the best markers available to track productivity and efficiency. Another limitation of our study is that these data reflect our regional experience and may not be representative of other institutions’ experience. Issues inherent to our clinical practice such as staffing pattern, supervision policy, consult and testing availability, and patient demographics may make our results unique to our clinical practice and not generalizable to other practice sites. The fact that our EM resident provider data are consistent with other published data is reassuring but not conclusive. Readers should be cautioned regarding using our data as a benchmark for comparison with their local experience. Along this line, other academic EDs should be encouraged to report their data so that national benchmarks can be developed.
Another limitation to our study is that our data collection spanned residency classes (January through December data collection with July through June EM resident class) and, hence, did not reflect one class’s experience. We believe this is more representative of actual provider level data, and less influenced by individual class nuances. Another limitation of our study is the fact that patient visits in which the resident did not sign the chart were classified as faculty-only visits and hence were not included in resident data. It is unclear what impact this limitation has on our data since our data collection was retrospective and actual patients’ charts were not reviewed to attempt to match resident handwriting with lack of resident signature. This should be kept in mind when interpreting the data. However, from departmental quality assurance data, it is known that this occurs in less than 10% of charts and therefore would not affect resident productivity numbers significantly.
CONCLUSIONS We provide data showing the productivity (number of patients seen per hour) and efficiency (length of stay) for EM residents in an academic ED. These data may be useful to program directors and medical directors in planning ED staffing patterns.—DANIEL DEBEHNKE, MD (
[email protected]), SHAWN O’BRIEN, MD, and ROBERT LESCHKE, MD, Department of Emergency Medicine, Medical College of Wisconsin, Milwaukee, WI Presented at the SAEM annual meeting, Boston, MA, May 1999. Key words. internship and residency; emergency medicine; efficiency; length of stay.
References 1. Langdorf MI, Strange G, Macneil P. Computerized tracking of emergency medicine resident clinical experience. Ann Emerg Med. 1990; 19:764–73. 2. Southall AC, Harris VV. Patient ED turnaround times: a comparative review. Am J Emerg Med. 1999; 17:151–3.
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Stroke Symptom Attribution and Time to Emergency Department Arrival: The Delay in Accessing Stroke Healthcare Study As the third leading cause of death and single largest reason for disability in the United States, stroke exacts an enormous toll on individuals and communities. In the past, permanent neurologic damage after an acute ischemic stroke was almost inevitable, as there were few effective acute therapies. Recent treatment innovations, however, have made it possible to reduce the neurologic impairment associated with ischemic stroke.1–3 The approved ‘‘therapeutic window’’ of thrombolytic therapy for ischemic stroke is three hours from onset of symptoms, but many stroke victims do not reach medical care within this time period.4–6 The reasons for this prolonged delay are not well understood.7–9 Several factors have been found to be associated with shorter delays from symptom onset to hospital arrival (out-of-hospital delay), including the presence of a bystander,10 calling 911,5,9–11 stroke subtype,4,8 sudden onset of symptoms,7 stroke severity,5–12 time of day,8,13,14 and the presence of symptoms on awakening.13 Whether individuals with acute stroke view their symptoms as signaling a stroke may also impact care-seeking behavior.6 Theoretically, stroke victims who attribute their symptoms to stroke may have shorter delay times than individuals who associate their symptoms with less-specific causes. Further, understanding differences in symptom identification is fundamental to developing tailored public information campaigns designed to shorten time to hospital presentation. The current study was conducted to describe the relationship between stroke symptom attribution and outof-hospital delay, including both the initial interval of time from the onset of symptoms to first contact with medical personnel and the total outof-hospital delay.
METHODS Study
Design. The methods used in this prospective registry of
patients presenting to the ED with signs or symptoms of stroke have been previously reported.10 The institutional review board on human subjects in research at the University of North Carolina at Chapel Hill approved the study protocol, and patients or next-of-kin gave informed consent to be interviewed.
Study Setting and Population. The University of North Carolina Hospitals, Chapel Hill, North Carolina, tertiary-care University Hospital, with approximately 600 beds and an ED volume of 36,000 visits per year, was the site of patient enrollment. Trained ED staff nurses enrolled 201 patients presenting with any of the following symptoms suggestive of stroke: sudden unilateral weakness or numbness of face, arm, or leg, sudden dimness or loss of vision, loss of speech, sudden severe headache, or unexplained dizziness. No age restrictions were applied. These broad eligibility criteria, based on symptoms at presentation rather than discharge diagnosis, resulted in the enrollment of many patients who were eventually ‘‘ruled out’’ for stroke. This sensitive criteria, however, allowed for the characterization of care-seeking behavior among a full spectrum of patients responding to stroke-like symptoms.
Study Protocol. A nurse in the ED conducted an interview with patients or next-of-kin regarding circumstances surrounding the onset of their symptoms, including the time of onset and their perception of symptoms. Patients were asked the open-ended question: ‘‘When the problems first started, what did you think was happening? ’’ The patients’ responses were categorized by the nurse into one of four categories: 1) having a stroke, 2) having a heart attack, 3) didn’t know what was happening, and 4) other. After patients were discharged from the hospital, the medical record was obtained and reviewed by a trained
abstractor, and information on the final discharge diagnosis and timing of diagnostic and therapeutic procedures was recorded.
Data Analysis. Two components of out-of-hospital delay were defined: 1) reaction interval and 2) total outof-hospital delay. Reaction interval was defined as the time from onset of symptoms to first contact (either by phone or in person) with medical personnel (physician, nurse, paramedic or other emergency medical personnel, or 911 operator). Total out-of-hospital delay was defined as time from onset of symptoms to arrival at the ED. Time of symptom onset was defined as the time that the signs or symptoms of the chief complaint began. For those who had symptoms immediately upon waking or were unable to identify a specific onset time, onset was defined as the last time the patient was known to be free of symptoms.
RESULTS Forty-two patients with total out-ofhospital delay more than 48 hours and two with missing data on symptoms attribution were excluded from the analyses. Eighty-seven patients in our sample received a final discharge diagnosis of stroke or transient ischemic attack (TIA). The average age of stroke/TIA patients was 68 years. As shown in Table 1, 52% (n = 45) of the stroke/TIA patients interpreted their symptoms as indicating they were having a stroke, while 24% (n = 21) attributed their symptoms to a condition other than stroke, and 24% (n = 21) could not attribute their symptoms to any specific cause. Those attributing symptoms to nonstroke conditions were more often female and less likely to have reported receiving information about stroke symptoms in the past. Incidences of calling 911 in response to symptoms were similar in all three attribution groups. Patients not attributing their symptoms to any specific condition were less likely to report having been given information about stroke in the past, and were less likely to have arrived in the ED by ambulance. The median reaction interval
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among patients not attributing their symptoms to any specific cause was similar to that for those attributing symptoms to nonstroke conditions and almost twice as long as that for patients attributing symptoms to stroke (2.5 hours, 2.5 hours, and 1.4 hours, respectively). Further, the shortest median (interquartile range) out-of-hospital delays occurred among patients attributing their symptoms to stroke [2.5 hours (1.8, 8.0)] and the longest among those attributing symptoms to a condition other than stroke [4.8 hours (2.7, 9.6)] (Table 1). Because of the small sample size, we combined patients attributing symptoms to nonstroke conditions with those reporting ‘‘don’t know.’’ The result was two categories of symptom attribution, stroke- and non-stroke-related. Patients in these two symptom attribution groups differed from one another in their delay from symptom onset to initial contact with medical personnel as well as in total out-of-hospital delay time. The median (interquartile range) delay from symptom onset to initial contact with medical personnel among those attributing their symp-
Williams et al. • DELAY IN STROKE CARE
toms to nonstroke conditions was nearly twice that of patients who identified their symptoms as stroke [2.5 hours (0.8, 6.0) and 1.4 hours (0.2, 4.7)], respectively (Fig. 1). Similarly, the median (interquartile range) total out-of-hospital delay was 4.3 hours (1.8, 7.0) among patients not attributing their symptoms to stroke vs 2.6 hours (1.8, 8.0) for those who thought they were having a stroke.
DISCUSSION We found that stroke/TIA patients presenting to the ED who associated their symptoms specifically to a ‘‘stroke’’ delayed less time in contacting medical personnel and subsequently experienced shorter outof-hospital delays than those who attributed symptoms to nonstroke conditions and those who could not attribute their symptoms to any specific cause. Patients correctly identifying their symptoms were also more likely to have received information on stroke in the past. These findings confirm those reported by Feldman et al.7 in which
patients attributing their symptoms to stroke presented for health care earlier than those attributing symptoms to nonstroke conditions. Having correctly identified one’s symptoms as stroke was not an independent predictor of delay time. In that study, however, the authors did not differentiate between reaction interval and total out-of-hospital delay. The findings from the present study differ with those reported by Williams et al.,5 in which symptom attribution was shown not to influence total out-of-hospital delay, defined as early (ⱕ3 hours) or late (>3 hours) arrival to the ED. Findings from the current study have implications for public information campaigns designed to improve timely access to health care for stroke. Increasing one’s ability to correctly interpret symptoms could reduce the interval of time from symptom onset to the initial response and help to reduce the total time from symptom onset to arrival in the ED. The ability to recognize symptoms is only partially dependent on accurate and sufficient knowledge of stroke warning signs. Knowledge alone may not be ade-
TABLE 1. Distribution of Population Characteristics by Type of Symptom Attribution Due to Stroke (n = 45)
Due to Nonstroke Conditions (n = 21)
Don’t Know (n = 21)
Total (n = 87)
Age High school
20 (44%) 11 (24%) 14 (31%)
10 (48%) 7 (33%) 4 (19%)
11 (52%) 7 (33%) 3 (14%)
41 25 21
Race/ethnicity White African-American Hispanic
28 (62%) 16 (36%) 1 (2%)
12 (57%) 9 (43%) 0
14 (67%) 7 (33%) 0
54 32 1
Arrival by ambulance
26 (58%)
11 (52%)
8 (38%)
45
Called 911
15 (33%)
6 (29%)
6 (29%)
27
Ever been given information about stroke symptoms
23 (51%)
8 (40%)
6 (30%)
37
Median reaction interval* (interquartile range)
1.4 hr (0.2, 4.7)
2.5 hr (0.5, 6.0)
2.5 hr (1.0, 6.0)
2.5 hr (0.8, 6.0)
Median total out-of-hospital delay† (interquartile range)
2.6 hr (1.8, 8.0)
4.8 hr (2.7, 9.6)
3.3 hr (1.4, 6.3)
4.3 hr (1.8, 7.0)
*Interval from onset of symptoms to initial reaction. †Interval from onset of symptom to ED arrival.
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quate to effect recognition of stroke symptoms at onset. While Kothari et al.6 reported that stroke patients who knew the warning signs of stroke were more likely to interpret their own symptoms as indicative of stroke, Williams et al.5 found that even though 38% of stroke patients reported that they knew the warning signs of stroke, only 25% correctly interpreted their own symptoms as stroke. More studies are needed in order to better understand the role of accuracy and depth of knowledge in symptom interpretation. There may be factors in addition to knowledge that govern whether a person with stroke symptoms correctly recognizes them as such and takes urgent and appropriate action. For some, that recognition may follow from increased knowledge of stroke warning signs and symptoms. For others, it may be determined by a combination of knowledge and other factors such as perceived risk,15 perceived seriousness of the symptoms,16 or the degree of similarity between personal symptoms and one’s conception of how a stroke occurs.16 Additional studies are needed to further elucidate factors that facilitate appropriate initial response to stroke symptoms. The success of public information campaigns to increase recognition of stroke signs and symptoms depends on an empirical investigation of this type. Available data indicate a serious stroke knowledge deficit in the population at large. A recent community survey indicated that 27% of individuals were unaware of any stroke warning signs and symptoms and only 58% could identify at least one of the five stroke warning signs established by the National Institute for Neurological Disorders and Stroke.17 Similarly, a study of a national sample of 750 adults conducted by the Gallup organization for the National Stroke Association reported that, when queried about the symptoms associated with stroke, 17% of the individuals had responses categorized as ‘‘don’t know/refused.’’ Clearly, more work needs to be done to educate the general public about stroke warning signs and symptoms. Message design might be enhanced by audience analyses that result in profiles of population sub-
Figure 1. Median hours (interquartile range) from symptom onset to initial reaction and to ED arrival by symptom attribution. groups, distinguished, among other factors, on the basis of specific cognitive predictors of stroke symptoms recognition. There is a promise in the use of public information campaigns to shorten delays in accessing health care for stroke.19,20 Future community intervention efforts will only benefit from a future refinement of targeted messages.
LIMITATIONS AND FUTURE QUESTIONS Limitations of the current study derive primarily from the sample size and the setting in which the data were collected. The relatively small number of patients enrolled in this study limits the strength and generalizability of its findings. Studies are needed with a larger number of hospitals in more diverse geographic settings. During data collection, approximately 50% of the interviews were conducted with the assistance of an informant, due to the severity of the patients’ conditions. Although we did not have direct measures of the type and extent of severity, the inability to complete the interview without assistance was considered a crude measure. Therefore, these data may be biased toward the symptom attribution characteristics of patients who reach the ED and those with milder episodes. Future studies might examine the extent to which patients’ recog-
nition of the onset of symptoms or that of other individuals in the immediate environment play a role in patients’ attribution of symptoms and therefore in influencing decision time and out-of-hospital delay. Understanding the influence of others on the decision to seek medical care has implications for community education targeting not only persons at high risk for stroke, but also community dwellers whose informed and decisive action may be critical for someone with an evolving stroke.
CONCLUSIONS Stroke patients presenting to the ED who attributed their symptoms to stroke tended to respond to their symptoms sooner and arrive in the ED faster than stroke patients who attributed their symptoms to other conditions. Increasing accurate symptom recognition among highrisk individuals in the community may play a major role in decreasing the time to hospital presentation after the onset of stroke symptoms.— JANICE E. WILLIAMS, PhD, MPH (
[email protected]), and WAYNE D. ROSAMOND, PhD, Department of Epidemiology, University of North Carolina, School of Public Health, Chapel Hill, NC; and DEXTER L. MORRIS, PhD, MD, Department of Emergency Medicine, University of North Carolina, School of Medicine, Chapel Hill, NC
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Key words. cerebral infarction; stroke; symptoms; emergency medicine; response time; prevention.
References 1. National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group. Tissue plasminogen activator for acute ischemic stroke. N Engl J Med. 1995; 333:1581–7. 2. Muticentre Acute Stroke Trial–Italy (MAST-I) Group. Randomized controlled trial of streptokinase, aspirin, and combination of both in treatment of acute ischemic stroke. Lancet. 1995; 346:1509– 14. 3. Hacke W, Kaste M, Fieschi C, et al. Intravenous thrombolysis with recombinant tissue plasminogen activator for acute hemispheric stroke: the European Cooperative Acute Stroke Study (ECASS). JAMA. 1995; 274:1017–25. 4. Alberts MJ, Bertels C, Dawson DV. An analysis of time of presentation after stroke. JAMA. 1990; 263:65–8. 5. Williams LS, Bruno A, Rouch D, Marriott DJ. Stroke patients’ knowledge of stroke: influence on time to presentation. Stroke. 1997; 28:912–5. 6. Kothari R, Sauerbeck L, Jauch E, et
Williams et al. • DELAY IN STROKE CARE
al. Patients’ awareness of stroke signs, symptoms, and risk factors. Stroke. 1997; 28:1871–5. 7. Feldman E, Gordon N, Brooks JM, et al. Factors associated with early presentation of acute stroke. Stroke. 1993; 24: 1805–10. 8. Fogelholm R, Murros K, Rissanen A, Ilmavirta M. Factors delaying hospital admission after acute stroke. Stroke. 1996; 27:398–400. 9. Bratina P, Greenberg L, Pasteur W, Grotta JC. Current emergency department management of stroke in Houston, Texas. Stroke. 1995; 26:409–14. 10. Rosamond WD, Gorton RA, Hinn AR, Hohenhaus SM, Morris DL. Rapid response to stroke symptoms: the Delay in Accessing Stroke Healthcare (DASH) Study. Acad Emerg Med. 1998; 5:45–51. 11. Barsan WG, Brott TG, Broderick JP, Haley EC, Levy DE, Marler JR. Urgent therapy for acute stroke. Stroke. 1994; 25:2132–7. 12. Jorgensen HS, Nakayama H, Reith J, Raaschou HO, Olsen TS. Factors delaying hospital admission in acute stroke: the Copenhagen Stroke Study. Neurology. 1996; 47:383–7. 13. Azzimondi G, Bassein L, Fiorani L. Variables associated with hospital arrival time after stroke. Effect of delay on the clinical efficacy of early treatment. Stroke. 1997; 28:537–42.
14. Barsan WG, Brott TG, Broderick JP, Haley EC, Levy DE, Marler JR. Time of hospital presentation in patients with acute stroke. Arch Intern Med. 1993; 153:2558–61. 15. Kreuter MW, Strecher VJ. Changing inaccurate perceptions of health risk: results from a randomized trial. Health Psychol. 1995; 14:56–63. 16. Ruston A, Clayton J, Calnan M. Patients’ action during their cardiac event: qualitative study exploring differences and modifiable factors. Br Med J. 1998; 316:1060–5. 17. Pancioli AM, Broderick J, Kothari R, et al. Public perception of stroke warning signs and knowledge of potential risk factors. JAMA. 1998; 279:1288–92. 18. National Stroke Association. Awareness and knowledge of stroke prevention: a study of adults, 50 years of age and over. Englewood, CO: National Stroke Association, 1996. 19. Alberts MJ, Perry A, Dawson DV, Bertels C. Effects of public and professional education on reducing the delay in presentation and referral of stroke patients. Stroke. 1992; 23;352–6. 20. Dornan WA, Stroink AR, Kattner KK, et al. A public education program is associated with a dramatic decrease in hospital arrival time in patients with acute stroke [abstract]. Stroke. 1999; 30: 232.
䢇
Instructions for Contributors to Clinical Pearls Clinical Pearls is a section of Academic Emergency Medicine that uses photographic images to provide visual clues for a case study presented as an unknown. Visual clinical findings make up a large part of the practice of emergency medicine (EM). ‘‘Capturing’’ these findings allows clinicians to share their experience and knowledge with others, making clinical photographs an excellent teaching tool. This section intends to stimulate academic emergency physicans to use clinical photography for augmenting their teaching of EM. Clinical Pearls manuscripts should be presented as case study ‘‘unknowns’’ and must be accompanied by a clinical photograph. Radiographs and other supporting data (ECGs, pathology specimens, Gram stains, etc.) are acceptable if they accompany a clinical photograph. A series of clinical photographs to demonstrate a progressive disease process is acceptable. Cases with a radiograph or ECG alone should be discussed with the section editor before submission. Manuscript preparation should follow the general Instructions for Authors found in AEM. Format of the section follows this general scheme: Title (usually the chief complaint of the patient), Chief Complaint, History of Present Illness, Physical Examination and Laboratory, Diagnosis, Discussion, Clinical Pearls (3 – 5 ‘‘take-home’’ points of the case), and References. The most original image available (slide, negative, or photograph) and two 5 ⫻ 7-inch color prints should accompany
the manuscript. The original image will be returned. Arrows, symbols, or labels identifying structures should be marked on the second print if necessary. Each print and slide should be labeled with the last name(s) of the contributors and an arrow indicating the top of the image. Contributors must provide the names, highest academic degrees, addresses, and phone and fax numbers of the photographer and all contributors. Acknowledgment of manuscript and photograph acceptance will be made in writing to the contributor. The section editor will have the photograph critiqued by a professional medical photographer to provide suggestions for improving photographic technique. The critique will become part of the published article. By submitting to the Clinical Pearls, the contributor allows the section editor to distribute the case and image to all EM residency programs in the United States as part of a prejournal mail-out. This activity allows programs to preview the case in didactic situations to enhance the learning from the case. It is the responsibility of the contributor to obtain patient consent for use of the photograph in a publication if the patient is in any way identifiable. Send manuscripts and images to AEM, 901 North Washington Avenue, Lansing, MI 48906. For additional information or questions, contact Larry Stack, phone: 615-936-0093; fax: 615-936-1316; e-mail:
[email protected]
